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Spatio-Temporal Analysis of the Impact of Landscape Changes on Vegetation and Land Surface Temperature over Tamil Nadu

Spatio-Temporal Analysis of the Impact of Landscape Changes on Vegetation and Land Surface... Article Spatio-Temporal Analysis of the Impact of Landscape Changes on Vegetation and Land Surface Temperature over Tamil Nadu 1 , , † 2 , † 1 2 Mohamed Shamsudeen * , Rajchandar Padmanaban , Pedro Cabral and Paulo Morgado NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal; pcabral@novaims.unl.pt Centre of Geographic Studies, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal; rajchandar@edu.ulisboa.pt (R.P.); paulo@campus.ul.pt (P.M.) * Correspondence: m20210707@novaims.unl.pt † These authors contributed equally to this work. Abstract: Land-use changes adversely may impact ecological entities and humans by affecting the water cycle, environmental changes, and energy balance at global and regional scales. Like many megaregions in fast emerging countries, Tamil Nadu, one of the largest states and most urbanized (49%) and industrial hubs in India, has experienced extensive landuse and landcover change (LULC). However, the extent and level of landscape changes associated with vegetation health, surface permeability, and Land Surface Temperature (LST) has not yet been quantified. In this study, we employed Random Forest (RF) classification on Landsat imageries from 2000 and 2020. We also computed vegetation health, soil moisture, and LST metrics for two decades from Landsat imageries to delineate the impact of landscape changes in Tamil Nadu using Google Earth Engine (GEE). The level of vegetation health and drought for 2020 was more accurately assessed by combining the Temperature Condition Index (TCI) and Vegetation Condition Index (VCI). A Soil moisture index was subsequently used to identify surface permeability. A 75% expansion in urban areas of Tamil Nadu Citation: Shamsudeen, M.; was detected mainly towards the suburban periphery of major cities between 2000 and 2020. We Padmanaban, R.; Cabral, P.; Morgado, observed an overall increase in the coverage of urban areas (built-up), while a decrease for vegetated P. Spatio-Temporal Analysis of the (cropland and forest) areas was observed in Tamil Nadu between 2000 and 2020. The Soil-Adjusted Impact of Landscape Changes on Vegetation and Land Surface Vegetation Index (SAVI) values showed an extensive decline in surface permeability and the LST Temperature over Tamil Nadu. Earth values showed an overall increase (from a maximum of 41 C to 43 C) of surface temperature in 2022, 3, 614–638. https://doi.org/ Tamil Nadu’s major cities with the highest upsurge for urban built-up areas between 2000 and 2020. 10.3390/earth3020036 Major cities built-up and non-vegetation areas in Tamil Nadu were depicted as potential drought hotspots. Our results deliver significant metrics for surface permeability, vegetation condition, surface Academic Editor: Ioannis Gitas temperature, and drought monitoring and urges the regional planning authorities to address the Received: 25 April 2022 current status and social-ecological impact of landscape changes and to preserve ecosystem services. Accepted: 20 May 2022 Published: 26 May 2022 Keywords: urban ecosystem services; landuse and landcover; Random Forest classification; land Publisher’s Note: MDPI stays neutral surface temperature; environmental monitoring; remote sensing with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction In the last three decades, the growth of landuse and landcover changes (LULC) have led to an enormous transformation in global landscape patterns [1–4]. Landscape changes Copyright: © 2022 by the authors. result from many factors, including variability in abiotic conditions, such as climate, to- Licensee MDPI, Basel, Switzerland. pography, and soils, biotic interactions that generate spatial patterning even under homo- This article is an open access article geneous environmental conditions and past and present patterns of human settlement. distributed under the terms and Land-use changes contributed to a reduction in vegetation health, surface permeability, and conditions of the Creative Commons the occurrence of Surface Heat Island (SHI), Urban Heat Island (UHI), and drought [5–8]. It Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ also drives climate change which affects humans and wildlife [9]. To enable sustainability 4.0/). and changes in the environmental structure, it is necessary to understand the subtleties of Earth 2022, 3, 614–638. https://doi.org/10.3390/earth3020036 https://www.mdpi.com/journal/earth Earth 2022, 3 615 landcover change [10]. It is given high importance as most of the metropolitan cities have encountered substantial landcover changes over the decades [11–13]. Henceforth, these metropolitan areas consume more energy and produce severe environmental problems and thus lend land, water, and air pollution, which affects the functions of ecosystems [5,14,15]. Land Surface Temperature (LST) is an important parameter that influences the partition of energy between ground and vegetation and determines the surface air temperature [14]. LST is also an important factor in global climate change, vegetation growth, and UHI [15,16]. A sudden increase in land-use changes also affects environmental factors, such as rainfall, temperature, and groundwater level, which threatens the development process of the urban areas. Thus, examining the impact of landscape changes on vegetation and LST is crucial for efficient urban planning and management of land-use zones [17,18]. In India, one-third of the people live in cities [19]. During the last decade, the 4% increasing trend in urbanization expresses that people from the Tamil Nadu state and other neighboring states and millions of north Indian states people have relocated from rural areas to a metropolitan city to find a job and make their lives in cities [20]. The urbanization level in India went from 27.7% to 31.1%, with an increasing amount of 3.3% from 2001 to 2011 [21,22]. The statistics show that the urban population level in India will increase to 600 million, which doubles the rate from the present scenario by 2031 [23]. Due to urbanization, the cities in India are affected by unhygienic health conditions and damage to ecosystems [24]. In India, Tamil Nadu secured the first rank in urbanization among the fifteen primary states listed by the country [25]. The state has attained the uppermost level of urbanization with 43.86% in the country, based on the 2001 census [26]. Around 27 million people live in the metropolitan city of Tamil Nadu [27]. The urbanization pattern of Tamil Nadu is dispersed with municipalities in almost every district [28]. This rapid rise in urbanization in Tamil Nadu leads to many problems, including deforestation, water scarcity, flood, pollution, and other environmental damages [29]. Thus analyzing the impact of landscape changes on vegetation and land surface temperature over Tamil Nadu is crucial for sustainable urban planning and management [30]. Global Remote Sensing (RS) and satellite imageries processing techniques delivered considerable potential for landscape change patterns and vegetation change analysis. Thus, this study attempts to examine the impact of landscape changes on vegetation and land surface temperature using RS. Several studies have been carried out to examine the LULC changes, which are important indicators for examining the relationship between the environment and anthropogenic activities that can be attained from satellite images and other image processing techniques [31–35]. These are especially useful for a state like Tamil Nadu, where ground/field monitoring data are not available and scarce [32,36]. But the availability of satellite images helps to overcome this issue, especially Google Earth Engine (GEE) cloud computing platform provides efficient methods and freely available spatial data for storing, accessing, and analyzing spatial datasets on the high-performance servers [37]. GEE was launched by Google in 2010 and makes freely available RS datasets via its internet-based Application Programming Interface (API) provided by Python and a JavaScript web-based Interactive Development Environment (IDE) used to detect changes, landuse changes, map trends, landcover changes and quantify differences on the Earth’s surfaces and atmospheres [37]. The existing studies have focused mainly on the spatio-temporal relationship between LULC and environmental changes [10,32,38–40]. However, the multivariate relationship between LULC changes, vegetation, soil moisture, and land surface temperature has not been analyzed widely. This paper aims to fill this gap by examining the LULC changes effect on landuse vegetation and land surface temperature using GEE. The availability of GEE codes helps future research to integrate different datasets from Tamil Nadu at different resolutions. The impact of land-use changes should be analyzed by considering the following research questions: (i) how could landscape changes make a variation in the Spatial and Temporal pattern of the land-use change? [41]; (ii) how could landscape changes affect the Earth 2022, 3 616 surface and land temperature conditions? [42], and (iii) how does the relationship exist between land-use change patterns and environmental factors (temperature, soil moisture, and vegetation) [43]. This paper is organized as follows: Sections 2.1 and 2.2 describe the study area and data used in this work. Section 2.3 provides information about the observation and assessment of LULC Classification, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), LST, Vegetation Condition Index (VCI), and Temperature Condition Index (TCI), respectively. Section 3 then combines the results from the individual stages of the research design to evaluate LULC changes to assess their impact on vegetation and LST. Finally, Sections 4 and 5 provide the discussion and conclusion, respectively. 2. Materials and Methods 2.1. Study Area Our study was undertaken in Tamil Nadu, a state located in the southern part of India [10], which has a seashore surrounded by one dimension and consists of various types of landscapes such as hilly terrain, wetlands, and forests [32]. It receives rainfall predominantly during the monsoon season in the western parts and interior districts, while the sea bordered districts receive rainfall from the emergence of cyclones [44]. Its capital city is Chennai and is surrounded on the north by Andhra Pradesh, on the east by the Bay of Bengal, on the west by Kerala, and on the south by the Indian Ocean (Figure 1). The Western Ghats and the Eastern Ghats mountain ranges converge in the Nilgiri hills in Tamil Nadu, making it the only state in India to have both. The total area of the state is 130,058 sq km making it the eleventh largest state in the country [45]. The geographic location of Tamil Nadu spans between 11.12 N 78.65 E with an average elevation of 247 m and a maximum of 2599 m. Tamil Nadu has the largest tourism industry in India [29]. Tamil Nadu is the most urbanized state in India, one of the foremost industrialized states, and the highest tax-paying state to the central government. The urbanization trend of Tamil Nadu is supposed to exaggerate in the upcoming years. The climate of the region is sub-humid continental monsoon with an average annual temperature of 28.8 C. The average yearly precipitation of the state is about 945 mm, of which 32% is through the southwest monsoon, and 48% is through the northeast monsoon. The cropping pattern in the majority of the states depends on the monsoon, if the onset of the monsoon is delayed or fails, then the probability of a drought event occurring is high [46]. 2.2. Data We used the Google Earth Engine (GEE) for most of the image processing and analysis for this paper. On the GEE computing platform, the static input variables are existing datasets, terrain, and geographic location, whereas the dynamic inputs are data acquired from RS [47]. Data were collected between October to December, which is usually the wet season because dry season data leads to low accuracy results on the vegetation and temperature changes while doing the mean of all images for every single year. So we only used the winter and monsoon data to classify landscape changes in vegetation along with the soil moisture over land surface temperature [46]. For the classification operation, data were collected from or uploaded to the GEE cloud computing platform. High-resolution satellite images from the Landsat sensor with a spatial resolution of 30 m and temporal resolution of 16-day images for the monsoon period October to December 2000, 2004, 2008, 2012, 2016, and 2020 were accessed from the GEE cloud computing platform. (Earth Engine Data Catalog: https://developers.google.com/earth-engine/datasets/catalog/, accessed on 1 January 2020) [48] (Table 1). Earth 2022, 3, FOR PEER REVIEW 4 Earth 2022, 3 617 Figure 1. Geographic location and area of Tamil Nadu State. The maps were generated using Figure 1. Geographic location and area of Tamil Nadu State. The maps were generated using Google Google Maps. Maps. The geographic location of Tamil Nadu spans between 11.12° N 78.65° E with an av- erage elevation of 247 m and a maximum of 2599 m. Tamil Nadu has the largest tourism industry in India [29]. Tamil Nadu is the most urbanized state in India, one of the foremost industrialized states, and the highest tax-paying state to the central government. The urbanization trend of Tamil Nadu is supposed to exaggerate in the upcoming years. The climate of the region is sub-humid continental monsoon with an average annual temperature of 28.8 °C. The average yearly precipitation of the state is about 945 mm, of which 32% is through the Earth 2022, 3 618 Table 1. Technical description of the Landsat imageries used in this study. Temporal Name Spatial Resolution Date of Acquisition Resolution 2000-10-10, 2000-10-17, 2000-10-19, 2000-10-26, 2000-11-04, 2000-11-20,2000-11-27, 2000-12-06, 2000-12-15, 2000-12-20 & 2000-12-29 2004-10-07, 2004-10-12, 2004-10-14, 2004-10-21, 2004-11-08, 2004-11-22, 2004-11-29, 2004-12-01, Landsat 7 Collection 1 Tier 1 2004-12-10, 2004-12-17 & 2004-12-26 TOA Reflectance 30 m 16 day 2008-10-09, 2008-10-16, 2008-10-18, 2008-10-23, LE07/C01/T1_SR 2008-11-01, 2008-11-03, 2008-11-19, 2008-12-03, 2008-12-12, 2008-12-21 & 2008-12-28 2012-10-04, 2012-10-11, 2012-10-13, 2012-10-27, 2012-11-05, 2012-11-14, 2012-11-21, 2012-11-30, 2012-12-07, 2012-12-14, 2012-12-18 & 2012-12-28 2016-10-07, 2016-10-15, 2016-10-24, 2016-10-31, 2016-11-08, 2016-11-16, 2016-11-25, 2016-12-02, Landsat 8 Collection 1 Tier 1 2016-12-10, 2016-12-18 & 2016-12-26 TOA Reflectance 30 m 16 day 2020-10-07, 2020-10-15, 2020-10-23, 2020-10-31, LC08/C01/T1_SR 2020-11-08, 2020-11-16, 2020-11-24, 2020-12-02, 2020-12-10, 2020-12-18 & 2020-12-26 2.3. Methods Earth 2022, 3, FOR PEER REVIEW 6 To summarize our research design, we provided a schematic representation for the methodological framework used in this study (Figure 2), which is described in the follow- ing subsections. Figure 2. Methodology for analyzing landscape change and vegetation health (productivity) (NDVI: Figure 2. Methodology for analyzing landscape change and vegetation health (productivity) (NDVI: Normalized Difference Vegetation Index; VCI: Vegetation Condition Index; LST: Land Surface Tem- Normalized Difference Vegetation Index; VCI: Vegetation Condition Index; LST: Land Surface Tem- perature; TCI: Temperature Condition Index; VHI: Vegetation Health Index; LULC: Land-use and perature; TCI: Temperature Condition Index; VHI: Vegetation Health Index; LULC: Land-use and Land cover). Land cover). 