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Background: Landslides area controversial issue worldwide and cause a wide range of impacts on the socio- economic systems of the affected community. However, empirical studies of affected environments remain inadequate for prediction and decision making. This study aims to estimate the direct impact of a massive landslide that occurred around areas with Kandyan home gardens (KHGs)in Aranayake, Sri Lanka. Results: Primary data were gathered by structured questionnaire from residents of the directly affected regions; the questionnaire data were combined with spatial data to acquire detailed information about the livelihoods and hazards at the household level. Satellite images were used to find affected land use and households prior to the landslide. Further, secondary data were obtained to assess the recovery cost. A multiple regression model was established to estimate the economic value of the home gardens. Field surveys and satellite images revealed that land-use practices during the past decades have caused environmental imbalance and have led to slope instability. Conclusions: The results reveal that 52% of household income is generated by the KHG and that the income level highly depends on the extent of the land (R = 0.85, p < 0.05). The extent of destroyed land that was obtained from the satellite images and the age of the KHG were used to develop a multiple regression model to estimate the economic loss of the KHG. It was found that the landslide affected region had been generating approximately US$ 160,000 annually from their home gardens toward the GDP of the country. This study found that almost all houses in the area were at risk of further sliding, and all of them were partially or entirely affected by the landslide. Among the affected households, 60% (40 houses) had completelycollapsed, whereas 40% (27 houses) were partially damaged. Because of these circumstances, the government must provide US $ 40,369 to recover the fully and partially damaged households. Finally, a lack of awareness and unplanned garden cultivation were the main contributing factors that increased the severity of the damage. Keywords: Socio-economy, Landslide, Direct loss from the landslide, Spatial data Background onset disasters such as landslide shave a massive impact Natural disasters are complex detrimental events that on human life and property. occur entirely beyond the control of humans (Alimoham- Landslides occur over a wide range of velocities and madlou et al., 2013). Natural disasters can be classified are recognized as the third most crucial natural disaster based on the speed of onset; some disasters occur within worldwide (Zillman, 1999). Landslides are usually trig- seconds (landslides), minutes (tornadoes) or hours (flash gered without warning, giving people less time to evacu- floods and tsunamis) and others may take months or years ate. Therefore, the direct impact of landslides on the to manifest themselves (droughts). Furthermore, rapid socio-economic system is crucial (Christopher, 2016). Landslides are responsible for significant loss of life and injury to people and their livestock as well as damage to * Correspondence: email@example.com Department of Forestry and Environmental Science, Faculty of Applied infrastructure, agricultural lands and housing (Schuster Science, Gangodawila, University of Sri Jayewardenepura, Nugegoda, Sri and Fleming, 1986;JRC, 2003; Blöchl and Braun, 2005; Lanka Guzzetti et al., 2012).Economic losses from landslides have Full list of author information is available at the end of the article © The Author(s). 2018 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. Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 2 of 12 been increasing over recent decades (Petley et al., 2005; (Jayawardana et al., 2014). The most rainfall is usually Guha-sapir et al., 2011; Guzzetti, 2012), mainly due to in- received during the monsoons. However, average rainfall creasing development and investment in landslide-prone amountvaries during the cyclone season. areas (Bandara et al., 2013;Petley et al., 2005). There are few studies that have attempted to quantify the General description of the Aranayake landslide impact of landslides on socio-economic systems (Mertens During the recent past, there has been no record of et al., 2017). In Sri Lanka, the socio-economic impacts from major landslides in the region. Therefore, people tend to landslides have not been studied adequately. Landslides in use the slopes for unplanned cultivation with poor sur- Sri Lanka were considered a minor disaster up until the late face water management and unplanned construction. twentieth century (Rathnaweera and Nawagamuwa, 2013). Consequently, people have less awareness of the possi- For instance, the annual average number of landslides was bility of disaster. However, evidence of paleo-landslides less than 50 until the year 2002. However, the frequency of can be observed throughout the region. Paleo-landslides landslide occurrence rapidly increased after 2003. Studies seem to have been active approximately 