Inventory Map Research Articles

Flood risk assessment using modern models integrated with GIS for susceptibility mapping of the M'zab Valley watershed (Algeria)

ABSTRACT Comprehensive flood control is needed because flooding can be catastrophic for people's lives and ways of living. This management must provide information on the hydrologic, geotechnical, environmental, social, and economic elements of floods. This work created maps for the flood susceptibility of the M'zab Valley using logistic regression (LR) and frequency ratio (FR). The main objective of the research was to strengthen the bivariate probability capabilities of FR and LR. A flood inventory map was created by extracting flood sites from multiple sources. The flood inventory was divided randomly into two parts: 30% was used for validation and the remaining 70% was used to train the models. Independent variable datasets included the distance from the river, rainfall, soil type, land use/land cover (LULC), elevation, drainage density, flow accumulation, and slope. The effect of each variable on flooding was assessed by comparing each independent variable with the dependent flood layer. The flood susceptibility map was assessed using the prediction rate approach on the validation dataset, which was not used for model construction. The accuracy assessment's findings revealed a prediction rate of FR 0.86 and LR 0.881, as well as a success rate of FR 0.924 and LR 0.924.

Flood vulnerability map of the Bagmati River basin, Nepal: a comparative approach of the analytical hierarchy process and frequency ratio model

AbstractThe analytical hierarchy process (AHP) and frequency ratio model (FR), along with the integration of GIS, have proven to be successful approaches for assessing flood-prone areas. However, in Nepal flood vulnerability mapping based on GIS decision analysis is limited. Thus, this study focused on comparing the data-driven FR method and expert knowledge-based AHP technique in a GIS environment to prepare a flood vulnerability map for the Bagmati River basin, helping to explore the gap in flood vulnerability mapping methodologies and approaches. By combining all class-weighted contributing factors, like elevation, precipitation, flow accumulation, drainage density, soil, distance from the river, land use land cover, normalized difference vegetative index, slope and topographic wetness index, the study evaluated the efficiency of FR and AHP in assessing flood vulnerability maps. An inventory map of floods containing 107 flood points was created. Subsequently, the flood vulnerability maps generated using FR and AHP models revealed that 9.30% and 11.36% of regions were in highly vulnerable areas, respectively. Receiver operating characteristics validated the model outcomes, indicating that the FR model’s accuracy of 91% outperformed the AHP model’s 84% accuracy. The study findings will assist decision-makers in enacting sustainable management techniques to reduce future damage in the Bagmati basin.

Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India

Landslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.

A Dataset for Species Distribution Modelling of Mangroves in Vietnam: Based on the National Forest Inventory Monitoring

Mangroves provides essential ecosystem services such as protection of coastal areas, carbon sequestration, and habitat provision for diverse species in coastal ecosystems. Species distribution models (SDMs) are powerful tools for predicting the potential distribution of mangrove species, which support impact assessments of climate changes on biodiversity and ecological functions of mangrove ecosystems. A comprehensive dataset for mangrove occurrence information derived from the Forest Inventory Map of Vietnam was designed to facilitate the building and projection of SDMs. The prediction data designed for training SDMs integrates ecological information including 701 field survey-based mangrove occurrences at the genus level and 21 environmental variables such as bioclimatic variables, digital elevation model and soil properties with 1 km spatial resolution. The projection data for provide sets of predictors aligned with four shared socioeconomic pathways scenarios representing two future periods to support the projection of SDM results under future climate conditions in Vietnam. This dataset serves as a valuable ecological information resource, enabling the modeling and predicting of potential mangrove habitats and distributions for the protection and restoration of mangroves in Vietnam under changing environmental conditions.

Application of Bivariate Statistical Method for Landslide Susceptibility Assessment along Srinagar-Bandipora Highway, North Kashmir Himalaya

The present study aims to create and evaluate a map indicating the potential of landslides along the Srinagar-Bandipora highway in the North Kashmir Himalaya region. Therefore, the study utilized the Bivariate Statistical Relative effect (RE) method to establish the correlation between casual factors (CFs) contributing to landslides and the actual landslide incidences. Additionally, fifteen different CFs were analyzed within a Geographic Information System (GIS) framework to assess the impact of these factors on landslides. Further, a comprehensive GIS-based inventory map was compiled, incorporating both primary and secondary data sources, to identify landslide locations. A landslide susceptibility zonation (LSZ) map was developed by integrating all identified landslide CFs along with their respective effect function values. Moreover, the results revealed that 21.06% of the study area was classified as having a very high to high susceptibility to landslides. To validate the accuracy of the produced map, 30% of landslide validation data was employed and the receiver operating characteristics (ROC) curve method was utilized, yielding an excellent result with an area under curve (AUC) value of 0.919 for the study area. Consequently, the findings of this study hold significant potential for mitigating landslide-related hazards and guiding land use planning projects.

