Slope Curvature Research Articles

Fungal community composition varies spatially in a commercial potato field in response to soil properties and topographic features

The factors influencing the spatial distribution of fungal communities are commonly examined over large spatial scales but not at smaller scales. Given this, the extent to which soil properties and topographic features contribute to the diversity and distribution of fungal communities in an agricultural field needs to be further explored. We investigated the spatial distribution of soil fungal community composition from a ~1100 m long transect with 83 sampling points in a commercial potato field with a rolling landform. The relative abundance of Ascomycota, Basidiomycota and Mortierellomycota showed medium to strong spatial dependence with an autocorrelation range varying from ~43 to 92 m, similar to the autocorrelation range of soil properties and topographic features. Most of the variability in fungal and saprotrophic community composition was explained by soil properties (15% and 11%, respectively) and spatial distance (16% and 15% respectively). While topographic features contributed 8% and 5% of variability to total fungi and saprotrophic community composition, respectively. The fungal and saprotrophic community compositions were correlated with SOC, pH and slope curvature, however, richness and Pielou’s evenness of the fungal communities and fungal biomass were not correlated with soil properties or topographic features. The results suggest that the spatial variation in fungal and saprotrophic community composition in response to soil properties and topographic features in this agricultural landscape was due to differences in assemblages of fungal Amplicon Sequence Variants (ASVs) but not in differences in the number of fungal ASVs or fungal biomass measured using phospholipids fatty acids.

Comparing 24-hour IOP fluctuation slope curve between newly diagnosed ocular hypertension and primary open-angle glaucoma

ObjectiveTo compare the 24-hour intraocular pressure (IOP) fluctuation slope curve between newly diagnosed patients with ocular hypertension (OHT) and primary open-angle glaucoma (POAG).Methods and analysisNewly diagnosed and untreated OHT and...

Landslide Susceptibility Modelling of Central Highland Part of Chaliyar River Basin, Kerala, India with Integrated Algorithms of Frequency Ratio and Shannon Entropy

An integrated landslide susceptibility analysis is carried out for the central highland region of the Chaliyar River Basin in Kerala, India using bivariate statistical methods, namely the Frequency Ratio (FR) and Shannon Entropy (SE). The study addresses the complex nature of landslides, influenced by natural as well as anthropogenic factors, with specific focus on assessing the landslide likelihood of the study area. The methodology involves a systematic approach of collecting the inventory data, identifying various landslide causative factors and developing their corresponding thematic maps, spatial analysis of landslide occurrence and causative factors using GIS software and generation of Landslide Susceptibility Model (LSM) employing FR and SE algorithm, followed by model validation. Various causative factors considered for the study include slope angle, slope aspect, slope curvature, elevation, lithology, drainage density, landuse and landcover (LULC), Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI). The FR and SE algorithm enable the spatial classification of the study area into four landslide susceptibility categories namely Low, Moderate, High, and Very High. Validation of both the LSMs was carried out using Landslide Density Index (LDI) and Area Under the Curve (AUC) methods. LDI demonstrate a positive fit for both the models, which is indicative of reliability of the susceptibility predictions of the study area. A slightly higher AUC value of SE model is an indication of a high accuracy rate of SE model over FR model. This research brings out a robust methodology for predicting and identifying the landslide risks of the study area.The outcomes of this study will help in developing effective strategies to manage the landslide hazards in geologically vulnerable areas. Keywords: Landslide Susceptibility, Landslide Conditioning Factors, Frequency Ratio, Shannon Entropy, Landslide Density Index(LDI), Area Under the Curve (AUC)

Utilization of Frequency Ratio and Logistic Regression Model for Landslide Susceptibility Mapping in Bogor Area

Java Island holds the highest record of landslide events in Indonesia. In 2021, the Bogor area, consisting of the city and regency of Bogor, recorded the highest number of landslides. These events further impact the fatalities, damage, and loss to society. Landslide mitigation should be considered to reduce the risk caused by landslide hazards. In this regard, a landslide susceptibility analysis is one of the fundamental steps in mitigation measures that can support policymakers in response to landslide disaster risk reduction. The location of landslide possibilities can be identified by mapping landslide susceptibility. Therefore, this study aims to produce a landslide susceptibility map (LSM) using a statistical frequency ratio method and logistic regression. The number of landslide inventories used in the model is about 822 events. To apply the model, the present study evaluates 13 influencing factors consisting of elevation, slope angle, slope aspect, slope curvature, Topographic Wetness Index (TWI), distance to river, lithological, distance to fault, soil type, annual rainfall, Normalized Difference Vegetation Index (NDVI), land use land cover (LULC), and road distance. The model performance is further evaluated using Area Under the ROC Curve (AUC). The frequency ratio (FR) and logistic regression (LR) models produce satisfactory results and have high predictions of future landslide occurrences with a score of 0.8317 and 0.8817, respectively.