2.3.1. Landuse and Landcover Classification Land-use classification describes both landuse and landcover (LULC) significantly [32]. LULC change classification helps the policymakers to understand the environmental change dynamics to ensure sustainable development [49]. Therefore, the classification of LULC into different levels is essential [32]. Together, these classification system levels are described as a hierarchy system that is useful in unfolding, monitoring, and forecasting LULC changes [39]. The sources of classifying LULC changes are mainly satellite images and aerial photographs [50,51]. In this study, we analyzed the changes in LULC with seven classes (Table 2) over two decades using Random Forest classification. The Random Forest classification technique is used to enhance the contiguities between the data points. The performance of RF re- quires the following steps [52,53]. Developing the N- Tree bootstrap model using satellite imageries. Based on DN values, develop an unpruned classification for each bootstrap model. In terms of DN values, gather the N-number of polygons. Select the number of classifications for land-use classes. Generate results of land-use classification. LULC classification was done in R studio (4.1.3) software based on classes and meth- ods for spatial data, Raster Geospatial Data Abstraction Library (Rgdal), Raster, and Ran- dom forests packages [54,55]. Earth 2022, 3 619 2.3.1. Landuse and Landcover Classification Land-use classification describes both landuse and landcover (LULC) significantly [32]. LULC change classification helps the policymakers to understand the environmental change dynamics to ensure sustainable development [49]. Therefore, the classification of LULC into different levels is essential [32]. Together, these classification system levels are described as a hierarchy system that is useful in unfolding, monitoring, and forecasting LULC changes [39]. The sources of classifying LULC changes are mainly satellite images and aerial photographs [50,51]. In this study, we analyzed the changes in LULC with seven classes (Table 2) over two decades using Random Forest classification. The Random Forest classification technique is used to enhance the contiguities between the data points. The performance of RF requires the following steps [52,53]. Table 2. Description of Landuse and Landcover (LULC) classes. No. LULC Classes Land-Uses Included in the Class 1 Settlement Urban, Rural, Mining 2 Waterbodies Rivers, Lakes, Ponds, Streams, and Canals 3 Forest Deciduous, Evergreen, Scrub Forest, and Swamp/Mangroves Agricultural plantation (tea, coffee, and rubber), Horticultural plantation 4 Plantation Agriculture (coconut, citrus fruits, orchards, fruits, and vegetable gardens), and Agro-horticultural plantation Cropped in more than two seasons 5 Crop Land Agriculture Paddy, rice, sugarcane, cotton, and groundnut Grass dominated land cover, Shrub and bush dominated land cover, and small 6 Fallow Land tree dominated land cover 7 Barren Land Bare Exposed Rock, Mixed Barren Land, and Sand dunes Developing the N- Tree bootstrap model using satellite imageries. Based on DN values, develop an unpruned classification for each bootstrap model. In terms of DN values, gather the N-number of polygons. Select the number of classifications for land-use classes. Generate results of land-use classification. LULC classification was done in R studio (4.1.3) software based on classes and methods for spatial data, Raster Geospatial Data Abstraction Library (Rgdal), Raster, and Random forests packages [54,55]. The land-use classification results were validated using the GEE images obtained from the year 2000 to 2020. Randomly 75 points were selected, and pixel values of these selected points were extracted for the various study periods. The accuracy assessment performed by comparing land-use classification extracted points with the GEE Image results in identifying those exact points on the GEE. To quantify the accuracy of the results, the kappa coefficient was used in the ERDAS Imagine (8.7) software [56]. The confusion matrix is used in calculating the user and producer accuracies. The kappa coefficient’s accuracy of more than 80% shows the desired land-use classification. Henceforth, the classified results are ancillary to the GEE reference data. The producer accuracy (1), user ’s accuracy (2), overall accuracy (3), and Kappa coefficient (4) are calculated using the following Equations [57]. Number o f accurately classi f ied pixels in the each category o f LULC ProducerAccuracy =  100 (1) Total number o f re f erence pixels in that category (The column total) Number o f accurately classi f ied pixels in the each category o f LULC User s Accuracy =  100 (2) Total number o f re f erence pixels in that category (The Row total) Earth 2022, 3 620 Total number o f accurately classi f ied pixels (Diagonal) Overall Accuracy =  100 (3) Total number o f re f erence pixels (Obs Exp) Kappa coe f f icient = (4) (1 Exp) where Obs = Observed Overall accuracy, represents accuracy reported in error matrix, Exp = Expected correct, represents correct classification. 2.3.2. Normalized Difference Vegetation Index (NDVI) NDVI estimates vegetation by calculating the disparity between infrared, in which vegetation strongly reflects light portions and red light, in which vegetation absorbs light portions [58]. The values of NDVI range from 1 to 1. In this contrast, negative values almost depict the water bodies [59]. On the other hand, the NDVI value near +1 indicates the portion with dense vegetation areas [60]. The value of NDVI around 0 may not be green leaves, which could be urbanized areas [61]. NDVI uses the following Formula (5) to estimate the vegetation areas [61]. NIR RED NDVI = (5) NIR + RED where NIR—Near-Infrared Band. The vegetation with high healthy condition reflects almost close to Near Infrared (NIR) and red in terms of higher different frequencies [58]. The formula results create inducements between the values from 1 to 1 [62]. The indication of low reflectance near red and high reflectance in near-infrared represents the high yield of NDVI [59]. In this study, we extracted NDVI values at regular intervals to differentiate the different growth stages of vegetation productivity and its coverage, i.e., as high productivity, low productivity, and medium productivity. The coverage changes and the productivity of vegetation are quantified using the R packages, Sp, Rgdal, Raster, and rts [54]. 2.3.3. Soil Adjusted Vegetation Index (SAVI) Surface permeability in the study area region was assessed using the Soil Adjusted Vegetation Index (SAVI). In general, SAVI specifies coverage of vegetation and health concerning soil moisture and thus accounts for the high inconsistency of built-up and non- built-up land cover in urban areas [63]. In SAVI, a transformation technique is presented to minimize the effects of external factors from spectral vegetation indices. SAVI controls for the influence of soil brightness in the NDVI and it reduces the brightness of soil-related noise in the coverage of vegetation estimation [32]. Vegetation health is strongly associated with surface permeability and thus provides an essential identification of impermeable surfaces, especially in urban areas. SAVI is calculated using Equation (6) [32]. (NIR + RED) SAVI =  (1 + L) (6) (NIR + RED + L) where RED is the pixel value of band 3 (RED), and the NIR band is the pixel value of Near-Infrared band 4. L—Soil brightness correction factor [64]. Dense vegetation and high permeable surface areas L = 0, Less vegetation portion and impermeable surface areas L = 1 [65]. Due to the vegetation and coverage of built-up land, the inconsistency level was fixed at L = 0.5 [66]. SAVI was calculated for the satellite images for the period from 2000 to 2020. The SAVI was extracted at natural breaks to differentiate surface permeability degrees. The computation of area coverage for soil permeability zone between 2000 to 2020 was done using SAVI. For each LULC class, the areal coverage and standard deviation of SAVI are computed. Earth 2022, 3 621 2.3.4. Land Surface Temperature (LST) In this study, the LST index was calculated for each pixel in the satellite images from 2000 to 2020 to quantity the radiative skin temperature of the land surface and its elements, which depends on the optical brightness and reflectance of the surface (Albedo) [32]. In general, low SAVI with soil and built-up lands having high albedo [67]. On the other hand, high SAVI with dense vegetation portions has a low albedo, indicating variability in the climatic condition over an urban and dense forest region associated with surface permeability degrees [68]. The LST of each pixel was calculated using Equation (7) [68]. L = fL L  Q Q g DN 1 + L (7) l max min CALMAX CALMIN MIN where, L = the spectral radiance that is scaled to Q in W/(m *sr *m) MAX CALMAX L = the spectral radiance that is scaled to Q in W/(m *sr *m) MIN CALMIN Q = the maximum quantized calibrated pixel value (corresponding to L ) in CALMAX MAX DN = 255 Q = the minimum quantized calibrated pixel value (corresponding to L ) in CALMIN MIN DN = 1 LST was applied to the satellite images for the period from 2000 to 2020. The LST was extracted at natural breaks to differentiate with SAVI. For each LULC class, the areal cover- age and standard deviation of LST were computed. Using Spearman correlation analysis, the quantification between the association of surface permeability and LST was achieved. 2.3.5. Vegetation Condition Index (VCI)—Drought Intensity and Temperature Condition Index Kogan’s vegetation condition index delivers the relative Normalized Difference Vege- tation Index changes concerning the historical NDVI pixel values [69]. VHI can be derived based on both the LST and NDVI [70]. It distinguishes the present vegetation index, i.e., NDVI or EVI (Enhanced Vegetation Index), with the values observed in the same data period of different previous years within particular significant pixel values. The VCI calculated as per the Formula (8) [71] shown below NDVI NDVI min VCI =  100 (8) NDVI NDVI max min where NDVI and NDVI are the maximum and minimum NDVI values for a pixel max min during a period of time [72]. As a resulting percentage of this formula, Table 3 represents bad to good vegetation conditions in different colors. Table 3. Interpretation of the VCI, recommended practice by the United Nations, office for outer space affairs UN—Spider knowledge portal. (Recommended Practice: Drought monitoring using the Vegetation Condition Index (VCI): https://un-spider.org/book/export/html/9206, accessed on 5 January 2020) [73]. Value Category >40% No drought 30–40% Light drought 20–30% Moderate Drought 10–20% Severe Drought 0–10% Extreme drought Earth 2022, 3 622 2.3.6. Temperature Condition Index (TCI) The Temperature Condition Index (TCI) proposed by Kogan was used to determine the stress effect on the vegetation possessed by the temperature condition and enormous wetness [74]. The implementation of TCI is anticipated by taking minimum and maximum temperatures and correcting them to project various vegetation types for the different temperature conditions represented by (9) [75] LST LST max TCI = (9) LST LST max min where LST—Land Surface Temperature, LSTmax, LSTmin—Maximum and Minimum values of Land Surface Temperature. 2.3.7. Vegetation Health Index (VHI) Vegetation Health Index (VHI) assesses the effectiveness of drought over the area by incorporating vegetation health (i.e.,) lower than average NDVI and higher than the temperature on the vegetation conditions [76,77]. Vegetation Health Condition is the combination of both VCI and TCI written as (10) [78] VHI = a VCI + (1 a) TCI (10) where VCI—Vegetation Condition Index, TCI—Temperature Condition Index, and — parameter which quantifies the involvement of each component in the vegetation health. 3. Results 3.1. Analyzing the Landuse Landcover Changes Seven LULC classes, namely urban settlements, forest, plantation agriculture, barren land, water bodies, fallow land, and crop-land agriculture, are considered for the analysis, with a spatial extent of about 130,159.46 km . Analysis of the accuracy of the classified LULC was achieved using user accuracy, producer accuracy, and kappa coefficient using reference data obtained from other sources. Accuracy assessment for 2000 obtained a kappa coefficient of 0.9 with a producer and user accuracy value of 95.62 and 92.15, respectively. For the year 2020, the kappa coefficient is 0.85 and 89.35, 94.16 for producer and user accuracy, respectively (Table 4). Table 4. Accuracy assessment for generated LULC map. 2000 2020 LULC Classes Producer Accuracy User Accuracy Producer Accuracy User Accuracy Settlement 85.06 80.21 79.14 84.27 Forest 89.12 89.95 82.26 91.66 Plantation Agriculture 88.75 91.64 84.59 86.74 Barren Land 93.41 86.35 86.03 88.35 Waterbodies 89.23 94.72 86.41 86.98 Fallow Land 92.39 84.83 87.26 92.83 Crop Land Agriculture 95.62 92.15 89.35 94.16 Kappa 0.90 0.85 Urban settlements and barren lands increased rapidly by 150% and 120%, respectively, while all other five classes decreased in considerable amounts (Figure 3). The aerial declination of classes forest, plantation-agriculture, water bodies, fallow land, and cropland- agriculture are 21%, 36%, 2%, 14%, and 4%, respectively. The sudden evolution of sub-urban Earth 2022, 3 623 periphery landforms from cropland to impervious constructions was observed, which is depicted as the rapid increase in the settlements as seen in Figure 3 as red patches. Chennai and its fringes had rapid urban development and other sub-urban cities also urbanized due to the migration of the population to cities for various purposes. The barren land also increased by 6477 km mainly due to urbanization, leaving the agricultural lands, leading to an increase in uncultivable barren land and water bodies change is around Earth 2022, 3, FOR PEER REVIEW 11 185 km , which is at an alarming rate of sign (Table 5). Thus, the landscape of Tamil Nadu is urbanized with the decrease in fertile cropland pastures over the last two decades and the conversion of those lands into barren landforms. Figure 3. LULC classification map for 2000 (Left) and 2020 (Right). Figure 3. LULC classification map for 2000 (Left) and 2020 (Right). Table 5. Comparative analysis between various classes over two decades. Table 5. Comparative analysis between various classes over two decades. Area (Sq.km) Area (Sq.km) LULC Changes LULC Changes Area (Sq.km) Area (Sq.km) LULC Changes LULC Changes LULC Class LULC Class 2000 2000 2020 2020 2000–2020 2000–2020 2000–2020 2000–2020 ( (%) %) Settlement 5103.29 12,721.7 7618.41 149.28 Settlement 5103.29 12,721.7 7618.41 149.28 Forest 21,954.34 17,407.97 −4546.37 −20.70 Forest 21,954.34 17,407.97 4546.37 20.70 Plantation Agriculture 9125.72 5905.68 −3220.04 −35.28 Plantation Agriculture 9125.72 5905.68 3220.04 35.28 Barren Land 5385.57 11,863.2 6477.63 120.27 Barren Land 5385.57 11,863.2 6477.63 120.27 Waterbodies 9274.63 9089.49 −185.14 −1.99 Waterbodies 9274.63 9089.49 185.14 1.99 Fallow Land 31,064.4 26,997.57 −4066.83 −13.09 Fallow Land 31,064.4 26,997.57 4066.83 13.09 Crop Land Agriculture 48,224.51 46,173.82 −2050.69 −4.25 Crop Land Agriculture 48,224.51 46,173.82 2050.69 4.25 TOTAL 130,159.46 130,159.43 TOTAL 130,159.46 130,159.43 3.2. Assessment of Vegetation Health Normalized Difference Vegetation Indices (NDVI) are primarily used to detect the presence of vegetation and the health status of existing plantations. For the analysis car- ried out over two decades in Tamil Nadu, the plantations are undergoing severe stress and agriculture is becoming difficult as the years pass on. From this study, nearly one- third of the entire area was stress-free in 2000 and the healthy vegetation pattern is clearly visible in river basins because of the high reflectance value in the NIR band (Figure 4). The year 2004 and 2008 has almost the same healthiness in the outpost with slight declination in the landscapes having high productivity compared to that of 2000. In 2012, there was around 50 percent increase in the area ranging between 0.1 to 0.2, which clearly reveals that few plantations are undergoing stress, whereas a lot of agricultural areas are left Earth 2022, 3 624 3.2. Assessment of Vegetation Health Normalized Difference Vegetation Indices (NDVI) are primarily used to detect the presence of vegetation and the health status of existing plantations. For the analysis carried out over two decades in Tamil Nadu, the plantations