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Modelling terrain erosion susceptibility of logged and regenerated forested region in northern Borneo through the Analytical Hierarchy Process (AHP) and GIS techniques

Modelling terrain erosion susceptibility of logged and regenerated forested region in northern... This research examines the susceptibility of logged and regenerated forest region to erosion through the application of the analytical hierarchy process (AHP) and geographical information systems (GIS). In order to estimate terrain erosion susceptibility, ten geo-environmental variables were taken into account as possible factors relevant to terrain erosion. They are slope, aspect, relative relief, slope length and steepness (LS) factor, curvature, landforms, topographic wetness index (TWI), stream power index (SPI), stream head density, and land use/land cover. Pairwise comparison matrixes were generated to derive the weightages and ratings of each variable and their classes. These were integrated to generate the terrain erosion susceptibility index (TESI) map. Among the variables used in the analysis the land use/land cover, slope, SPI, stream head density, and LS factor were shown to have high contribution towards terrain erosion susceptibility. The areas with a concave slopes > 25° and high relative relief, LS factor, TWI, and stream head densities were found to be more susceptible to erosion such as gullying or landslides. The conversion of TESI into terrain erosion susceptibility zonation (TESZ) map shown that 25% of the total area is highly susceptible to erosion. Among this, 10% of the area possesses a very high vulnerability to landslides and gullying or soil slips and these areas coincide with logging roads and skidder trails. Linear regression analysis between TESI and TESZ with spatial distribution of mean annual rainfall in the region does not show any significant relationships (p > 0.10). However, high rainfall triggers rapid downstream movement of unsupported slopes in the region. The terrain erosion susceptibility zonation map expresses the realistic condition of logged terrain matching with field observations in the area in terms of erosion. The results can serve as basic data for future development programs in the region, in any projects where the terrain susceptibility is critical by planning infrastructure to avoid high risk zones. Keywords: Geo-environmental variable, Terrain susceptibility, AHP, GIS, Borneo * Correspondence: vijithh@gmail.com Department of Applied Geology, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009 Miri, Sarawak, Malaysia © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 2 of 18 Introduction Park et al. 2013; Shit et al. 2015; Althuwaynee et al. Erosion, either as soil loss or landslides, is the natural 2016; Rahmati et al. 2017; Torri et al. 2018; Othman et denudation process or a stage of geomorphic evolution al. 2018). Although the output of susceptibility analysis of terrain which is responsible for generating different may vary in name such as landslide susceptibility zon- topographical features (Thornbury 1969). The natural ation (LSZ) map or gully erosion susceptibility map, the erosional or denudational process will take place at given analysis techniques and geo-environmental variables rate and any recent changes in the normal rate of ero- used in the modelling are generally similar. Further sion may reflect changes in the equilibrium condition of details of different techniques used to analyse terrain the terrain due to anthropogenic causes. Erosion and erosion susceptibility can be found in Aleotti and allied mass wasting problems are common in hilly areas, Chowdhury (1999), Guzzetti et al. (1999), van Westen but their severity will vary depending on the geo- (2000), Brenning (2005), Huabin et al. (2005), and van environmental factors involved. Steep sloping, highly Westen et al. (2006). elevated rugged terrain may be fragile in terms of geo- In the present study, an attempt has been made to logical, vegetation, and climatic factors making it more model and classify the upper catchment regions of the vulnerable to erosion, which may be aggravated by Baram River (Sarawak, Malaysia) in terms of susceptibil- human induced developmental activities (Fadul et al. ity of the terrain to erosion due to gullying, soil slip, and 1999). The fragility of such terrains can be termed as landslides. The region considered possesses very weak susceptibility to erosion. Assessment of the susceptibility geological formations (tightly folded sedimentary rocks of the terrain to erosion and classification into different of various lithologies) covered by dense forest. During susceptibility zones is an important step to understand- the last few decades, the study area has undergone in- ing an area’s vulnerability to erosion for development of tense terrain modification and forest clearing through proper management plans and mitigation strategies (Dai timber harvesting and logging road construction which and Lee 2002; Ayalew et al. 2004; Bijukchhen et al. 2013; increased the vulnerability of the terrain to erosion Erener et al. 2016; Pham et al. 2017). Susceptibility map- (Fig. 1). As a result of episodes of heavy rainfall, areas ping is generally used in landslide and gully erosion with high vulnerability to erosion will flow or slide modelling, the goal of which is to identify potentially downhill to valley streams and may deposit large quan- vulnerable areas which are those with several critical tities of sediment in the rivers downstream. Very few variables. To understand the susceptibility of a region to studies have reported on terrain susceptibility to erosion erosion, either as landslides or gullying, different in Sarawak and the reported studies deal with the soil methods which use expert opinion (qualitative), statis- erosion assessment using soil loss equations (USLE tical prediction (quantitative), or both may be applied /RUSLE) (Besler 1987; de Neergaard et al. 2008; Vijith et using geographical information systems (GIS). al. 2018a, 2018b; Vijith and Dodge-Wan 2018). Prior to In order to assess the susceptibility, a number of geo- 2018, no studies were reported from the selected upper environmental variables such as geomorphology, slope, catchment region of the Baram River. land use, lithology, etc., as well as palaeo locations of the The present study is an initial attempt to assess terrain phenomena have been used (Kheir et al. 2007; Akgün erosion susceptibility and can be used as a basic and and Türk 2011; Dewitte et al. 2015; Kavzoglu et al. 2014; valuable information while planning for roads and other Gómez-Gutiérrez et al. 2015; Chen et al. 2016a; Garosi infrastructure developments. The study area lacks a et al. 2018). Among these, most of the parameters con- database of previous information related to erosion sidered as natural parameters and the land use/land (gullying and landslides in particular) and due to the cover existed in the area is only man made i.e. it was relatively inaccessible nature of the terrain, it is difficult mainly controlled human activity. Expert opinion to map the locations of slides or gullies by direct obser- method relies on the field knowledge and expertise of vation in the field. To overcome these limitations, a the analyst to determine the influence and weights of well-defined and tested predictive analysis model, i.e. the each parameter and parameter classes, whereas statis- analytical hierarchy process (AHP) which uses a combin- tical techniques use well defined bivariate or multivariate ation of expert opinion and statistical measurements, analysis techniques through dependent and independent was applied in this research. Numerous researchers have variables to determine the relative importance of each used the analytical hierarchy process to estimate the sus- variable (Bourenane et al. 2015; Rahmati et al. 2016). ceptibility to landslide or soil erosion in other parts of The suitability and selection of methods to produce sus- the world and found it to be successful in predicting the ceptibility map is often heavily depend on the availability vulnerability of the region based on the parameters used of data sets of independent geo-environmental variables (Komac 2006; Neaupane and Piantanakulchai 2006; particularly information on previous incidents of land- Yoshimatsu and Abe 2006; Yalcin 2008; Nekhay et al. slides or gullies (Lucà et al. 2011; Conoscenti et al. 2013; 2009; Svoray et al. 2012; Reis et al. 2012; Kayastha et al. Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 3 of 18 Fig. 1 Slope failures observed in the study area 2013; Pourghasemi et al. 2012, 2013a; Youssef 2015; northeast (NE) - southwest (SW) to north northeast Althuwaynee et al. 2016; Sangchini et al. 2016; Rahaman (NNE) - south southwest (SSW) trend. The drainage and Aruchamy 2017; Arabameri et al. 2018b). The find- pattern is predominantly dendritic but the presence of ings of the present research will facilitate the identifica- trellis and parallel pattern in the region indicates the tion of areas critically vulnerable to erosion and influence of lithology and structural features on the landslides and thus provides an opportunity to avoid risk development of drainage networks. Geomorphological associated with terrain susceptibility while implementing features vary from highly elevated steep sloping escarp- the developmental schemes in the region. ments to low lying flat regions of fluvial floodplains. Hills and mounds show highly complex shapes with a Study area sharp crests to rounded tops. The area receives an an- A forested region in the interior Sarawak, which has nual average rainfall of approximately 4600 mm from the undergone vegetation changes and terrain alteration due two dominant monsoon seasons viz., southwest and to logging activities was selected for the present analysis. northeast monsoons. Rainfall shows high spatial and The study area covers a total area of 2105 km and con- temporal variations (Vijith and Dodge-Wan 2018) Vege- tains two major subwatersheds of the Baram River tation cover varies from dense primary forest to open namely Sungai Patah and Sungai Akah which are located spaces of barren land. The majority of the study area is between north latitudes 3° 13′ 15″ to 3° 41′ 50″ and covered with forests of different types and density, east longitudes 114° 35′ 42″ to 115° 13′ 20″ (Fig. 2). followed by mixed agricultural land (mainly hill paddy Though the subwatersheds differ in shape, both have cultivation) and then the open spaces with no vegetation similar terrain and geological characteristics. The area is related to road development, villages, and logging. Initial highly undulating with elevations between 37 m to 1578 field observations indicated that the development of log- m asl. The bed rock consists of sedimentary rocks of ging roads and log trail (skidding and pulling trails) have Paleocene, Oligocene, and Miocene ages. Most of the rendered the terrain more susceptible to erosion by study area consists of Oligocene shale and sandstone, changing the continuity of the hills through toe cutting with areas of Paleocene deep water sediments composed and removal of the protective vegetation cover. of shale and sandstone with occasional conglomerate and limestone, and Miocene shale and sandstone. Nu- Materials and methods merous anticlines, synclines, and local fractures are The Sungai Akah and Sungai Patah catchments of the present in the area showing tight folds with a common Baram River were selected for the present research as Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 4 of 18 Fig. 2 Study area location map this is a data poor region. Mapping of terrain erosion classification with field verification. Land use/land cover is susceptibility is considered as the preliminary step to the most significant factor under the influence of an- understand the risks of soil erosion and landslide. An thropogenic activities which modify the protective vegeta- erosion susceptibility map was generated using several tion cover. Different software used for the generation of geo-environmental variables derived from various re- variables and final analysis are ArcGIS version 9.3 and mote sensing data sources such as digital elevation SAGA version 2.1, which operates in the raster GIS envir- model (DEM) and satellite images. The digital elevation onment and the cell size for this analysis was fixed as model, downloaded from the earth explorer (http:// 30 × 30 m. The significance and methodology applied to earthexplorer.usgs.gov) website of U. S Geological Sur- obtain each variable is described in text, as well as the vey. Shuttle Radar Topographic Mission (SRTM) data of weightages attributed to each class of each variable. 