Fast and Highly Accurate Zonal Wavefront Reconstruction from Multi-Directional Slope and Curvature Information Using Subregion Cancelation

The wavefront reconstruction is a crucial step in determining the performance of wavefront detection instruments. The wavefront reconstruction algorithm is primarily evaluated in three dimensions: accuracy, speed, and noise immunity. In this paper, we propose a hybrid zonal reconstruction algorithm that introduces slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions by dividing the neighbor sampling points into subregions in groups of four. By canceling the same parameters in integration equations, an algorithm using multi-directional slope–curvature information is achieved with only two sets of integration equations in each subregion, reducing the processing time. Simulation experiments show that the relative root-mean-square reconstruction error of this algorithm is improved by about 4 orders of magnitude compared with existing algorithms that use multi-directional slope information or slope–curvature information alone. Compared with the hybrid multi-directional slope–curvature algorithm, the proposed algorithm can reduce computation time by about 50% as well as provide better noise immunity and reconstruction accuracy. Finally, the validity of the proposed algorithm is verified by the null test experiment.

Assessment of flood risk using Hierarchical Analysis Process method and Remote Sensing systems through arid catchment in southeastern Tunisia

Flood hazard mapping is a fundamental tool for estimating vulnerability in flood-prone areas and assessing flood risk in arid areas, enabling better management. This study specifically focuses on analyzing flood risk in the Gabes catchment, South-Tunisia, which is marked by exceptional flash floods attributed to climate change, notably in the past fifty years. For this aim, a combination of Geographic Information Systems (GIS) coupled to Remote Sensing Techniques and the Analytic Hierarchy Process (AHP) method was applied in this work to identify flood-prone regions within the Gabes catchment. The selected physical parameters are rainfall, slope, slope curvature, elevation, land cover, distance from river, drainage density, and soil type. The relative significance of each parameter was analyzed within a pairwise matrix. Eventually, a regional flood risk map was performed that shows the global susceptibility index classification of various areas within the catchment. Notably, flood risk zones were predominantly concentrated in the coastal areas, accounting for 57.54% of Gabes catchment. The major findings of this study, focusing on potential flood risks in arid regions, offer crucial insights for decision-makers, providing essential data for flood preparedness and mitigation strategies. Nevertheless, it is still required to validate the obtained results with field records and flood inventory maps.

Landslide Susceptibility and Risk Mapping in the Tectonic Ensemble Comprising of Eastern Himalayan Zone, Northeast India and Bhutan using Logistic Regression and Random Forest Techniques

Abstract The mountainous terrain of the East-Northeast tectonic ensemble covers an approximate area of 3,11,420 km2 comprising the Eastern Himalayan zone, Northeast India and Bhutan is prone to mass movements. The increasing trend of landslide occurrences in the last few decades sets loud alarm bells for mapping the landslide hotspot zones in terms of Landslide Susceptibility and Risk. Initially, a landslide inventory of around 9751 landslides has been prepared, of which 6826 (70%) has been randomly picked as training dataset and the remaining 2925 (30%) as the testing dataset. Thereafter, Random Forest (RF) and binary Logistic Regression (LR) based Landslide Susceptibility Zonation (LSZ) have been prepared through twelve predictor layers viz. Slope Angle, Slope Aspect, Slope Curvature, Distance to Drainage, Distance to Lineament, Landform, Surface Geology, Distance to Road, Normalized Difference Vegetation Index (NDVI), Landuse/ Landcover (LULC), Rainfall and Epicentre Proximity. Both the Landslide Susceptibility Index (LSI) maps are divided into five classes, viz. ‘low’, ‘moderate’, ‘high’, ‘very high’ and ’severe’, which are then validated statistically by drawing a comparison with the 30% testing chronological landslide inventory database. The statistical index based accuracy assessment in terms of Area Under the Curve (AUC) exhibits an LR model AUC of 0.776 and RF model AUC of 0.820. However, it has been observed that the RF model strongly correlates with the testing inventory dataset exhibiting that around 46% of the landslide-prone terrain is classified in the ‘high to severe’ zones with 41% inventory landslides occurring in these zones. Integrating the Random Forest (RF) based LSI thematic layer with the socio-economic vulnerability layers like the Number of Households and Population Density have demarcated around 20.78% of the region under ‘very high to severe’ socio-economic risk as convoluted by landslide susceptibility in the terrain. The present findings are expected to be useful in urban development and town planning with appropriate slope management and land-use planning.