Increased Sensitivity and Accelerated Response of Vegetation to Water Variability in China from 1982 to 2022

Understanding how plants adapt to shifting water availability is imperative for predicting ecosystem vulnerability to drought. However, the spatial–temporal dynamics of the plant–water relationship remain uncertain. In this study, we employed the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI4g), an updated version succeeding GIMMS NDVI3g spanning from 1982 to 2022. We integrated this dataset with the multiple scale Standardized Precipitation Evapotranspiration Index (SPEI 1 to 24) to investigate the spatial–temporal variability of sensitivity and lag in vegetation growth in response to water variability across China. Our findings indicate that over 83% of China’s vegetation demonstrates positive sensitivity to water availability, with approximately 66% exhibiting a shorter response lag (lag < 1 month). This relationship varies across aridity gradients and diverges among plant functional types. Over 66% of China’s vegetation displays increased sensitivity to water variability and 63% manifests a short response lag to water changes over the past 41 years. These outcomes significantly contribute to understanding vegetation dynamics in response to changing water conditions, implying a heightened susceptibility of vegetation to drought in a future warming world.

Landslide susceptibility analysis and hazard zonation of Koh-e-Suleman range using weighted overlay technique

A healthy environment improves the quality of life on Earth, while natural disasters affect the quality of the environment. An area’s Landslide Hazard Zonation (LHZ) provides key information about the susceptibility to natural or manmade disasters. Using the Weighted Overlay Technique, this research aims to develop the landslide susceptibility and zonation mapping of Koh-e-Suleman Range (a hill station around the heritage site). Six causative factors are evaluated: geology, elevation, slope inclination, distance from streams, type of soil, and annual rainfall intensity, considering equal risk influence for all the factors. All causative factor maps were generated to yield equal classes and provide uniformity to the results except for soil types found in the study area. The findings indicate that the study area may comprise four landslide hazard zones with high, medium, low, and very low-intensity susceptibility of landslides. The accuracy of the final LHZ map is verified by comparing the hazard zonation area percentages with the global landslide inventory map from a macro-study of NASA. It was found that the study areas largely fall within low- and very low-hazard zones.

Landslide Risk Assessments through Multicriteria Analysis

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.

Enhancing landslide inventory mapping through Multi-SAR image analysis: a comprehensive examination of current landslide zones

Enhancing landslide inventory mapping through Multi-SAR image analysis: a comprehensive examination of current landslide zones

Soil erosion susceptibility prediction using ensemble hybrid models with multicriteria decision-making analysis: Case study of the Medjerda basin, northern Africa

Soil erosion is considered one of the most prevalent natural hazards in semiarid regions, leading to the instability of ecosystems and human life. The main purpose of this research was to investigate and analyze soil erosion susceptibility maps in the Medjerda basin in northern Africa. This study utilizes four ensemble models based on the analytical hierarchy process (AHP) multicriteria decision-making analysis, namely, deep learning neural network AHP (DLNN-AHP), frequency ratio AHP (FR-AHP), Monte Carlo AHP (MC-AHP), and fuzzy AHP (F-AHP). Eight predictor variables were considered as inputs to the model, namely, the slope degree, digital elevation model (DEM), topographic wetness index (TWI), distance to river (DFR), distance to road (DFRD), normalized difference vegetation index (NDVI), rainfall erosivity (R), factor and soil erodibility factor (K). Soil erosion inventory maps were developed from field surveys and satellite images. The dataset was randomly divided into 70% for training and 30% for testing. The performances of the utilized models were compared using a receiver operating characteristic (ROC) curve. The results highlighted that all the models utilized exhibited good performance, with DLNN-AHP (93.1%) exhibiting slight superiority, followed by FR-AHP (90.9%), F-AHP (88.9%), and MC-AHP (88.5%). Among the influencing factors, the distance to the river and rainfall erosivity had the most significant impacts on the incidence of soil erosion. Moreover, the current findings revealed that 38.3% of the study area is extremely highly susceptible to soil erosion. The results of this study can aid in developing decision-support tools for planners and managers aiming to mitigate the adverse effects of soil erosion.