30 m was used after it had been clipped to the study area In order to generate the terrain susceptibility map of boundary and the voids filled by the Fill DEM module the study area by analyzing the contribution of each available in the spatial analyst extension of ArcGIS soft- variable which makes the terrain susceptible to erosion, ware. The filled elevation dataset was then used to derive the analytical hierarchy process (AHP) technique devel- variables such as slope, aspect, relative relief, slope oped by Saaty (1980) was used. This methods has the length and steepness (LS) factor, curvature, landforms, capability of integrating expert knowledge, field informa- topographic wetness index (TWI), and stream power tion, and relative statistics together. AHP is a semi- index (SPI). Stream networks were produced from the quantitative, multi-criteria decision support technique digital elevation model and stream head points were ex- which is used to generate high quality and precise tracted to calculate the stream head density map. The decisions through the application of the matrix based parameters are natural features of the region and terrain pairwise comparison of the contributing factors which and not affected by anthropogenic activities. Landsat 8 determine the results of the phenomenon or the process OLI images of the area acquired on 28th March 2015, (Saaty 1990; Saaty 1994; Saaty and Vargas 2001). The which reflect the current land use pattern were used to pairwise comparison will be carried out based on the produce the land use/land cover map through supervised different ratings of each variable or feature classes on Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 5 of 18 the basis of relative importance varying from 1 to 9. reference to horizontal. Nature of the slope varies from Each value in the relative importance can be assigned to gentle to steep and this controls different geomorphic variable or variable classes based on the subjective processes such as erosion, transportation, and deposition judgement of relative importance. Relative weight of the in relation to the rainfall-runoff characteristics of the re- variables used in the matrices can be determined by gion (Foumelis et al. 2004; Gómez-Gutiérrez et al. 2015; generations of eigenvectors and the consistency of the Sangchini et al. 2016; Arabameri et al. 2018a). Gentle variable can be assessed by calculating the consistency slopes are expected to induce less terrain slips due to index (CI) as given below (Saaty 1990) (Eq. 1): low shear stresses (Lee et al. 2004). High slope values show the highest susceptibility to erosion although verti- ðÞ λmax‐n cal terrain surfaces, and very high slopes having exposed CI ¼ ð1Þ ðÞ n‐1 bedrock show less susceptibility to terrain erosion due to less or nil soil cover (Dewitte et al. 2015; Rahmati et where, λ is the largest or principal eigenvalue of the max al. 2017; Torri et al. 2018). In order to generate the analysed matrix and n is the order of the square matrix. slope, hydrologically corrected (void filled) SRTM DEM This inconsistency index can also be expressed as were used and the slope map generated shown a range consistency ratio (CR) which determine the suitability of varies from 0 to 75°. Then the slope was reclassified into individual parameters and their classes to be included in the following gentle to very critical seven classes 0–5°, the analysis and was given by the Eq. (2): 5°-10°, 10°-15°, 15°-25°, 25°-35°, 35°-45°, and > 45° and the relative percentage of area covered by individual CI CR ¼ ð2Þ slope class shown high spatial variation. Among the RI slope classes, a large percentage of the study area falls where, RI is the random index i.e. consistency index for within the slope class 15°-25° (31%), followed by 10°-15° a random square matrix of the same size proposed by (20%), and 25°-35° (17%). It was also observed that, the Saaty (1980). The cut-off of the CR was fixed as less than higher slope classes in the range of 35–45° and > 45° oc- or equal to 0.1 so that if CR of the analyzed variable is cupied comparatively reduced areas of 9% and 1% only found to be higher than the cut-off, the variable will be respectively. Seven slope classes were ranked by attribut- omitted from the analysis. ing factor scores from 1 to 9 to generate the pairwise matrix. The attribution was based on the assumption Preparation of terrain Erosion susceptibility zonation that there is a regular increase in risk across all the slope (TESZ) map classes. Considering the influence of the slope over the In order to map the areas susceptible to terrain erosion terrain stability, relative weightages were then calculated and classify them based on the severity and criticality of and these vary from 0.0274 to 0.2432 (Table 1). Higher risk, a number of distinct geo-environmental variables ratings are noted in areas having a slope higher than 35 were considered. The combined effects of multiple vari- in the study area. ables in terrain susceptibility were characterised through Slope aspect indicates the direction of the terrain slope the application of analytical hierarchy process (AHP) with respect to north and varies from − 1 to 359°, in based influence measuring technique, which is consid- which the negative value represents flat surface (Prasan- ered a powerful and supportive multiple criteria decision nakumar et al. 2011). Aspect of the terrain have direct making tool (Malczewski 1999; Yasser et al. 2013; Chen and indirect control over terrain processes and condi- et al. 2016b). Ten individual factors were used. They are: tions such as soil moisture, vegetation cover, and soil slope, aspect, relative relief, LS factor, curvature, land- thickness by exposing the surface to sunlight and or forms, TWI, SPI, stream head density, and land use/land heavy rain (Clerici et al. 2006; Meten et al. 2015). In cover (Fig. 3a-j). The contribution of each parameter in most of the landslide and gully erosion modelling stud- the terrain susceptibility as a single unit and individual ies, slope aspects is taken as an important variable (Reis feature classes in the parameters were determined by the et al. 2012; Pourghasemi et al. 2013b; Rahmati et al. cross comparison matrices analysed through the AHP 2016; Sangchini et al. 2016; Menggenang and Samanta and output rating was considered as the weight of each 2017; Othman et al. 2018). In the present research, a parameter and their class. Table 1 shows the pairwise slope aspect map was generated from the elevation comparison matrix, consistency ratio, and the weightings surface and classified into nine classes which are: flat, N, of individual parameters, and their classes considered in NE, E, SE, S, SW, W, and NW based on the orientation the analysis. i.e. which way the terrain is facing. Considering the area In all analysis which deals with terrain susceptibility, distribution of the individual aspect class in the study the primary factor considered is the terrain slope, which area, most of the slope aspects classes cover similar represents the inclination of the topography with areas (13%) except flat terrain which is very rare (0.30%). Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 6 of 18 Fig. 3 Geo-environmental variables used in the analysis a Slope b Aspect c Relative relief d Slope length and steepness (LS) e Curvature f Landforms g topographic wetness index (TWI) h Stream power index (SPI) i Stream head density j Land use/land cover Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 7 of 18 Table 1 Pair-wise comparison matrix, ratings, and consistency ratio of the variables classes and individual variables used in the present study Variables Classes 1 2 3 4 5 6 7 8 9 10 Rating / weights Slope 0–5 1 1/3 1/4 1/5 1/7 1/8 1/9 0.0270 5–10 1 3/4 3/5 3/7 3/8 3/9 0.0810 10–15 1 4/5 4/7 4/8 4/9 0.1081 15–25 1 5/7 5/8 5/9 0.1351 25–35 1 7/8 7/9 0.1891 35–45 1 8/9 0.2162 > 45 1 0.2432 Aspect Flat 1 1/2 1/2 1/3 1/4 1/5 1/7 1/9 1/6 0.0256 N 1 1 2/3 2/4 2/5 2/7 2/9 2/6 0.0512 NE 1 2/3 2/4 2/5 2/7 2/9 2/6 0.0512 E 1 3/4 3/5 3/7 3/9 3/6 0.0769 SE 1 4/5 4/7 4/9 4/6 0.1025 S 1 5/7 5/9 5/6 0.1282 SW 1 7/9 7/6 0.1794 W 1 9/6 0.2307 NW 1 0.1538 Relative relief < 100 1 1/3 1/5 1/7 1/9 0.04 100–200 1 3/5 3/7 3/9 0.12 200–300 1 5/7 5/9 0.2 300–400 1 7/9 0.28 > 400 1 0.36 Slope length and Steepness (LS) 5 1 1/3 1/7 1/9 0.05 10 1 3/7 3/9 0.15 15 1 7/9 0.35 > 15 1 0.45 Curvature Concave 1 9 9/7 0.5294 Flat 1 1/7 0.0588 Convex 1 0.4117 Landforms Deeply incised stream 1 2/4 2/9 2/1 2/6 2/3 2/5 2/2 2/3 2/7 0.0476 Midslope drainages 1 4/9 4/1 4/6 4/3 4/5 4/2 4/3 4/7 0.0952 Upland drainages 1 9 9/6 9/3 9/5 9/2 9/3 9/7 0.2142 U shaped valleys 1 1/6 1/3 1/5 1/2 1/3 1/7 0.0238 Plains 1 6/3 6/5 6/2 6/3 6/7 0.1428 Open slopes 1 3/5 3/2 3/3 3/7 0.0714 Upper slopes 1 5/2 5/3 5/7 0.1190 Local ridges 1 2/3 2/7 0.0476 Midslope ridges 1 3/7 0.0714 Mountain tops 1 0.1666 Topographic wetness index (TWI) Low (< 5) 1 1/4 1/9 0.0714 Moderate(5–10) 1 4/9 0.2857 High (> 10) 1 0.6428 Stream power index (SPI) < 0 1 1/2 1/3 1/4 1/6 1/8 1/9 0.0303 0–1 1 2/3 2/4 2/6 2/8 2/9 0.0606 Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 8 of 18 Table 1 Pair-wise comparison matrix, ratings, and consistency ratio of the variables classes and individual variables used in the present study (Continued) Variables Classes 1 2 3 4 5 6 7 8 9 10 Rating / weights 1–2 1 3/4 3/6 3/8 3/9 0.0909 2–3 1 4/6 3/8 4/9 0.1212 3–4 1 6/8 6/9 0.1818 4–5 1 8/9 0.2424 > 5 1 0.2727 Stream head density Low (< 15) 1 1/4 1/9 0.0714 Medium (15–20) 1 4/9 0.2857 High > 20 1 0.6428 Land use/land cover (LULC) Water 1 1/1 1/8 1/2 1/1 1/2 1/9 1/9 1/1 0.0294 Upper montane forest 1 1/8 1/2 1/1 1/2 1/9 1/9 1/1 0.0294 Secondary forest 1 8/2 8/1 8/2 8/9 8/9 8/1 0.2359 Primary forest 1 2/1 2/2 2/9 2/9 2/1 0.0588 Pebble cobble 1 1/2 1/9 1/9 1/1 0.0294 Paddy 1 2/6 2/9 2/1 0.0588 Mixed agriculture 1 9/9 9/1 0.2647 Exposed soil (barren) 1 9/1 0.2647 Artificial surface 1 0.0294 Consistency ratio (CR): < 0.0001 Variables(as single unit) Slope 1 8/1 8/4 8/5 8/3 8/2 8/4 8/7 8/6 8/9 0.1633 Aspect 1 1/4 1/5 1/3 1/2 1/4 1/7 1/6 1/9 0.0204 Relative Relief 1 4/5 4/3 4/2 4/4 4/7 4/6 4/9 0.0816 Slope length and steepness 1 5/3 5/2 5/4 5/7 5/6 5/9 0.1020 Curvature 1 3/2 3/4 3/7 3/6 3/9 0.0612 Landform 1 2/4 2/7 2/6 2/9 0.0408 TWI 1 4/7 4/6 4/9 0.0816 SPI 1 7/6 7/9 0.1429 Stream head density 1 6/9 0.1224 LULC 1 0.1837 Consistency ratio (CR): < 0.00003 NE facing slopes were also below average (10.70%). Before 2017). The relative relief of the study area was generated applying the relative weightages to individual aspect class, from the digital elevation model using the neighborhood slope instability observed during the field visit was consid- range function available in the spatial analyst extension of ered. During the field visit, it was noted that, west facing ArcGIS software by keeping the unit size of the area as 1 slopes in general as well as southwest and northwest show km . The relative relief calculated for the study area ranges 2 2 more incidence of slope failure and gully erosion than any from 46 m/km to 692 m/km andwas then dividedinto 2 2 others. Therefore, while attributing the factor scores to five classes which are: < 100 m/km , 100–200 m/km , 200– 2 2 2 generate the pairwise matrix, higher scores were given to 300 m/km ,300–400 m/km and, > 400 m/km .Consider- slopes facing west, southwest, and northwest directions ing the area distribution of individual classes of relative re- and relative weightages or ratings were calculated which lief in the selected study area, the majority (95%) falls vary in the range of 0.0256 to 0.2307. within the three classes from 100 to 400 m/km .Within Another important parameter which controls the ter- this 95%, more than 43% of the total area has relative relief rain stability is the change in elevation in the unit area in the range of 200–300 m/km followed by 30% of the which is termed as relative relief. Terrains with higher total area with relative relief in the range of 100–200 m/ 2 2 relative relief indicates higher runoff and less infiltration km and 22% of the area in the range of 300–400 m/km .It and shows higher susceptibility to erosion (Raja et al. was noted that very low and very high relative relief zones Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 9 of 18 2 2 (< 100 m/km and > 400 m/km ) cover significantly less ranges from − 30 to + 32, i.e. from concave surfaces to areas of 1% and 4% respectively only. Review of previous flat and convex surfaces. In the study area, the topog- works carried out in landslide and gully erosion modelling raphy consists of both concave and convex curvature which used the theme relative relief as a parameter indi- surfaces which together cover 94% of total area whereas cates higher potentiality of areas with high relative relief in flat areas only cover 6%. This is due to the complex and conditioning for erosion (Foumelis et al. 2004; Zhu et al. highly undulating nature of folded sedimentary rocks 2014; Pourghasemi et al. 2013a; Sangchini et al. 2016). within the study area. Considering the shape of the land Basedonthisprior andproveninformation, inthe present surface, both concave and convex surfaces possess sus- research while attributing the factor scores to generate the ceptibility to erosion. But in the study area, during the pairwise matrix, higher scores were given to relative relief field visits, it was noted that compared to convex surface class having higher values and lower scores were assigned the more gullies are observed in a concave surfaces. Fur- to low relative relief class. The relative weightages thus ther, while considering the previous studies reported calculated varied from 0.040 to 0.36. from other parts of the world, most studies marked con- LS factor corresponds to the combined effect of slope cave surfaces as more vulnerable to gullying and erosion length and its steepness, which have direct bearing on (Pourghasemi et al. 2012; Meten et al. 2015; Youssef the erosion and the transportation potential of an area 2015; Raja et al. 2017). Therefore, while assigning the (Pourghasemi et al. 2013b; Vijith and Dodge-Wan 2018). factor scores, more importance was given to concave An area with high slope and elongated nature has high curvature than convex by attributing higher scores and potential for generating runoff and this directly influ- the calculated ratings are 0.0588 (flat), 0.4117 (convex), ences the development of rills in the terrain in response and 0.5294 (concave). to heavy rainfall (Haan et al. 1994; Panagos et al. 2015; In order to produce a reliable terrain erosion suscepti- Correa-Muñoz and Higidio-Castro 2017). Therefore, in bility map, the specific landforms present in the study the present analysis the LS factor was considered and area needs to be included in the analysis. Landforms