The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy)

Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China

China has been subject to rapid urban expansion and afforestation since the economic reform in 1978. However, the influence of land use and cover changes (LUCCs) and human activities on landslide occurrence is often ignored in landslide susceptibility mapping and zonation (LSMZ). In this study, Enshi City, China, was selected as the study area because of dramatic LUCCs during the last two decades. This study divided landslide affecting factors (AFs) into base affecting factors (BAFs) and land-related affecting factors (LAFs), and 15 landslide susceptibility maps were created by three different types of models. The results showed that the combination 6 of heuristic multi-layer perceptron model with LAFs (HMLP-LAFC₆) model obtained the highest model performance. In addition, any factor combinations of HMLP-LAF model outperformed other two types of models, and the use of land use and cover (LULC) in different periods as well as LUCCs may significantly impact the model performance. Given that land policy adjustments are normally core drivers of LUCC in China, a land planning based LSMZ framework was proposed, which is suitable for LSMZ in rapid LUCC regions with radical land policies. Finally, this paper strongly suggests developing more hybrid models that coupling dynamic AFs, clarifying the quantitative boundaries of time-irrelevant and dynamic AFs, increasing the accuracy of LULC prediction, and improving the abilities of bilateral understanding for effective, integrated, and systematic management of land planning and landslide hazards.

A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps

Empirical evidence shows that climate, deforestation and informal housing (i.e. unregulated construction practices typical of fast-growing developing countries) can increase landslide occurrence. However, these environmental changes have not been considered jointly and in a dynamic way in regional or national landslide susceptibility assessments. This gap might be due to a lack of models that can represent large areas (>100km²) in a computationally efficient way, while simultaneously considering the effect of rainfall infiltration, vegetation and housing. We therefore suggest a new method that uses a hillslope-scale mechanistic model to generate regional susceptibility maps under changing climate and informal urbanisation, which also accounts for existing uncertainties. An application in the Caribbean shows that the landslide susceptibility estimated with the new method and associated with a past rainfall-intensive hurricane identifies ~67.5 % of the landslides observed after that event. We subsequently demonstrate that the hypothetical expansion of informal housing (including deforestation) increases landslide susceptibility more (+20 %) than intensified rainstorms due to climate change (+6 %). However, their combined effect leads to a much greater landslide occurrence (up to +40 %) than if the two drivers were considered independently. Results demonstrate the importance of including both land cover and climate change in landslide susceptibility assessments. Furthermore, by modelling mechanistically the overlooked dynamics between urban growth and climate change, our methodology can provide quantitative information of the main landslide drivers (e.g. quantifying the relative impact of deforestation vs informal urbanisation) and locations where these drivers are or might become most detrimental for slope stability. Such information is often missing in data-scarce developing countries but is key for supporting national long-term environmental planning, for targeting financial efforts, as well as for fostering national or international investments for landslide mitigation.

An objective absence data sampling method for landslide susceptibility mapping

The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mahalanobis distances (MD). We demonstrate this method on landslide susceptibility mapping of three subdistricts (Upazilas) of the Rangamati district, Bangladesh, and compare the results with the landslide susceptibility map produced based on the slope-based absence data sampling method. Using the 15 landslide causal factors, including slope, aspect, and plan curvature, we first determine the critical value of 23.69 based on the Chi-square distribution with 14 degrees of freedom. This critical value was then used to determine the sampling space for 261 random absence data. In comparison, we chose another set of the absence data based on a slope threshold of < 3°. The landslide susceptibility maps were then generated using the random forest model. The Receiver Operating Characteristic (ROC) curves and the Kappa index were used for accuracy assessment, while the Seed Cell Area Index (SCAI) was used for consistency assessment. The landslide susceptibility map produced using our proposed method has relatively high model fitting (0.87), prediction (0.85), and Kappa values (0.77). Even though the landslide susceptibility map produced by the slope-based sampling also has relatively high accuracy, the SCAI values suggest lower consistency. Furthermore, slope-based sampling is highly subjective; therefore, we recommend using MD -based absence data sampling for landslide susceptibility mapping.