Comparing Frequency Ratio and Analytical Hierarchy Models for Landslide Susceptibility in the Dharan Sub-Metropolitan Region of Eastern Nepal

This study compares the application of Frequency Ratio (FR) (statistical/quantitative) and Analytical Hierarchy Process (AHP) (heuristic/semi-quantitative) for landslide susceptibility analysis in Dharan Sub-Metropolitan Region of eastern Nepal. Nine different thematic layers including slope, aspect, curvature, geology, land use, distance to roads, distance to thrusts, topographic position index (TPI), and distance to streams were used for analysis. A landslide inventory map was prepared by identifying the landslides in recent Google Earth images, and it was then verified by field observations. The resulting susceptibility map with different susceptibility levels was validated using the Area Under the Curve (AUC) method, and the resulting AUC values were determined to be 78% for FR and 76.8% for AHP. Both methods offer a reliable strategy for landslide susceptibility mapping with a good prediction rate. The FR model enclosed 41%, 31%, 20%, 7% and 1% of the area as very low, low, moderate, high, and very high susceptibility, respectively, whereas AHP showed 21%, 24%, 27%, 21% and 7% of the area for the respective susceptibility levels. The specific zones identified within the range from very low to very high susceptibility provide valuable insights for local authorities, planners, and decision-makers, allowing them to identify areas susceptible to landslides and implement mitigation measures to aid in targeted risk management efforts.

Mapping and analysis of land subsidence in Awka south L.G.A using sentinel 1 data

This study mapped and analysed land subsidence in Awka South, Anambra State, Nigeria with the view of determining the trend and rate of ground displacements within the study area. Its objectives are to model and analyze time-dependent ground displacements of Awka between 2019 and 2020; ascertain the trend, of the ground displacements between 2019 and 2020; determine the vulnerability of land subsidence in Awka South between 2019 and 2020 and to develop an inventory map to show land subsidence susceptibility areas in Awka South. The methodology involved TopSAR Split to split each sub swath with selected bursts into a separate product, application of Orbit File to acquire the satellite orbit file, back Geocoding to co-registers the four S-1 SLC split products (January and December of 2019 and that of 2020) of the same sub-swath using the orbits of the two products, enhanced spectral diversity to implement the network enhanced spectral diversity (NESD) method for TOPS coregistration, interferogram to compute (complex) interferogram, deburst to limited the results to the burst covering the study area, phase removal to estimate and subtract topographic phase from the interferogram, multilook and Goldstein Phase Filtering and Unwrap Phase to reduce speckle noise the residues in the phase unwrapping step respectively then displacement and range doppler terrain corrections to convert interferometric phase to displacement map. The results showed that subsidence in Awka South was ranging between a minimum of 2.33m to a maximum of 9.76m in 2019 and a minimum of 3.99m to a maximum of 11.22m in 2020. The results also indicated that Awka South had a trend of change of 6.95 % at the annual rate of change of 3.47m/y. The results indicated an expanding/increasing Subsidence in Awka South. The results obtained from the Land Subsidence vulnerability revealed high-risk zone occupied 33% of the entire area, spanning 5634.78 hectares, while Moderate risk zone occupied 16.18% spanning 2763.47 hectares while low risk zone occupied 50.82% spanning of 8678.22 hectares. The maps provided in this study is recommended as a spatial data addition for a decision support system on land subsidence issues.

GIS-based statistical analysis for landslide susceptibility evaluation and zonation mapping: A case from Blue Nile Gorge, Gohatsion-Dejen road corridor, Central Ethiopia

Globally, landslides in mountainous regions are considered to be major geo-hazards which are frequent, intensive and devastating that result in extensive destruction to people's lives, land, property and other infrastructure. To mitigate landslide-associated risks, identification, evaluation, and delineation of susceptible areas is essential for proper strategic planning and management. The present study was conducted in Blue Nile Gorge in the highlands of Central Ethiopia, about 185 km from Addis-Ababa, along the Gohatsion-Dejen road corridor. Landslides are frequently reported in the study area, particularly during the rainy season. The objective of the present study was to evaluate landslide susceptibility and to prepare a landslide susceptibility zoning (LSZ) map. For this purpose a statistical information value (IV) model was used that took into account various factors including slope material, elevation, slope, aspect, land-use and land-cover, proximity to the road, and proximity to streams. Each of these factors was classified based on different conditions like landslide concentration, topography, geology, and land-use and land-cover types. The landslide inventory mapping was conducted through field observations and Google Earth image interpretation. Later, the influence of each causative factor on past landslides was used to calculate their respective IVs. The statistical IVs were further used to evaluate the landslide susceptibility. The LSZ map produced from this study shows that 22.3 % of the study area falls into a very high susceptibility zone, 23.1 % into a high, 18.8 % in moderate, 23.1 % into a low, and the remaining 12.8 % into a low susceptibility zone. The LSZ map was validated using the Receiver Operating Characteristics Curve (ROC), landslide density index (LDI), and past landslide data. All validation methods showed acceptable results. The prepared LSZ map can be used for safe land-use planning of the area.