generated from the digital elevation model through the controls many spatial topographic erosional and deposi- methodology proposed by Moore and Burch (1986a, tional processes and was an integral part of geomorpho- 1986b) using SAGA 2.1. The generated LS factor value metry (Seif 2014). Surface runoff, soil moisture varies from 0 to 25 and was divided into four classes distribution, vegetation characteristic, and even the which are: < 5, 5–10, 10–15, and > 15 considering its water quality are influenced by the specific landforms contribution to erosion susceptibility. Within the study (Mokarram et al. 2015). Therefore in the present re- area of Sungai Patah and Sungai Akah watersheds, 50% search, the topographic position index based landform of the terrain has low LS values (< 5) and 41% has LS classification proposed by Weiss (2001) was selected to value between 5 and 10. Only 9% of the area has LS generate the landforms using digital elevation model. value of 10–15 and only 1% has LS value over 15. Soil Topographic position index analysis identified ten land- and gully erosion modelling conducted by researchers in forms in the Sungai Akah and Patah area. They are various locations identified the role of higher LS factor deeply incised streams, midslope drainages, upland in initiating erosion and transportation of material from drainages, U-shaped valleys, plains, open slopes, upper a region downstream (Nekhay et al. 2009; Pourghasemi slopes, local ridges, midslope ridges, and mountain tops. et al. 2012; Shit et al. 2015; Arabameri et al. 2018a). Within the study area, 39% of the total area is covered by Based on this in the present research also, while assign- deeply incised streams whereas mountain tops cover 30%. ing the factor scores, more importance were given to Besides these, local ridges (12%), upland drainages (10%), classes showing high LS factor values and relative U-shaped valleys (4%), and upper slopes (3%) are also weightages were calculated which vary in the range of present. The remaining three landform classes (midslope 0.05 to 0.45. drainages, open slopes, midslope ridges, and plains) cover The topographic curvature used in the analysis repre- less 1% of the total area only. Later, by considering the sents the shape of the slope or topography which has relative importance of individual landforms over the ter- direct bearing on the erosion by either concentrating rain susceptibility to erosion as explained in the previous runoff or dispersing it (Lee and Sambath 2006; Fischer studies conducted to model the landside susceptibility in et al. 2012). Topographic curvature may show an up- various regions (Costanzo et al. 2012; Tien Bui et al. 2012; ward convex surface (positive curvature) or upwardly Oh and Lee 2017), factor scores were fixed and ratings concave surface (negative curvature), or it may be flat were calculated. Calculated ratings vary from 0.0142 (zero curvature) (Alkhasawneh et al. 2013). In order to (Plain) to 0.2142 (Upland drainages). understand the influence of the shape of the surface The parameters discussed above all contribute to a slope over terrain susceptibility, curvature was generated certain extent to increase the susceptibility of the terrain from the DEM. Topographic curvature in the study area to erosion. In addition, the contribution of water flow in Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 10 of 18 the terrain to enhance the susceptibility was also consid- Another parameter of interest is stream head density ered by means of topographic wetness index (TWI), de- which indicates the number of stream origin points per rived from the digital elevation model. TWI considers the unit area. Analysis of channel head locations can the upslope contributing area and its slope to quantify provide insight into the controls on drainage density as the steady state wetness and water flow across the region well as the response of landscapes to climatic change (Pourghasemi et al. 2013a). TWI generated for the study and indication about the rate of susceptibility of that ter- area shows values in the range of 1 to 25 which have rain (Wadge 1988; Montgomery and Dietrich 1989; Lin been divided into three classes which are: < 5, 5–10, and Oguchi 2004). In the present study, the stream head and > 10. Considering the wetness potential of the area density was calculated by extracting the starting points through TWI classes, 30% of the total study area was of all 1st order streams in the study area. Using the found to have low wetness index (TWI < 5) whereas density function available in the spatial analyst extension most of the area (62% of the total area) shown moderate of ArcGIS, stream head density was calculated for 1 km wetness index (5–10), while remaining 8% of the area and the calculated density values were found to vary has high TWI values (> 10). To take into account differ- from 8 to 25 N/km . Reclassification of stream head ent level of contribution of TWI to terrain erosion sus- density in to three classes which are: low (< 15 N/km ), 2 2 ceptibility, landslide, and gully erosion susceptibility medium (15–20 N/km ), and high (> 20 N/km )was studies carried out in different locations were considered then used for the calculation of individual weights. It was (Wang et al. 2015; Chen et al. 2017; Arabameri et al. noted that, 21% of the total study area has low stream 2018b). It was noted that, in most studies high TWI has head density whereas 71% of the area has moderate dens- high impact on erosion and in the present study, the ity, and remaining 7% of the area only has high density. relative scores of individual TWI classes were assigned Areas having high stream head density is more susceptible based on the TWI values i.e. lower score were attributed to erosion, especially by the development of gully head to low TWI and vice versa. The calculated weightages and continuous erosion downstream. Based on the density varies from 0.0714 (TWI < 5) to 0.6428 (TWI > 10). classes and its impact on terrain erosion susceptibility, the Another parameter is stream power index (SPI), which relative scores of the stream head density classes were estimates the capacity of streams to potentially modify assigned. Further, ratings were calculated and it varies in the geomorphology of an area through gully erosion and the range of 0.0714 to 0.6428 indicating varying contribu- transportation. SPI is the measure of the erosive power tion towards the terrain susceptibility. of flowing water by considering the relationship between In erosion susceptibility analysis, the existing land use/ discharge and specific catchment area (Chen and Yu land cover of the area under consideration also plays a 2011; Pourghasemi et al. 2013b). SPI highlights areas in vital role by providing information about the condition which overland flow has higher erosive power in the of vegetative protection against erosion and many re- catchment (Wilson and Gallant 2000). This makes the searchers found land use/land cover to be a dominant use of SPI a significant parameter of interest in erosion variable in erosion susceptibly (Dai and Lee 2002; Glade and terrain susceptibility modelling. SPI was calculated 2003; Beguería 2006; Leh et al. 2013; Galve et al. 2015; for the study area using the stream power index module Mandal and Mandal 2018; Vuillez et al. 2018; Abdulkar- available in SAGA 2.1 software based on the digital eem et al. 2019). It is also one of the key factors under elevation model as input data. SPI of the Baram study anthropogenic influence i.e. reflective of human disturb- area varies from − 13 to 7 indicating the differential ero- ance of vegetation cover due to logging, clearing for roads, sive power of the streams in the region. Higher values and/or agriculture. In the present study, the land use/land indicate the likely overland flow paths during storms or cover map of the area was derived from Landsat 8 OLI severe erosive rainfall pointing to potential areas for images acquired on 28th March 2015, through the super- gullying or other areas susceptible of erosion. The SPI vised classification with extensive ground truth points map prepared was reclassified into seven classes which from field observations. The segmentation of Landsat are: < 0, 0–1, 1–2, 2–3, 3–4, 4–5, and > 5. Most of the image into classified land use/land cover map has identi- study area (63%) showed SPI value less than 0. High SPI fied and mapped the following land use/land cover classes represent areas where high slopes and flow accumula- in the area: water, secondary forest, primary forest, mon- tions exist which indicate enhanced with erosive poten- tane forest, mixed agriculture, paddy, exposed soil tial (Gómez-Gutiérrez et al. 2015; Arabameri et al. (barren), artificial surfaces, and pebbles, cobbles in river 2018a). Considering the SPI values and their contribu- beds. The supervised classification indicate that more than tion towards terrain susceptibility and gullying, the 56% of the total area was covered by secondary forests and relative scores were added to each class in a simple pro- 27% of the area was covered by primary forests. It was also gression and rating was calculated and the rating varies noted that, land use activities like mixed agricultural land in the range of 0.0303 to 0.2727. and exposed barren land, which alter the terrain condition Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 11 of 18 in the region, cover 8.8 and 1.8% of the total area respect- Result and discussion ively. The other land use/land cover classes together cover Ten geo-environmental variables which are potentially less than 5.5% of the total area, in which upper montane responsible for changing the stability of the terrain ren- forests cover 3.35% of the area. Further, when determining dering it more susceptible to erosion were considered the relative influence of individual land use/land cover quantitatively to assess the susceptibility of the forested classes in terrain susceptibility, previous study which detail region of Sarawak to erosion using the AHP technique. the influence of individual land use classes in soil erosion Among the ten variables used to generate the terrain vulnerability of the area was taken into account (Vijith erosion susceptibility index (TESI) map, the variables and Dodge-Wan 2018). For the AHP, the weight was such as land use/land cover, slope, stream power index, calculated for individual land use/land cover classes based stream head density, and slope length and steepness on the relative importance assigned to each class and factors shown maximum influence (> 0.10) followed the varied in the range of 0.0294 to 0.2647. Among the differ- relative relief and topographic wetness index (0.08). ent classes, the exposed barren land, mixed agriculture Other variables such as curvature (0.06) and landform acquired the highest rating of 0.2647 followed by second- (0.04) shown moderate influence, whereas aspect was ary forest (0.2352) whereas the upper montane forest and found to be the lowest influencing variable with a rank artificial surface showed the lowest weight (0.0294). of 0.02. Even though, the variable ranks differ, the selec- Higher weight shown by the exposed barren land, mixed tion of the variables in the present analysis are found to agriculture, and secondary forest in soil erosion study be optimum by showing the CR less than the cut-off (Vijith et al. 2018a, 2018b) indicates the strong influence value (0.00003). Besides this, the weight factor calculated of these land use classes on terrain susceptibility. for the individual variable classes indicates a varying de- In order to produce the terrain erosion susceptibility gree of influences within the parameter and between the zonation (TESZ) map, the ranking of individual parame- parameters. It was also noted that the relative weighting ters was carried out to assign their relative contribution of variable classes indicates the variability of influences. before assigning the calculated weight to each parameter Among the variables considered, the land use/land cover, classes. The parameter ranking indicated that land use/ terrain with slope > 25° having west, southwest, and land cover is the highest influencing parameter with a northwest orientations, relative relief > 300 m/km ; high rating of 0.183 followed by slope (0.163), stream power LS factor, and concavity, having high TWI, upland drain- index (0.142), and stream head density (0.122). The ages and mountain top landforms, high stream head other parameters such as aspect, relative relief, LS factor, density are showing high relative weights among the curvature, landforms, and topographic wetness index classes and contributing more to the terrain susceptibil- were found to have less influence. Reliability of each par- ity. The integration of weighted variables in the raster ameter to be included in the analysis was determined by calculator resulted terrain erosion susceptibility index examining the consistency ratio (CR) and it was noted (TESI) map showing the susceptibility ranges from 0.07 that all the parameters shown CR below the proposed to 0.34 indicating spatial distribution of different degree cut-off of 0.1, so none were omitted from the analysis. of susceptibility to erosion (Fig. 4a). The TESI map gen- Finally, the weights calculated for individual parameter erated shows varying distribution of higher and lower classes were assigned to the respective parameters to susceptibility indexes all over the area without showing produce the weighted maps and using the raster calcula- any particular pattern, which make it difficult to identify tor option of the spatial analyst, individual themes were and differentiate the regions which showing nil or low integrated to produce the terrain erosion