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

To assess the status of hotspots and research trends on geographic information system (GIS)-based landslide susceptibility (LS), we analysed 1142 articles from the Thomas Reuters Web of Science Core Collection database published during 2001-2020 by combining bibliometric and content analysis. The paper number, authors, institutions, corporations, publication sources, citations, and keywords are noted as sub/categories for the bibliometric analysis. Thematic LS data, including the study site, landslide inventory, conditioning factors, mapping unit, susceptibility models, and mode fit/prediction performance evaluation, are presented in the content analysis. Then, we reveal the advantages and limitations of the common approaches used in thematic LS data and summarise the development trends. The results indicate that the distribution of articles shows clear clusters of authors, institutions, and countries with high academic activity. The application of remote sensing technology for interpreting landslides provides a more convenient and efficient landslide inventory. In the landslide inventory, most of the sample strategies representing the landslides are point and polygon, and the most frequently used sample subdividing strategy is random sampling. The scale effects, lack of geographic consistency, and no standard are key problems in landslide conditioning factors. Feature selection is used to choose the factors that can improve the model's accuracy. With advances in computing technology and artificial intelligence, LS models are changing from simple qualitative and statistical models to complex machine learning and hybrid models. Finally, five future research opportunities are revealed. This study will help investigators clarify the status of LS research and provide guidance for future research.

A Heuristic Method to Evaluate the Effect of Soil Tillage on Slope Stability: A Pilot Case in Central Italy

Among the various predisposing factors of rainfall-induced shallow landslides, land use is constantly evolving, being linked to human activities. Between different land uses, improper agricultural practices can have a negative impact on slope stability. Indeed, unsustainable soil tillage can modify the mechanical properties of the soils, leading to a possible increase of the instability phenomena. However, the effects of soil tillage on slope stability are poorly investigated. To address this topic, the PG_TRIGRS model (a probabilistic, geostatistic-based extension of TRIGRS) was applied to a cultivated, landslide-prone area in central Italy, thoroughly studied and periodically monitored through systematic image analysis and field surveys. A heuristic approach was adopted to quantitatively evaluate the effect of soil tillage on the mechanical properties of the soil: after a first run of the model with unbiased parameters, the slope stability analysis was carried out assuming several percentages of reduction of the effective soil cohesion to mimic an increasing impact of soil tillage on the strength conditions. Then, a comparison between observed landslides and the spatial distribution of the probability of failure derived from the application of PG_TRIGRS was carried out. A back analysis with contingency matrix and skill scores was adopted to search for the best compromise between correct and incorrect model outcomes. The results show that soil tillage caused a 20 to 30% reduction in soil cohesion in the analyzed area.

Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping

Among several natural and anthropogenic conditioning factors that control slope instability, heavy rainfall is a key factor in terms of triggering landslide events. In the Mediterranean region, Crete suffers the frequent occurrence of heavy rainstorms that act as a triggering mechanism for landslides. The Mediterranean island of Crete suffers from frequent occurrences of heavy rainstorms, which often trigger landslides. Therefore, the spatial and temporal study of recent storm/landslide events and the projection of potential future events is crucial for long-term sustainable land use in Crete and Mediterranean landscapes with similar geomorphological settings, especially with climate change likely to produce bigger and more frequent storms in this region. Geoinformatic technologies, mainly represented by remote sensing (RS) and Geographic Information Systems (GIS), can be valuable tools towards the analysis of such events. Considering an administrative unit of Crete (municipality of Rethymnon) for investigation, the present study focused on using RS and GIS-based approaches to: (i) detect landslides triggered by heavy rainstorms during February 2019; (ii) determine the interaction between the triggering factor of rainfall and other conditioning factors; and (iii) estimate the spatial component of a hazard map by spatially indicating the possibility for rainfall-triggered landslides when similar rainstorms take place in the future. Both landslide detection and hazard mapping outputs were validated by field surveys and empirical analysis, respectively. Based on the validation results, geoinformatic technologies can provide an ideal methodological framework for the acquisition of landslide-related knowledge, being particularly beneficial to land-use planning and decision making, as well as the organization of emergency actions by local authorities.

GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility

Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), decision table (DTable), radial basis function network (RBFN), and stochastic gradient descent (SGD), are proposed in this study to delineate landslide susceptibility zones. First, a landslide inventory map was randomly divided into 828 (75%) samples and 276 (25%) samples for training and validation, respectively. Second, the One-R technique was utilized to analyze the importance of 14 variables. Then, the prediction capability of the four models was validated and compared in terms of different statistical indices (accuracy (ACC) and Cohen’s kappa coefficient (k)) and the areas under the curve (AUC) in the receiver operating characteristic curve. The results showed that the SGD model performed best (AUC = 0.897, ACC = 80.98%, and k = 0.62), followed by the BN (AUC = 0.863, ACC = 78.80%, and k = 0.58), RBFN (AUC = 0.846, ACC = 77.36%, and k = 0.55), and DTable (AUC = 0.843, ACC = 76.45%, and k = 0.53) models. The susceptibility maps revealed that the DS railway segments from Puyang town to Dengsheng village are in high and very high-susceptibility zones.

Inventory of Historical and Recent Earthquake-Triggered Landslides and Assessment of Related Susceptibility by GIS-Based Analytic Hierarchy Process: The Case of Cephalonia (Ionian Islands, Western Greece)

Cephalonia, located in the middle of the central Ionian Islands, has been affected by destructive earthquakes during both the instrumental and the historical period. Despite the fact that it is widely studied from several scientific viewpoints, limited research has been conducted so far regarding the earthquake-triggered landslides (ETL) and the related susceptibility. In the context of the present study, an inventory with 67 ETL from 11 earthquakes that occurred from 1636 to 2014 is presented. Given this record, the study further examines the ETL susceptibility exploiting 10 landslide causal factors in the frame of a GIS-based Analytic Hierarchy Process (AHP). Four factors (i.e., slope, PGA, tectonic structures and lithology) were associated in a higher degree to the locations where ETL occurred on the island. Based on the comparison of the ETL inventory and the landslide susceptibility index (LSI) map, the distribution of ETL in Cephalonia is not random, as their majority (82%) were generated within high to critically high susceptible zones. This fact, along with the AUC values of 80.3%, reveals a fair-to-good accuracy of the landslide susceptibility assessment and indicate that the contribution of the studied variables to the generation of ETL was effectively determined.

Impact of Land Use/Land Cover Change on Landslide Susceptibility in Rangamati Municipality of Rangamati District, Bangladesh

Landslide susceptibility depends on various causal factors such as geology, land use/land cover (LULC), slope, and elevation. Unlike other factors that are relatively stable over time, LULC is a dynamic factor associated with human activities. This study evaluates the impact of LULC change on landslide susceptibility in the Rangamati municipality of Rangamati district, Bangladesh, based on three LULC scenarios—the existing (2018) LULC, the proposed LULC (proposed in 2010, but not yet implemented), and the simulated LULC of 2028—using artificial neural network (ANN)-based cellular automata. The random forest model was used for landslide susceptibility mapping. The model showed good accuracy for all three LULC scenarios (existing: 82.7%; proposed: 81.4%; and 2028: 78.3%) and strong positive correlations (>0.8) between different landslide susceptibility maps. LULC is either the third or fourth most important factor in these scenarios, suggesting that is has a moderate impact on landslide susceptibility. Future LULC changes will likely increase landslide susceptibility, with up to 14.5% increases in the high susceptibility zone for both the proposed and simulated LULC scenarios. These findings may help policymakers carry out proper urban planning and highlight the importance of considering landslide susceptibility in LULC planning.

School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education

This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multi-natural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current number of elementary schools is relatively much higher. This study represents the first to attempt to integrate these four parameters—accessibility, multi natural hazard, biohazard, comfort index, and land value—to determine potential areas for new schools to achieve educational equity targets.

Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China

Landslides occur frequently in Lixian County, China, and land use has changed significantly in recent decades. We obtained land use data for the years 1980, 2000, and 2018, as well as three landslide susceptibility maps from a Random Forest model. Agricultural land, low coverage grassland, water area, and urban, rural and other construction land were prone to landslides. Landslide susceptibility was low in areas of woodland, moderate and high coverage grassland, bare rock land, desert and tundra. Areas with high landslide susceptibility were mainly located in the catchment of the study region, and a 2.61% decrease in high landslide susceptibility areas over the 38-year period was primarily driven by changes in agricultural and rural land. By contrast, a 1.42% increase in low landslide susceptibility areas over the 38-year period was driven by changes in moderate and high coverage woodland and moderate coverage grassland. There is a need for effective management measures to be implemented because areas with high landslide susceptibility are still present. We also found that human aggregations, or the absence of these, vary in their effects on the areas of Lixian County most susceptible to landslides.

Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis

The study objective was to derive a susceptibility model for shallow landslides that could include process-related non-stationary variables, to be adaptable to climate changes. We selected the territory of the Mont-Emilius and Mont-Cervin Mountain Communities (northern Italy) as the study area. To define summary variables related to landslide predisposing and triggering processes, we investigated the relationships between landslide occurrences and intense rainfall and snowmelt events (period 1991-2020). For landslide susceptibility mapping, we set up a Generalized Additive Model. We defined a reference model through variable penalization (relief, NDVI, land cover and geology predictors). Similarly, we optimized a model including the climate variables, checking their smooth functions to ensure physical plausibility. Finally, we validated the optimized model through a k-fold cross-validation and performed an evaluation based on contingency tables, area under the receiver operating characteristic curve (AUROC) and variable importance (decrease in explained variance). The climate variables that resulted as being statistically and physically significant are the effective annual number of rainfall events with intensity-duration characteristics above a defined threshold (EAT_ean) and the average number of melting events occurring in a hydrological year (ME_n). In the optimized model, EAT_ean and ME_n accounted for 5% of the explained deviance. Compared to the reference model, their introduction led to an increase in true positive rate and AUROC of 2.4% and 0.8%, respectively. Also, their inclusion caused a transition of the vulnerability class in 11.0% of the study area. The k-fold validation confirmed the statistical significance and physical plausibility of the meteorological variables in 74% (EAT_ean) and 93% (ME_n) of the fitted models. Our results demonstrate the validity of the proposed approach to introduce process-related, non-stationary, physically-plausible climate variables within a shallow landslide susceptibility analysis. Not only do the variables improve the model performance, but they make it adaptable to map the future evolution of landslide susceptibility including climate changes.

Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study

Land use/land cover (LULC) changes may alter the risk of landslide occurrence. While LULC has often been considered as a static factor representing present-day LULC, historical LULC dynamics have recently begun to attract more attention. The study objective was to assess the effect of LULC legacies of nearly 200 years on landslide susceptibility models in two Austrian municipalities (Waidhofen an der Ybbs and Paldau). We mapped three cuts of LULC patterns from historical cartographic documents in addition to remote-sensing products. Agricultural archival sources were explored to provide also a predictor on cumulative biomass extraction as an indicator of historical land use intensity. We use historical landslide inventories derived from high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), which are reported to have a biased landslide distribution on present-day forested areas and agricultural land. We asked (i) if long-term LULC legacies are important and reliable predictors and (ii) if possible inventory biases may be mitigated by LULC legacies. For the assessment of the LULC legacy effect on landslide occurrences, we used generalized additive models (GAM) within a suitable modeling framework considering various settings of LULC as predictor, and evaluated the effect with well-established diagnostic tools. For both municipalities, we identified a high density of landslides on present-day forested areas, confirming the reported drawbacks. With the use of LULC legacy as an additional predictor, it was not only possible to account for this bias, but also to improve model performances.

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

Assessment of Landscape Change of Lesser Himalayan Road Corridor of Uttarakhand, India