GIS-based MCDM approach for landslide hazard zonation mapping in east Gojjam zone, central Ethiopia

Landslides are prevalent in the Ethiopian highlands, particularly in the east Gojjam zone, which is highly affected by landslide problems. This research was carried out in the east Gojjam zone, northwestern Ethiopia. The study area is part of an economically important area in the country, and it is the main source of water for the Grand Ethiopian Renaissance Dam (GERD). The main objective of this work was to undertake a detailed inventory of past landslide locations and prediction of present and future landslide hazards, as well as the preparation of a landslide zonation map in the East Gojjam zone by using the Analytical Hierarchy Process (AHP) with the GIS technique. The parameters used for this study were slope degree, slope aspect, land use and land cover, road proximity, rainfall, lithology, altitude, and river proximity. The various causative parameters were collected from the field, and suitable modifications were made to the thematic maps. Finally, the ratings for various parameters were used as the basis to prepare the LHZ map in GIS windows. The landslide susceptibility and inventory mapping were produced in the GIS environment. The results of the study show that the main driving factors for the landslide hazards in the area were river proximity, rainfall, and manmade activities. Validation of this LHZ map revealed that more than 80% of past landslides match within the "high hazard zone" and reasonably accepted the rationality of the adopted methodology. The considered parameters, as well as their evaluation of the production of LHZ-Map, were confirmed. The produced landslide inventory map is very important for urban planners, agricultural studies, environmentalists, and future landslide hazardous prevention and mitigation strategies.

HOTSED: A new integrated model for assessing potential hotspots of sediment sources and related sediment dynamics at watershed scale

In this paper we introduce HOTSED, a novel, innovative GIS-based model designed for assessing potential hotspots of sediment dynamics at watershed scale. HOTSED integrates geomorphic spatial information with both structural and functional properties of connectivity. HOTSED provides a single and intuitive output that depicts the location of sediment source hotspots. Moreover, it enables the identification of “relative hazard” classes for sediment production and related effects. The general methodological framework is based on the initial elaboration of an Inventory Map (IM) of sediment-related landforms and processes, along with the implementation of a corresponding database. Subsequently, we used data stored in the IM to estimate the geomorphic Potential of Sediment Sources (PSS) through a relative scoring system. Furthermore, we computed Structural Sediment Connectivity (STC) and the Potential for Sediment Transport (PST) by combining terrain and hydrological parameters, vegetation roughness, and rainfall erosivity. Afterwards, PSS, STC, and PST components are integrated through a raster-based calculation method yielding the HOTSED model. We tested the HOTSED procedure in the upper Val d’Arda-Mignano watershed, which is a representative geomorphologically highly active Mediterranean area of the Northern Apennines (Italy). Through photointerpretation, terrain analysis, and fieldwork, we mapped sediment-related geomorphic features for a total of 4640 ha including: badlands and gullies (0.26%), rill-interrill erosion (15.03%), fluvial erosion (0.03%), landslides (70.06%), litho-structural erosional systems (0.87%), slope deposits (12.56%), and alluvial deposits (1.19%). HOTSED revealed hotspots with a very high hazard potential located near main channels or upstream of the reservoir. These areas are often linked with active landslides highly connected to the drainage system and frequently associated with other processes like bank erosion or surficial soil erosion. The model also highlighted linear hotspots corresponding to drainages flowing alongside or intersecting complex geomorphic systems such as landslides. Furthermore, HOTSED identified areas where sediments are stored in depositional landforms, exhibiting a low hazard potential, considering both low geomorphic potential and sediment connectivity. Our conceptual model is generally applicable but proves to be particularly effective in areas characterized by complex and polygenetic geomorphic systems, such as the Northern Apennines. HOTSED offers a valuable tool for watershed authorities to support sustainable watershed and reservoir management.