susceptibility susceptibility and very high susceptibility. index (TESI) map using the equation (Eq. 3): In order to understand the spatial extent of different severity of erosion susceptibility, the TESI map was re- Terrain erosion susceptibility index ðTESIÞ¼ classified into five discrete classes based on the suscepti- bility index values namely nil, low, moderate, high, and Slope  0:163 þ Aspect  0:020 wt wt very high zones (Fig. 4b). The reclassification of the TESI þ Relative relief  0:081 þ LS factor  0:102 wt wt to terrain erosion susceptibility zonation (TESZ) map þ Curvature  0:061 þ Land forms  0:040 wt wt facilitated the calculation of areal extent of different sus- þ TWI  0:081 þ SPI  0:142 wt wt ceptibility zones. The areas falling under each erosion þ Stream head density  0:122 wt susceptibility class are given in Table 2 and shown in þ Land use=land cover  0:183 wt Fig. 5. The final TESZM showed that 10.3% of the study area is categorised as having very high susceptibility to ð3Þ erosion and these areas appears to be distributed differ- where, is the relative weights of classes in individual ent places in the region. In addition, high erosion sus- wt variable. ceptibility zones occupy 14.9% whereas moderate and Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 12 of 18 Fig. 4 a Terrain erosion susceptibility index (TESI) maps and b Classified terrain erosion susceptibility zonation (TESZ) map low susceptibility zones covers 25.8 and 27.1% of the susceptibility zones mostly occur in the flanks of the area respectively. It was also noted that 17.47% of the mountains rather than in the valleys. In addition, in study area is not prone to erosion. Besides this, 4% of some places these zones show linear patterns which can the area was not included in the final analysis as there is be linked directly with the road structure and skidder no data in these zones due to thick cloud and cloud trails. For the development of roads in the area, the con- shadow on satellite image. An attempt has been made to tinuity of hills with concave or convex slopes has been understand the spatial characteristics of the erosion sus- removed by the toe cutting and this will increase the ceptibility zones by overlying the TESZ with the exagger- susceptibility to erosion and lead to the development of ated terrain model. It was found that the higher erosion soil slumps triggered by the heavy rainfall. The clustered Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 13 of 18 Table 2 Terrain erosion susceptibility classes derived from the reclassification of TESI Terrain erosion susceptibility classes Area (km ) Area (%) Probability of terrain erosion Nil 367.84 17.47 No chance of erosion, mostly low lying areas. Low 572.01 27.17 Very low probability. Mostly affected by the run-off from the higher elevation Moderate 544.77 25.88 Medium probability, may directly involve in erosion or affected as part of falling from the top High 313.22 14.88 High certainty of erosion either as slide or gullying. Need attention in such areas crossing road sections. Very High 217.31 10.32 Very high certainty of erosion either as slide or gullying. To be monitored during the heavy rainy seasons. No data 89.85 4.27 No data is available due to cloud and shadow in the image. Not considered in the analysis nature of the higher erosion susceptibility indicates the and annual rainfall is shown in Fig. 6. It was noted that logging activity and shifting cultivation, which exposes mean monthly rainfall varies between 238 mm (June) to the terrain by removing the protective tree cover. 532 mm (November) with long term mean monthly and annual rainfall of 352 mm 4227 mm respectively. A Rainfall distribution spatial distribution map was generated by considering Though different geo-environmental variables make the the mean annual rainfall calculated for each rain gauge terrain susceptible to erosion, the amount and intensity for use in further analysis (Fig. 7). Mean rainfall ranges of rainfall which falls in an area acts as the triggering between 3654 to 4862 mm with higher rainfall generally mechanism which can initiate movement of soil, debris, located in southwest part of the study area, especially and other overburden downstream. In most studies, between the rain gauges Long Naha’ah and Long Akah rainfall distribution is included as a theme to statistically whereas comparatively lower rainfall is noted in north- model the land susceptibility to erosion (Sangchini et al. ern and north-eastern part of the study area. 2016). In the present research, rainfall distribution in the In order to assess the contribution of rainfall to terrain study area was considered separately and analysed to susceptibility leading to slope failure, 200 random identify the areas with high possibility of terrain erosion (unconditional and unstratified) points (pixel size 30 × susceptibility. Therefore, 5 year rainfall data were 30 m) were generated within the study area boundary collected from the Department of Irrigation and Drain- and mean annual rainfall, TESI, and TESZ values corre- age (DID) Malaysia corresponding to four rain gauges sponding to each point were extracted. The extracted located in the study area and six around the area. Mean values of TESI and TESZ were compared by linear re- monthly rainfall distribution and 5 year mean monthly, gression with mean annual rainfall to study the possible Fig. 5 Area distribution of terrain erosion susceptibility zones Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 14 of 18 Fig. 6 Mean monthly and annual rainfall distribution in the study area role of local rainfall amount and distribution over terrain mean annual rainfall distribution and TESZ shows susceptibility (Lyra et al. 2014; Teodoro et al. 2016; Brito absence of correlation (Fig. 8b). P values (p > 0.10) also et al. 2017) (Fig. 8). Linear regression plot of mean indicates no or nil dependency between the dependant annual rainfall and TESI indicates very low or nil correl- (terrain susceptibility) and independent (rainfall) vari- ation (Fig. 8a). Similarly, the linear regression plot of the ables in the region. Although high rainfall in general is a Fig. 7 Spatial distribution of mean annual rainfall in the study area Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 15 of 18 Fig. 8 Linear regression plots explaining the relationship between rainfall a TESI and b TESZ factor in increasing terrain erosion susceptibility, at a variables considered in the analysis through the AHP specific local scale (pixel size 30 × 30 m area), the higher technique facilitated the identification of the most cru- amount of rainfall received in parts of the region does cial variables which render the terrain more susceptible not appear to significantly influence the local site to erosion. Though all these variables were found to be specific terrain susceptibility. However, other geo- contributing to erosion susceptibility to various degrees, environmental variables considered play more significant the determination of ranks through relative ratio high- roles in rendering the terrain more susceptible to ero- lights that land use/land cover, slope, stream power sion in specific local areas. index, stream head density, and LS factor are the most crucial variables. In the study area, the places which are Conclusion exposed (barren land) with concave slopes having slope The characteristic probability of erosion proneness of a exceeding 25° and facing west, southwest, and northwest, sample catchment with regenerated and logged tropical with relative relief higher than 300 m/km and high LS rain forest region in Sarawak, northern Borneo, was suc- factor, TWI and stream head density are found to be the cessfully carried out in the present study using raster most vulnerable to erosion. These areas are identified GIS and AHP technique. Terrain variables derived from via the TESI and TESZ maps. the digital elevation model such as slope, aspect, relative TESZ map generated by the reclassification of TESI relief, LS factor, curvature, landforms, TWI, SPI, stream into five distinct groups show the spatial pattern of ero- head density, and the land use/land cover interpreted sion susceptibility in terms of its severity. It was found from the satellite images were integrated in the raster that 10 and 14% of the total area comes under the very based GIS environment after deriving the determinant high and high erosion susceptibility zones. The higher ranking and weights for the variables and variable clas- susceptibility was found to be characteristic of high ele- ses. The generation of rankings and weightages for the vated hills and slopes which undergo rapid changes. Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 16 of 18 However, areas with nil and low potential of erosion sus- Akgün, A., and N. Türk. 2011. Mapping erosion susceptibility by a multivariate statistical method: A case study from the Ayvalık region, NW Turkey. ceptibility together constitute 44% and the moderate Computers & Geosciences 37 (9): 1515–1524. susceptibility zones occupy 25% of the total study area. Aleotti, P., and R. Chowdhury. 1999. Landslide hazard assessment: Summary Considering the influence of rainfall in the region, the review and new perspectives. Bulletin of Engineering Geology and the Environment. 58: 21–44. entire study area receives what can be considered high Alkhasawneh, M.S., U.K. Ngah, L.T. Tay, M. Isa, N. Ashidi, and M.S. Al-batah. 2013. tropical rainfall. Analysis of 200 randomly distributed Determination of important topographic factors for landslide mapping analysis using pixel sized area (30 m × 30 m) suggests that at local scale MLP network. The Scientific World Journal. https://doi.org/10.1155/2013/415023. Althuwaynee, O.F., B. Pradhan, and S. Lee. 2016. 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Environmental Earth combined with other factors such as slope, LS factor. Sciences. 77 (17): 628. Along with this, the high amount of rainfall recorded Ayalew, L., H. Yamagishi, and N. Ugawa. 2004. Landslide susceptibility mapping throughout the region induces movement of unsup- using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata prefecture, Japan. Landslides 1 (1): 73–81. ported and toe-cut slopes to move downstream. The Beguería, S. 2006. Changes in land cover and shallow landslide activity: A case findings of the present study give a better understanding study in the Spanish Pyrenees. Geomorphology. 74 (1–4): 196–206. of the region in terms of erosional characteristics. The Besler, H. 1987. Slope properties, slope processes and soil erosion risk in the tropical rain forest of Kalimantan Timur (Indonesian Borneo). Earth surface findings can be used for planning of new roads, settle- Processes and landforms 12 (2): 195–204. ments by developing and implementing erosion reduc- Bijukchhen, S.M., P. Kayastha, and M.R. Dhital. 2013. A comparative evaluation of tion and terrain protection measures. heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, East Nepal. Arabian Journal of Geosciences Abbreviations 6 (8): 2727–2743. AHP: Analytical Hierarchy Process; CI: Consistency Index; GIS: Geographical Bourenane, H., Y. Bouhadad, M.S. Guettouche, and M. Braham. 2015. GIS-based Information Systems; LS factor: Slope Length and Steepness factor; landslide susceptibility zonation using bivariate statistical and expert LSZ: Landslide Susceptibility Zonation; RUSLE: Revised Universal Soil Loss approaches in the city of Constantine (Northeast Algeria). Bulletin of Equation; SPI: Stream Power Index; SRTM: Shuttle Radar Topographic Mission; Engineering Geology and the Environment 74 (2): 337–355. TESI: Terrain Erosion Susceptibility Index; TESZ: Terrain Erosion Susceptibility Brenning, A. 2005. Spatial prediction models for landslide hazards: Review, comparison Zonation; TWI: Topographic Wetness Index; USLE: Universal Soil Loss and evaluation. Natural Hazards and Earth System Science 5: 853–862. Equation Brito, T.T., J.F. Oliveira-Júnior, G.B. Lyra, G. Gois, and M. Zeri. 2017. Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil. Acknowledgements Meteorology and Atmospheric Physics 129 (5): 469–478. The authors wish to thank Sarawak Energy Berhad for funding this research Chen, C.Y., and F.C. Yu. 2011. Morphometric analysis of debris flows and their under the Project “Mapping of Soil Erosion Risk”. They also thank Curtin source areas using GIS. Geomorphology. 129 (3–4): 387–397. University Malaysia for facilities and other assistance and the Department of Chen, W., H. Chai, X. Sun, Q. Wang, X. Ding, and H. Hong. 2016a. A GIS-based Irrigation and Drainage (DID), Malaysia for providing rainfall data. Authors are comparative study of frequency ratio, statistical index and weights-of- also thankful to the Editor in Chief, and anonymous reviewers for their evidence models in landslide susceptibility mapping. Arabian Journal of critical reviews, constructive comments, and suggestions which significantly Geosciences. 9 (3): 204. improved the quality of the manuscript. Chen, W., W. Li, H. Chai, E. Hou, X. Li, and X. Ding. 2016b. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty Authors’ contributions factor (CF) models for the Baozhong region of Baoji city, China. VH done technical, scientific analysis of the research and developed the Environmental Earth Sciences 75 (1): 1–14. manuscript. 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Modelling terrain erosion susceptibility of logged and regenerated forested region in northern Borneo through the Analytical Hierarchy Process (AHP) and GIS techniques

Geoenvironmental Disasters , Volume 6 (1) – Jul 12, 2019

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Springer Journals
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Copyright © 2019 by The Author(s).