are undergoing severe stress and agriculture is becoming difficult as the years pass on. From this study, nearly one-third of the entire area was stress-free in 2000 and the healthy vegetation pattern is clearly visible Earth 2022, 3, FOR PEER REVIEW 12 in river basins because of the high reflectance value in the NIR band (Figure 4). The year 2004 and 2008 has almost the same healthiness in the outpost with slight declination in the landscapes having high productivity compared to that of 2000. In 2012, there was around uncultivated, which is derived from the analysis of high productivity landform changes 50 percent increase in the area ranging between 0.1 to 0.2, which clearly reveals that few in comparison with 2008 in which change is around 20%. By comparative analysis of plantations are undergoing stress, whereas a lot of agricultural areas are left uncultivated, NDVI maps between 2016 and 2020, the southern portion of the study area is undergoing which is derived from the analysis of high productivity landform changes in comparison severe stress, as is clearly visible with a decrease in highly productive land by around with 2008 in which change is around 20%. By comparative analysis of NDVI maps between 45%. From the overall analysis of the healthiness of the vegetation carried over for two 2016 and 2020, the southern portion of the study area is undergoing severe stress, as is decades, we learned that areas with no plantation increased by 218% and low productivity clearly visible with a decrease in highly productive land by around 45%. From the overall (0.1–0.3) areas increased adversely by about 600% and the average productive land (0.3– analysis of the healthiness of the vegetation carried over for two decades, we learned that 0.5) changes around 7.7% and there is a decrease in the healthy regions by about 75% areas with no plantation increased by 218% and low productivity (0.1–0.3) areas increased roughly (Table 6). Southern Tamil Nadu has an unhealthy vegetation pattern, while the adversely by about 600% and the average productive land (0.3–0.5) changes around 7.7% western portion possesses good healthiness due to the Western Ghats existing in that par- and there is a decrease in the healthy regions by about 75% roughly (Table 6). Southern ticular belt. Tamil Nadu has an unhealthy vegetation pattern, while the western portion possesses good healthiness due to the Western Ghats existing in that particular belt. (a) (b) Figure 4. Cont. Earth 2022, 3, FOR PEER REVIEW 13 Earth 2022, 3 625 (c) (d) (e) (f) Figure 4. NDVI map for Tamil Nadu depicting the healthiness of the vegetation (a) 2000, (b) 2004, Figure 4. NDVI map for Tamil Nadu depicting the healthiness of the vegetation (a) 2000, (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. (c) 2008, (d) 2012, (e) 2016, (f) 2020. Earth 2022, 3 626 Table 6. Change in the areal distribution of the NDVI classes for Tamil Nadu for two decades. Change Change Change Change Area Coverage Area Coverage Area Coverage in Area in Area in Area in Area Vegeta- 2 2 2 (km ) (km ) (km ) Cover- Cover- Cover- Cover- NDVI tion age age age age Classes Produc- 2000– 2008– 2016– 2000– tivity 2000 2004 2008 2012 2016 2020 2004 2012 2020 2020 (%) (%) (%) (%) 1–0.1 No 6507.97 9111.16 40.13 10,412.75 11,714.35 12.50 18,222.32 20,695.35 13.57 218.00 0.1–0.2 Low 2603.18 3904.78 50.68 5206.37 7809.56 50.00 11,714.35 13,146.10 12.22 405.73 0.2–0.3 Low 1301.59 3253.98 50.00 3774.62 4555.58 20.68 7809.56 10,282.59 31.66 690.00 0.3–0.4 Average 57,270.16 50,762.18 11.36 54,666.97 66,381.32 21.42 67,813.07 68,333.71 0.76 19.31 0.4–0.5 Average 27,333.48 29,285.87 7.17 26,682.68 15,619.13 41.46 10,542.91 10,152.43 3.70 62.85 0.5–1 High 35,143.05 33,841.45 3.70 29,416.03 24,079.48 18.14 14,057.21 7549.24 46.29 78.51 3.3. Soil Moisture Assessment SAVI is the ideal index to assess soil moisture which is evaluated based on the surface permeability. When the SAVI value ranges between 1 to 0.1, then the area may be either water bodies or areas with no soil moisture, i.e., impermeable surfaces; similarly, when the SAVI value increases, the soil moisture tends to increase due to an increase in the soil penetration capability. From this analysis, due to the population explosion, deterioration of soil moisture was observed, which is mainly due to an increase in impermeable surfaces. In 2000, nearly 40% of Tamil Nadu had high to very high soil moisture, which was spatially scattered over the state (Figure 5). Similarly, in 2004, very high-high soil moisture areas diminished to 20% and are seen in the Western Ghats and along the water sources. By 2008, soil moisture regained around 35% and there was a considerable rise in the areas having no permeability. Again in 2012, soil moisture deteriorated, which resulted in the increase in areas having medium soil moisture. This leads to stress in the vegetation. From 2012, soil moisture followed a decreasing trend only, i.e., high soil moisture areas get converted into low to no moisture areas. In 2020, nearly 90% of the area fell in the no-medium soil moisture range. By the comparative study of SAVI between 2000 and 2020, areas with no permeability increased by 27.67% and an increase of 76.57% and 107.91% for low and medium permeable areas, respectively, whereas the high and very high permeable areas decreased by 79.41% and 69.52%, respectively (Table 7). The southeast coastal areas of Tamil Nadu are facing continuous declination of soil moisture and a similar trend is visualized in the northwestern parts also. Earth 2022, 3, FOR PEER REVIEW 15 Earth 2022, 3 627 (a) (b) (c) (d) Figure 5. Cont. Earth 2022, 3, FOR PEER REVIEW 16 Earth 2022, 3 628 (e) (f) Figure 5. SAVI map for Tamil Nadu depicting the soil moisture related with permeability (a) 2000, Figure 5. SAVI map for Tamil Nadu depicting the soil moisture related with permeability (a) 2000, (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. Table 7. Change analysis of soil moisture for Tamil Nadu for two decades between 2000–2020. Table 7. Change analysis of soil moisture for Tamil Nadu for two decades between 2000–2020. 2 2 2 Area Coverage (km ) Change Area Coverage (km ) Change Area Coverage (km ) Change Change Change Change Change Change in Area in Area Surface in Area in Area Area Coverage Area Coverage Area Coverage in Area in Area in Area in Area SAVI Covera Covera 2 2 2 (km ) (km ) (km ) PSurface ermeabi Coverag Coverag Cover- Cover- Cover- Cover- Classes 2000 2004 ge 2008 2012 ge 2016 2020 SAVI Perme- lity age age e 2age 016– e 2 age 000– Classes 2000– 2008– ability 2000– 2008– 2016– 2000– 2020(%) 2020(%) 2004(%) 2012(%) 2000 2004 2008 2012 2016 2020 2004 2012 2020 2020 −1–0.1 No 71473.47 77499.89 8.43 78610.48 83528.91 6.25 86158.26 91253.43 5.91 27.67 (%) (%) (%) (%) 0.1–0.2 Low 6897.59 8520.95 23.53 6024.32 8072.63 34.00 10349.75 12179.67 17.68 76.57 1–0.1 No 71,473.47 77,499.89 8.43 78,610.48 83,528.91 6.25 86,158.26 91,253.43 5.91 27.67 0.2–0.3 Medium 7352.34 16824.72 128.83 5102.44 10412.58 104.07 13385.42 15286.84 14.20 107.91 0.1–0.2 Low 6897.59 8520.95 23.53 6024.32 8072.63 34.00 10,349.75 12,179.67 17.68 76.57 0.3–0.5 High 21270.30 10981.54 −48.37 19301.68 12861.74 −33.36 7039.58 4378.71 −37.97 −79.41 0.2–0.3 Medium 7352.34 16,824.72 128.83 5102.44 10,412.58 104.07 13,385.42 15,286.84 14.20 107.91 Very 0.5–1 23165.76 16332.36 −29.49 21120.54 15283.6 −27.63 13226.47 7060.81 −46.61 −69.52 0.3–0.5 High High 21,270.30 10,981.54 48.37 19,301.68 12,861.74 33.36 7039.58 4378.71 37.97 79.41 Very 0.5–1 23,165.76 16,332.36 29.49 21,120.54 15,283.6 27.63 13,226.47 7060.81 46.61 69.52 High 3.4. Calculating Land Surface Temperature LST is an important parameter related to urban expansion, which increases due to an i3.4. ncrea Calculating se in the tLand errainSurface tempera Temperatur ture. In 2e000, the average LST ranges between 18–23 °C and the emissivity was more in the southern part as these areas had a higher LST of around 40 LST is an important parameter related to urban expansion, which increases due to an °C and most of the coastal areas in the northern part of Tamil Nadu and the western re- increase in the terrain temperature. In 2000, the average LST ranges between 18–23 C and gions registers an LST between 25–30 °C (Figure 6). By 2004, the LST average in Tamil the emissivity was more in the southern part as these areas had a higher LST of around 40 C Nadu declined to a few degrees due to the decrease in LST in the southern part. After and most of the coastal areas in the northern part of Tamil Nadu and the western regions 2004, due to various factors like urbanization and industrialization, LST kept on increas- registers an LST between 25–30 C (Figure 6). By 2004, the LST average in Tamil Nadu ing gradually, with the interior parts of Tamil Nadu experiencing a severe rise in LST. declined to a few degrees due to the decrease in LST in the southern part. After 2004, due Between 2008–2012, there was a bump in the trend in the entire study area, with a 30% to various factors like urbanization and industrialization, LST kept on increasing gradually, increase in the range between 35–42 °C and a decrease in the range between 15–20 °C by with the interior parts of Tamil Nadu experiencing a severe rise in LST. Between 2008–2012, 28%. In 2016, it also followed a similar trend with increased LST in the sub-urban areas there was a bump in the trend in the entire study area, with a 30% increase in the range ex bec tw ep ete t n he 35 W –4 es 2 ter Cna G nd haatsd r eu cnn reais ng e i in n t th he e w ran eg steer bn p etw or ee tin ons 15 of –20 TaC mb il N y 2a 8d % u.. I In n 2020 2016,, ag it a ai ls n o , t fhe oll o L w ST ed in acs rea ims iled ar t b ry en 1d 0% w i itn h tihe nc r re aa ng sed e of LS 3 T5i– n 42 th ° eC s a und b-u a rb rou annd are a as 147% excep d tetc hre eas We es in te rth n e G a hrea ats running in the western portions of Tamil Nadu. In 2020, again, the LST increased by 10% ranging between 15–25 °C. Thus, the time series analysis of LST reveals that there has been Earth 2022, 3 629 Earth 2022, 3, FOR PEER REVIEW 17 in the range of 35–42 C and around a 147% decrease in the area ranging between 15–25 C. Thus, the time series analysis of LST reveals that there has been an increase in the LST over an increase in the LST over two decades enormously due to various reasons and condi- tw tio o n ds ec th aa dt p es reen vo ar im l. An ous iln ycd re uas e e to in v ar arie o a b usy r 282% eason w s ain th d a cn on Ld SiT ti o of n s mto hra e t t p hra en v 3 ai5 l ° . A C o n b in sc erv rea ed se in area by 282% with an LST of more than 35 C observed and 12.46% and 31.66% increases and 12.46% and 31.66% increases in temperature ranging between 25–30 °C and 30–35 °C, inres tem pec pe tir v ael tu yr , e w rh aerea ngin sg LS be T t ra we n eg n in 2g 5 b –3 et 0wC eea nn 1d 5– 320 0– 3 °C 5 an Cd , r 2 e0 sp –25 ec t°C ive d ly e,cw reh ase ere da b sy L 6 S6% T r an ang di ng be 68% twe , e rn esp 15 e– c2 tiv 0 ely ( C a Tn ad ble 2 8 0– ).2 T 5he C ar d ee ac s w reait sh ed mb oy un 6t6 a% ino au ns r d e 6gio 8%n , s a res ls po e f ca ticv ed a ely ( rT ise ab in le L 8S ). in The the interior and the southern part experiences severe climatic changes due to urban ex- areas with mountainous regions also faced a rise in LS in the interior and the southern part pansion and a decrease in green cover. experiences severe climatic changes due to urban expansion and a decrease in green cover. (a) (b) (c) (d) Figure 6. Cont. Earth 2022, 3, FOR PEER REVIEW 18 Earth 2022, 3 630 (e) (f) Figure 6. LST map for Tamil Nadu representing the terrain temperature over two decades (a) 2000, Figure 6. LST map for Tamil Nadu representing the terrain temperature over two decades (a) 2000, (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. Table 8. Change analysis of LST for Tamil Nadu for two decades between 2000–2020. Table 8. Change analysis of LST for Tamil Nadu for two decades between 2000–2020. Area Coverage Change Area Coverage Change Area Coverage Change in Change 2 2 2 LST (km ) in Area (km ) in Area (km ) Area in Area Change Change Change Change Classe Area Coverage Coverag Area Coverage Coverag Area Coverage Coverage Coverag in Area in Area in Area in Area 2 2 2 (km ) (km ) (km ) s (°C) 2000 2004 e 2000– 2008 2012 e 2008– 2016 2020 2016– e 2000– LST Cover- Cover- Cover- Cover- 2004(%) 2012(%) 2020(%) 2020 (%) Classes age age age age 15–20 39325.74 36129.23 −8.12 32427.97 23257.45 −28.27 18138.61 13175.31 −27.36 −66.49 ( C) 2000– 2008– 2016– 2000– 2000 2004 2008 2012 2016 2020 20–25 29118.11 25574.10 −12.17 22142.12 18269.29 −17.49 13462.45 9252.15 −31.27 −68.22 2004 2012 2020 2020 25–30 16134.25 15604.11 (%) −3.28 12219.51 14367.75 17 (%) .58 17021.38 18145.74 6.6 (%) 0 12.(%) 46 30–35 20029.62 21303.74 6.36 22401.62 23295.88 3.99 24313.57 26372.68 8.46 31.66 15–20 39,325.74 36,129.23 8.12 32,427.97 23,257.45 28.27 18,138.61 13,175.31 27.36 66.49 35–41 11325.36 12335.21 8.91 14536.49 16394.36 12.78 19149.60 21048.45 9.91 85.85 20–25 29,118.11 25,574.10 12.17 22,142.12 18,269.29 17.49 13,462.45 9252.15 31.27 68.22 >41 14226.38 19213.07 35.05 26431.75 34574.73 30.80 38073.84 42157.11 10.72 196.33 25–30 16,134.25 15,604.11 3.28 12,219.51 14,367.75 17.58 17,021.38 18,145.74 6.60 12.46 3.5. Comparative Study between NDVI, SAVI, and LST Associated with Each LULC Class 30–35 20,029.62 21,303.74 6.36 22,401.62 23,295.88 3.99 24,313.57 26,372.68 8.46 31.66 In general, SAVI and NDVI are positively correlated, while the LST has a negative 35–41 11,325.36 12,335.21 8.91 14,536.49 16,394.36 12.78 19,149.60 21,048.45 9.91 85.85 correlation with these two indices. Analysis of the Spearman coefficient reveals that set- >41 14,226.38 19,213.07 35.05 26,431.75 34,574.73 30.80 38,073.84 42,157.11 10.72 196.33 tlements exhibit the highest coefficient value of 0.0061 with the increase in coefficient val- ues (Table 9). This is because urban areas have fewer plantations and very low permea- 3.5. Comparative Study between NDVI, SAVI, and LST Associated with Each LULC Class bility, which has led to an increase in LST for two decades in Tamil Nadu. Forest and water bodies have the least coefficient values due to healthy dense green cover and high In general, SAVI and NDVI are positively correlated, while the LST has a negative moisture content. The correlation values tend to increase from 2000 to 2020 because of a correlation with these two indices. Analysis of the Spearman coefficient reveals that settle- decrease in plantation and an increase in impervious layer concentration in Tamil Nadu. ments exhibit the highest coefficient value of 0.0061 with the increase in coefficient values Thus, to avoid the consequences of LST changes, planners and administrators should fo- (Table 9). This is because urban areas have fewer plantations and very low permeability, cus on increasing the green cover in urban areas where surface permeability is low to which has led to an increase in LST for two decades in Tamil Nadu. Forest and water avoid the impactful effect on ecosystem services. bodies have the least coefficient values due to healthy dense green cover and high moisture content. The correlation values tend to increase from 2000 to 2020 because of a decrease in plantation and an increase in impervious layer concentration in Tamil Nadu. Thus, to avoid the consequences of LST changes, planners and administrators should focus on increasing the green cover in urban areas where surface permeability is low to avoid the impactful effect on ecosystem services. Earth 2022, 3 631 Table 9. Spearman correlation between Normalized Difference Vegetation Indices (NDVI), Soil- adjusted vegetation index (SAVI), and land surface temperature (LST) by landuse and landcover (LULC) zones. All correlation coefficients are statistically significant at p  0.01. Correlation Coefficient p-Values LULC Class 2000 2020 Settlement 0.28 0.51 0.0061 Forest 0.06 0.11 0.0011 Plantation Agriculture 0.11 0.20 0.0049 Barren Land 0.18 0.24 0.0023 Waterbodies 0.15 0.17 0.0017 Fallow Land 0.17 0.27 0.0031 Crop Land Agriculture 0.12 0.22 0.0048 3.6. Assessing the Drought Intensity for 2020 Tamil Nadu experiences a tropical climate, which receives rainfall from monsoons or cyclones, depressions prevailing in the Bay of Bengal. The annual vegetation cycle of Tamil Nadu primarily depends on the southwest and northeast monsoon. If any failure in the monsoon occurs, then disaster begins. VCI is the commonly used index to portray the impacts of drought on vegetation irrespective of the climatic variations. When the VCI value exceeds 60, then crops sustain no impacts due to drought and when it falls below 20, severe impacts occur. In Tamil Nadu, during the year 2020, the drought-prone areas like southern coastal districts and a few western interior districts experience severe impacts due to drought leading to a subsequent deficit in crop yield. The northern districts and Western Ghats (VCI > 60) experience no vegetation stress and vegetation remains healthy because monsoon in those particular regions onset at the correct timeframe and provides enough rainfall for crops. The moderate drought occurs primarily in the outer fringes of severe drought-affected regions and certain riparian parts of the Cauvery deltaic regions, with VCI values ranging between 20 to 60 (Figure 7). 