Landslide susceptibility assessment along the National Highway-244 from Batote to Doda, J & K, India: A study based on the Frequency Ratio Method

The National Highway-244 is highly susceptible to landslide occurrences, frequently resulting in road blockades and causing significant hardships for the local population. These landslides pose a threat to human lives, property and the environment, leading to substantial losses. In this study, an attempt has been made to carry out landslide susceptibility assessment through frequency ratio method along the National Highway-244 utilizing GIS and statistical computations. It considers eight parameters, which include topographical (slope, slope aspect, slope curvature, hill shade, and relief), anthropogenic (distance to road and distance to river) and geological parameters that mostly influence the occurrence of landslides in the area under investigation. The present study focuses only along National Highway-244 and data has been gathered from field visits and secondary sources. The results of the study inferred that the area under investigation falls into different susceptibility zones, namely very high, high, moderate, low, and very low, covering approximately 15%, 31%, 27%, 19%, and 8%, respectively of the total area. This study reveals that a considerable proportion, around 73%, of the study area falls within very high to moderate susceptibility zones. The conclusions drawn from this study hold significant implications for stakeholders and also provide valuable insights for future planning and infrastructure development, enabling them to make informed decisions. By considering the susceptibility zones identified in this study, stakeholders can implement appropriate measures to mitigate the impact of landslides, ensuring the safety and stability of the region.

Frequency ratio analysis to determine the landslide susceptibility in East Sikkim district of Indian Himalayan region

Landslides are a significant natural disaster causing damage to many mountainous regions worldwide including the Indian Himalayan region. In the East Sikkim district of the Eastern Himalayas, the most used bivariate frequency ratio (FR) model was utilized with high-resolution satellite imagery to understand the susceptibility of the region to landslides. Conditioning factors such as slope aspect, slope angle, slope curvature, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), lithology, and geomorphology were considered in the analysis. LULC is the most crucial factor contributing to landslide susceptibility with a normalized FR value of 14.1. Slope and geomorphology followed closely with values of 12.5 and 11.8 respectively. In contrast, the least important factors were slope aspect and lithology with values of 8.7 and 9.3 respectively. These results can be used to prioritize landslide conditioning factors (LCF) and generate a final landslide susceptibility map (LSM). By adding the values of all LCFs, a landslide susceptibility index was obtained, and the LSM was zoned into high, medium, and low susceptibility classes covering 23.4%, 44.4%, and 32.2% of the study area respectively. The validity of the method used was confirmed using a receiver operating characteristic curve which yielded an accuracy of 78%. The findings highlight the importance of LULC, slope, and geomorphology as critical factors in landslide susceptibility in the East Sikkim district of the Eastern Himalayas.

Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China

Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km2 area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.

Assessment of two methods on zoning wildfire propagation in Itacolomi State Park, Minas Gerais State, Brazil

This study aimed to assess the wild fire propagation risk to wildfires in the Itacolomi State Park, in Minas Gerais State, Brazil, using GIS and to compare the efficiency of the incident solar radiation over the aspect variable. The following variables were used: land cover/use (LCU), slope (SLP), slope curvature (CUR), aspect (ASP) and incident solar radiation (SOL). The weights of each variable were calculated from the ratio between the total area and the burned area of each class in order to generate the fire propagation risk maps. Fire data from 2016 to 2019 were used for validation. When the moderate risk class was considered susceptible, inadequate precision was observed for both methods (ASP and SOL). On the other hand, when the moderate class was considered non-susceptible to fire, the results presented moderate accuracy. Furthermore, the methods using SOL and ASP showed similar results. The results can guide fire mitigation actions on the park.

Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms

Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management.

Spatial prediction of soil erosion risk using knowledge-driven method in Malaysia's Steepland Agriculture Forested Valley.

Soil is a fundamental resource with its value vital to the world's ecosystem. Due to the fact that soil is one of the bases of all terrestrial life, humans cannot survive without it. However, soil erosion has jeopardized soil sustainability and affected the environmental quality, leaving a bad impact if these issues were not tackled at an earlier phase. Many research has been done to predict soil erosion susceptibility areas using different methods. This research aims to classify the contributing factors of soil erosion according to the risk and generate a soil erosion risk prediction map in Cameron Highlands. Thus, this research focuses on a knowledge-driven method that uses Analytical Hierarchy Process (AHP) technique to achieve the objectives. This technique consists of weighing the factors adopted by comparing pairs of factors that control erosion in this area through experts' opinions. 15 factors have been chosen to build the prediction map. Result shows that rainfall erosivity is the main factor contributing to soil erosion in Cameron Highlands which is 0.110, followed by land use (0.095), slope steepness (0.089), soil texture (0.079), NDVI (0.079), TWI (0.072), slope length (0.065), slope aspect (0.064), slope altitude (0.062), SPI (0.061), lithology (0.060), slope curvature (0.054), drainage density (0.049), distance to road (0.029) and distance to stream (0.025). The west part of the study area was exposed to a high risk of soil erosion. This research will give the decision-makers, policymakers and planners insight into minimizing the soil erosion problem and suggest better precautions and solutions to overcome this severe environmental problem in the more advanced phase.

Effects of contour farming and tillage practices on soil erosion processes in a hummocky watershed. A model-based case study highlighting the role of tramline tracks

To reduce water erosion, one of the most severe land degradation processes on arable soils, conservation measures such as contour farming or reduced tillage practices can be applied. The general effectiveness of these measures on reducing soil erosion is well understood, however, little is known about the influence on tramline tracks, which have considerable impact on the transport of sediment and runoff. This study assesses the impact of reduced and conventional tillage and slope-related tramline directions on runoff and soil erosion rates at catchment level. For this purpose, a published and freely available dataset was used consisting of soil loss and runoff data modelled for a single soil erosion event considering courses and structural properties of tramlines (cf. Saggau et al., 2022). The orientation of tramlines against the slope direction was defined as contouring efficiency (Cef) and calculated by an approach that relates slope and tramline directions. Finally, results of the statistical analyses are compared with mapped soil erosion data. Results show that i) high-resolution derivation of Cef, based on tramlines, shows reasonable results in capturing variations in slope curvatures within the fields, ii) increased Cefs can significantly reduce runoff and soil erosion in tramlines and, iii) reduced tillage indicates significantly lower runoff and soil erosion rates in tramlines and cultivated areas. The results reveal that tramlines have strong interrelations with surface runoff, water connectivity and soil erosion. The consideration of tramlines in process-based models should be employed in other model approaches as it improves assessments of soil erosion risk and efficiencies of conservation measures, which are necessary in taking appropriate actions in support of soil and water conservation.

Freely accessible inventory and spatial distribution of large-scale landslides in Xianyang City, Shaanxi Province, China

In this study, we used high-resolution optical satellite images on the Google Earth platform to map large-scale landslides in Xianyang City, Shaanxi Province, China. After mapping, a comprehensive and detailed large-scale landslide inventory that contains 2924 large-scale landslides was obtained. We analyzed the spatial distribution of landslides with seven influencing factors, including elevation, slope angle, aspect, curvature, lithology, distance to a river, and distance to the fault. Landslide Number, Landslide Area, Landslide Number Density (LND), and Landslide Area Percentage (LAP) were selected as indexes for the spatial distribution analysis. The results show that the number and area of landslides in the elevation range of 1000–1200 ​m is the highest. The highest number of landslides was observed in the slope angle of 25°–30°. North-facing slopes are prone to sliding. The area and number of landslides are the largest when the slope curvature ranges from −1.28 to 0. The LND and LAP reach their maxima when the slope curvature is less than −2.56. Areas covered by the Tertiary stratum with weakened fine-grained sandstone and siltstone show the highest LND and LAP values. Regarding distance to a river, the LAP peaks in the range of 300–600 ​m, whereas the LND peaks in an area larger than 2100 ​m. The values of LND and LNP rise as the distance from the faults increases, except for the locations 30 ​km away from active faults. This phenomenon is because active faults in this area pass through the plain areas, while landslides mostly occur in mountainous areas. The cataloging of landslide development in Xianyang City provides a significant scientific foundation for future research on landslides. In addition, the spatial distribution results are useful for landslide hazard prevention decisions and provide valuable references in this area.