The spatio-temporal monitoring and understanding of the pattern of land-use and land-cover (LULC) change in the Himalayas are essential for sustainable development, especially from environmental planning and management perspective. The increasing anthropogenic activities and climate change in the Siwalik and Lesser Himalayas have substantially experienced rapid change in the natural landscape; however, detailed investigation and documentation of such observed changes are limited. This study aims to assess the LULC changes along the Kalsi-Chakrata road corridor located in the Lesser Himalayan region of Uttarakhand state of India using remote sensing and geographic information system (GIS) for the periods 2000-2010 and 2010-2019. The LULC maps were generated from multi-temporal satellite images of the Landsat -7 Enhanced Thematic Mapper Plus (ETM+) series for 2000 and 2010, and the Linear Imaging Self-Scanning System IV (LISS IV) images from Resourcesat-1 for 2019. The extent of spatial landscape changes occurred in different LULC classes was performed through the cross-tabulation change matrix in the GIS module up to the individual village level. The results indicate that the forest cover of the area was intensively converted to open areas, sparse vegetation, and different land-use categories. These included agricultural land, built-up areas, and decreased from 47.27 % in 2000 to 36.66 % in 2019. During the same period, the open areas and agricultural areas were increased by 15.86 % and 4.49 %, respectively. Moreover, the built-up areas (both urban and rural settlements) were progressively increased from 0.33% in 2000 to 0.56 % in 2019. The conversion of forests and sparsely vegetative areas to agricultural land and rural settlements is closely associated with the increasing anthropogenic activities due to population growth, tourism, movement of heavy vehicles for mining and other economic activities. The changes in land-cover to land use classes are more prominent in Samalta Dadauli, Nithala, Bhugtari, and Udapalta villages located between Kalsi and Sahiya town. The reported maximum transition of forest areas into the open area, agricultural land, and sparse vegetation indicates the possible scarcity of water, which could link with the incidence of climatic or seasonal variation in the Lesser Himalayan terrain to the hydro-geomorphic and anthropogenic processes. The trend in LULC change at the village level gave the insight to help to prioritize future mitigation planning and sustainable development that are exceedingly convenient for the planners, policymakers, and local authorities for comprehensive forest management, biodiversity strategies, and necessary conservation

Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model

Landslide susceptibility mapping (LSM) is a cost-effective tool for landslide hazard mitigation. To date, no nationwide landslide susceptibility maps have been produced for the entire Kenyan territory. Hence, this work aimed to develop a landslide susceptibility map at the national level in Kenya using the fuzzy analytic hierarchy process method. First, a hierarchical evaluation index system containing 10 landslide contributing factors and their subclasses was established to produce a susceptibility map. Then, the weights of these indexes were determined through pairwise comparisons, in which triangular fuzzy numbers (TFNs) were employed to scale the relative importance based on the opinions of experts. Ultimately, these weights were merged in a hierarchical order to obtain the final landslide susceptibility map. The entire Kenyan territory was divided into five susceptibility levels. Areas with very low susceptibility covered 5.53% of the Kenyan territory, areas with low susceptibility covered 20.58%, areas with the moderate susceptibility covered 29.29%, areas with high susceptibility covered 29.16%, and areas with extremely high susceptibility covered 15.44% of Kenya. The resulting map was validated using an inventory of 425 historical landslides in Kenya. The results indicated that the TFN-AHP model showed a significantly improved performance (AUC = 0.86) compared with the conventional AHP (AUC = 0.72) in LSM for the study area. In total, 31.53% and 29.88% of known landslides occurred within the “extremely high” and “high” susceptibility zones, respectively. Only 8.24% and 1.65% of known landslides fell within the “low” and “very low” susceptibility zones, respectively. The map obtained as a result of this study is beneficial to inform planning and land resource management in Kenya.

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Landslide spatial probability and size are two essential components of landslide susceptibility. However, in existing slope-unit-based landslide susceptibility assessment methods, landslide size has not been explicitly considered. This paper developed a novel slope-unit based approach for landslide susceptibility assessment that explicitly incorporates landslide size. This novel approach integrates the predicted occurrence probability (spatial probability) of landslides and predicted size (area) of potential landslides for a slope-unit to obtain a landslide susceptibility value for that slope-unit. The results of a case study showed that, from a quantitative point of view, integrating spatial probability and size in slope-unit-based landslide susceptibility assessment can bring remarkable increases of AUC (Area under the ROC curve) values. For slope-unit-based scenarios using the logistic regression method and the neural network method, the average increase of AUC brought by incorporating landslide size is up to 0.0627 and 0.0606, respectively. Slope-unit-based landslide susceptibility models incorporating landslide size had utilized the spatial extent information of historical landslides, which was dropped in models not incorporating landslide size, and therefore can make potential improvements. Nevertheless, additional case studies are still needed to further evaluate the applicability of the proposed approach.

Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data

Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In this study, we derive proxies from multi-sensoral high resolution remote sensing data to investigate both the location of slums and the location of slopes. We do so for seven cities on three continents. Using a chi-squared test of homogeneity, we compare the locations of formal areas with that of slums. Contrary to the perception indirectly stated in the literature, we find that slums are in none of the sample cities predominantly located in these exposed areas. In five out of seven cities, the spatial share of slums on hills steeper than 10° is even less than 5% of all slums. However, we also find a higher likelihood of slums occurring in these exposed areas than of formal settlements. In six out of seven sample cities, the probability that a slum is located in steep areas is higher than for a formal settlement. As slums mostly feature higher population densities, these findings reveal a clear tendency that slum residents are more likely to settle in exposed areas.

Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover

In June 2009 and September 2014, the Styrian Basin in Austria was affected by extreme events of heavy thunderstorms, triggering thousands of landslides. Since the relationship between intense rainfall, land cover/land use (LULC), and landslide occurrences is still not fully understood, our objective was to develop a model design that allows to assess landslide susceptibility specifically for past triggering events. We used generalized additive models (GAM) to link land surface, geology, meteorological, and LULC variables to observed slope failures. Accounting for the temporal variation in landslide triggering, we implemented an innovative spatio-temporal approach for landslide absence sampling. We assessed model performance using k-fold cross-validation in space and time to estimate the area under the receiver operating characteristic curve (AUROC). Furthermore, we analyzed the variable importance and its relationship to landslide occurrence. Our results showed that the models had on average acceptable to outstanding landslide discrimination capabilities (0.81–0.94 mAUROC in space and 0.72–0.95 mAUROC in time). Furthermore, meteorological and LULC variables were of great importance in explaining the landslide events (e.g., five-day rainfall 13.6–17.8% mean decrease in deviance explained), confirming their usefulness in landslide event analysis. Based on the present findings, future studies may assess the potential of this approach for developing future storylines of slope instability based on climate and LULC scenarios.

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

The Sustainable Development Goals (SDGs) have been in effect since 2015 to continue the progress of the Millennium Development Goals. Some of the SDGs are expected to be achieved by 2020, while others by 2030. Among the 17 SDGs, SDG 15 is particularly dedicated to environmental resources (e.g., forest, wetland, land). These resources are gravely threatened by human-induced climate change and intense anthropogenic activities. In Bangladesh, one of the most climate-vulnerable countries, climate change and human interventions are taking a heavy toll on environmental resources. Ensuring the sustainability of these resources requires regular monitoring and evaluation to identify challenges, concerns, and progress of environmental management. Remote sensing has been used as an effective tool to monitor and evaluate these resources. As such, many studies on Bangladesh used various remote-sensing approaches to conduct research on the issues related to SDG 15, particularly on forest, wetland, erosion, and landslides. However, we lack a comprehensive view of the progress, challenges, concerns, and future outlook of the goal and its targets. In this study, we sought to systematically review the remote-sensing studies related to SDG 15 (targets 15.1–15.3) to present developments, analyze trends and limitations, and provide future directions to ensure sustainability. We developed several search keywords and finally selected 53 articles for review. We discussed the topical and methodological trends of current remote-sensing works. In addition, limitations were identified and future research directions were provided.

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Despite recent progress in landslide susceptibility mapping, a holistic method is still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object-based random forest in modeling the susceptibility of protected and non-protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these two forest areas to landslides. After surveying the landslide events, segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object-based image analysis was used for deriving anthropogenic-induced forest loss and fragmentation. The layers of logging and mining were obtained from available historical data. Landslide samples were extracted from field observations, satellite images, and available database. A single database was generated including all conditioning and triggering object features, and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non-protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non-protected forest. The topographic features obtained about 60% of the importance values with the domination of the topographic ruggedness index and slope in the protected forest. Although the importance of topographic features was reduced to 36% in the non-protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non-protected forest to landslides.