3.7. Monitoring the Vegetation Health by VHI Vegetation health generally depends upon the water availability for the survival of the vegetation prevailing. Water availability, i.e., soil moisture that is related to surface temperature, is also considered the major factor for deriving the health index. VHI also considers the VCI, which is related to stress either because of water availability or stress due to diseases that exist in the crop. VHI analyses of all the aforementioned conditions and mapping of the health of the crop, almost 35% of Tamil Nadu has stressed vegetation distribution, and the Western Ghats have healthy vegetation characteristics because of healthy vegetation circumstances and high moisture content (Figure 8). In the case of a failed monsoon, VHI can be improved by providing water supply to crops in the correct proportion by implementing precision agriculture methodologies or changing the irrigation pattern to avoid stress. Earth 2022, 3, FOR PEER REVIEW 20 Earth 2022, 3 632 Earth 2022, 3, FOR PEER REVIEW 21 Figure 7. VCI map (drought intensity map) of Tamil Nadu for 2020. Figure 7. VCI map (drought intensity map) of Tamil Nadu for 2020. 3.7. Monitoring the Vegetation Health by VHI Vegetation health generally depends upon the water availability for the survival of the vegetation prevailing. Water availability, i.e., soil moisture that is related to surface temperature, is also considered the major factor for deriving the health index. VHI also considers the VCI, which is related to stress either because of water availability or stress due to diseases that exist in the crop. VHI analyses of all the aforementioned conditions and mapping of the health of the crop, almost 35% of Tamil Nadu has stressed vegetation distribution, and the Western Ghats have healthy vegetation characteristics because of healthy vegetation circumstances and high moisture content (Figure 8). In the case of a failed monsoon, VHI can be improved by providing water supply to crops in the correct proportion by implementing precision agriculture methodologies or changing the irriga- tion pattern to avoid stress. Figure 8. Analyzing the vegetation health in 2020 using VHI for Tamil Nadu. Figure 8. Analyzing the vegetation health in 2020 using VHI for Tamil Nadu. 3.8. Comparative Study between VCI, VHI, and LST Associated with Each LULC Class Indices related to vegetation like VCI and VHI are positively correlated, while the LST is negatively correlated to that of the vegetation indices. Drought is the primary caus- ative factor for increasing LST, as clearly portrayed; as drought leads to the decline of crop health, this will result in the emergence of LST. The settlement has the highest spearman coefficient values of 0.0064 with increases in coefficient values of −0.31 and −0.54 for 2000 and 2020, respectively, while the forest has the minimal correlation of 0.0012 among all the LULC classes considered for our analysis (Table 10). Cropland and plantation have almost similar correlation values due to their similar characteristics. However, the corre- lation values for each class increased from 2000 to 2020 due to urbanization and a decrease in green cover resulting in increased LST. Table 10. Spearman correlation between Vegetation Condition Index (VCI), Vegetation Health In- dex (VHI), and Land Surface Temperature (LST) by landuse and landcover (LULC) zones. All cor- relation coefficients are statistically significant at p ≤ 0.01. Correlation Coefficient LULC Class p-Values 2000 2020 Settlement −0.31 −0.54 0.0064 Forest −0.09 −0.15 0.0012 Plantation Agriculture −0.12 −0.22 0.0048 Barren Land −0.20 −0.26 0.0025 Waterbodies −0.13 −0.15 0.0016 Fallow Land −0.18 −0.28 0.0032 Crop Land Agriculture −0.14 −0.24 0.0050 Earth 2022, 3 633 3.8. Comparative Study between VCI, VHI, and LST Associated with Each LULC Class Indices related to vegetation like VCI and VHI are positively correlated, while the LST is negatively correlated to that of the vegetation indices. Drought is the primary causative factor for increasing LST, as clearly portrayed; as drought leads to the decline of crop health, this will result in the emergence of LST. The settlement has the highest spearman coefficient values of 0.0064 with increases in coefficient values of 0.31 and 0.54 for 2000 and 2020, respectively, while the forest has the minimal correlation of 0.0012 among all the LULC classes considered for our analysis (Table 10). Cropland and plantation have almost similar correlation values due to their similar characteristics. However, the correlation values for each class increased from 2000 to 2020 due to urbanization and a decrease in green cover resulting in increased LST. Table 10. Spearman correlation between Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Land Surface Temperature (LST) by landuse and landcover (LULC) zones. All correlation coefficients are statistically significant at p  0.01. Correlation Coefficient p-Values LULC Class 2000 2020 Settlement 0.31 0.54 0.0064 Forest 0.09 0.15 0.0012 Plantation Agriculture 0.12 0.22 0.0048 Barren Land 0.20 0.26 0.0025 Waterbodies 0.13 0.15 0.0016 Fallow Land 0.18 0.28 0.0032 Crop Land Agriculture 0.14 0.24 0.0050 4. Discussion Our study on LULC changes in Tamil Nadu over two decades reveals that there is abundant growth in urban areas and fallow landscapes monotonically, about 150% and 120%, respectively, and shrinkage in cropland and plantation agriculture landforms by a notable margin. These changes are mainly due to the movement of people from the rural areas to the towns in Tamil Nadu, notably Chennai, Coimbatore, Trichy, and Madurai, seeking employment by leaving their owning premises, which results in the conversion of agricultural lands into fallow lands and similarity due to high demand urban-fringes are plotted and sold, leading to increase in settlements results in urbanization [25]. Due to the boom in construction, there is an increase in the percentage of impervious layers in landforms, which leads to several other consequences like reduction in green cover, degradation of groundwater, and saltwater intrusion in coastal belts [79], for example, Vedanta Sterlite copper industry (currently closed for violating environmental measures) in Tuticorin and thermal power stations in Tuticorin and Ennore extracted millions of gallons of groundwater drives saltwater intrusion and, toxic discharges degrades the groundwater in the coastal belts of Tuticorin and Chennai, respectively [80,81]. Another example, nu- clear power plants in Kalpakkam and Kudankulam also degrade the groundwater and environment due to the discharge of heavy metals and other toxic substances [36]. Surface permeability is assessed using SAVI, a modified NDVI index, which shows that the urban areas and fallow lands have very low surface permeability, resulting in an upsurge of LST [42]. From the NDVI map, it is clear that the healthiness of the vegetation deterio- rates during the period of study, and the reality is that major basins (Cauvery basin and Thamirabarani basin) experience stress in the plantation growth, due to inadequate water availability. The stopping or delayed release of Cauvery water from Karnataka dam’s by the Karnataka government is also a driving force of water scarcity in the Cauvery basin and several districts of Tamil Nadu [82]. Thanjavur (the rice bowl of Tamil Nadu) residing in Earth 2022, 3 634 the Cauvery delta plain experiences severe degradation in the healthiness of the crop after 2008, which has severe impacts on the economy of the state. Thus, proper planning must be employed in deltaic and basin areas to renovate the agricultural landforms to achieve sustainable development. VHI and VCI are used to show the healthiness of the vegetation in relation to drought and temperature, respectively. When a certain area is influenced by drought, health deteriorates, and rises in temperatures also cause stress in vegetation due to decreases in soil moisture; however, agricultural crops prevail due to water supply provided by farmers from various water sources. The main consequence of urbanization is global warming, i.e., an increase in earth temperature to a certain degree is clearly envisaged in the LST map; there is a spike in LST values of about 4–5 C in two decades. Due to deforestation, which minimizes the natural cooling effect, LST may rise further, causing severe health ailments to the people residing in those areas because of the trap of heat energy by the built-up areas and small roads with the added heat from the air conditioners, vehicle discharge still exacerbates the effect [83]. Southern districts like Rameswaram, Thoothukudi, Virudhunagar, Kanyaku- mari, Sivagangai, and part of Tirupur had the higher LST values in 2020; however, it can be controlled by improving green cover and erecting artificial groundwater recharge structures to enhance the growth of trees in urban areas, thereby, there will be increased chances of rainfall due to a phenomenon called evapotranspiration [4]. Recently, the LST value upsurges in Kanyakumari districts may be due to the conversation of agriculture and farmland into a national highway project implemented by the National Democratic Alliance (NDA) government. Future research should focus on the Kanyakumari district to identify the cause of the sudden increase in LST. Urban Sprawl (US) is another major influential factor in the conversion of land- scapes [84,85]. Urban areas tend to grow on the outer fringes of the major cities, which results in the shrinkage of production landforms and water bodies being encroached upon, leading to severe impacts on ecosystems. Our study showcases the growth of towns from 2000 to 2020 in the fringes; in this process, the people start to reside in those outer fringes and they start facing troubles like lack of infrastructures, sewage treatment, and proper water supply. To avoid those circumstances, proper urban planning and environmental management practices should be done to achieve sustainable development in those ar- eas [27]. With the increase in urban population and unsustainable replacement of green cover into impervious layer results to cause severe ecological and economic damages. Due to urbanization and population rise ESV (Ecosystem Service Value) falls rapidly, which results in a shortage of multi-utility services provided to the people with needs. Due to the conversion of agricultural land to other landscapes, there is a huge loss in ESV and Gross Domestic Product (GDP) of the state between 2000 and 2020 and demand for food grains increases; thus, ESV and GDP should be evaluated in future research. As the transformation of agricultural or forest land into impervious layers is uncontrollable in developing countries, certain norms of regulations should be incorporated strictly on the public to reduce the impacts of the transformation of landforms [31]. Similarly, certain schemes like the Conversion of Cropland to Forest Program (CCFP) can be implemented, and subsidies can be provided to the farmers who are involved in such schemes, which can be helpful for communities who are all switching their location to urban areas due to any reasons and gaining more ecosystem values. The comparative study of various indices using spearman’s coefficient shows that when the vegetation health and soil moisture deteriorate, then the surface temperature increases, and simultaneously the presence of the severity of drought and the health of vegetation deteriorates, then the area is highly susceptible to the upsurge of LST. The combined approach of RS, geographical information system (GIS), and LULC change the analysis associated with vegetation and temperature change measures provide useful information and efficient methods and modeling landscape changes on vegetation and land surface temperatures in Tamil Nadu. Therefore, this study also contributes several indices and indicators to monitor drought and vegetation health in Tamil Nadu. It also Earth 2022, 3 635 delivers useful information for urban planning and sustainable development as well as mitigation of environmental impact in Tamil Nadu. 5. Conclusions In this study, the creation of a LULC map using the RF Algorithm for the entire Tamil Nadu to analyze the spatial and temporal pattern of landcover changes and the accuracy of the LULC map was carried out. We found a rapid increase in the urban settlements and barren lands while the agricultural landscape decreased alarmingly due to various anthropogenic activities from 2000 to 2020. Various indices like NDVI, SAVI, LST, VHI, and VCI were derived using RS data for six different years during the last two decades. The NDVI map was used to identify the vegetation health based on reflectance from chlorophyll pigment. Using NIR imagery, soil moisture was studied using SAVI, which identified the regions having low surface permeability and the areas having low moisture. The increase in LST values reflects the relationship between urbanization and its impacts on climatic conditions. The intensity of drought in Tamil Nadu is depicted by VCI, while VHI is used to deduce the health of vegetation by including the soil moisture with the NDVI, which will be used to portray the emergence of heat islands in future research. LST is inversely related to VHI and VCI, and the Spearman coefficient relationship shows that urban areas are at a higher risk for the evolution of heat islands. To conclude, our study is done primarily to analyze the impact of landscape changes on vegetation and LST, and a relationship is deduced along with the identification of hotspot areas for the emergence of LST in various districts in Tamil Nadu and plan developmental proposals at the landscape level and maintain the economic benefits and ecological gains of the different land cover classes and monitor the region. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/earth3020036/s1. Figure S1: Enhanced vegetation index (EVI) for Tamil Nadu 2000 and 2020; Figure S2: Surface Urban Heat Island for Tamil Nadu 2020; Figure S3: Temperature conditon index for Tamil Nadu 2020; Figure S4: NDVI difference between 2000 and 2020; Figure S5: EVI difference between 2000 and 2020. Author Contributions: Conceptualization, M.S. and R.P.; methodology, M.S. and R.P.; software, M.S. and R.P.; validation, M.S., R.P., P.C. and P.M.; formal analysis, M.S., R.P. and P.C.; investigation, M.S. and R.P.; resources, M.S., R.P. and P.C.; data curation, M.S., R.P., P.C. and P.M.; writing—original draft preparation, M.S. and R.P.; writing—review and editing, M.S., R.P., P.C. and P.M.; visualization, M.S. and R.P.; supervision, R.P. and P.C.; project administration, R.P. and P.C.; funding acquisition, M.S., R.P. and P.C. All authors have read and agreed to the published version of the manuscript. 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Spatio-Temporal Analysis of the Impact of Landscape Changes on Vegetation and Land Surface Temperature over Tamil Nadu