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Environment; Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
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2197-8670
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10.1186/s40677-019-0124-x
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Abstract

This research examines the susceptibility of logged and regenerated forest region to erosion through the application of the analytical hierarchy process (AHP) and geographical information systems (GIS). In order to estimate terrain erosion susceptibility, ten geo-environmental variables were taken into account as possible factors relevant to terrain erosion. They are slope, aspect, relative relief, slope length and steepness (LS) factor, curvature, landforms, topographic wetness index (TWI), stream power index (SPI), stream head density, and land use/land cover. Pairwise comparison matrixes were generated to derive the weightages and ratings of each variable and their classes. These were integrated to generate the terrain erosion susceptibility index (TESI) map. Among the variables used in the analysis the land use/land cover, slope, SPI, stream head density, and LS factor were shown to have high contribution towards terrain erosion susceptibility. The areas with a concave slopes > 25° and high relative relief, LS factor, TWI, and stream head densities were found to be more susceptible to erosion such as gullying or landslides. The conversion of TESI into terrain erosion susceptibility zonation (TESZ) map shown that 25% of the total area is highly susceptible to erosion. Among this, 10% of the area possesses a very high vulnerability to landslides and gullying or soil slips and these areas coincide with logging roads and skidder trails. Linear regression analysis between TESI and TESZ with spatial distribution of mean annual rainfall in the region does not show any significant relationships (p > 0.10). However, high rainfall triggers rapid downstream movement of unsupported slopes in the region. The terrain erosion susceptibility zonation map expresses the realistic condition of logged terrain matching with field observations in the area in terms of erosion. The results can serve as basic data for future development programs in the region, in any projects where the terrain susceptibility is critical by planning infrastructure to avoid high risk zones. Keywords: Geo-environmental variable, Terrain susceptibility, AHP, GIS, Borneo * Correspondence: vijithh@gmail.com Department of Applied Geology, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009 Miri, Sarawak, Malaysia © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 2 of 18 Introduction Park et al. 2013; Shit et al. 2015; Althuwaynee et al. Erosion, either as soil loss or landslides, is the natural 2016; Rahmati et al. 2017; Torri et al. 2018; Othman et denudation process or a stage of geomorphic evolution al. 2018). Although the output of susceptibility analysis of terrain which is responsible for generating different may vary in name such as landslide susceptibility zon- topographical features (Thornbury 1969). The natural ation (LSZ) map or gully erosion susceptibility map, the erosional or denudational process will take place at given analysis techniques and geo-environmental variables rate and any recent changes in the normal rate of ero- used in the modelling are generally similar. Further sion may reflect changes in the equilibrium condition of details of different techniques used to analyse terrain the terrain due to anthropogenic causes. Erosion and erosion susceptibility can be found in Aleotti and allied mass wasting problems are common in hilly areas, Chowdhury (1999), Guzzetti et al. (1999), van Westen but their severity will vary depending on the geo- (2000), Brenning (2005), Huabin et al. (2005), and van environmental factors involved. Steep sloping, highly Westen et al. (2006). elevated rugged terrain may be fragile in terms of geo- In the present study, an attempt has been made to logical, vegetation, and climatic factors making it more model and classify the upper catchment regions of the vulnerable to erosion, which may be aggravated by Baram River (Sarawak, Malaysia) in terms of susceptibil- human induced developmental activities (Fadul et al. ity of the terrain to erosion due to gullying, soil slip, and 1999). The fragility of such terrains can be termed as landslides. The region considered possesses very weak susceptibility to erosion. Assessment of the susceptibility geological formations (tightly folded sedimentary rocks of the terrain to erosion and classification into different of various lithologies) covered by dense forest. During susceptibility zones is an important step to understand- the last few decades, the study area has undergone in- ing an area’s vulnerability to erosion for development of tense terrain modification and forest clearing through proper management plans and mitigation strategies (Dai timber harvesting and logging road construction which and Lee 2002; Ayalew et al. 2004; Bijukchhen et al. 2013; increased the vulnerability of the terrain to erosion Erener et al. 2016; Pham et al. 2017). Susceptibility map- (Fig. 1). As a result of episodes of heavy rainfall, areas ping is generally used in landslide and gully erosion with high vulnerability to erosion will flow or slide modelling, the goal of which is to identify potentially downhill to valley streams and may deposit large quan- vulnerable areas which are those with several critical tities of sediment in the rivers downstream. Very few variables. To understand the susceptibility of a region to studies have reported on terrain susceptibility to erosion erosion, either as landslides or gullying, different in Sarawak and the reported studies deal with the soil methods which use expert opinion (qualitative), statis- erosion assessment using soil loss equations (USLE tical prediction (quantitative), or both may be applied /RUSLE) (Besler 1987; de Neergaard et al. 2008; Vijith et using geographical information systems (GIS). al. 2018a, 2018b; Vijith and Dodge-Wan 2018). Prior to In order to assess the susceptibility, a number of geo- 2018, no studies were reported from the selected upper environmental variables such as geomorphology, slope, catchment region of the Baram River. land use, lithology, etc., as well as palaeo locations of the The present study is an initial attempt to assess terrain phenomena have been used (Kheir et al. 2007; Akgün erosion susceptibility and can be used as a basic and and Türk 2011; Dewitte et al. 2015; Kavzoglu et al. 2014; valuable information while planning for roads and other Gómez-Gutiérrez et al. 2015; Chen et al. 2016a; Garosi infrastructure developments. The study area lacks a et al. 2018). Among these, most of the parameters con- database of previous information related to erosion sidered as natural parameters and the land use/land (gullying and landslides in particular) and due to the cover existed in the area is only man made i.e. it was relatively inaccessible nature of the terrain, it is difficult mainly controlled human activity. Expert opinion to map the locations of slides or gullies by direct obser- method relies on the field knowledge and expertise of vation in the field. To overcome these limitations, a the analyst to determine the influence and weights of well-defined and tested predictive analysis model, i.e. the each parameter and parameter classes, whereas statis- analytical hierarchy process (AHP) which uses a combin- tical techniques use well defined bivariate or multivariate ation of expert opinion and statistical measurements, analysis techniques through dependent and independent was applied in this research. Numerous researchers have variables to determine the relative importance of each used the analytical hierarchy process to estimate the sus- variable (Bourenane et al. 2015; Rahmati et al. 2016). ceptibility to landslide or soil erosion in other parts of The suitability and selection of methods to produce sus- the world and found it to be successful in predicting the ceptibility map is often heavily depend on the availability vulnerability of the region based on the parameters used of data sets of independent geo-environmental variables (Komac 2006; Neaupane and Piantanakulchai 2006; particularly information on previous incidents of land- Yoshimatsu and Abe 2006; Yalcin 2008; Nekhay et al. slides or gullies (Lucà et al. 2011; Conoscenti et al. 2013; 2009; Svoray et al. 2012; Reis et al. 2012; Kayastha et al. Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 3 of 18 Fig. 1 Slope failures observed in the study area 2013; Pourghasemi et al. 2012, 2013a; Youssef 2015; northeast (NE) - southwest (SW) to north northeast Althuwaynee et al. 2016; Sangchini et al. 2016; Rahaman (NNE) - south southwest (SSW) trend. The drainage and Aruchamy 2017; Arabameri et al. 2018b). The find- pattern is predominantly dendritic but the presence of ings of the present research will facilitate the identifica- trellis and parallel pattern in the region indicates the tion of areas critically vulnerable to erosion and influence of lithology and structural features on the landslides and thus provides an opportunity to avoid risk development of drainage networks. Geomorphological associated with terrain susceptibility while implementing features vary from highly elevated steep sloping escarp- the developmental schemes in the region. ments to low lying flat regions of fluvial floodplains. Hills and mounds show highly complex shapes with a Study area sharp crests to rounded tops. The area receives an an- A forested region in the interior Sarawak, which has nual average rainfall of approximately 4600 mm from the undergone vegetation changes and terrain alteration due two dominant monsoon seasons viz., southwest and to logging activities was selected for the present analysis. northeast monsoons. Rainfall shows high spatial and The study area covers a total area of 2105 km and con- temporal variations (Vijith and Dodge-Wan 2018) Vege- tains two major subwatersheds of the Baram River tation cover varies from dense primary forest to open namely Sungai Patah and Sungai Akah which are located spaces of barren land. The majority of the study area is between north latitudes 3° 13′ 15″ to 3° 41′ 50″ and covered with forests of different types and density, east longitudes 114° 35′ 42″ to 115° 13′ 20″ (Fig. 2). followed by mixed agricultural land (mainly hill paddy Though the subwatersheds differ in shape, both have cultivation) and then the open spaces with no vegetation similar terrain and geological characteristics. The area is related to road development, villages, and logging. Initial highly undulating with elevations between 37 m to 1578 field observations indicated that the development of log- m asl. The bed rock consists of sedimentary rocks of ging roads and log trail (skidding and pulling trails) have Paleocene, Oligocene, and Miocene ages. Most of the rendered the terrain more susceptible to erosion by study area consists of Oligocene shale and sandstone, changing the continuity of the hills through toe cutting with areas of Paleocene deep water sediments composed and removal of the protective vegetation cover. of shale and sandstone with occasional conglomerate and limestone, and Miocene shale and sandstone. Nu- Materials and methods merous anticlines, synclines, and local fractures are The Sungai Akah and Sungai Patah catchments of the present in the area showing tight folds with a common Baram River were selected for the present research as Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 4 of 18 Fig. 2 Study area location map this is a data poor region. Mapping of terrain erosion classification with field verification. Land use/land cover is susceptibility is considered as the preliminary step to the most significant factor under the influence of an- understand the risks of soil erosion and landslide. An thropogenic activities which modify the protective vegeta- erosion susceptibility map was generated using several tion cover. Different software used for the generation of geo-environmental variables derived from various re- variables and final analysis are ArcGIS version 9.3 and mote sensing data sources such as digital elevation SAGA version 2.1, which operates in the raster GIS envir- model (DEM) and satellite images. The digital elevation onment and the cell size for this analysis was fixed as model, downloaded from the earth explorer (http:// 30 × 30 m. The significance and methodology applied to earthexplorer.usgs.gov) website of U. S Geological Sur- obtain each variable is described in text, as well as the vey. Shuttle Radar Topographic Mission (SRTM) data of weightages attributed to each class of each variable. 