Mathematical model of ammonia nitrogen transport from soil to runoff on irregular slopes

The loss of nitrogen on sloping land causes a decline in land productivity and non-point source pollution. Research on nitrogen loss typically occurs on land with a uniform slope. However, irregular slopes are the most widely distributed types of slope in nature. Ignoring the effects of irregular slopes may cause misestimations of nitrogen loss on sloped land. In this study, a mathematical model was established to describe the process of ammonia nitrogen transfer from the surface soil to runoff under different slope shapes and rainfall intensities. The slopes were generalized as parts of circles; a slope curvature parameter was proposed to describe topographic and hydraulic differences. The model was evaluated using nine slope degrees (concave slopes of middle-down: 10, 20, 30, and 40 cm; uniform slope; and convex slopes of middle-up: 10, 20, 30, and 40 cm) and three rainfall intensities (0.125, 0.083, and 0.042 cm min−1). Runoff rates were fitted using the established runoff model based on Horton’s infiltration equation. The ammonia nitrogen transfer from the soil layer to runoff was built based on the diffusion model. The governing equations of the models were solved numerically. The study found that the mean runoff rates of concave slopes were significantly lower than those of convex slopes under rainfall intensities of 0.083 and 0.042 cm min−1. At a rainfall intensity of 0.042 cm min−1, the mean ammonia nitrogen loss rates of concave slopes decreased by 21.70% to 34.17% compared to the uniform slope, while the loss rates of convex slopes increased by 18.49% to 58.97% (p < 0.05). Similar trends were observed at rainfall intensities of 0.083 and 0.125 cm min−1. Overall, the runoff rates, ammonia nitrogen concentration and ammonia nitrogen loss rate tended to decrease with an increasing slope curvature parameter. The simulated results for the runoff rate and ammonia nitrogen transport process were consistent with the observed data, with R2 values ranging from 0.80 to 0.98 for the runoff rate and 0.64 to 0.95 for the ammonia nitrogen concentration. Further correlation analyses were performed to determine the relationships among the slope curvature, rainfall intensity, hydraulic, and nutrient transport parameters. The results showed that these parameters can be described as a combined linear function of the slope curvature parameter and rainfall intensity. Moreover, the sensitivity of the model to various parameters showed that the slope shape and curvature had an important impact on nitrogen transport. This model can be used to improve the theoretical basis for developing nitrogen-loss control methods.

Effects of slope curvature, ridge height and layered roofs on wind pressures on traditional-Chinese-style gable-hip and gable roofs

Curved slope profiles, high ridges and layered roofs are important features of traditional Chinese buildings, and they affect wind pressures and loads on roofs. Wind loadings on traditional Chinese hip roofs have been studied (Shan et al., 2018). However, traditional Chinese gable-hip roofs and gable roofs are as important as hip roofs, and their wind loading characteristics need to be investigated. Wind tunnel tests are performed on linear-slope gable-roof building models, as well as traditional Chinese building models with gable- and gable-hip roofs to investigate and estimate the necessary wind-resistant capacity of cladding tiles. Effects of roof curvature, ridge height and layered roofs on wind loadings on traditional Chinese gable roofs and traditional Chinese gable-hip roofs are investigated. Roof curvature of a gable roof can enlarge the peak area-averaged pressure and increase the resultant lift force. With increase in ridge height of a traditional Chinese gable roof, peak pressures increase while resultant lift forces decrease. Layered roofs decrease peak pressures but increase resultant lift forces on double-layer gable-hip roofs. As a result, wind pressures on traditional Chinese roofs can be significantly influenced by all three unique shape characteristics.