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Article Spatio-Temporal Analysis of the Impact of Landscape Changes on Vegetation and Land Surface Temperature over Tamil Nadu 1 , , † 2 , † 1 2 Mohamed Shamsudeen * , Rajchandar Padmanaban , Pedro Cabral and Paulo Morgado NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal; pcabral@novaims.unl.pt Centre of Geographic Studies, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal; rajchandar@edu.ulisboa.pt (R.P.); paulo@campus.ul.pt (P.M.) * Correspondence: m20210707@novaims.unl.pt † These authors contributed equally to this work. Abstract: Land-use changes adversely may impact ecological entities and humans by affecting the water cycle, environmental changes, and energy balance at global and regional scales. Like many megaregions in fast emerging countries, Tamil Nadu, one of the largest states and most urbanized (49%) and industrial hubs in India, has experienced extensive landuse and landcover change (LULC). However, the extent and level of landscape changes associated with vegetation health, surface permeability, and Land Surface Temperature (LST) has not yet been quantified. In this study, we employed Random Forest (RF) classification on Landsat imageries from 2000 and 2020. We also computed vegetation health, soil moisture, and LST metrics for two decades from Landsat imageries to delineate the impact of landscape changes in Tamil Nadu using Google Earth Engine (GEE). The level of vegetation health and drought for 2020 was more accurately assessed by combining the Temperature Condition Index (TCI) and Vegetation Condition Index (VCI). A Soil moisture index was subsequently used to identify surface permeability. A 75% expansion in urban areas of Tamil Nadu Citation: Shamsudeen, M.; was detected mainly towards the suburban periphery of major cities between 2000 and 2020. We Padmanaban, R.; Cabral, P.; Morgado, observed an overall increase in the coverage of urban areas (built-up), while a decrease for vegetated P. Spatio-Temporal Analysis of the (cropland and forest) areas was observed in Tamil Nadu between 2000 and 2020. The Soil-Adjusted Impact of Landscape Changes on Vegetation and Land Surface Vegetation Index (SAVI) values showed an extensive decline in surface permeability and the LST Temperature over Tamil Nadu. Earth values showed an overall increase (from a maximum of 41 C to 43 C) of surface temperature in 2022, 3, 614–638. https://doi.org/ Tamil Nadu’s major cities with the highest upsurge for urban built-up areas between 2000 and 2020. 10.3390/earth3020036 Major cities built-up and non-vegetation areas in Tamil Nadu were depicted as potential drought hotspots. Our results deliver significant metrics for surface permeability, vegetation condition, surface Academic Editor: Ioannis Gitas temperature, and drought monitoring and urges the regional planning authorities to address the Received: 25 April 2022 current status and social-ecological impact of landscape changes and to preserve ecosystem services. Accepted: 20 May 2022 Published: 26 May 2022 Keywords: urban ecosystem services; landuse and landcover; Random Forest classification; land Publisher’s Note: MDPI stays neutral surface temperature; environmental monitoring; remote sensing with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction In the last three decades, the growth of landuse and landcover changes (LULC) have led to an enormous transformation in global landscape patterns [1–4]. Landscape changes Copyright: © 2022 by the authors. result from many factors, including variability in abiotic conditions, such as climate, to- Licensee MDPI, Basel, Switzerland. pography, and soils, biotic interactions that generate spatial patterning even under homo- This article is an open access article geneous environmental conditions and past and present patterns of human settlement. distributed under the terms and Land-use changes contributed to a reduction in vegetation health, surface permeability, and conditions of the Creative Commons the occurrence of Surface Heat Island (SHI), Urban Heat Island (UHI), and drought [5–8]. It Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ also drives climate change which affects humans and wildlife [9]. To enable sustainability 4.0/). and changes in the environmental structure, it is necessary to understand the subtleties of Earth 2022, 3, 614–638. https://doi.org/10.3390/earth3020036 https://www.mdpi.com/journal/earth Earth 2022, 3 615 landcover change [10]. It is given high importance as most of the metropolitan cities have encountered substantial landcover changes over the decades [11–13]. Henceforth, these metropolitan areas consume more energy and produce severe environmental problems and thus lend land, water, and air pollution, which affects the functions of ecosystems [5,14,15]. Land Surface Temperature (LST) is an important parameter that influences the partition of energy between ground and vegetation and determines the surface air temperature [14]. LST is also an important factor in global climate change, vegetation growth, and UHI [15,16]. A sudden increase in land-use changes also affects environmental factors, such as rainfall, temperature, and groundwater level, which threatens the development process of the urban areas. Thus, examining the impact of landscape changes on vegetation and LST is crucial for efficient urban planning and management of land-use zones [17,18]. In India, one-third of the people live in cities [19]. During the last decade, the 4% increasing trend in urbanization expresses that people from the Tamil Nadu state and other neighboring states and millions of north Indian states people have relocated from rural areas to a metropolitan city to find a job and make their lives in cities [20]. The urbanization level in India went from 27.7% to 31.1%, with an increasing amount of 3.3% from 2001 to 2011 [21,22]. The statistics show that the urban population level in India will increase to 600 million, which doubles the rate from the present scenario by 2031 [23]. Due to urbanization, the cities in India are affected by unhygienic health conditions and damage to ecosystems [24]. In India, Tamil Nadu secured the first rank in urbanization among the fifteen primary states listed by the country [25]. The state has attained the uppermost level of urbanization with 43.86% in the country, based on the 2001 census [26]. Around 27 million people live in the metropolitan city of Tamil Nadu [27]. The urbanization pattern of Tamil Nadu is dispersed with municipalities in almost every district [28]. This rapid rise in urbanization in Tamil Nadu leads to many problems, including deforestation, water scarcity, flood, pollution, and other environmental damages [29]. Thus analyzing the impact of landscape changes on vegetation and land surface temperature over Tamil Nadu is crucial for sustainable urban planning and management [30]. Global Remote Sensing (RS) and satellite imageries processing techniques delivered considerable potential for landscape change patterns and vegetation change analysis. Thus, this study attempts to examine the impact of landscape changes on vegetation and land surface temperature using RS. Several studies have been carried out to examine the LULC changes, which are important indicators for examining the relationship between the environment and anthropogenic activities that can be attained from satellite images and other image processing techniques [31–35]. These are especially useful for a state like Tamil Nadu, where ground/field monitoring data are not available and scarce [32,36]. But the availability of satellite images helps to overcome this issue, especially Google Earth Engine (GEE) cloud computing platform provides efficient methods and freely available spatial data for storing, accessing, and analyzing spatial datasets on the high-performance servers [37]. GEE was launched by Google in 2010 and makes freely available RS datasets via its internet-based Application Programming Interface (API) provided by Python and a JavaScript web-based Interactive Development Environment (IDE) used to detect changes, landuse changes, map trends, landcover changes and quantify differences on the Earth’s surfaces and atmospheres [37]. The existing studies have focused mainly on the spatio-temporal relationship between LULC and environmental changes [10,32,38–40]. However, the multivariate relationship between LULC changes, vegetation, soil moisture, and land surface temperature has not been analyzed widely. This paper aims to fill this gap by examining the LULC changes effect on landuse vegetation and land surface temperature using GEE. The availability of GEE codes helps future research to integrate different datasets from Tamil Nadu at different resolutions. The impact of land-use changes should be analyzed by considering the following research questions: (i) how could landscape changes make a variation in the Spatial and Temporal pattern of the land-use change? [41]; (ii) how could landscape changes affect the Earth 2022, 3 616 surface and land temperature conditions? [42], and (iii) how does the relationship exist between land-use change patterns and environmental factors (temperature, soil moisture, and vegetation) [43]. This paper is organized as follows: Sections 2.1 and 2.2 describe the study area and data used in this work. Section 2.3 provides information about the observation and assessment of LULC Classification, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), LST, Vegetation Condition Index (VCI), and Temperature Condition Index (TCI), respectively. Section 3 then combines the results from the individual stages of the research design to evaluate LULC changes to assess their impact on vegetation and LST. Finally, Sections 4 and 5 provide the discussion and conclusion, respectively. 2. Materials and Methods 2.1. Study Area Our study was undertaken in Tamil Nadu, a state located in the southern part of India [10], which has a seashore surrounded by one dimension and consists of various types of landscapes such as hilly terrain, wetlands, and forests [32]. It receives rainfall predominantly during the monsoon season in the western parts and interior districts, while the sea bordered districts receive rainfall from the emergence of cyclones [44]. Its capital city is Chennai and is surrounded on the north by Andhra Pradesh, on the east by the Bay of Bengal, on the west by Kerala, and on the south by the Indian Ocean (Figure 1). The Western Ghats and the Eastern Ghats mountain ranges converge in the Nilgiri hills in Tamil Nadu, making it the only state in India to have both. The total area of the state is 130,058 sq km making it the eleventh largest state in the country [45]. The geographic location of Tamil Nadu spans between 11.12 N 78.65 E with an average elevation of 247 m and a maximum of 2599 m. Tamil Nadu has the largest tourism industry in India [29]. Tamil Nadu is the most urbanized state in India, one of the foremost industrialized states, and the highest tax-paying state to the central government. The urbanization trend of Tamil Nadu is supposed to exaggerate in the upcoming years. The climate of the region is sub-humid continental monsoon with an average annual temperature of 28.8 C. The average yearly precipitation of the state is about 945 mm, of which 32% is through the southwest monsoon, and 48% is through the northeast monsoon. The cropping pattern in the majority of the states depends on the monsoon, if the onset of the monsoon is delayed or fails, then the probability of a drought event occurring is high [46]. 2.2. Data We used the Google Earth Engine (GEE) for most of the image processing and analysis for this paper. On the GEE computing platform, the static input variables are existing datasets, terrain, and geographic location, whereas the dynamic inputs are data acquired from RS [47]. Data were collected between October to December, which is usually the wet season because dry season data leads to low accuracy results on the vegetation and temperature changes while doing the mean of all images for every single year. So we only used the winter and monsoon data to classify landscape changes in vegetation along with the soil moisture over land surface temperature [46]. For the classification operation, data were collected from or uploaded to the GEE cloud computing platform. High-resolution satellite images from the Landsat sensor with a spatial resolution of 30 m and temporal resolution of 16-day images for the monsoon period October to December 2000, 2004, 2008, 2012, 2016, and 2020 were accessed from the GEE cloud computing platform. (Earth Engine Data Catalog: https://developers.google.com/earth-engine/datasets/catalog/, accessed on 1 January 2020) [48] (Table 1). Earth 2022, 3, FOR PEER REVIEW 4 Earth 2022, 3 617 Figure 1. Geographic location and area of Tamil Nadu State. The maps were generated using Figure 1. Geographic location and area of Tamil Nadu State. The maps were generated using Google Google Maps. Maps. The geographic location of Tamil Nadu spans between 11.12° N 78.65° E with an av- erage elevation of 247 m and a maximum of 2599 m. Tamil Nadu has the largest tourism industry in India [29]. Tamil Nadu is the most urbanized state in India, one of the foremost industrialized states, and the highest tax-paying state to the central government. The urbanization trend of Tamil Nadu is supposed to exaggerate in the upcoming years. The climate of the region is sub-humid continental monsoon with an average annual temperature of 28.8 °C. The average yearly precipitation of the state is about 945 mm, of which 32% is through the Earth 2022, 3 618 Table 1. Technical description of the Landsat imageries used in this study. Temporal Name Spatial Resolution Date of Acquisition Resolution 2000-10-10, 2000-10-17, 2000-10-19, 2000-10-26, 2000-11-04, 2000-11-20,2000-11-27, 2000-12-06, 2000-12-15, 2000-12-20 & 2000-12-29 2004-10-07, 2004-10-12, 2004-10-14, 2004-10-21, 2004-11-08, 2004-11-22, 2004-11-29, 2004-12-01, Landsat 7 Collection 1 Tier 1 2004-12-10, 2004-12-17 & 2004-12-26 TOA Reflectance 30 m 16 day 2008-10-09, 2008-10-16, 2008-10-18, 2008-10-23, LE07/C01/T1_SR 2008-11-01, 2008-11-03, 2008-11-19, 2008-12-03, 2008-12-12, 2008-12-21 & 2008-12-28 2012-10-04, 2012-10-11, 2012-10-13, 2012-10-27, 2012-11-05, 2012-11-14, 2012-11-21, 2012-11-30, 2012-12-07, 2012-12-14, 2012-12-18 & 2012-12-28 2016-10-07, 2016-10-15, 2016-10-24, 2016-10-31, 2016-11-08, 2016-11-16, 2016-11-25, 2016-12-02, Landsat 8 Collection 1 Tier 1 2016-12-10, 2016-12-18 & 2016-12-26 TOA Reflectance 30 m 16 day 2020-10-07, 2020-10-15, 2020-10-23, 2020-10-31, LC08/C01/T1_SR 2020-11-08, 2020-11-16, 2020-11-24, 2020-12-02, 2020-12-10, 2020-12-18 & 2020-12-26 2.3. Methods Earth 2022, 3, FOR PEER REVIEW 6 To summarize our research design, we provided a schematic representation for the methodological framework used in this study (Figure 2), which is described in the follow- ing subsections. Figure 2. Methodology for analyzing landscape change and vegetation health (productivity) (NDVI: Figure 2. Methodology for analyzing landscape change and vegetation health (productivity) (NDVI: Normalized Difference Vegetation Index; VCI: Vegetation Condition Index; LST: Land Surface Tem- Normalized Difference Vegetation Index; VCI: Vegetation Condition Index; LST: Land Surface Tem- perature; TCI: Temperature Condition Index; VHI: Vegetation Health Index; LULC: Land-use and perature; TCI: Temperature Condition Index; VHI: Vegetation Health Index; LULC: Land-use and Land cover). Land cover). 