Relation between land cover and landslide susceptibility in Val d'Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction

The effects of land use and land cover (LULC) dynamics on landslide susceptibility are not fully understood. This study evaluates the influence of LULC on landslide susceptibility and assesses the historic and future LULC changes in a high mountain region. A detailed inventory map showing the distribution of landslides was prepared based on the 2013 episode in Val d'Aran, Pyrenees (Spain). This inventory showed that LULC clearly affected landslide susceptibility. Both the number of landslides and the landslide density triggered in grassland and meadow was highest (52% and 2.0 landslides/km²). In contrast, the landslide density in areas covered by forest and shrubs was much lower (15% and 0.4 landslides/km², and 23% and 1.7 landslides/km², respectively). Historical changes of LULC between 1946 and 2013 were determined by comparing aerial photographs. The results indicated that the forest and shrub areas increased by 68 and 65%, respectively; whereas grassland and scree areas decreased by 33 and 52%. Urban area also increased by 532%, especially between 1990 and 2001. Future LULC was predicted until 2097 using TerrSet software. The results showed that the forest area and urban area increased by 57 and 43%, severally; while shrubs, grassland and scree area decreased by 28, 46 and 78%, respectively. Heuristic and deterministic models were applied to create susceptibility maps, which classified the study area into four susceptibility degrees from very low to high. The maps were validated by the 2013 landslide dataset and showed satisfactory results using receiver operating characteristics curves and density graph method. Then, susceptibility maps until 2097 were calculated by the heuristic model and results revealed that landslide susceptibility will decrease by 48% for high-susceptible areas. In contrast, the areas of very-low susceptibility degree will increase 95%, while medium and low-susceptible areas will be more or less constant. This study only includes the effect of future LULC changes on the landslide susceptibility and does not analyze the future impacts of climate changes and the variation of rainfall conditions. Nevertheless, the results may be used as support for land management guidelines to reduce the risk of slope instabilities.

Comparison of Statistical Analysis Models for Susceptibility Assessment of Earthquake-Triggered Landslides: A Case Study from 2015 Earthquake in Lefkada Island

The main purpose of this study is to comparatively assess the susceptibility of earthquake-triggered landslides in the island of Lefkada (Ionian Islands, Greece) using two different statistical analysis models, a bivariate model represented by frequency ratio (FR), and a multivariate model represented by logistic regression (LR). For the implementation of the models, the relationship between geo-environmental factors contributing to landslides and documented events related to the 17th November 2015 earthquake was investigated by geographic information systems (GIS)-based analysis. A landslide inventory with events attributed to the specific earthquake was prepared using satellite imagery interpretation and field surveys. Eight factors: Elevation, slope angle, slope aspect, distance to main road network, distance to faults, land cover, geology, and peak ground acceleration (PGA), were considered and used as thematic data layers. The prediction capability of the models and the accuracy of the resulting susceptibility maps were tested by a standard validation method, the receiver operator characteristic (ROC) analysis. Based on the validation results, the output map with the highest reliability could potentially constitute an ideal basis for use within regional spatial planning as well as for the organization of emergency actions by local authorities.

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.

Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future

Land cover is one of the most important conditioning factors in landslide susceptibility analysis. Usually it is considered as a static factor, but it has proven to be dynamic, with changes occurring even in few decades. In this work the influence of land cover changes on landslide susceptibility are analyzed for the past and for future scenarios. For the application, an area representative of the hilly-low mountain sectors of the Italian Southern Apennines was chosen (Rivo basin, in Molise Region). With this purpose landslide inventories and land cover maps were produced for the years 1954, 1981 and 2007. Two alternative future scenarios were created for 2050, one which follows the past trend (2050-trend), and another one more extreme, foreseeing a decrease of forested and cultivated areas (2050-alternative). The landslide susceptibility analysis was performed using the Spatial Multi-Criteria Evaluation method for different time steps, investigating changes to susceptibility over time. The results show that environmental dynamics, such as land cover change, affect slope stability in time. In fact there is a decrease of susceptibility in the past and in the future 2050-trend scenario. This is due to the increase of forest or cultivated areas, that is probably determined by a better land management, water and soil control respect to other land cover types such as shrubland, pasture or bareland. Conversely the results revealed by the alternative scenario (2050-alternative), show how the decrease in forest and cultivated areas leads to an increase in landslide susceptibility. This can be related to the assumed worst climatic condition leading to a minor agricultural activity and lower extension of forested areas, possibly associated also to the effects of forest fires. The results suggest that conscious landscape management might contribute to determine a significant reduction in landslide susceptibility.