500–1000 years undertaken by the National Building Research Organization ago (Jayasinghe, 2016); hence, people living in Aranayake of Sri Lanka (NBRO) revealed that the number of landslides have few experiences of a landslide in their lifetime. In increased due to increasing human intervention such as fact, observing old landslides is a good indication that unplanned cultivation, non-engineered construction, and the area has unstable geology and that more landslides deforestation. are likely in the future. The Aranayake region experi- In general, most of the socio-economic impact assess- enced 435 mm of cumulative rainfall from 14-May-2016 ments on landslides are limited due to a lack of data to 17-May-2016 (~ 72 h). The exceptionally high rainfall (Deheragoda, 2008). Lossesfrom landslides can be esti- was mainly due to the development of a low-pressure mated through the integration of field investigation, zone around Sri Lanka caused by a tropical cyclone in socio-economic surveys, and remote sensing. Moreover, the Indian Ocean. This sustained torrential rainfall recent studies have revealed the complexity involved in triggered a landslide on 17-May-2016 at approximately the quantification of the direct impact that landslides 16.30–17. 00 h. Thelandslide buried houses and property have on socio-economic systems (Mertens et al., 2016). and resulted in massive casualties. According to field Agroforestry makes a significant contribution observation, this landslide was a debris flow landslide tothesocio-economic system of rural communities in Sri having a very complex translational model. Lanka. In general, agro-forests are located on slopes, and most are vulnerable to landslides. Because of the finan- Socio-economic background of the area cial benefits of agro-forestry and home gardens, the rural The population of Aranayake is approximately 68,464 community is engaged in many agricultural activities, with 1:1 male to female ratio. Overall, 47% of the resi- which means the land is at higher risk. dents are permanently or temporarily employed. The This study differs from other recent studies on the high rate of dependency reaches 53% of the total popula- impact of landslides in many ways. First, it attempts to tion. More than 50% of the population is included in the investigate an overview of landslides. Second, it focuses labor force, and most of them are engaged in home on the use of integrated remote sensing to quantify garden and plantation agriculture. Although recorded socio-economic losses in agro-forest system named incomes are low, people have alternative income sources Kandyan Home Garden (KHG) system to estimate the and food security from their home gardens. direct impact of a massive landslide on household income and property damage as a case study. The traditional home gardens and agroforestry Aranayake traditional home gardens and the agroforestry Study area system clearly reflect the typical KHG system in the wet Physical setting zone of the country. Home gardens in the Aranayake A tragic landslide resulted in a catastrophic situation, region have a functional relationship with the occupants burying parts of the two rural villages of Elangapitiya related to economic, biophysical and social aspects. The and Pallebage. Those villages belong to the Aranayake Aranayake KHG consists of a mixture of annual and divisional secretariat in Kegalle, Sri Lanka (Fig. 1). perennial crops, such as tea, rubber, paddies, cardamom, Aranayake is a mountainous region in the wet zone of black paper, jackfruit, coconut, and cocoa. The crops are the country. The area receives heavy rains during the not grown according to any specific pattern and appear rainy periods (May–September, southwest monsoon; Oc- to be in a random, intimately mixed pattern. According tober–November, inter-monsoon) and bright sunshine to the typical pattern of KHGs, tea land is available on during the dry season (March–December). The average steeps slopes, rubber exists in moderate terrain and flat annual rainfall is from 2500 mm to 3000 mm terrain is for paddies. In addition, minor crops can be Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 3 of 12 Fig. 1 Figure showing land-use of Landslide affected GN divisions; Debathgama, Pallebage, and Elangapitiya. Land use based on 1:50000 maps seen near households. The most fundamental social questionnaire was revised according to the responses. benefit of KHGs is their direct contribution to a secure The survey mainly covered various income sources, household food supply. The livelihood benefits of KHGs, social capital, household demography, household type, however, are well beyond the food supply. In general, living condition, land-use type, KHG production and selling excess KHG production significantly improves landslide experience. the financial status of the community. The KHG system It was decided thatdata collection needed to be main- was significantly damaged by the Aranayake landslide tained at a high precision with a 95% confidence level reducing the income and food security of the region. according to the Department of Senses and Statistics