2.3.1. Landuse and Landcover Classification Land-use classification describes both landuse and landcover (LULC) significantly [32]. LULC change classification helps the policymakers to understand the environmental change dynamics to ensure sustainable development [49]. Therefore, the classification of LULC into different levels is essential [32]. Together, these classification system levels are described as a hierarchy system that is useful in unfolding, monitoring, and forecasting LULC changes [39]. The sources of classifying LULC changes are mainly satellite images and aerial photographs [50,51]. In this study, we analyzed the changes in LULC with seven classes (Table 2) over two decades using Random Forest classification. The Random Forest classification technique is used to enhance the contiguities between the data points. The performance of RF re- quires the following steps [52,53]. Developing the N- Tree bootstrap model using satellite imageries. Based on DN values, develop an unpruned classification for each bootstrap model. In terms of DN values, gather the N-number of polygons. Select the number of classifications for land-use classes. Generate results of land-use classification. LULC classification was done in R studio (4.1.3) software based on classes and meth- ods for spatial data, Raster Geospatial Data Abstraction Library (Rgdal), Raster, and Ran- dom forests packages [54,55]. Earth 2022, 3 619 2.3.1. Landuse and Landcover Classification Land-use classification describes both landuse and landcover (LULC) significantly [32]. LULC change classification helps the policymakers to understand the environmental change dynamics to ensure sustainable development [49]. Therefore, the classification of LULC into different levels is essential [32]. Together, these classification system levels are described as a hierarchy system that is useful in unfolding, monitoring, and forecasting LULC changes [39]. The sources of classifying LULC changes are mainly satellite images and aerial photographs [50,51]. In this study, we analyzed the changes in LULC with seven classes (Table 2) over two decades using Random Forest classification. The Random Forest classification technique is used to enhance the contiguities between the data points. The performance of RF requires the following steps [52,53]. Table 2. Description of Landuse and Landcover (LULC) classes. No. LULC Classes Land-Uses Included in the Class 1 Settlement Urban, Rural, Mining 2 Waterbodies Rivers, Lakes, Ponds, Streams, and Canals 3 Forest Deciduous, Evergreen, Scrub Forest, and Swamp/Mangroves Agricultural plantation (tea, coffee, and rubber), Horticultural plantation 4 Plantation Agriculture (coconut, citrus fruits, orchards, fruits, and vegetable gardens), and Agro-horticultural plantation Cropped in more than two seasons 5 Crop Land Agriculture Paddy, rice, sugarcane, cotton, and groundnut Grass dominated land cover, Shrub and bush dominated land cover, and small 6 Fallow Land tree dominated land cover 7 Barren Land Bare Exposed Rock, Mixed Barren Land, and Sand dunes Developing the N- Tree bootstrap model using satellite imageries. Based on DN values, develop an unpruned classification for each bootstrap model. In terms of DN values, gather the N-number of polygons. Select the number of classifications for land-use classes. Generate results of land-use classification. LULC classification was done in R studio (4.1.3) software based on classes and methods for spatial data, Raster Geospatial Data Abstraction Library (Rgdal), Raster, and Random forests packages [54,55]. The land-use classification results were validated using the GEE images obtained from the year 2000 to 2020. Randomly 75 points were selected, and pixel values of these selected points were extracted for the various study periods. The accuracy assessment performed by comparing land-use classification extracted points with the GEE Image results in identifying those exact points on the GEE. To quantify the accuracy of the results, the kappa coefficient was used in the ERDAS Imagine (8.7) software [56]. The confusion matrix is used in calculating the user and producer accuracies. The kappa coefficient’s accuracy of more than 80% shows the desired land-use classification. Henceforth, the classified results are ancillary to the GEE reference data. The producer accuracy (1), user ’s accuracy (2), overall accuracy (3), and Kappa coefficient (4) are calculated using the following Equations [57]. Number o f accurately classi f ied pixels in the each category o f LULC ProducerAccuracy =  100 (1) Total number o f re f erence pixels in that category (The column total) Number o f accurately classi f ied pixels in the each category o f LULC User s Accuracy =  100 (2) Total number o f re f erence pixels in that category (The Row total) Earth 2022, 3 620 Total number o f accurately classi f ied pixels (Diagonal) Overall Accuracy =  100 (3) Total number o f re f erence pixels (Obs Exp) Kappa coe f f icient = (4) (1 Exp) where Obs = Observed Overall accuracy, represents accuracy reported in error matrix, Exp = Expected correct, represents correct classification. 2.3.2. Normalized Difference Vegetation Index (NDVI) NDVI estimates vegetation by calculating the disparity between infrared, in which vegetation strongly reflects light portions and red light, in which vegetation absorbs light portions [58]. The values of NDVI range from 1 to 1. In this contrast, negative values almost depict the water bodies [59]. On the other hand, the NDVI value near +1 indicates the portion with dense vegetation areas [60]. The value of NDVI around 0 may not be green leaves, which could be urbanized areas [61]. NDVI uses the following Formula (5) to estimate the vegetation areas [61]. NIR RED NDVI = (5) NIR + RED where NIR—Near-Infrared Band. The vegetation with high healthy condition reflects almost close to Near Infrared (NIR) and red in terms of higher different frequencies [58]. The formula results create inducements between the values from 1 to 1 [62]. The indication of low reflectance near red and high reflectance in near-infrared represents the high yield of NDVI [59]. In this study, we extracted NDVI values at regular intervals to differentiate the different growth stages of vegetation productivity and its coverage, i.e., as high productivity, low productivity, and medium productivity. The coverage changes and the productivity of vegetation are quantified using the R packages, Sp, Rgdal, Raster, and rts [54]. 2.3.3. Soil Adjusted Vegetation Index (SAVI) Surface permeability in the study area region was assessed using the Soil Adjusted Vegetation Index (SAVI). In general, SAVI specifies coverage of vegetation and health concerning soil moisture and thus accounts for the high inconsistency of built-up and non- built-up land cover in urban areas [63]. In SAVI, a transformation technique is presented to minimize the effects of external factors from spectral vegetation indices. SAVI controls for the influence of soil brightness in the NDVI and it reduces the brightness of soil-related noise in the coverage of vegetation estimation [32]. Vegetation health is strongly associated with surface permeability and thus provides an essential identification of impermeable surfaces, especially in urban areas. SAVI is calculated using Equation (6) [32]. (NIR + RED) SAVI =  (1 + L) (6) (NIR + RED + L) where RED is the pixel value of band 3 (RED), and the NIR band is the pixel value of Near-Infrared band 4. L—Soil brightness correction factor [64]. Dense vegetation and high permeable surface areas L = 0, Less vegetation portion and impermeable surface areas L = 1 [65]. Due to the vegetation and coverage of built-up land, the inconsistency level was fixed at L = 0.5 [66]. SAVI was calculated for the satellite images for the period from 2000 to 2020. The SAVI was extracted at natural breaks to differentiate surface permeability degrees. The computation of area coverage for soil permeability zone between 2000 to 2020 was done using SAVI. For each LULC class, the areal coverage and standard deviation of SAVI are computed. Earth 2022, 3 621 2.3.4. Land Surface Temperature (LST) In this study, the LST index was calculated for each pixel in the satellite images from 2000 to 2020 to quantity the radiative skin temperature of the land surface and its elements, which depends on the optical brightness and reflectance of the surface (Albedo) [32]. In general, low SAVI with soil and built-up lands having high albedo [67]. On the other hand, high SAVI with dense vegetation portions has a low albedo, indicating variability in the climatic condition over an urban and dense forest region associated with surface permeability degrees [68]. The LST of each pixel was calculated using Equation (7) [68]. L = fL L  Q Q g DN 1 + L (7) l max min CALMAX CALMIN MIN where, L = the spectral radiance that is scaled to Q in W/(m *sr *m) MAX CALMAX L = the spectral radiance that is scaled to Q in W/(m *sr *m) MIN CALMIN Q = the maximum quantized calibrated pixel value (corresponding to L ) in CALMAX MAX DN = 255 Q = the minimum quantized calibrated pixel value (corresponding to L ) in CALMIN MIN DN = 1 LST was applied to the satellite images for the period from 2000 to 2020. The LST was extracted at natural breaks to differentiate with SAVI. For each LULC class, the areal cover- age and standard deviation of LST were computed. Using Spearman correlation analysis, the quantification between the association of surface permeability and LST was achieved. 2.3.5. Vegetation Condition Index (VCI)—Drought Intensity and Temperature Condition Index Kogan’s vegetation condition index delivers the relative Normalized Difference Vege- tation Index changes concerning the historical NDVI pixel values [69]. VHI can be derived based on both the LST and NDVI [70]. It distinguishes the present vegetation index, i.e., NDVI or EVI (Enhanced Vegetation Index), with the values observed in the same data period of different previous years within particular significant pixel values. The VCI calculated as per the Formula (8) [71] shown below NDVI NDVI min VCI =  100 (8) NDVI NDVI max min where NDVI and NDVI are the maximum and minimum NDVI values for a pixel max min during a period of time [72]. As a resulting percentage of this formula, Table 3 represents bad to good vegetation conditions in different colors. Table 3. Interpretation of the VCI, recommended practice by the United Nations, office for outer space affairs UN—Spider knowledge portal. (Recommended Practice: Drought monitoring using the Vegetation Condition Index (VCI): https://un-spider.org/book/export/html/9206, accessed on 5 January 2020) [73]. Value Category >40% No drought 30–40% Light drought 20–30% Moderate Drought 10–20% Severe Drought 0–10% Extreme drought Earth 2022, 3 622 2.3.6. Temperature Condition Index (TCI) The Temperature Condition Index (TCI) proposed by Kogan was used to determine the stress effect on the vegetation possessed by the temperature condition and enormous wetness [74]. The implementation of TCI is anticipated by taking minimum and maximum temperatures and correcting them to project various vegetation types for the different temperature conditions represented by (9) [75] LST LST max TCI = (9) LST LST max min where LST—Land Surface Temperature, LSTmax, LSTmin—Maximum and Minimum values of Land Surface Temperature. 2.3.7. Vegetation Health Index (VHI) Vegetation Health Index (VHI) assesses the effectiveness of drought over the area by incorporating vegetation health (i.e.,) lower than average NDVI and higher than the temperature on the vegetation conditions [76,77]. Vegetation Health Condition is the combination of both VCI and TCI written as (10) [78] VHI = a VCI + (1 a) TCI (10) where VCI—Vegetation Condition Index, TCI—Temperature Condition Index, and — parameter which quantifies the involvement of each component in the vegetation health. 3. Results 3.1. Analyzing the Landuse Landcover Changes Seven LULC classes, namely urban settlements, forest, plantation agriculture, barren land, water bodies, fallow land, and crop-land agriculture, are considered for the analysis, with a spatial extent of about 130,159.46 km . Analysis of the accuracy of the classified LULC was achieved using user accuracy, producer accuracy, and kappa coefficient using reference data obtained from other sources. Accuracy assessment for 2000 obtained a kappa coefficient of 0.9 with a producer and user accuracy value of 95.62 and 92.15, respectively. For the year 2020, the kappa coefficient is 0.85 and 89.35, 94.16 for producer and user accuracy, respectively (Table 4). Table 4. Accuracy assessment for generated LULC map. 2000 2020 LULC Classes Producer Accuracy User Accuracy Producer Accuracy User Accuracy Settlement 85.06 80.21 79.14 84.27 Forest 89.12 89.95 82.26 91.66 Plantation Agriculture 88.75 91.64 84.59 86.74 Barren Land 93.41 86.35 86.03 88.35 Waterbodies 89.23 94.72 86.41 86.98 Fallow Land 92.39 84.83 87.26 92.83 Crop Land Agriculture 95.62 92.15 89.35 94.16 Kappa 0.90 0.85 Urban settlements and barren lands increased rapidly by 150% and 120%, respectively, while all other five classes decreased in considerable amounts (Figure 3). The aerial declination of classes forest, plantation-agriculture, water bodies, fallow land, and cropland- agriculture are 21%, 36%, 2%, 14%, and 4%, respectively. The sudden evolution of sub-urban Earth 2022, 3 623 periphery landforms from cropland to impervious constructions was observed, which is depicted as the rapid increase in the settlements as seen in Figure 3 as red patches. Chennai and its fringes had rapid urban development and other sub-urban cities also urbanized due to the migration of the population to cities for various purposes. The barren land also increased by 6477 km mainly due to urbanization, leaving the agricultural lands, leading to an increase in uncultivable barren land and water bodies change is around Earth 2022, 3, FOR PEER REVIEW 11 185 km , which is at an alarming rate of sign (Table 5). Thus, the landscape of Tamil Nadu is urbanized with the decrease in fertile cropland pastures over the last two decades and the conversion of those lands into barren landforms. Figure 3. LULC classification map for 2000 (Left) and 2020 (Right). Figure 3. LULC classification map for 2000 (Left) and 2020 (Right). Table 5. Comparative analysis between various classes over two decades. Table 5. Comparative analysis between various classes over two decades. Area (Sq.km) Area (Sq.km) LULC Changes LULC Changes Area (Sq.km) Area (Sq.km) LULC Changes LULC Changes LULC Class LULC Class 2000 2000 2020 2020 2000–2020 2000–2020 2000–2020 2000–2020 ( (%) %) Settlement 5103.29 12,721.7 7618.41 149.28 Settlement 5103.29 12,721.7 7618.41 149.28 Forest 21,954.34 17,407.97 −4546.37 −20.70 Forest 21,954.34 17,407.97 4546.37 20.70 Plantation Agriculture 9125.72 5905.68 −3220.04 −35.28 Plantation Agriculture 9125.72 5905.68 3220.04 35.28 Barren Land 5385.57 11,863.2 6477.63 120.27 Barren Land 5385.57 11,863.2 6477.63 120.27 Waterbodies 9274.63 9089.49 −185.14 −1.99 Waterbodies 9274.63 9089.49 185.14 1.99 Fallow Land 31,064.4 26,997.57 −4066.83 −13.09 Fallow Land 31,064.4 26,997.57 4066.83 13.09 Crop Land Agriculture 48,224.51 46,173.82 −2050.69 −4.25 Crop Land Agriculture 48,224.51 46,173.82 2050.69 4.25 TOTAL 130,159.46 130,159.43 TOTAL 130,159.46 130,159.43 3.2. Assessment of Vegetation Health Normalized Difference Vegetation Indices (NDVI) are primarily used to detect the presence of vegetation and the health status of existing plantations. For the analysis car- ried out over two decades in Tamil Nadu, the plantations are undergoing severe stress and agriculture is becoming difficult as the years pass on. From this study, nearly one- third of the entire area was stress-free in 2000 and the healthy vegetation pattern is clearly visible in river basins because of the high reflectance value in the NIR band (Figure 4). The year 2004 and 2008 has almost the same healthiness in the outpost with slight declination in the landscapes having high productivity compared to that of 2000. In 2012, there was around 50 percent increase in the area ranging between 0.1 to 0.2, which clearly reveals that few plantations are undergoing stress, whereas a lot of agricultural areas are left Earth 2022, 3 624 3.2. Assessment of Vegetation Health Normalized Difference Vegetation Indices (NDVI) are primarily used to detect the presence of vegetation and the health status of existing plantations. For the analysis carried out over two decades in Tamil Nadu, the plantations are undergoing severe stress and agriculture is becoming difficult as the years pass on. From this study, nearly one-third of the entire area was stress-free in 2000 and the healthy vegetation pattern is clearly visible