Landslide susceptibility assessment using different rainfall event-based landslide inventories: advantages and limitations

AbstractThe present work aims to evaluate potential sources of uncertainty associated with rainfall-triggered event-based landslide inventories within the framework of landslide susceptibility assessment. Therefore, this study addresses the following questions: (i) How representative is an event-based landslide inventory map of the total landslide activity and distribution in a study area?; (ii) How reliable is an event-based landslide susceptibility map?; (iii) How appropriate is an event-based landslide inventory map for independently validating a landslide susceptibility map? To address these questions, two independent and contrasting rainfall event-based landslide inventories were used, together with a historical landslide inventory, to assess landslide susceptibility for different types of landslides in a study area located north of Lisbon, Portugal. The results revealed the following findings: (i) contrasting rainfall critical conditions for failure can trigger similar landslide types, although they may vary in size and be spatially constrained by different predisposing conditions, particularly lithology and soil type; (ii) landslide susceptibility models using event-based landslide inventories are not reliable in the study area, regardless of the landslide inventory map used for training and validation; and (iii) complementary sources of uncertainty results from using incomplete historical landslide inventories to assess landslide susceptibility and non-totally independent landslide inventories for modeling validation. The present study enhances the understanding of regional landslide susceptibility patterns based on contrasting rainfall-trigger conditions, providing valuable information to minimize exposure; to design regional landslide early warning systems for specific rainfall-trigger landslide events; and to improve the response and preparedness of civil protection services.

Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii

ABSTRACT Climate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an essential technique for assessing areas prone to wildfires. In this study, we proposed the combination of the damage proxy map (DPM) and differenced normalized burn ratio (dNBR) method to generate a precise wildfire inventory map and used it to predict areas susceptible to wildfire. The wildfire susceptibility maps were produced using frequency ratio (FR), convolutional neural network (CNN), and long short-term memory (LSTM)-based deep learning and their performances were compared. We implemented the proposed method on Maui Island, Hawaii, where wildfires frequently occur. We started the process by generating a wildfire inventory map from 2019 to 2023 based on the DPM method applied to Sentinel-1 synthetic aperture radar (SAR) data combined with a dNBR map retrieved from Sentinel-2 data. The wildfire inventory was randomly divided into a training dataset (70%) and a testing dataset (30%). Fifteen wildfire-related factors, including topographical, meteorological, land use, environmental, and anthropological factors, were selected to predict wildfires. The wildfire-related factors were selected by conducting study literature and considering spatial correlation analysis based on the FR method, information gain ratio analysis (IGR), and multicollinearity assessment using tolerance (TOL) and variance inflation factor (VIF) metrics. The level of susceptibility of an area to wildfire is divided into five, namely very high, high, moderate, low, and very low. The FR, CNN, and LSTM produced wildfire susceptibility maps with similar patterns, significantly influenced by land use and rainfall factors. The highly susceptible areas are located on gentle slopes covered by agricultural land and unhealthy vegetation, and these areas have low rainfall intensity but receive high levels of solar radiation. Meanwhile, areas with relatively low susceptibility occur in forests with high levels of wet canopy evaporation. The prediction results were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and the CNN performed slightly better than the FR and LSTM, with AUC values of 0.879, 0.877, and 0.870, respectively. Hence, the use of the CNN algorithm in the proposed method is appropriate, specifically for the study area. In addition, the susceptibility map provides information on susceptible areas on Maui Island, Hawaii, to increase public awareness.

Landslide susceptibility modeling in the Kulfo river catchment, rift valley, Ethiopia: An integrated geospatial and statistical analysis

Landslides occur when debris, rocks, or soil particles move downward. Examining the susceptibility of landslides is essential for safeguarding human well-being and assessing the consequences of landslides on the natural surroundings and ecosystems. This study utilized the frequency ratio technique to evaluate the probability of landslides happening in the Kulfo River watershed, situated in the Rift Valley area of Ethiopia. In order to ensure a comprehensive analysis, many data sources were employed, including satellite images, geological data, and historical records of landslides. This study developed a systematic approach to assess the probability of landslides by considering ten (10) influential factors: land use/land cover, slope, aspect, elevation, curvature, lithology, proximity to lineament, and normalized difference vegetation index (NDVI). The ten influential elements were prioritised based on literature review, expert knowledge, and preliminary study area analysis. The aforementioned causal elements are integrated with a comprehensive landslide inventory map. The sensitivity of the study's area to landslides was mapped using a frequency ratio (FR) model. Subsequently, it was classified as representing various degrees of vulnerability, spanning from extremely minimal to quite significant. The effectiveness of the suggested model was measured by evaluating the accuracy of the generated map of landslide susceptibility by the area under the curve (AUC) technique. Based on the most recent study results, the success rate curve has an area under the curve (AUC) of 79.6%, which indicates a highly satisfactory level of performance. Policymakers may utilize the findings of this study to make educated decisions on how to mitigate the risks of landslides in relation to land use and preparedness for disasters.