of Sri Lanka standards. Sampling was done based on a Method proportionate stratified random sampling method from Field investigation both villages (Kumar, 2007). Additionally, the following Several exploratory field investigations were done after formula was used to determine the sample size (Eq. (1); the landslide to grasp the overall view. Calibrated hand- Mathers et al., 2007). held GPS was used to collect all field information. To analyze the related socio-economic conditions during n ¼ Z σ=E ð1Þ α=2 the field visits, detailed studies were done on human settlement and topography. where n = sample size, Zα/2 = confidence level, σ = Sampling and primary data collection standard deviation, and E = error. Primary data were gathered by structured questionnaire According to the equation, the estimated sample size from two directly affected Grama Niladhari (GN) divi- was 120under 90% accuracy levels. In this study, sions (Fig. 1). Before data collection, a pilot survey was 127households were selected as the primary data source conducted on 30 randomly selected houses, and the (592 individuals). Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 4 of 12 Secondary data collection and analysis prediction models. Land size and number of years of Secondary information and maps were predominantly cultivation are the typicalparameters used for estimating used to evaluate the socio-economic status before the the values (Mohan et al., 2006). Economic values also landside. Socio-economic data were obtained from the quantify the benefit provided by home gardens (Galahena recently updated database in the Aranayake Divisional et al., 2013; Langellotto, 2014). According to the literature, Secretariat. The 1:10,000 land-use data were obtained the following multiple regression model was used to esti- from the Land-use and Policy Planning Department mate the economic value of KHG production destroyed (LUPPD) of Sri Lanka. The collected information and by the landslide (Eq. (2)). The model was established using maps were used to evaluate socio-economic conditions. the primary data obtained from the affected villages. The present study integrates the socio-economic and GIS data simultaneously for the landslide impact assess- Y ¼ α þ β X þ β X ð2Þ 1 2 1 2 ment. Socio-economic data were analyzed by the descriptivestatistical method, chi-squired test,and correl- where y = economic value of a home garden; α, β and ation analysisusingSPSS 19 software. Conversely, the β = regression coefficients; x = land area in sq.m; and 2 1 spatial data processing and analyses have been incorpo- x = number of years in cultivation. rated using ArcGIS 10.2. Direct loss from the landslide was determined by asses- sing loss on agricultural land, damage to cultivation and Landslide inventory and affected area mapping households. Further, all the replacement costs for land- Landslide boundary demarcation and mapping are slide related damage are considered in loss estimation. essential to study the extent of damage (Guzzetti, 2006). During the past decades, use of satellite information Results and discussion for landslide investigation has increased significantly The results from social surveys revealed that the affected (Guzzetti et al., 2012). For instance, landslide damage villages of Aranayake (Elagipitia and Pallebage) are agri- in forest terrain has been identified by high-resolution culturally based rural areas depending on KHGs (Fig. 1). Google-Earth images with the help of many other The unexpected landslide completely destroyed a large attributes (Guzzetti et al., 2012, Qiong et al., 2013). land area and was one of the largest landslides recorded Cloud-free Google-Earth images of the Aranayake in Sri Lankan history. Fourteen families were completely landslide area were acquired. According to the images, buried, and 127 lives were lost in this landslide. These the landslide had a clear boundary; thus, boundary results have been identified as a common feature of demarcation was able to be accurate. In addition, ground many massive landslides (Alimohammadlou et al., 2013). truth GPS locations were incorporated to minimize The village community has a middle-income level based errors. The collected information was converted to poly- on the per-capita income of the country (Table 1). De- gon data using geographical information systems (GIS; scriptive statistics of collected primary data related to Raghuvanshi et al., 2015). total monthly income, contribution of KHG for monthly In addition to boundary demarcation, the Google-Earth income, savings and expenditure with respect to age of data have the highest accuracy in household mapping the KHG is summarized in the table. However, income (Escamilla et al., 2014). Therefore, the affected households also depends on the diversity of KHGs. It is clear that were mapped using Google-Earth images before the the highest land area has been cultivated during the past incident and superimposed on the inventory map. In this two decades (n = 54; Table 1). Therefore, it can be con- exercise, the location of the remaining households was clude that land use of the region