The role of land use changes in the distribution of shallow landslides

The role of land use dynamics on shallow landslide susceptibility remains an unresolved problem. Thus, this work aims to assess the influence of land use changes on shallow landslide susceptibility. Three shallow landslide-prone areas that are representative of peculiar land use settings in the Oltrepò Pavese (North Apennines) are analysed: the Rio Frate, Versa and Alta Val Tidone catchments. These areas were affected by widespread land abandonment and modifications in agricultural practices from 1954 to 2012 and relevant shallow landslide phenomena in 2009, 2013 and 2014. A multi-temporal land use change analysis allows us to evaluate the degree of transformation in the three investigated areas and the influence of these changes on the susceptibility to shallow landslides. The results show that the three catchments were characterised by pronounced land abandonment and important changes in agricultural practices. In particular, abandoned cultivated lands that gradually recovered through natural grasses, shrubs and woods were identified as the land use change classes that were most prone to shallow landslides. Additionally, the negative qualities of the agricultural maintenance practices increased the surface water runoff and consequently intensified erosion processes and instability phenomena. Although the land use was identified as the most important predisposing factor in all the study areas, some cases existed in which the predisposition of certain areas to shallow landslides was influenced by the combined effect of land use changes and the geological conditions, as highlighted by the high susceptibility of slopes that are characterised by adverse local geological (thick soils derived from clayey-marly bedrocks) and geomorphological (slope angle higher than 25°) conditions. Thus, the achieved results are particularly useful to understand the best land conservation strategies to be adopted to reduce instability phenomena and the consequent economic losses in areas that are strongly linked to agricultural land use in these territories.

Major Natural Disasters in China, 1985-2014: Occurrence and Damages

This study aimed to describe the characteristics of natural disasters and associated losses from 1985 to 2014. The Mann-Kendall method was used to detect any long-term trends and abrupt changes. Hotspot analysis was conducted to detect the spatial clusters of disasters. We found an increasing trend in the occurrence of integrated natural disasters (tau = 0.594, p < 0.001), particularly for floods (tau = 0.507, p < 0.001), landslides (tau = 0.365, p = 0.009) and storms (tau = 0.289, p = 0.032). Besides, there was an abrupt increase of natural disasters in 1998–2000. Hotspots of droughts, floods, landslides and storms were identified in central, southern, southwest and southeast areas of China, respectively. Annual deaths from integrated natural disasters were decreasing (tau = −0.237, p = 0.068) at about 32 persons/year, decreasing at 17 persons/year for floods (tau = −0.154, p = 0.239), and decreasing at approximately 12 persons/year for storms (tau = −0.338, p = 0.009). No significant trend was detected in inflation-adjusted damages while a declining trend was detected in the ratio of year damage against GDP (gross domestic product). In conclusion, there has been an increasing trend in occurrence of natural disasters in China with the absence of an increase in life and economic losses. Despite the progress in the disaster adaption, there will be great challenges in disaster control for China in the future.

Future Forest Cover Change Scenarios with Implications for Landslide Risk: An Example from Buzau Subcarpathians, Romania

This study focuses on future forest cover change in Buzau Subcarpathians, a landslide prone region in Romania. Past and current trends suggest that the area might expect a future increase in deforestation. We developed spatially explicit scenarios until 2040 to analyze the spatial pattern of future forest cover change and potential changes to landslide risk. First, we generated transition probability maps using the weights of evidence method, followed by a cellular automata allocation model. We performed expert interviews, to develop two future forest management scenarios. The Alternative scenario (ALT) was defined by 67% more deforestation than the Business as Usual scenario (BAU). We integrated the simulated scenarios with a landslide susceptibility map. In both scenarios, most of deforestation was projected in areas where landslides are less likely to occur. Still, 483 (ALT) and 276 (BAU) ha of deforestation were projected on areas with a high-landslide occurrence likelihood. Thus, deforestation could lead to a local-scale increase in landslide risk, in particular near or adjacent to forestry roads. The parallel process of near 10% forest expansion until 2040 was projected to occur mostly on areas with high-landslide susceptibility. On a regional scale, forest expansion could so result in improved slope stability. We modeled two additional scenarios with an implemented landslide risk policy, excluding high-risk zones. The reduction of deforestation on high-risk areas was achieved without a drastic decrease in the accessibility of the areas. Together with forest expansion, it could therefore be used as a risk reduction strategy.