Assessing gully erosion susceptibility dynamics using information value and hazard index methods: A case study of Agulu-Nanka watershed, Southeast Nigeria

Gully erosion is one of the major causes of land degradation leading to the loss of productive lands globally. The Benue Trough in Nigeria is plagued with the problem of gully erosion, with a myriad of natural and anthropogenic factors considered as contributory factors. In this study, quantitative and qualitative methods were combined to evaluate gully susceptibility in the Agulu-Nanka watershed located in the lower part of Benue Trough between 2000 and 2018. Landsat 7 and 8 images, Digital Elevation Model (DEM), geological, soil, and rainfall erosivity maps were used to generate the gully erosion controlling factors including vegetation cover, lithology, soil, land use/cover, slope, curvature, aspect and rainfall erosivity. Additionally, high resolution image was used to develop gully inventory map. The information value method was used evaluate the influence of various classes of gully erosion controlling factors on gully distribution in the watershed. This enabled objective derivation of weights for the various classes of these factors. Information value analysis reveal that low percentage vegetation cover (<10 %), Ameki Group lithological unit, high slope gradient (17 − 43°) and Dystric Fluvisols soil type have the highest influence on gully erosion in the watershed. Since the information value method does not consider interrelationship between factors, the rank sum technique was used to derive factor weights based on the relationship between the factors. To develop the gully susceptibility map for the Agulu-Nanka watershed for the years 2000 and 2018, the erosion controlling factors were integrated using factor class weights derived through information value and factor weights derived through rank sum method. The analysis of gully erosion hazard maps show that approximately 48 % of the watershed was under low erosion risk over the period of 18 years. Areas under moderate, high and severe erosion risk decreased from 27.45 %, 9.45 % and 0.27 % of the watershed in year 2000, to 21.30 %, 8.41 %, and 0.20 % of the watershed respectively in 2018. On the other hand, the areas under negligible erosion risk increased from 14.69 % in year 2000, to 21.26 % of the watershed in 2018. The validation of the gully erosion susceptibility map for years 2000 and 2018 using the Receiver Operating Curve (ROC) showed ROC-AUC values of 0.81 % and 0.88 % respectively suggesting good model results. While the result of study indicates that restoration efforts through afforestation is yielding some result, the minimal reduction in the size of areas under severe and high gully erosion susceptibilities indicate that additional mitigation measures need to be combined with intensified afforestation efforts to effectively combat gully erosion in the watershed.

Remote Sensing and GIS-Based Accuracy Assessment of LULC Map and Landslide Susceptibility Prediction for Meghalaya, India

ABSTRACT Through this study, a Landslide Susceptibility Map (LSM) has been developed for the Meghalaya state, India using an Analytical Hierarchy Process (AHP). According to a 2012 Geological Survey of India report, the annual average number of landslides in Meghalaya is nearly 30, which is due to a combination of mountains, steep slopes, and excessive rainfall, leading the state to suffer a huge loss of life and property from landslides. For effective management of the current landslide situation, information about prior landslides is needed. Therefore, the landslide inventory map is prepared with 380 previously occurred events. The Landslide inventory records were separated into training samples (70%) and testing samples (30%) for the purpose of validation. In this regard, the present study has 15 conditioning factors, i.e., slope, rainfall, elevation, relative relief, aspect, distance from the road, curvature, distance from the stream, LULC, lineament density, geomorphology, geology, NDVI, MSAVI, NDWI, which are used to develop susceptibility map. Classification and accuracy assessment of LULC is carried out with segregation as 77% vegetation, 16.4% range land, 3.1% built area, 2.8% crops, 0.4% waterbodies, and 0.3% others (bare land, flooded vegetation, etc.). The Kappa for LULC categorization is 0.92, which is quite satisfactory and suggests that the LULC categorization is reliable. The developed susceptibility map is classified into four different classes, low susceptibility (35%), moderate susceptibility (41%), high susceptibility (20%), and very high susceptibility (4%), and has been verified using physical and Receiver Operating Characteristics (ROC) techniques. Results show that anticipated susceptibility classes are in good match with previous landslide events. The prepared map is reliable and can be used for land-use planning of the state in the future.