has been affected also mapped for cross-validation. significantly during that period. This land use change The affected area map was developed by overlaying contributed to increase the landslide vulnerability of the the landslide inventory map with different thematic region. It was found that the landslide affected region layers such as land-use type, building distribution, trans- has been generating approximately US$ 160,000 annu- portation networks and water streams in the region. ally from their home gardens and plantations (Tea, Then, the affected area map was used to find the area Rubber and Paddy) toward the GDP. Thus, the results covered by different land-use categories. In addition, real revealed that both the social and economic systems were damage values for different land-use types were esti- highly influenced by the landslide, especially the KHGs in mated using the affected area map incorporated with a the region (Fig. 1 and Table 1). unit value for each land-use category. Overview of the landslide Model economic value of KHG and direct loss estimation During the field visits, it was found that a huge amount There is no direct method to analyze the economic value of rocks and debris was piled up at the base of the of home gardens. Generally, they are estimated by mountain, largely consisting of gneissic boulders more Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 5 of 12 Table 1 Summary of the monthly economic status of the houses and home gardens were located in the damaged household in Aranayaka landslide area. Data obtained from a slope, which is higher than 35 degrees (Figs. 3 and 4). The structuredquestionnaire survey entire area had been cultivated with minor export crops AB C D E (cloves, cardamom, and pepper) and fruits being common USD USD USD USD Acre in KHGs (Perera and Rajapakse, 1991). Most of the access roads were madeofconcrete or asphalt. Age of KHG, < 10 years (n =5) Major landslide contributing factors have been identi- Min 77 161 110 12 1.00 fied by detailed assessment. The escarpment slope of the Max 103 194 148 15 2.00 mountain was made up of metamorphic rocks having Avg 88 171 124 13 1.43 high joint/fracture density and thin soil overburden. SD 11 14 16 2 0.42 Weathering conditions of the exposed slide surface of Age of KHG, 10–20 years (n = 54) the basement rock revealed weakening along the mica and feldspar-rich layers. This mica and feldspar in the Min 77 161 110 12 1.00 hornblende and granite gneiss can weaken the surface Max 129 258 213 23 2.75 by intensive chemical weathering (Sajinkumar et al., Avg 99 194 154 15 1.81 2011). In addition, due to un-planned tea cultivation and SD 13 30 31 4 0.49 KHGs on the upper region of the slope, rainwater Age of KHG, 20–30 years (n = 38) infiltration was quite significant. Consequently, high Min 90 161 110 12 1.25 pore-water pressure built by the heavy, prolonged rain- fall generated strong destabilizing forces on the slope Max 142 258 213 23 3.00 (Matsuura et al., 2008). The excess pore-water pressure Avg 107 202 154 17 2.06 within the soil could have caused the reduction of shear SD 14 32 28 4 0.41 strength and the boulders that floated on the moving Age of KHG, 30–40 years (n = 20) debris (Kang et al., 2017). Min 103 194 110 15 2.00 Max 142 258 213 23 3.00 Awareness of landslide mitigation According to an eyewitness, there was heavy rainfall a Avg 122 236 160 21 2.55 few days before the landslide. The mountain stood calm SD 12 26 35 3 0.31 and quiet during this rain, and no one noticed any clue Age of KHG, 40–50 years (n = 10) of a possible disaster. It is well known that heavy rain is Min 129 194 123 15 2.00 the main reason for massive landslides on vulnerable Max 142 258 213 23 3.25 slopes (Gariano and Guzzetti, 2016). The villagers were Avg 132 239 172 21 2.73 awakened for a possible incident but not evacuated because there was no appropriate evacuation plan. SD 5 23 27 3 0.33 Permanent evacuation from the possible landslide area is n No of hHouse hold A Monthly income from KHG usually avoided due to the misleading behavior of the B Total monthly income officials during the relocation of the residences. Despite C Monthly expenditure D Avarage monthly saving this, it is necessary to practice successful emergency E Own land size evacuation to protect the community (Huang et al., 2015). The evacuation of people is often a combinedef- than 10 m in diameter. In general, most of the human fort of the relevant government officials; however, there settlements are spread around the affected base area. are no such systems commonly practiced in Sri Lanka. The average width around the landslide scarp is Only the NBRO issues warning awareness messages to approximately 350–350 m, the height is approximately the general public during heavy rainfall. During the early 50–75 m, and the widestpart of the slide is approximately hours of the day that the landslide occurred, cracks with 600 m. The affected home gardens and a natural vegeta- muddy groundwater appeared inside one house, indicat- tion cover could be observed in the surrounding area and ing a sign of the landslide. However, all