Earth 2022, 3, FOR PEER REVIEW 12 in river basins because of the high reflectance value in the NIR band (Figure 4). The year 2004 and 2008 has almost the same healthiness in the outpost with slight declination in the landscapes having high productivity compared to that of 2000. In 2012, there was around uncultivated, which is derived from the analysis of high productivity landform changes 50 percent increase in the area ranging between 0.1 to 0.2, which clearly reveals that few in comparison with 2008 in which change is around 20%. By comparative analysis of plantations are undergoing stress, whereas a lot of agricultural areas are left uncultivated, NDVI maps between 2016 and 2020, the southern portion of the study area is undergoing which is derived from the analysis of high productivity landform changes in comparison severe stress, as is clearly visible with a decrease in highly productive land by around with 2008 in which change is around 20%. By comparative analysis of NDVI maps between 45%. From the overall analysis of the healthiness of the vegetation carried over for two 2016 and 2020, the southern portion of the study area is undergoing severe stress, as is decades, we learned that areas with no plantation increased by 218% and low productivity clearly visible with a decrease in highly productive land by around 45%. From the overall (0.1–0.3) areas increased adversely by about 600% and the average productive land (0.3– analysis of the healthiness of the vegetation carried over for two decades, we learned that 0.5) changes around 7.7% and there is a decrease in the healthy regions by about 75% areas with no plantation increased by 218% and low productivity (0.1–0.3) areas increased roughly (Table 6). Southern Tamil Nadu has an unhealthy vegetation pattern, while the adversely by about 600% and the average productive land (0.3–0.5) changes around 7.7% western portion possesses good healthiness due to the Western Ghats existing in that par- and there is a decrease in the healthy regions by about 75% roughly (Table 6). Southern ticular belt. Tamil Nadu has an unhealthy vegetation pattern, while the western portion possesses good healthiness due to the Western Ghats existing in that particular belt. (a) (b) Figure 4. Cont. Earth 2022, 3, FOR PEER REVIEW 13 Earth 2022, 3 625 (c) (d) (e) (f) Figure 4. NDVI map for Tamil Nadu depicting the healthiness of the vegetation (a) 2000, (b) 2004, Figure 4. NDVI map for Tamil Nadu depicting the healthiness of the vegetation (a) 2000, (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. (c) 2008, (d) 2012, (e) 2016, (f) 2020. Earth 2022, 3 626 Table 6. Change in the areal distribution of the NDVI classes for Tamil Nadu for two decades. Change Change Change Change Area Coverage Area Coverage Area Coverage in Area in Area in Area in Area Vegeta- 2 2 2 (km ) (km ) (km ) Cover- Cover- Cover- Cover- NDVI tion age age age age Classes Produc- 2000– 2008– 2016– 2000– tivity 2000 2004 2008 2012 2016 2020 2004 2012 2020 2020 (%) (%) (%) (%) 1–0.1 No 6507.97 9111.16 40.13 10,412.75 11,714.35 12.50 18,222.32 20,695.35 13.57 218.00 0.1–0.2 Low 2603.18 3904.78 50.68 5206.37 7809.56 50.00 11,714.35 13,146.10 12.22 405.73 0.2–0.3 Low 1301.59 3253.98 50.00 3774.62 4555.58 20.68 7809.56 10,282.59 31.66 690.00 0.3–0.4 Average 57,270.16 50,762.18 11.36 54,666.97 66,381.32 21.42 67,813.07 68,333.71 0.76 19.31 0.4–0.5 Average 27,333.48 29,285.87 7.17 26,682.68 15,619.13 41.46 10,542.91 10,152.43 3.70 62.85 0.5–1 High 35,143.05 33,841.45 3.70 29,416.03 24,079.48 18.14 14,057.21 7549.24 46.29 78.51 3.3. Soil Moisture Assessment SAVI is the ideal index to assess soil moisture which is evaluated based on the surface permeability. When the SAVI value ranges between 1 to 0.1, then the area may be either water bodies or areas with no soil moisture, i.e., impermeable surfaces; similarly, when the SAVI value increases, the soil moisture tends to increase due to an increase in the soil penetration capability. From this analysis, due to the population explosion, deterioration of soil moisture was observed, which is mainly due to an increase in impermeable surfaces. In 2000, nearly 40% of Tamil Nadu had high to very high soil moisture, which was spatially scattered over the state (Figure 5). Similarly, in 2004, very high-high soil moisture areas diminished to 20% and are seen in the Western Ghats and along the water sources. By 2008, soil moisture regained around 35% and there was a considerable rise in the areas having no permeability. Again in 2012, soil moisture deteriorated, which resulted in the increase in areas having medium soil moisture. This leads to stress in the vegetation. From 2012, soil moisture followed a decreasing trend only, i.e., high soil moisture areas get converted into low to no moisture areas. In 2020, nearly 90% of the area fell in the no-medium soil moisture range. By the comparative study of SAVI between 2000 and 2020, areas with no permeability increased by 27.67% and an increase of 76.57% and 107.91% for low and medium permeable areas, respectively, whereas the high and very high permeable areas decreased by 79.41% and 69.52%, respectively (Table 7). The southeast coastal areas of Tamil Nadu are facing continuous declination of soil moisture and a similar trend is visualized in the northwestern parts also. Earth 2022, 3, FOR PEER REVIEW 15 Earth 2022, 3 627 (a) (b) (c) (d) Figure 5. Cont. Earth 2022, 3, FOR PEER REVIEW 16 Earth 2022, 3 628 (e) (f) Figure 5. SAVI map for Tamil Nadu depicting the soil moisture related with permeability (a) 2000, Figure 5. SAVI map for Tamil Nadu depicting the soil moisture related with permeability (a) 2000, (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. Table 7. Change analysis of soil moisture for Tamil Nadu for two decades between 2000–2020. Table 7. Change analysis of soil moisture for Tamil Nadu for two decades between 2000–2020. 2 2 2 Area Coverage (km ) Change Area Coverage (km ) Change Area Coverage (km ) Change Change Change Change Change Change in Area in Area Surface in Area in Area Area Coverage Area Coverage Area Coverage in Area in Area in Area in Area SAVI Covera Covera 2 2 2 (km ) (km ) (km ) PSurface ermeabi Coverag Coverag Cover- Cover- Cover- Cover- Classes 2000 2004 ge 2008 2012 ge 2016 2020 SAVI Perme- lity age age e 2age 016– e 2 age 000– Classes 2000– 2008– ability 2000– 2008– 2016– 2000– 2020(%) 2020(%) 2004(%) 2012(%) 2000 2004 2008 2012 2016 2020 2004 2012 2020 2020 −1–0.1 No 71473.47 77499.89 8.43 78610.48 83528.91 6.25 86158.26 91253.43 5.91 27.67 (%) (%) (%) (%) 0.1–0.2 Low 6897.59 8520.95 23.53 6024.32 8072.63 34.00 10349.75 12179.67 17.68 76.57 1–0.1 No 71,473.47 77,499.89 8.43 78,610.48 83,528.91 6.25 86,158.26 91,253.43 5.91 27.67 0.2–0.3 Medium 7352.34 16824.72 128.83 5102.44 10412.58 104.07 13385.42 15286.84 14.20 107.91 0.1–0.2 Low 6897.59 8520.95 23.53 6024.32 8072.63 34.00 10,349.75 12,179.67 17.68 76.57 0.3–0.5 High 21270.30 10981.54 −48.37 19301.68 12861.74 −33.36 7039.58 4378.71 −37.97 −79.41 0.2–0.3 Medium 7352.34 16,824.72 128.83 5102.44 10,412.58 104.07 13,385.42 15,286.84 14.20 107.91 Very 0.5–1 23165.76 16332.36 −29.49 21120.54 15283.6 −27.63 13226.47 7060.81 −46.61 −69.52 0.3–0.5 High High 21,270.30 10,981.54 48.37 19,301.68 12,861.74 33.36 7039.58 4378.71 37.97 79.41 Very 0.5–1 23,165.76 16,332.36 29.49 21,120.54 15,283.6 27.63 13,226.47 7060.81 46.61 69.52 High 3.4. Calculating Land Surface Temperature LST is an important parameter related to urban expansion, which increases due to an i3.4. ncrea Calculating se in the tLand errainSurface tempera Temperatur ture. In 2e000, the average LST ranges between 18–23 °C and the emissivity was more in the southern part as these areas had a higher LST of around 40 LST is an important parameter related to urban expansion, which increases due to an °C and most of the coastal areas in the northern part of Tamil Nadu and the western re- increase in the terrain temperature. In 2000, the average LST ranges between 18–23 C and gions registers an LST between 25–30 °C (Figure 6). By 2004, the LST average in Tamil the emissivity was more in the southern part as these areas had a higher LST of around 40 C Nadu declined to a few degrees due to the decrease in LST in the southern part. After and most of the coastal areas in the northern part of Tamil Nadu and the western regions 2004, due to various factors like urbanization and industrialization, LST kept on increas- registers an LST between 25–30 C (Figure 6). By 2004, the LST average in Tamil Nadu ing gradually, with the interior parts of Tamil Nadu experiencing a severe rise in LST. declined to a few degrees due to the decrease in LST in the southern part. After 2004, due Between 2008–2012, there was a bump in the trend in the entire study area, with a 30% to various factors like urbanization and industrialization, LST kept on increasing gradually, increase in the range between 35–42 °C and a decrease in the range between 15–20 °C by with the interior parts of Tamil Nadu experiencing a severe rise in LST. Between 2008–2012, 28%. In 2016, it also followed a similar trend with increased LST in the sub-urban areas there was a bump in the trend in the entire study area, with a 30% increase in the range ex bec tw ep ete t n he 35 W –4 es 2 ter Cna G nd haatsd r eu cnn reais ng e i in n t th he e w ran eg steer bn p etw or ee tin ons 15 of –20 TaC mb il N y 2a 8d % u.. I In n 2020 2016,, ag it a ai ls n o , t fhe oll o L w ST ed in acs rea ims iled ar t b ry en 1d 0% w i itn h tihe nc r re aa ng sed e of LS 3 T5i– n 42 th ° eC s a und b-u a rb rou annd are a as 147% excep d tetc hre eas We es in te rth n e G a hrea ats running in the western portions of Tamil Nadu. In 2020, again, the LST increased by 10% ranging between 15–25 °C. Thus, the time series analysis of LST reveals that there has been Earth 2022, 3 629 Earth 2022, 3, FOR PEER REVIEW 17 in the range of 35–42 C and around a 147% decrease in the area ranging between 15–25 C. Thus, the time series analysis of LST reveals that there has been an increase in the LST over an increase in the LST over two decades enormously due to various reasons and condi- tw tio o n ds ec th aa dt p es reen vo ar im l. An ous iln ycd re uas e e to in v ar arie o a b usy r 282% eason w s ain th d a cn on Ld SiT ti o of n s mto hra e t t p hra en v 3 ai5 l ° . A C o n b in sc erv rea ed se in area by 282% with an LST of more than 35 C observed and 12.46% and 31.66% increases and 12.46% and 31.66% increases in temperature ranging between 25–30 °C and 30–35 °C, inres tem pec pe tir v ael tu yr , e w rh aerea ngin sg LS be T t ra we n eg n in 2g 5 b –3 et 0wC eea nn 1d 5– 320 0– 3 °C 5 an Cd , r 2 e0 sp –25 ec t°C ive d ly e,cw reh ase ere da b sy L 6 S6% T r an ang di ng be 68% twe , e rn esp 15 e– c2 tiv 0 ely ( C a Tn ad ble 2 8 0– ).2 T 5he C ar d ee ac s w reait sh ed mb oy un 6t6 a% ino au ns r d e 6gio 8%n , s a res ls po e f ca ticv ed a ely ( rT ise ab in le L 8S ). in The the interior and the southern part experiences severe climatic changes due to urban ex- areas with mountainous regions also faced a rise in LS in the interior and the southern part pansion and a decrease in green cover. experiences severe climatic changes due to urban expansion and a decrease in green cover. (a) (b) (c) (d) Figure 6. Cont. Earth 2022, 3, FOR PEER REVIEW 18 Earth 2022, 3 630 (e) (f) Figure 6. LST map for Tamil Nadu representing the terrain temperature over two decades (a) 2000, Figure 6. LST map for Tamil Nadu representing the terrain temperature over two decades (a) 2000, (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. (b) 2004, (c) 2008, (d) 2012, (e) 2016, (f) 2020. Table 8. Change analysis of LST for Tamil Nadu for two decades between 2000–2020. Table 8. Change analysis of LST for Tamil Nadu for two decades between 2000–2020. Area Coverage Change Area Coverage Change Area Coverage Change in Change 2 2 2 LST (km ) in Area (km ) in Area (km ) Area in Area Change Change Change Change Classe Area Coverage Coverag Area Coverage Coverag Area Coverage Coverage Coverag in Area in Area in Area in Area 2 2 2 (km ) (km ) (km ) s (°C) 2000 2004 e 2000– 2008 2012 e 2008– 2016 2020 2016– e 2000– LST Cover- Cover- Cover- Cover- 2004(%) 2012(%) 2020(%) 2020 (%) Classes age age age age 15–20 39325.74 36129.23 −8.12 32427.97 23257.45 −28.27 18138.61 13175.31 −27.36 −66.49 ( C) 2000– 2008– 2016– 2000– 2000 2004 2008 2012 2016 2020 20–25 29118.11 25574.10 −12.17 22142.12 18269.29 −17.49 13462.45 9252.15 −31.27 −68.22 2004 2012 2020 2020 25–30 16134.25 15604.11 (%) −3.28 12219.51 14367.75 17 (%) .58 17021.38 18145.74 6.6 (%) 0 12.(%) 46 30–35 20029.62 21303.74 6.36 22401.62 23295.88 3.99 24313.57 26372.68 8.46 31.66 15–20 39,325.74 36,129.23 8.12 32,427.97 23,257.45 28.27 18,138.61 13,175.31 27.36 66.49 35–41 11325.36 12335.21 8.91 14536.49 16394.36 12.78 19149.60 21048.45 9.91 85.85 20–25 29,118.11 25,574.10 12.17 22,142.12 18,269.29 17.49 13,462.45 9252.15 31.27 68.22 >41 14226.38 19213.07 35.05 26431.75 34574.73 30.80 38073.84 42157.11 10.72 196.33 25–30 16,134.25 15,604.11 3.28 12,219.51 14,367.75 17.58 17,021.38 18,145.74 6.60 12.46 3.5. Comparative Study between NDVI, SAVI, and LST Associated with Each LULC Class 30–35 20,029.62 21,303.74 6.36 22,401.62 23,295.88 3.99 24,313.57 26,372.68 8.46 31.66 In general, SAVI and NDVI are positively correlated, while the LST has a negative 35–41 11,325.36 12,335.21 8.91 14,536.49 16,394.36 12.78 19,149.60 21,048.45 9.91 85.85 correlation with these two indices. Analysis of the Spearman coefficient reveals that set- >41 14,226.38 19,213.07 35.05 26,431.75 34,574.73 30.80 38,073.84 42,157.11 10.72 196.33 tlements exhibit the highest coefficient value of 0.0061 with the increase in coefficient val- ues (Table 9). This is because urban areas have fewer plantations and very low permea- 3.5. Comparative Study between NDVI, SAVI, and LST Associated with Each LULC Class bility, which has led to an increase in LST for two decades in Tamil Nadu. Forest and water bodies have the least coefficient values due to healthy dense green cover and high In general, SAVI and NDVI are positively correlated, while the LST has a negative moisture content. The correlation values tend to increase from 2000 to 2020 because of a correlation with these two indices. Analysis of the Spearman coefficient reveals that settle- decrease in plantation and an increase in impervious layer concentration in Tamil Nadu. ments exhibit the highest coefficient value of 0.0061 with the increase in coefficient values Thus, to avoid the consequences of LST changes, planners and administrators should fo- (Table 9). This is because urban areas have fewer plantations and very low permeability, cus on increasing the green cover in urban areas where surface permeability is low to which has led to an increase in LST for two decades in Tamil Nadu. Forest and water avoid the impactful effect on ecosystem services. bodies have the least coefficient values due to healthy dense green cover and high moisture content. The correlation values tend to increase from 2000 to 2020 because of a decrease in plantation and an increase in impervious layer concentration in Tamil Nadu. Thus, to avoid the consequences of LST changes, planners and administrators should focus on increasing the green cover in urban areas where surface permeability is low to avoid the impactful effect on ecosystem services. Earth 2022, 3 631 Table 9. Spearman correlation between Normalized Difference Vegetation Indices (NDVI), Soil- adjusted vegetation index (SAVI), and land surface temperature (LST) by landuse and landcover (LULC) zones. All correlation coefficients are statistically significant at p  0.01. Correlation Coefficient p-Values LULC Class 2000 2020 Settlement 0.28 0.51 0.0061 Forest 0.06 0.11 0.0011 Plantation Agriculture 0.11 0.20 0.0049 Barren Land 0.18 0.24 0.0023 Waterbodies 0.15 0.17 0.0017 Fallow Land 0.17 0.27 0.0031 Crop Land Agriculture 0.12 0.22 0.0048 3.6. Assessing the Drought Intensity for 2020 Tamil Nadu experiences a tropical climate, which receives rainfall from monsoons or cyclones, depressions prevailing in the Bay of Bengal. The annual vegetation cycle of Tamil Nadu primarily depends on the southwest and northeast monsoon. If any failure in the monsoon occurs, then disaster begins. VCI is the commonly used index to portray the impacts of drought on vegetation irrespective of the climatic variations. When the VCI value exceeds 60, then crops sustain no impacts due to drought and when it falls below 20, severe impacts occur. In Tamil Nadu, during the year 2020, the drought-prone areas like southern coastal districts and a few western interior districts experience severe impacts due to drought leading to a subsequent deficit in crop yield. The northern districts and Western Ghats (VCI > 60) experience no vegetation stress and vegetation remains healthy because monsoon in those particular regions onset at the correct timeframe and provides enough rainfall for crops. The moderate drought occurs primarily in the outer fringes of severe drought-affected regions and certain riparian parts of the Cauvery deltaic regions, with VCI values ranging between 20 to 60 (Figure 7). 