30 m was used after it had been clipped to the study area In order to generate the terrain susceptibility map of boundary and the voids filled by the Fill DEM module the study area by analyzing the contribution of each available in the spatial analyst extension of ArcGIS soft- variable which makes the terrain susceptible to erosion, ware. The filled elevation dataset was then used to derive the analytical hierarchy process (AHP) technique devel- variables such as slope, aspect, relative relief, slope oped by Saaty (1980) was used. This methods has the length and steepness (LS) factor, curvature, landforms, capability of integrating expert knowledge, field informa- topographic wetness index (TWI), and stream power tion, and relative statistics together. AHP is a semi- index (SPI). Stream networks were produced from the quantitative, multi-criteria decision support technique digital elevation model and stream head points were ex- which is used to generate high quality and precise tracted to calculate the stream head density map. The decisions through the application of the matrix based parameters are natural features of the region and terrain pairwise comparison of the contributing factors which and not affected by anthropogenic activities. Landsat 8 determine the results of the phenomenon or the process OLI images of the area acquired on 28th March 2015, (Saaty 1990; Saaty 1994; Saaty and Vargas 2001). The which reflect the current land use pattern were used to pairwise comparison will be carried out based on the produce the land use/land cover map through supervised different ratings of each variable or feature classes on Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 5 of 18 the basis of relative importance varying from 1 to 9. reference to horizontal. Nature of the slope varies from Each value in the relative importance can be assigned to gentle to steep and this controls different geomorphic variable or variable classes based on the subjective processes such as erosion, transportation, and deposition judgement of relative importance. Relative weight of the in relation to the rainfall-runoff characteristics of the re- variables used in the matrices can be determined by gion (Foumelis et al. 2004; Gómez-Gutiérrez et al. 2015; generations of eigenvectors and the consistency of the Sangchini et al. 2016; Arabameri et al. 2018a). Gentle variable can be assessed by calculating the consistency slopes are expected to induce less terrain slips due to index (CI) as given below (Saaty 1990) (Eq. 1): low shear stresses (Lee et al. 2004). High slope values show the highest susceptibility to erosion although verti- ðÞ λmax‐n cal terrain surfaces, and very high slopes having exposed CI ¼ ð1Þ ðÞ n‐1 bedrock show less susceptibility to terrain erosion due to less or nil soil cover (Dewitte et al. 2015; Rahmati et where, λ is the largest or principal eigenvalue of the max al. 2017; Torri et al. 2018). In order to generate the analysed matrix and n is the order of the square matrix. slope, hydrologically corrected (void filled) SRTM DEM This inconsistency index can also be expressed as were used and the slope map generated shown a range consistency ratio (CR) which determine the suitability of varies from 0 to 75°. Then the slope was reclassified into individual parameters and their classes to be included in the following gentle to very critical seven classes 0–5°, the analysis and was given by the Eq. (2): 5°-10°, 10°-15°, 15°-25°, 25°-35°, 35°-45°, and > 45° and the relative percentage of area covered by individual CI CR ¼ ð2Þ slope class shown high spatial variation. Among the RI slope classes, a large percentage of the study area falls where, RI is the random index i.e. consistency index for within the slope class 15°-25° (31%), followed by 10°-15° a random square matrix of the same size proposed by (20%), and 25°-35° (17%). It was also observed that, the Saaty (1980). The cut-off of the CR was fixed as less than higher slope classes in the range of 35–45° and > 45° oc- or equal to 0.1 so that if CR of the analyzed variable is cupied comparatively reduced areas of 9% and 1% only found to be higher than the cut-off, the variable will be respectively. Seven slope classes were ranked by attribut- omitted from the analysis. ing factor scores from 1 to 9 to generate the pairwise matrix. The attribution was based on the assumption Preparation of terrain Erosion susceptibility zonation that there is a regular increase in risk across all the slope (TESZ) map classes. Considering the influence of the slope over the In order to map the areas susceptible to terrain erosion terrain stability, relative weightages were then calculated and classify them based on the severity and criticality of and these vary from 0.0274 to 0.2432 (Table 1). Higher risk, a number of distinct geo-environmental variables ratings are noted in areas having a slope higher than 35 were considered. The combined effects of multiple vari- in the study area. ables in terrain susceptibility were characterised through Slope aspect indicates the direction of the terrain slope the application of analytical hierarchy process (AHP) with respect to north and varies from − 1 to 359°, in based influence measuring technique, which is consid- which the negative value represents flat surface (Prasan- ered a powerful and supportive multiple criteria decision nakumar et al. 2011). Aspect of the terrain have direct making tool (Malczewski 1999; Yasser et al. 2013; Chen and indirect control over terrain processes and condi- et al. 2016b). Ten individual factors were used. They are: tions such as soil moisture, vegetation cover, and soil slope, aspect, relative relief, LS factor, curvature, land- thickness by exposing the surface to sunlight and or forms, TWI, SPI, stream head density, and land use/land heavy rain (Clerici et al. 2006; Meten et al. 2015). In cover (Fig. 3a-j). The contribution of each parameter in most of the landslide and gully erosion modelling stud- the terrain susceptibility as a single unit and individual ies, slope aspects is taken as an important variable (Reis feature classes in the parameters were determined by the et al. 2012; Pourghasemi et al. 2013b; Rahmati et al. cross comparison matrices analysed through the AHP 2016; Sangchini et al. 2016; Menggenang and Samanta and output rating was considered as the weight of each 2017; Othman et al. 2018). In the present research, a parameter and their class. Table 1 shows the pairwise slope aspect map was generated from the elevation comparison matrix, consistency ratio, and the weightings surface and classified into nine classes which are: flat, N, of individual parameters, and their classes considered in NE, E, SE, S, SW, W, and NW based on the orientation the analysis. i.e. which way the terrain is facing. Considering the area In all analysis which deals with terrain susceptibility, distribution of the individual aspect class in the study the primary factor considered is the terrain slope, which area, most of the slope aspects classes cover similar represents the inclination of the topography with areas (13%) except flat terrain which is very rare (0.30%). Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 6 of 18 Fig. 3 Geo-environmental variables used in the analysis a Slope b Aspect c Relative relief d Slope length and steepness (LS) e Curvature f Landforms g topographic wetness index (TWI) h Stream power index (SPI) i Stream head density j Land use/land cover Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 7 of 18 Table 1 Pair-wise comparison matrix, ratings, and consistency ratio of the variables classes and individual variables used in the present study Variables Classes 1 2 3 4 5 6 7 8 9 10 Rating / weights Slope 0–5 1 1/3 1/4 1/5 1/7 1/8 1/9 0.0270 5–10 1 3/4 3/5 3/7 3/8 3/9 0.0810 10–15 1 4/5 4/7 4/8 4/9 0.1081 15–25 1 5/7 5/8 5/9 0.1351 25–35 1 7/8 7/9 0.1891 35–45 1 8/9 0.2162 > 45 1 0.2432 Aspect Flat 1 1/2 1/2 1/3 1/4 1/5 1/7 1/9 1/6 0.0256 N 1 1 2/3 2/4 2/5 2/7 2/9 2/6 0.0512 NE 1 2/3 2/4 2/5 2/7 2/9 2/6 0.0512 E 1 3/4 3/5 3/7 3/9 3/6 0.0769 SE 1 4/5 4/7 4/9 4/6 0.1025 S 1 5/7 5/9 5/6 0.1282 SW 1 7/9 7/6 0.1794 W 1 9/6 0.2307 NW 1 0.1538 Relative relief < 100 1 1/3 1/5 1/7 1/9 0.04 100–200 1 3/5 3/7 3/9 0.12 200–300 1 5/7 5/9 0.2 300–400 1 7/9 0.28 > 400 1 0.36 Slope length and Steepness (LS) 5 1 1/3 1/7 1/9 0.05 10 1 3/7 3/9 0.15 15 1 7/9 0.35 > 15 1 0.45 Curvature Concave 1 9 9/7 0.5294 Flat 1 1/7 0.0588 Convex 1 0.4117 Landforms Deeply incised stream 1 2/4 2/9 2/1 2/6 2/3 2/5 2/2 2/3 2/7 0.0476 Midslope drainages 1 4/9 4/1 4/6 4/3 4/5 4/2 4/3 4/7 0.0952 Upland drainages 1 9 9/6 9/3 9/5 9/2 9/3 9/7 0.2142 U shaped valleys 1 1/6 1/3 1/5 1/2 1/3 1/7 0.0238 Plains 1 6/3 6/5 6/2 6/3 6/7 0.1428 Open slopes 1 3/5 3/2 3/3 3/7 0.0714 Upper slopes 1 5/2 5/3 5/7 0.1190 Local ridges 1 2/3 2/7 0.0476 Midslope ridges 1 3/7 0.0714 Mountain tops 1 0.1666 Topographic wetness index (TWI) Low (< 5) 1 1/4 1/9 0.0714 Moderate(5–10) 1 4/9 0.2857 High (> 10) 1 0.6428 Stream power index (SPI) < 0 1 1/2 1/3 1/4 1/6 1/8 1/9 0.0303 0–1 1 2/3 2/4 2/6 2/8 2/9 0.0606 Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 8 of 18 Table 1 Pair-wise comparison matrix, ratings, and consistency ratio of the variables classes and individual variables used in the present study (Continued) Variables Classes 1 2 3 4 5 6 7 8 9 10 Rating / weights 1–2 1 3/4 3/6 3/8 3/9 0.0909 2–3 1 4/6 3/8 4/9 0.1212 3–4 1 6/8 6/9 0.1818 4–5 1 8/9 0.2424 > 5 1 0.2727 Stream head density Low (< 15) 1 1/4 1/9 0.0714 Medium (15–20) 1 4/9 0.2857 High > 20 1 0.6428 Land use/land cover (LULC) Water 1 1/1 1/8 1/2 1/1 1/2 1/9 1/9 1/1 0.0294 Upper montane forest 1 1/8 1/2 1/1 1/2 1/9 1/9 1/1 0.0294 Secondary forest 1 8/2 8/1 8/2 8/9 8/9 8/1 0.2359 Primary forest 1 2/1 2/2 2/9 2/9 2/1 0.0588 Pebble cobble 1 1/2 1/9 1/9 1/1 0.0294 Paddy 1 2/6 2/9 2/1 0.0588 Mixed agriculture 1 9/9 9/1 0.2647 Exposed soil (barren) 1 9/1 0.2647 Artificial surface 1 0.0294 Consistency ratio (CR): < 0.0001 Variables(as single unit) Slope 1 8/1 8/4 8/5 8/3 8/2 8/4 8/7 8/6 8/9 0.1633 Aspect 1 1/4 1/5 1/3 1/2 1/4 1/7 1/6 1/9 0.0204 Relative Relief 1 4/5 4/3 4/2 4/4 4/7 4/6 4/9 0.0816 Slope length and steepness 1 5/3 5/2 5/4 5/7 5/6 5/9 0.1020 Curvature 1 3/2 3/4 3/7 3/6 3/9 0.0612 Landform 1 2/4 2/7 2/6 2/9 0.0408 TWI 1 4/7 4/6 4/9 0.0816 SPI 1 7/6 7/9 0.1429 Stream head density 1 6/9 0.1224 LULC 1 0.1837 Consistency ratio (CR): < 0.00003 NE facing slopes were also below average (10.70%). Before 2017). The relative relief of the study area was generated applying the relative weightages to individual aspect class, from the digital elevation model using the neighborhood slope instability observed during the field visit was consid- range function available in the spatial analyst extension of ered. During the field visit, it was noted that, west facing ArcGIS software by keeping the unit size of the area as 1 slopes in general as well as southwest and northwest show km . The relative relief calculated for the study area ranges 2 2 more incidence of slope failure and gully erosion than any from 46 m/km to 692 m/km andwas then dividedinto 2 2 others. Therefore, while attributing the factor scores to five classes which are: < 100 m/km , 100–200 m/km , 200– 2 2 2 generate the pairwise matrix, higher scores were given to 300 m/km ,300–400 m/km and, > 400 m/km .Consider- slopes facing west, southwest, and northwest directions ing the area distribution of individual classes of relative re- and relative weightages or ratings were calculated which lief in the selected study area, the majority (95%) falls vary in the range of 0.0256 to 0.2307. within the three classes from 100 to 400 m/km .Within Another important parameter which controls the ter- this 95%, more than 43% of the total area has relative relief rain stability is the change in elevation in the unit area in the range of 200–300 m/km followed by 30% of the which is termed as relative relief. Terrains with higher total area with relative relief in the range of 100–200 m/ 2 2 relative relief indicates higher runoff and less infiltration km and 22% of the area in the range of 300–400 m/km .It and shows higher susceptibility to erosion (Raja et al. was noted that very low and very high relative relief zones Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 9 of 18 2 2 (< 100 m/km and > 400 m/km ) cover significantly less ranges from − 30 to + 32, i.e. from concave surfaces to areas of 1% and 4% respectively only. Review of previous flat and convex surfaces. In the study area, the topog- works carried out in landslide and gully erosion modelling raphy consists of both concave and convex curvature which used the theme relative relief as a parameter indi- surfaces which together cover 94% of total area whereas cates higher potentiality of areas with high relative relief in flat areas only cover 6%. This is due to the complex and conditioning for erosion (Foumelis et al. 2004; Zhu et al. highly undulating nature of folded sedimentary rocks 2014; Pourghasemi et al. 2013a; Sangchini et al. 2016). within the study area. Considering the shape of the land Basedonthisprior andproveninformation, inthe present surface, both concave and convex surfaces possess sus- research while attributing the factor scores to generate the ceptibility to erosion. But in the study area, during the pairwise matrix, higher scores were given to relative relief field visits, it was noted that compared to convex surface class having higher values and lower scores were assigned the more gullies are observed in a concave surfaces. Fur- to low relative relief class. The relative weightages thus ther, while considering the previous studies reported calculated varied from 0.040 to 0.36. from other parts of the world, most studies marked con- LS factor corresponds to the combined effect of slope cave surfaces as more vulnerable to gullying and erosion length and its steepness, which have direct bearing on (Pourghasemi et al. 2012; Meten et al. 2015; Youssef the erosion and the transportation potential of an area 2015; Raja et al. 2017). Therefore, while assigning the (Pourghasemi et al. 2013b; Vijith and Dodge-Wan 2018). factor scores, more importance was given to concave An area with high slope and elongated nature has high curvature than convex by attributing higher scores and potential for generating runoff and this directly influ- the calculated ratings are 0.0588 (flat), 0.4117 (convex), ences the development of rills in the terrain in response and 0.5294 (concave). to heavy rainfall (Haan et al. 1994; Panagos et al. 2015; In order to produce a reliable terrain erosion suscepti- Correa-Muñoz and Higidio-Castro 2017). Therefore, in bility map, the specific landforms present in the study the present analysis the LS factor was considered and area needs to be included in the analysis. Landforms generated from the digital elevation model through the controls many spatial topographic erosional and deposi- methodology proposed by Moore and Burch (1986a, tional processes and was an integral part of geomorpho- 1986b) using SAGA 2.1. The generated LS factor value metry (Seif 2014). Surface runoff, soil moisture varies from 0 to 25 and was divided into four classes distribution, vegetation characteristic, and even the which are: < 5, 5–10, 10–15, and > 15 considering its water quality are influenced by the specific landforms contribution to erosion susceptibility. Within the study (Mokarram et al. 2015). Therefore in the present re- area of Sungai Patah and Sungai Akah watersheds, 50% search, the topographic position index based