the villages were the middle of the slide; few houses not damaged. In not fully aware of this matter. addition, a quite rapid and muddy groundwater flow could In contrast, the socialsurvey reveals 80% of the residents be observedon the right-side of the landslide, which is still are not ready to leave their homes, mainly due to flowing and forming small water streams. The debris at wealthy KHG and traditional beliefs. Therefore, it is the toe of the landslide could be split into two regions. necessary to build specific awareness programs for such The left side is approximately 75–125 m in width, and the socio-economic systems and to educate residents on right-side is approximately 350–450 m (Fig. 2). Many natural warning signs and the severity of disasters Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 6 of 12 Fig. 2 Figure illustrates escarpment of the landslide, evidence for human intervention in escarpment and above it and difficult translational path of the debris flow (Bhatia, 2013). Further, the community should have a essential mitigations will be able to control the infiltration proper evacuation plan and integrated emergency manage- capacity available in the KHG and to stabilize the prevail- ment mechanism (Dorasamy, 2017). Worldwide experi- ing conditions (Pushpakumara et al., 2012). ence has proposed community-based mitigation activities for landslides (Shum and Lam, 2011). Moreover, essential Impact of landslides on rural socio-economic systems mitigation activities have not been implanted in many Human settlements are randomly spread along the landslide prone areas in Sri Lanka. Community-based landslide affected slope (Figs. 3 and 4) and are wide- short-term mitigation measures such as surface drainage spread in rural Sri Lanka (MHCPU, 1996). Due to gentle control, application of erosion controls and dewatering of slope conditions, the houses are mainly located in the high elevated groundwater can be implemented. Those middle and foot regions. The inventory map reveals that Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 7 of 12 Fig. 3 Figure illustrates before the landslide occurred, just after the incident with settlements that were destroyed by the landslide and 9 months after the incident the adverse impact at the middle and foot regions are preserving indigenous knowledge and culture. According mainly due to wideranging debris movements, which is a to the remote sensing data, land uses such as tea (59%), well-known characteristic for debris flows worldwide rubber (18%), home garden (13%) and paddies (10%) (Gao and Sang, 2017). The debris flow flooded over the covered 33.7,10.2, 7.2 and 5.8 ha, respectively, within the different land uses with thicknesses of 1.5–3.5 m. How- affected area (Table 2). Temporal analyses revealed that ever, the initial region of the landslide had relatively less the natural vegetation in the affected region had been re- impact on human settlements. Therefore, to mitigate moved for plantations and home gardens during the past possible damage, disaster risk preparation is necessary decades (Figs. 2 and 3). Change in land cover is consid- (Ardaya et al., 2017). More than 3 billion people live in ered the primary cause for debris flow slides during the developing world in rural areas that comprise farm- intense rainfall (Schneider et al., 2010). Different trees ing communities (Godoy, 2010). Aligning with global have different root patterns and penetration depth, and rural communities, most of the landslide-prone district they can impact the stability of a slope under different in rural Sri Lanka, such as Aranayake, depends on soil conditions (Vergani et al., 2017). Despite short-term KHGs. Cash crop products such as tea and rubber pro- socio-economic gain by changing vegetation, this study vide a regular source of monthly income in Aranayake. reveals that slope instability is another alarming However, it was found that 98% of the tea-growing areas socio-economic issue. Recent past historical data show are owned by medium-scale producers. Therefore, tea those minor export crop earnings in the Aranayake area production has an indirect contribution to the income have increased by 215% with the remarkable develop- of the local community. Conversely, minor export crops ment of value-added products (MMECP, 2013). Descrip- in KHGs such as pepper, cloves, and cardamom provide tive statistics revealed 52% of the household income is direct additional income (Jacob and Alles, 1987; Perera covered by KHGs. The average monthly income of and Rajapakse, 1991). KHGs not only strengthen the Aranayake households is US$ 205, and the mean household economy but also sustain food security by monthly expenditure is US$ 157 (Table 1). Conversely, providing fruits, vegetables, and paddies for consump- per capita income per month is US$ 300 with expend- tion. The present study observed multiple social benefits iture of US$ 270 (CBSL, 2016). These findings indicate from traditional home gardens, such as improving family that the income and expenditures of the region are lower health and human capacity, empowering women, and than the