Normal fault rupture propagation in overburdened rock and its impact on the ground deformation profile based on a model experimental approach

Rupture propagation and ground deformation are two major issues encountered during fault propagation. However, few studies have investigated the rupture propagation and ground deformation of a crossing fault field. In this study, three large-scale experimental tests for simulating normal faulting in crossing fault field were conducted to investigate the propagation characteristics and patterns of fault ruptures. In the conducted tests, different factors were considered, such as the fault dip Df, fault width Wf and amount of normal faulting h. The result revealed that the fault rupture pattern was mainly affected by Df, while Wf could affect the initial rupture length and ground tensioned cracks. Three fault rupture patterns can be distinguished under different Df values. In the former two patterns, the main fault rupture trace is in the form of a nonlinear logarithmic spiral line with an outcropping direction of 45°-ψp/2. Although the ground deformation profiles can be fitted roughly by empirical equation, the local deformations at fault fracture zone are not usually predicted well. The Gompertz function can describe the growth of ground settlement in a fault-crossing rock mass field. Therefore, Gompertz was introduced to characterize the growth and geometrical features of ground settlement due to normal faulting. Finally, bell-shaped ground slope curves were identified mathematically according to the first derivation of the Gompertz function. Based on the geometrical parameters, the ground deformation characteristics were furtherly revealed. This research would assist engineers in selecting sites and designing facilities in regions with weak fault fracture zones.

AHP GIS-Based Gully Erosion Susceptibility Modelling in Tropical Volcano Environment

Gully erosion is the most destructive form of erosion and contributes significantly to the accumulation of water sediment in an area. This study aims to map the gully erosion susceptibility in the Kodil watershed, Central Java, Indonesia. The base data used in this study are from Sentinel-2 with a resolution of 10 m and DEMNAS (National Digital Elevation Model of Indonesia) with a resolution of 8 m. The Analytical Hierarchy Process (AHP) was used to assess the weight of each parameter that influences the gully erosion susceptibility. Overall, there were ten parameters used in this study, namely a) elevation (m); b) slope gradient (0); c) slope aspect; d) slope curvature; e) topographic wetness index; f) distance from the river (m); g) distance from the road (m); h) land use; i) soil texture; and j) soil aggregate. An accuracy test used the AUC (Area under Curve). The results of spatial data analysis proved that 855 ha (6.34%) of land in the Kodil DAS has a high susceptibility to gully erosion. Testing with AUC shows that the AUC training value was 0.742, the AUC test was 0.737, and the accumulative AUC value was 0.73, so the model results fall into the good category. The novel aspect of this study is the use of texture and soil aggregate parameters that have never been studied before. The decision to employ soil parameters is inextricably linked to the characteristics of the research site, which has extremely thick soil layers with high clay content. The gully erosion susceptibility model helps plan the development of land resources in its sustainability aspect. Keywords: gully erosion, analytical hierarchy process, susceptibility modeling, tropical volcano environment. https://doi.org/10.55463/issn.1674-2974.50.2.20

Characterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia

Topography plays a significant role in determining bushfire severity over a hilly landscape. However, complex interrelationships between topographic variables and bushfire severity are difficult to quantify using traditional statistical methods. More recently, different Machine Learning (ML) models are becoming popular in characterising complex relationships between different environmental variables. Yet, few studies have specifically evaluated the suitability of ML models in predictive bushfire severity analysis. Hence, the aim of this research is twofold. First, to determine suitable ML models by assessing their performances in bushfire severity predictions using remote sensing data analytics, and second to identify and investigate topographic variables influencing bushfire severity. The results showed that Random Forest (RF) and Gradient Boosting (GB) models had their distinct advantages in predictive modelling of bushfire severity. RF model showed higher precision (86% to 100%) than GB (59% to 72%) while predicting low, moderate, and high severity classes. Whereas GB model demonstrated better recall, i.e., completeness of positive predictions (56% to 75%) than RF (49% to 61%) for those classes. Closer investigations on topographic characteristics showed a varying relationship of severity patterns across different morphological landform classes. Landforms having lower slope curvatures or with unchanging slopes were more prone to severe burning than those landforms with higher slope curvatures. Our results provide insights into how topography influences potential bushfire severity risks and recommends purpose-specific choice of ML models.