landform of the terrain has low LS values (< 5) and 41% has LS classification proposed by Weiss (2001) was selected to value between 5 and 10. Only 9% of the area has LS generate the landforms using digital elevation model. value of 10–15 and only 1% has LS value over 15. Soil Topographic position index analysis identified ten land- and gully erosion modelling conducted by researchers in forms in the Sungai Akah and Patah area. They are various locations identified the role of higher LS factor deeply incised streams, midslope drainages, upland in initiating erosion and transportation of material from drainages, U-shaped valleys, plains, open slopes, upper a region downstream (Nekhay et al. 2009; Pourghasemi slopes, local ridges, midslope ridges, and mountain tops. et al. 2012; Shit et al. 2015; Arabameri et al. 2018a). Within the study area, 39% of the total area is covered by Based on this in the present research also, while assign- deeply incised streams whereas mountain tops cover 30%. ing the factor scores, more importance were given to Besides these, local ridges (12%), upland drainages (10%), classes showing high LS factor values and relative U-shaped valleys (4%), and upper slopes (3%) are also weightages were calculated which vary in the range of present. The remaining three landform classes (midslope 0.05 to 0.45. drainages, open slopes, midslope ridges, and plains) cover The topographic curvature used in the analysis repre- less 1% of the total area only. Later, by considering the sents the shape of the slope or topography which has relative importance of individual landforms over the ter- direct bearing on the erosion by either concentrating rain susceptibility to erosion as explained in the previous runoff or dispersing it (Lee and Sambath 2006; Fischer studies conducted to model the landside susceptibility in et al. 2012). Topographic curvature may show an up- various regions (Costanzo et al. 2012; Tien Bui et al. 2012; ward convex surface (positive curvature) or upwardly Oh and Lee 2017), factor scores were fixed and ratings concave surface (negative curvature), or it may be flat were calculated. Calculated ratings vary from 0.0142 (zero curvature) (Alkhasawneh et al. 2013). In order to (Plain) to 0.2142 (Upland drainages). understand the influence of the shape of the surface The parameters discussed above all contribute to a slope over terrain susceptibility, curvature was generated certain extent to increase the susceptibility of the terrain from the DEM. Topographic curvature in the study area to erosion. In addition, the contribution of water flow in Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 10 of 18 the terrain to enhance the susceptibility was also consid- Another parameter of interest is stream head density ered by means of topographic wetness index (TWI), de- which indicates the number of stream origin points per rived from the digital elevation model. TWI considers the unit area. Analysis of channel head locations can the upslope contributing area and its slope to quantify provide insight into the controls on drainage density as the steady state wetness and water flow across the region well as the response of landscapes to climatic change (Pourghasemi et al. 2013a). TWI generated for the study and indication about the rate of susceptibility of that ter- area shows values in the range of 1 to 25 which have rain (Wadge 1988; Montgomery and Dietrich 1989; Lin been divided into three classes which are: < 5, 5–10, and Oguchi 2004). In the present study, the stream head and > 10. Considering the wetness potential of the area density was calculated by extracting the starting points through TWI classes, 30% of the total study area was of all 1st order streams in the study area. Using the found to have low wetness index (TWI < 5) whereas density function available in the spatial analyst extension most of the area (62% of the total area) shown moderate of ArcGIS, stream head density was calculated for 1 km wetness index (5–10), while remaining 8% of the area and the calculated density values were found to vary has high TWI values (> 10). To take into account differ- from 8 to 25 N/km . Reclassification of stream head ent level of contribution of TWI to terrain erosion sus- density in to three classes which are: low (< 15 N/km ), 2 2 ceptibility, landslide, and gully erosion susceptibility medium (15–20 N/km ), and high (> 20 N/km )was studies carried out in different locations were considered then used for the calculation of individual weights. It was (Wang et al. 2015; Chen et al. 2017; Arabameri et al. noted that, 21% of the total study area has low stream 2018b). It was noted that, in most studies high TWI has head density whereas 71% of the area has moderate dens- high impact on erosion and in the present study, the ity, and remaining 7% of the area only has high density. relative scores of individual TWI classes were assigned Areas having high stream head density is more susceptible based on the TWI values i.e. lower score were attributed to erosion, especially by the development of gully head to low TWI and vice versa. The calculated weightages and continuous erosion downstream. Based on the density varies from 0.0714 (TWI < 5) to 0.6428 (TWI > 10). classes and its impact on terrain erosion susceptibility, the Another parameter is stream power index (SPI), which relative scores of the stream head density classes were estimates the capacity of streams to potentially modify assigned. Further, ratings were calculated and it varies in the geomorphology of an area through gully erosion and the range of 0.0714 to 0.6428 indicating varying contribu- transportation. SPI is the measure of the erosive power tion towards the terrain susceptibility. of flowing water by considering the relationship between In erosion susceptibility analysis, the existing land use/ discharge and specific catchment area (Chen and Yu land cover of the area under consideration also plays a 2011; Pourghasemi et al. 2013b). SPI highlights areas in vital role by providing information about the condition which overland flow has higher erosive power in the of vegetative protection against erosion and many re- catchment (Wilson and Gallant 2000). This makes the searchers found land use/land cover to be a dominant use of SPI a significant parameter of interest in erosion variable in erosion susceptibly (Dai and Lee 2002; Glade and terrain susceptibility modelling. SPI was calculated 2003; Beguería 2006; Leh et al. 2013; Galve et al. 2015; for the study area using the stream power index module Mandal and Mandal 2018; Vuillez et al. 2018; Abdulkar- available in SAGA 2.1 software based on the digital eem et al. 2019). It is also one of the key factors under elevation model as input data. SPI of the Baram study anthropogenic influence i.e. reflective of human disturb- area varies from − 13 to 7 indicating the differential ero- ance of vegetation cover due to logging, clearing for roads, sive power of the streams in the region. Higher values and/or agriculture. In the present study, the land use/land indicate the likely overland flow paths during storms or cover map of the area was derived from Landsat 8 OLI severe erosive rainfall pointing to potential areas for images acquired on 28th March 2015, through the super- gullying or other areas susceptible of erosion. The SPI vised classification with extensive ground truth points map prepared was reclassified into seven classes which from field observations. The segmentation of Landsat are: < 0, 0–1, 1–2, 2–3, 3–4, 4–5, and > 5. Most of the image into classified land use/land cover map has identi- study area (63%) showed SPI value less than 0. High SPI fied and mapped the following land use/land cover classes represent areas where high slopes and flow accumula- in the area: water, secondary forest, primary forest, mon- tions exist which indicate enhanced with erosive poten- tane forest, mixed agriculture, paddy, exposed soil tial (Gómez-Gutiérrez et al. 2015; Arabameri et al. (barren), artificial surfaces, and pebbles, cobbles in river 2018a). Considering the SPI values and their contribu- beds. The supervised classification indicate that more than tion towards terrain susceptibility and gullying, the 56% of the total area was covered by secondary forests and relative scores were added to each class in a simple pro- 27% of the area was covered by primary forests. It was also gression and rating was calculated and the rating varies noted that, land use activities like mixed agricultural land in the range of 0.0303 to 0.2727. and exposed barren land, which alter the terrain condition Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 11 of 18 in the region, cover 8.8 and 1.8% of the total area respect- Result and discussion ively. The other land use/land cover classes together cover Ten geo-environmental variables which are potentially less than 5.5% of the total area, in which upper montane responsible for changing the stability of the terrain ren- forests cover 3.35% of the area. Further, when determining dering it more susceptible to erosion were considered the relative influence of individual land use/land cover quantitatively to assess the susceptibility of the forested classes in terrain susceptibility, previous study which detail region of Sarawak to erosion using the AHP technique. the influence of individual land use classes in soil erosion Among the ten variables used to generate the terrain vulnerability of the area was taken into account (Vijith erosion susceptibility index (TESI) map, the variables and Dodge-Wan 2018). For the AHP, the weight was such as land use/land cover, slope, stream power index, calculated for individual land use/land cover classes based stream head density, and slope length and steepness on the relative importance assigned to each class and factors shown maximum influence (> 0.10) followed the varied in the range of 0.0294 to 0.2647. Among the differ- relative relief and topographic wetness index (0.08). ent classes, the exposed barren land, mixed agriculture Other variables such as curvature (0.06) and landform acquired the highest rating of 0.2647 followed by second- (0.04) shown moderate influence, whereas aspect was ary forest (0.2352) whereas the upper montane forest and found to be the lowest influencing variable with a rank artificial surface showed the lowest weight (0.0294). of 0.02. Even though, the variable ranks differ, the selec- Higher weight shown by the exposed barren land, mixed tion of the variables in the present analysis are found to agriculture, and secondary forest in soil erosion study be optimum by showing the CR less than the cut-off (Vijith et al. 2018a, 2018b) indicates the strong influence value (0.00003). Besides this, the weight factor calculated of these land use classes on terrain susceptibility. for the individual variable classes indicates a varying de- In order to produce the terrain erosion susceptibility gree of influences within the parameter and between the zonation (TESZ) map, the ranking of individual parame- parameters. It was also noted that the relative weighting ters was carried out to assign their relative contribution of variable classes indicates the variability of influences. before assigning the calculated weight to each parameter Among the variables considered, the land use/land cover, classes. The parameter ranking indicated that land use/ terrain with slope > 25° having west, southwest, and land cover is the highest influencing parameter with a northwest orientations, relative relief > 300 m/km ; high rating of 0.183 followed by slope (0.163), stream power LS factor, and concavity, having high TWI, upland drain- index (0.142), and stream head density (0.122). The ages and mountain top landforms, high stream head other parameters such as aspect, relative relief, LS factor, density are showing high relative weights among the curvature, landforms, and topographic wetness index classes and contributing more to the terrain susceptibil- were found to have less influence. Reliability of each par- ity. The integration of weighted variables in the raster ameter to be included in the analysis was determined by calculator resulted terrain erosion susceptibility index examining the consistency ratio (CR) and it was noted (TESI) map showing the susceptibility ranges from 0.07 that all the parameters shown CR below the proposed to 0.34 indicating spatial distribution of different degree cut-off of 0.1, so none were omitted from the analysis. of susceptibility to erosion (Fig. 4a). The TESI map gen- Finally, the weights calculated for individual parameter erated shows varying distribution of higher and lower classes were assigned to the respective parameters to susceptibility indexes all over the area without showing produce the weighted maps and using the raster calcula- any particular pattern, which make it difficult to identify tor option of the spatial analyst, individual themes were and differentiate the regions which showing nil or low integrated to produce the terrain erosion susceptibility susceptibility and very high susceptibility. index (TESI) map using the equation (Eq. 3): In order to understand the spatial extent of different severity of erosion susceptibility, the TESI map was re- Terrain erosion susceptibility index ðTESIÞ¼ classified into five discrete classes based on the suscepti- bility index values namely nil, low, moderate, high, and Slope  0:163 þ Aspect  0:020 wt wt very high zones (Fig. 4b). The reclassification of the TESI þ Relative relief  0:081 þ LS factor  0:102 wt wt to terrain erosion susceptibility zonation (TESZ) map þ Curvature  0:061 þ Land forms  0:040 wt wt facilitated the calculation of areal extent of different sus- þ TWI  0:081 þ SPI  0:142 wt wt ceptibility zones. The areas falling under each erosion þ Stream head density  0:122 wt susceptibility class are given in Table 2 and shown in þ Land use=land cover  0:183 wt Fig. 5. The final TESZM showed that 10.3% of the study area is categorised as having very high susceptibility to ð3Þ erosion and these areas appears to be distributed differ- where, is the relative weights of classes in individual ent places in