national average. Despite this, low household Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 8 of 12 Fig. 4 Figure illustrates the outline of the landslide superimposed onto the land use of the region, distribution of different land use classes in the affected area expenditure is an absolute gain for the community. As a result,there is increased savings with an annual average ratio of 12%. Unfortunately, the Aranayake landslide has completely abolished such self-dependent socio-economic Table 2 Estimate the economic value of the productions from selected land-use categories available in the landslide area systems. Additionally, the communities surrounding the landslide are now abandoned, and their inhabitants are Affected Land-used living in shelters. Most of landslide prone rural Sri Lanka Landuse Area Economic value of Total Economic production value that has similar socio-economic conditions is now at risk (Jacob and Alles, 1987). Thus, there should be provisions hectares acres per Acer per year (SLR) (SLR) for proper socio-economic development and land-use Tea 33.68 83.22 540,000.00 44,941,481 planning to mitigate landslide disasters in the current Rubber 10.21 25.23 45,000.00 1,135,324 environment. Home 7.24 17.89 42,684.00 763,634 The regression model reveals that mean monthly Garden income has a strong positive correlation with the culti- Paddy 5.8 14.33 35,000.00 501,623 vated land area of individual home gardens (R = 0.85, p Total 56.93 140.68 47,342,061.55 < 0.05; Fig. 5). It is concluded that farmers with large Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 9 of 12 Fig. 5 Figure illustrates a linear relationship between the cultivated land area of individual home gardens size in m farming area might have higher productivity per unit land the slope (Keith and Broadhead, 2011). Additionally, the and are encouraged to use the land intensively. According survey reveals that 90% of local people do not have to damage done by the landslide to the agricultural poten- minimum knowledge of the causative factors of landslides tial of the region (Table 1 and Table 2), it is well under- and proper land-use plans for steep slopes. stood that farmers who havemore significant land are willing to conserve the environment, but it was not ad- Regression model of the economic value of KHGs equately done for Aranayake. Hence, comprehensive Despite the interest, economic analyses after massive guidelines, especially on groundwater and surface water landslides have not been done in Sri Lanka or for any management during heavy rainfall, will be needed to pro- part of the world. Hence, there is no established model tect them from massive landslides on any slope (Masaba to assess actual damage. This study focuses on evaluat- et al., 2017). However, this study recognizes the lack of ing the level of KHG damage using a regression model such knowledge within the farming community. (Mohan et al., 2006). Primary information acquired Educational background is quite a distinct factor for directly from two affected villages is given in Table 1. disaster mitigation. The Aranayake region has educational Those data were used to model economic value using a backgrounds ranging from primary to graduate levels proposed model (Eq. (3)). The result from the multiple (Fig. 6). The results revealed that, despite the many schools regression model on net economic value for KHGs (Y in available in the region, the majority of the community (< $) indicates that land size (X in m ) and years in cultiva- 55%) has ordinary level (junior high school) qualifications. tion (X in years) are statistically significant (p < 0.05). More than 30% have completed advanced level examin- ation (senior high school) and have qualified for govern- Y ¼ 2196 þ 10:51X þ 20:840X ð3Þ 1 2 ment jobs. In general, previous studies indicate the positive relationship between educational level and household income (Saadv and Adam, 2016). However, household Because of the uniform land use pattern in the region, income in Aranayake is independent of the level of educa- this model can be used to predict the economic value of tion (P < 0.05). Thisfinding may be due to additional finan- the landslide affected home gardens in any location. The cial gain from household farming regardless of education total home garden affected area obtained from the level. This trend may lead to less protection for the natural remote sensing data is approximately 72,400 m (Area of environment among the rural community. Despite the KHG = 7.2 Hectares, Table 2), and from the primary level of education, people in the rural community are data, the average age of the KHG has been assumed less aware of the stability of the prevailing environment as25years. Therefore, the estimated economic value for and of proper land management compared to concern the entire extent of KHGs in the region is US$ 4927. for economic benefits. Human activities contribute to This sort of estimation helps to assess the real damage changing land cover types that increase slope instability to socio-economic conditions of the affected area. and landslide risk (Proper et al., 2014). Additionally, it is useful to evaluate the possible evacu- Consequently, increased soil infiltration from poor ation of affected people and to consider their past water management in plantations and KHGs, destabilize socio-economic status for necessary subsidy estimates. Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 10 of 12 Fig. 6 Figure illustrates educational level in percentage wise in the region Evaluation of other economic losses household income is highly dependent on the affected Economic losses by landslide affected regions are quite land size. significant. The highest income generated from tea was In an attempt to compensate for income loss after a US$ 144,769 per year. From this income, only 2% is landslide, household members in our sample seek shared with the general rural community (US$ 2896). self-employment or labor. The income obtained by this Other cultivation such as rubber, KHGs, and paddies employment or labor does not adequately compensate generate US$ 7231, US$ 4864 and US$ 3195 per year, for income lost due to landslides. Due to the landslide, respectively, indicating the landslide affected region has the most economic activity was abandoned, which is not been generating around US$ 160,000 for annual GDP. In actually accounted for in the evaluation. addition to the disturbance, emotions and sentimentality This study concluded that removing natural vegetation cannot be calculated in financial terms. Nevertheless, if and plantations causes an imbalanced runoff-to-infiltration arbitrarily equated, this estimate would run into millions. ratio destabilizing the slope. This result reflects that agri- This study revealed that landslides in rural areas could culture and the plantation-based socio-economic system severely affecthousehold income as much as in other are favorable for causing landslides, especially in the parts of the world (Msilimba, 2009; Haigh, 2012). paleo-landslide environment. The results of the survey Cost estimates for the damaged houses are quite sig- show that awareness of landslides and mitigation are the nificant. This study found that almost all houses in the critical issues of the socio-economic system. This reduces a area are at risk for further sliding, and all of them were significant level of their income from KHGs. Based on the partially or entirely affected by the landslide. Among the findings, two ultimate conclusions can be made to revive affected households, 60% (40 houses) had completelycol- the life of affected people. They are as follows: create lapsed whereas 40% (27houses) were partially damaged. appropriate job opportunities apart from agriculture and The department of valuation for Sri Lanka has estimated introduce suitable cash crops by considering bioengineer- the values of the collapsed houses as SU$ 7806. Partially ing approaches. Integrated spatial data can effectively be collapsed houses were estimated according to the level used for accurate loss estimates of the direct impact of of damage. Eventually, it was found that the total cost of landslides, and they can be used in the decision-making damaged houses is approximately US$ 40,369. process of the affected socio-economic system. Further, any changes in the frequency, intensity, and exposure to landslides require an economic assessment Conclusions framework that takes into consideration household in- The results indicate that the plantations and KHGs on come, including the contribution of home gardens. This- steep slopes are vulnerable to landslides. Landslides have framework is important since a proper understanding of significant impact on community income sources and possible damage can lead to more effective emergency households, and higher costs are incurred for subse- management and to the development of mitigation and quent rehabilitation and ongoing maintenance. It has preparedness activities designed to reduce the loss of been further observed that the severity of the impact on lives andthe associated economy. Perera et al. Geoenvironmental Disasters (2018) 5:11 Page 11 of 12 Acknowledgements Dorasamy, M., M. Raman, and M. Kaliannan. 2017. Integrated community We thank Mr. Yatagedara from Devisional Secretariat office Aranayake, to emergency management and awareness system: A knowledge management provide real estate values losses of affected household. We also system for disaster support. Technological Forecasting,and Social Change. acknowledge the Director General of National Building Research Elsevier Inc 121: 139–167. Organization for great support by providing landslide information. We Escamilla, V., M. Emch, and L. Dandalo. 2014. Sampling at the community level by extend our thanks to B.Sc. in Regional Science Planning students at SANASA using satellite imagery and geographical analysis, 690–694. New York: World Campus-Sri Lanka for their contribution to conduct social survey. 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Geoenvironmental Disasters – Springer Journals
Published: Dec 1, 2018
Keywords: Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
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