3.7. Monitoring the Vegetation Health by VHI Vegetation health generally depends upon the water availability for the survival of the vegetation prevailing. Water availability, i.e., soil moisture that is related to surface temperature, is also considered the major factor for deriving the health index. VHI also considers the VCI, which is related to stress either because of water availability or stress due to diseases that exist in the crop. VHI analyses of all the aforementioned conditions and mapping of the health of the crop, almost 35% of Tamil Nadu has stressed vegetation distribution, and the Western Ghats have healthy vegetation characteristics because of healthy vegetation circumstances and high moisture content (Figure 8). In the case of a failed monsoon, VHI can be improved by providing water supply to crops in the correct proportion by implementing precision agriculture methodologies or changing the irrigation pattern to avoid stress. Earth 2022, 3, FOR PEER REVIEW 20 Earth 2022, 3 632 Earth 2022, 3, FOR PEER REVIEW 21 Figure 7. VCI map (drought intensity map) of Tamil Nadu for 2020. Figure 7. VCI map (drought intensity map) of Tamil Nadu for 2020. 3.7. Monitoring the Vegetation Health by VHI Vegetation health generally depends upon the water availability for the survival of the vegetation prevailing. Water availability, i.e., soil moisture that is related to surface temperature, is also considered the major factor for deriving the health index. VHI also considers the VCI, which is related to stress either because of water availability or stress due to diseases that exist in the crop. VHI analyses of all the aforementioned conditions and mapping of the health of the crop, almost 35% of Tamil Nadu has stressed vegetation distribution, and the Western Ghats have healthy vegetation characteristics because of healthy vegetation circumstances and high moisture content (Figure 8). In the case of a failed monsoon, VHI can be improved by providing water supply to crops in the correct proportion by implementing precision agriculture methodologies or changing the irriga- tion pattern to avoid stress. Figure 8. Analyzing the vegetation health in 2020 using VHI for Tamil Nadu. Figure 8. Analyzing the vegetation health in 2020 using VHI for Tamil Nadu. 3.8. Comparative Study between VCI, VHI, and LST Associated with Each LULC Class Indices related to vegetation like VCI and VHI are positively correlated, while the LST is negatively correlated to that of the vegetation indices. Drought is the primary caus- ative factor for increasing LST, as clearly portrayed; as drought leads to the decline of crop health, this will result in the emergence of LST. The settlement has the highest spearman coefficient values of 0.0064 with increases in coefficient values of −0.31 and −0.54 for 2000 and 2020, respectively, while the forest has the minimal correlation of 0.0012 among all the LULC classes considered for our analysis (Table 10). Cropland and plantation have almost similar correlation values due to their similar characteristics. However, the corre- lation values for each class increased from 2000 to 2020 due to urbanization and a decrease in green cover resulting in increased LST. Table 10. Spearman correlation between Vegetation Condition Index (VCI), Vegetation Health In- dex (VHI), and Land Surface Temperature (LST) by landuse and landcover (LULC) zones. All cor- relation coefficients are statistically significant at p ≤ 0.01. Correlation Coefficient LULC Class p-Values 2000 2020 Settlement −0.31 −0.54 0.0064 Forest −0.09 −0.15 0.0012 Plantation Agriculture −0.12 −0.22 0.0048 Barren Land −0.20 −0.26 0.0025 Waterbodies −0.13 −0.15 0.0016 Fallow Land −0.18 −0.28 0.0032 Crop Land Agriculture −0.14 −0.24 0.0050 Earth 2022, 3 633 3.8. Comparative Study between VCI, VHI, and LST Associated with Each LULC Class Indices related to vegetation like VCI and VHI are positively correlated, while the LST is negatively correlated to that of the vegetation indices. Drought is the primary causative factor for increasing LST, as clearly portrayed; as drought leads to the decline of crop health, this will result in the emergence of LST. The settlement has the highest spearman coefficient values of 0.0064 with increases in coefficient values of 0.31 and 0.54 for 2000 and 2020, respectively, while the forest has the minimal correlation of 0.0012 among all the LULC classes considered for our analysis (Table 10). Cropland and plantation have almost similar correlation values due to their similar characteristics. However, the correlation values for each class increased from 2000 to 2020 due to urbanization and a decrease in green cover resulting in increased LST. Table 10. Spearman correlation between Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Land Surface Temperature (LST) by landuse and landcover (LULC) zones. All correlation coefficients are statistically significant at p  0.01. Correlation Coefficient p-Values LULC Class 2000 2020 Settlement 0.31 0.54 0.0064 Forest 0.09 0.15 0.0012 Plantation Agriculture 0.12 0.22 0.0048 Barren Land 0.20 0.26 0.0025 Waterbodies 0.13 0.15 0.0016 Fallow Land 0.18 0.28 0.0032 Crop Land Agriculture 0.14 0.24 0.0050 4. Discussion Our study on LULC changes in Tamil Nadu over two decades reveals that there is abundant growth in urban areas and fallow landscapes monotonically, about 150% and 120%, respectively, and shrinkage in cropland and plantation agriculture landforms by a notable margin. These changes are mainly due to the movement of people from the rural areas to the towns in Tamil Nadu, notably Chennai, Coimbatore, Trichy, and Madurai, seeking employment by leaving their owning premises, which results in the conversion of agricultural lands into fallow lands and similarity due to high demand urban-fringes are plotted and sold, leading to increase in settlements results in urbanization [25]. Due to the boom in construction, there is an increase in the percentage of impervious layers in landforms, which leads to several other consequences like reduction in green cover, degradation of groundwater, and saltwater intrusion in coastal belts [79], for example, Vedanta Sterlite copper industry (currently closed for violating environmental measures) in Tuticorin and thermal power stations in Tuticorin and Ennore extracted millions of gallons of groundwater drives saltwater intrusion and, toxic discharges degrades the groundwater in the coastal belts of Tuticorin and Chennai, respectively [80,81]. Another example, nu- clear power plants in Kalpakkam and Kudankulam also degrade the groundwater and environment due to the discharge of heavy metals and other toxic substances [36]. Surface permeability is assessed using SAVI, a modified NDVI index, which shows that the urban areas and fallow lands have very low surface permeability, resulting in an upsurge of LST [42]. From the NDVI map, it is clear that the healthiness of the vegetation deterio- rates during the period of study, and the reality is that major basins (Cauvery basin and Thamirabarani basin) experience stress in the plantation growth, due to inadequate water availability. The stopping or delayed release of Cauvery water from Karnataka dam’s by the Karnataka government is also a driving force of water scarcity in the Cauvery basin and several districts of Tamil Nadu [82]. Thanjavur (the rice bowl of Tamil Nadu) residing in Earth 2022, 3 634 the Cauvery delta plain experiences severe degradation in the healthiness of the crop after 2008, which has severe impacts on the economy of the state. Thus, proper planning must be employed in deltaic and basin areas to renovate the agricultural landforms to achieve sustainable development. VHI and VCI are used to show the healthiness of the vegetation in relation to drought and temperature, respectively. When a certain area is influenced by drought, health deteriorates, and rises in temperatures also cause stress in vegetation due to decreases in soil moisture; however, agricultural crops prevail due to water supply provided by farmers from various water sources. The main consequence of urbanization is global warming, i.e., an increase in earth temperature to a certain degree is clearly envisaged in the LST map; there is a spike in LST values of about 4–5 C in two decades. Due to deforestation, which minimizes the natural cooling effect, LST may rise further, causing severe health ailments to the people residing in those areas because of the trap of heat energy by the built-up areas and small roads with the added heat from the air conditioners, vehicle discharge still exacerbates the effect [83]. Southern districts like Rameswaram, Thoothukudi, Virudhunagar, Kanyaku- mari, Sivagangai, and part of Tirupur had the higher LST values in 2020; however, it can be controlled by improving green cover and erecting artificial groundwater recharge structures to enhance the growth of trees in urban areas, thereby, there will be increased chances of rainfall due to a phenomenon called evapotranspiration [4]. Recently, the LST value upsurges in Kanyakumari districts may be due to the conversation of agriculture and farmland into a national highway project implemented by the National Democratic Alliance (NDA) government. Future research should focus on the Kanyakumari district to identify the cause of the sudden increase in LST. Urban Sprawl (US) is another major influential factor in the conversion of land- scapes [84,85]. Urban areas tend to grow on the outer fringes of the major cities, which results in the shrinkage of production landforms and water bodies being encroached upon, leading to severe impacts on ecosystems. Our study showcases the growth of towns from 2000 to 2020 in the fringes; in this process, the people start to reside in those outer fringes and they start facing troubles like lack of infrastructures, sewage treatment, and proper water supply. To avoid those circumstances, proper urban planning and environmental management practices should be done to achieve sustainable development in those ar- eas [27]. With the increase in urban population and unsustainable replacement of green cover into impervious layer results to cause severe ecological and economic damages. Due to urbanization and population rise ESV (Ecosystem Service Value) falls rapidly, which results in a shortage of multi-utility services provided to the people with needs. Due to the conversion of agricultural land to other landscapes, there is a huge loss in ESV and Gross Domestic Product (GDP) of the state between 2000 and 2020 and demand for food grains increases; thus, ESV and GDP should be evaluated in future research. As the transformation of agricultural or forest land into impervious layers is uncontrollable in developing countries, certain norms of regulations should be incorporated strictly on the public to reduce the impacts of the transformation of landforms [31]. Similarly, certain schemes like the Conversion of Cropland to Forest Program (CCFP) can be implemented, and subsidies can be provided to the farmers who are involved in such schemes, which can be helpful for communities who are all switching their location to urban areas due to any reasons and gaining more ecosystem values. The comparative study of various indices using spearman’s coefficient shows that when the vegetation health and soil moisture deteriorate, then the surface temperature increases, and simultaneously the presence of the severity of drought and the health of vegetation deteriorates, then the area is highly susceptible to the upsurge of LST. The combined approach of RS, geographical information system (GIS), and LULC change the analysis associated with vegetation and temperature change measures provide useful information and efficient methods and modeling landscape changes on vegetation and land surface temperatures in Tamil Nadu. Therefore, this study also contributes several indices and indicators to monitor drought and vegetation health in Tamil Nadu. It also Earth 2022, 3 635 delivers useful information for urban planning and sustainable development as well as mitigation of environmental impact in Tamil Nadu. 5. Conclusions In this study, the creation of a LULC map using the RF Algorithm for the entire Tamil Nadu to analyze the spatial and temporal pattern of landcover changes and the accuracy of the LULC map was carried out. We found a rapid increase in the urban settlements and barren lands while the agricultural landscape decreased alarmingly due to various anthropogenic activities from 2000 to 2020. Various indices like NDVI, SAVI, LST, VHI, and VCI were derived using RS data for six different years during the last two decades. The NDVI map was used to identify the vegetation health based on reflectance from chlorophyll pigment. Using NIR imagery, soil moisture was studied using SAVI, which identified the regions having low surface permeability and the areas having low moisture. The increase in LST values reflects the relationship between urbanization and its impacts on climatic conditions. The intensity of drought in Tamil Nadu is depicted by VCI, while VHI is used to deduce the health of vegetation by including the soil moisture with the NDVI, which will be used to portray the emergence of heat islands in future research. LST is inversely related to VHI and VCI, and the Spearman coefficient relationship shows that urban areas are at a higher risk for the evolution of heat islands. To conclude, our study is done primarily to analyze the impact of landscape changes on vegetation and LST, and a relationship is deduced along with the identification of hotspot areas for the emergence of LST in various districts in Tamil Nadu and plan developmental proposals at the landscape level and maintain the economic benefits and ecological gains of the different land cover classes and monitor the region. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/earth3020036/s1. Figure S1: Enhanced vegetation index (EVI) for Tamil Nadu 2000 and 2020; Figure S2: Surface Urban Heat Island for Tamil Nadu 2020; Figure S3: Temperature conditon index for Tamil Nadu 2020; Figure S4: NDVI difference between 2000 and 2020; Figure S5: EVI difference between 2000 and 2020. Author Contributions: Conceptualization, M.S. and R.P.; methodology, M.S. and R.P.; software, M.S. and R.P.; validation, M.S., R.P., P.C. and P.M.; formal analysis, M.S., R.P. and P.C.; investigation, M.S. and R.P.; resources, M.S., R.P. and P.C.; data curation, M.S., R.P., P.C. and P.M.; writing—original draft preparation, M.S. and R.P.; writing—review and editing, M.S., R.P., P.C. and P.M.; visualization, M.S. and R.P.; supervision, R.P. and P.C.; project administration, R.P. and P.C.; funding acquisition, M.S., R.P. and P.C. All authors have read and agreed to the published version of the manuscript. 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Journal

EarthMultidisciplinary Digital Publishing Institute

Published: May 26, 2022

Keywords: urban ecosystem services; landuse and landcover; Random Forest classification; land surface temperature; environmental monitoring; remote sensing

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