the region. In addition, high erosion sus- wt variable. ceptibility zones occupy 14.9% whereas moderate and Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 12 of 18 Fig. 4 a Terrain erosion susceptibility index (TESI) maps and b Classified terrain erosion susceptibility zonation (TESZ) map low susceptibility zones covers 25.8 and 27.1% of the susceptibility zones mostly occur in the flanks of the area respectively. It was also noted that 17.47% of the mountains rather than in the valleys. In addition, in study area is not prone to erosion. Besides this, 4% of some places these zones show linear patterns which can the area was not included in the final analysis as there is be linked directly with the road structure and skidder no data in these zones due to thick cloud and cloud trails. For the development of roads in the area, the con- shadow on satellite image. An attempt has been made to tinuity of hills with concave or convex slopes has been understand the spatial characteristics of the erosion sus- removed by the toe cutting and this will increase the ceptibility zones by overlying the TESZ with the exagger- susceptibility to erosion and lead to the development of ated terrain model. It was found that the higher erosion soil slumps triggered by the heavy rainfall. The clustered Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 13 of 18 Table 2 Terrain erosion susceptibility classes derived from the reclassification of TESI Terrain erosion susceptibility classes Area (km ) Area (%) Probability of terrain erosion Nil 367.84 17.47 No chance of erosion, mostly low lying areas. Low 572.01 27.17 Very low probability. Mostly affected by the run-off from the higher elevation Moderate 544.77 25.88 Medium probability, may directly involve in erosion or affected as part of falling from the top High 313.22 14.88 High certainty of erosion either as slide or gullying. Need attention in such areas crossing road sections. Very High 217.31 10.32 Very high certainty of erosion either as slide or gullying. To be monitored during the heavy rainy seasons. No data 89.85 4.27 No data is available due to cloud and shadow in the image. Not considered in the analysis nature of the higher erosion susceptibility indicates the and annual rainfall is shown in Fig. 6. It was noted that logging activity and shifting cultivation, which exposes mean monthly rainfall varies between 238 mm (June) to the terrain by removing the protective tree cover. 532 mm (November) with long term mean monthly and annual rainfall of 352 mm 4227 mm respectively. A Rainfall distribution spatial distribution map was generated by considering Though different geo-environmental variables make the the mean annual rainfall calculated for each rain gauge terrain susceptible to erosion, the amount and intensity for use in further analysis (Fig. 7). Mean rainfall ranges of rainfall which falls in an area acts as the triggering between 3654 to 4862 mm with higher rainfall generally mechanism which can initiate movement of soil, debris, located in southwest part of the study area, especially and other overburden downstream. In most studies, between the rain gauges Long Naha’ah and Long Akah rainfall distribution is included as a theme to statistically whereas comparatively lower rainfall is noted in north- model the land susceptibility to erosion (Sangchini et al. ern and north-eastern part of the study area. 2016). In the present research, rainfall distribution in the In order to assess the contribution of rainfall to terrain study area was considered separately and analysed to susceptibility leading to slope failure, 200 random identify the areas with high possibility of terrain erosion (unconditional and unstratified) points (pixel size 30 × susceptibility. Therefore, 5 year rainfall data were 30 m) were generated within the study area boundary collected from the Department of Irrigation and Drain- and mean annual rainfall, TESI, and TESZ values corre- age (DID) Malaysia corresponding to four rain gauges sponding to each point were extracted. The extracted located in the study area and six around the area. Mean values of TESI and TESZ were compared by linear re- monthly rainfall distribution and 5 year mean monthly, gression with mean annual rainfall to study the possible Fig. 5 Area distribution of terrain erosion susceptibility zones Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 14 of 18 Fig. 6 Mean monthly and annual rainfall distribution in the study area role of local rainfall amount and distribution over terrain mean annual rainfall distribution and TESZ shows susceptibility (Lyra et al. 2014; Teodoro et al. 2016; Brito absence of correlation (Fig. 8b). P values (p > 0.10) also et al. 2017) (Fig. 8). Linear regression plot of mean indicates no or nil dependency between the dependant annual rainfall and TESI indicates very low or nil correl- (terrain susceptibility) and independent (rainfall) vari- ation (Fig. 8a). Similarly, the linear regression plot of the ables in the region. Although high rainfall in general is a Fig. 7 Spatial distribution of mean annual rainfall in the study area Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 15 of 18 Fig. 8 Linear regression plots explaining the relationship between rainfall a TESI and b TESZ factor in increasing terrain erosion susceptibility, at a variables considered in the analysis through the AHP specific local scale (pixel size 30 × 30 m area), the higher technique facilitated the identification of the most cru- amount of rainfall received in parts of the region does cial variables which render the terrain more susceptible not appear to significantly influence the local site to erosion. Though all these variables were found to be specific terrain susceptibility. However, other geo- contributing to erosion susceptibility to various degrees, environmental variables considered play more significant the determination of ranks through relative ratio high- roles in rendering the terrain more susceptible to ero- lights that land use/land cover, slope, stream power sion in specific local areas. index, stream head density, and LS factor are the most crucial variables. In the study area, the places which are Conclusion exposed (barren land) with concave slopes having slope The characteristic probability of erosion proneness of a exceeding 25° and facing west, southwest, and northwest, sample catchment with regenerated and logged tropical with relative relief higher than 300 m/km and high LS rain forest region in Sarawak, northern Borneo, was suc- factor, TWI and stream head density are found to be the cessfully carried out in the present study using raster most vulnerable to erosion. These areas are identified GIS and AHP technique. Terrain variables derived from via the TESI and TESZ maps. the digital elevation model such as slope, aspect, relative TESZ map generated by the reclassification of TESI relief, LS factor, curvature, landforms, TWI, SPI, stream into five distinct groups show the spatial pattern of ero- head density, and the land use/land cover interpreted sion susceptibility in terms of its severity. It was found from the satellite images were integrated in the raster that 10 and 14% of the total area comes under the very based GIS environment after deriving the determinant high and high erosion susceptibility zones. The higher ranking and weights for the variables and variable clas- susceptibility was found to be characteristic of high ele- ses. The generation of rankings and weightages for the vated hills and slopes which undergo rapid changes. Vijith and Dodge-Wan Geoenvironmental Disasters (2019) 6:8 Page 16 of 18 However, areas with nil and low potential of erosion sus- Akgün, A., and N. Türk. 2011. Mapping erosion susceptibility by a multivariate statistical method: A case study from the Ayvalık region, NW Turkey. ceptibility together constitute 44% and the moderate Computers & Geosciences 37 (9): 1515–1524. susceptibility zones occupy 25% of the total study area. Aleotti, P., and R. Chowdhury. 1999. Landslide hazard assessment: Summary Considering the influence of rainfall in the region, the review and new perspectives. Bulletin of Engineering Geology and the Environment. 58: 21–44. entire study area receives what can be considered high Alkhasawneh, M.S., U.K. Ngah, L.T. Tay, M. Isa, N. Ashidi, and M.S. Al-batah. 2013. tropical rainfall. Analysis of 200 randomly distributed Determination of important topographic factors for landslide mapping analysis using pixel sized area (30 m × 30 m) suggests that at local scale MLP network. The Scientific World Journal. https://doi.org/10.1155/2013/415023. Althuwaynee, O.F., B. Pradhan, and S. Lee. 2016. A novel integrated model for rainfall is not strongly correlated with erosion suscepti- assessing landslide susceptibility mapping using CHAID and AHP pair-wise bility. The field observations and the erosion susceptibil- comparison. International Journal of Remote Sensing. 37 (5): 1190–1209. ity map indicates that the root causes of the terrain Arabameri, A., B. Pradhan, H.R. Pourghasemi, K. Rezaei, and N. Kerle. 2018a. Spatial modelling of gully Erosion using GIS and R programing: A comparison susceptibility are modification of land use and the devel- among three data mining algorithms. Applied Sciences. 8 (8): 1369. opment of logging roads, and skidder trails. Barren areas Arabameri, A., K. Rezaei, H.R. Pourghasemi, S. Lee, and M. Yamani. 2018b. GIS- reduce the stability of the terrain and particularly when based gully erosion susceptibility mapping: A comparison among three data- driven models and AHP knowledge-based technique. Environmental Earth combined with other factors such as slope, LS factor. Sciences. 77 (17): 628. Along with this, the high amount of rainfall recorded Ayalew, L., H. Yamagishi, and N. Ugawa. 2004. Landslide susceptibility mapping throughout the region induces movement of unsup- using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata prefecture, Japan. Landslides 1 (1): 73–81. ported and toe-cut slopes to move downstream. The Beguería, S. 2006. Changes in land cover and shallow landslide activity: A case findings of the present study give a better understanding study in the Spanish Pyrenees. Geomorphology. 74 (1–4): 196–206. of the region in terms of erosional characteristics. The Besler, H. 1987. Slope properties, slope processes and soil erosion risk in the tropical rain forest of Kalimantan Timur (Indonesian Borneo). Earth surface findings can be used for planning of new roads, settle- Processes and landforms 12 (2): 195–204. ments by developing and implementing erosion reduc- Bijukchhen, S.M., P. Kayastha, and M.R. Dhital. 2013. A comparative evaluation of tion and terrain protection measures. heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, East Nepal. Arabian Journal of Geosciences Abbreviations 6 (8): 2727–2743. AHP: Analytical Hierarchy Process; CI: Consistency Index; GIS: Geographical Bourenane, H., Y. Bouhadad, M.S. Guettouche, and M. Braham. 2015. GIS-based Information Systems; LS factor: Slope Length and Steepness factor; landslide susceptibility zonation using bivariate statistical and expert LSZ: Landslide Susceptibility Zonation; RUSLE: Revised Universal Soil Loss approaches in the city of Constantine (Northeast Algeria). Bulletin of Equation; SPI: Stream Power Index; SRTM: Shuttle Radar Topographic Mission; Engineering Geology and the Environment 74 (2): 337–355. TESI: Terrain Erosion Susceptibility Index; TESZ: Terrain Erosion Susceptibility Brenning, A. 2005. Spatial prediction models for landslide hazards: Review, comparison Zonation; TWI: Topographic Wetness Index; USLE: Universal Soil Loss and evaluation. Natural Hazards and Earth System Science 5: 853–862. Equation Brito, T.T., J.F. Oliveira-Júnior, G.B. Lyra, G. Gois, and M. Zeri. 2017. Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil. Acknowledgements Meteorology and Atmospheric Physics 129 (5): 469–478. The authors wish to thank Sarawak Energy Berhad for funding this research Chen, C.Y., and F.C. Yu. 2011. Morphometric analysis of debris flows and their under the Project “Mapping of Soil Erosion Risk”. They also thank Curtin source areas using GIS. Geomorphology. 129 (3–4): 387–397. University Malaysia for facilities and other assistance and the Department of Chen, W., H. Chai, X. Sun, Q. Wang, X. Ding, and H. Hong. 2016a. A GIS-based Irrigation and Drainage (DID), Malaysia for providing rainfall data. Authors are comparative study of frequency ratio, statistical index and weights-of- also thankful to the Editor in Chief, and anonymous reviewers for their evidence models in landslide susceptibility mapping. Arabian Journal of critical reviews, constructive comments, and suggestions which significantly Geosciences. 9 (3): 204. improved the quality of the manuscript. Chen, W., W. Li, H. Chai, E. Hou, X. Li, and X. Ding. 2016b. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty Authors’ contributions factor (CF) models for the Baozhong region of Baoji city, China. VH done technical, scientific analysis of the research and developed the Environmental Earth Sciences 75 (1): 1–14. manuscript. DDW read and suggested few amendments in the analysis and Chen, W., X. Xie, J. Wang, B. Pradhan, H. Hong, D.T. Bui, Z. Duan, and J. Ma. 2017. structure of the manuscript and also reviewed the final manuscript, including A comparative study of logistic model tree, random forest, and classification language correction. All authors read and approved the final manuscript. and regression tree models for spatial prediction of landslide susceptibility. Catena. 151: 147–160. Funding Clerici, A., S. Perego, C. Tellini, and P. Vescovi. 2006. A GIS-based automated This research was carried out as part of the project “Mapping of Soil Erosion procedure for landslide susceptibility mapping by the conditional analysis Risk” funded by Sarawak Energy Berhad (RD01/2014(C)), Malaysia. method: The Baganza valley case study (Italian northern Apennines). Environmental Geology 50 (7): 941–961. Availability of data and materials Conoscenti, C., V. Agnesi, S. Angileri, C. Cappadonia, E. Rotigliano, and M. 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