Topographic Factors Research Articles

Estimation and Spatial Distribution of Individual Tree Aboveground Biomass in a Chinese Fir Plantation in the Dabieshan Mountains of Western Anhui, China

Understanding aboveground biomass (AGB) and its spatial distribution is key to evaluating the productivity and carbon sink effect of forest ecosystems. In this study, a 123-year-old Chinese fir forest in the Dabieshan Mountains of western Anhui Province was used as the research subject. Using AGB data calculated from field measurements of individual Chinese fir trees (diameter at breast height [DBH] and height) and spectral vegetation indices derived from unmanned aerial vehicle (UAV) remote sensing images, a random forest regression model was developed to predict individual tree AGB. This model was then used to estimate the AGB of individual Chinese fir trees. Combined with digital elevation model (DEM) data, the effects of topographic factors on the spatial distribution of AGB were analyzed. We found that remote sensing spectral vegetation indices obtained by UAVs can be used to predict the AGB of individual Chinese fir trees, with the normalized difference vegetation index (NDVI) and the optimized soil-adjusted vegetation index (OSAVI) being two important predictors. The estimated AGB of individual Chinese fir trees was 339.34 Mg·ha−1 with a coefficient of variation of 23.21%. At the local scale, under the influence of elevation, slope, and aspect, the AGB of individual Chinese fir trees showed a distribution pattern of decreasing from the middle to the northwest and southeast along the northeast-southwest trend. The effect of elevation on AGB was influenced by slope and aspect; AGB on steep slopes was higher than on gentle slopes, and the impact of slope on AGB was influenced by aspect. Additionally, AGB on north-facing slopes was higher than on south-facing slopes. Our results suggest that local environmental factors such as elevation, slope, and aspect should be considered in future Chinese fir plantation management and carbon sink assessments in the Dabieshan Mountains of western Anhui, China.

Assessing habitat suitability for aoudad (Ammotragus lervia) reintroduction in southeastern morocco to promote ecotourism

The objective of this study is to address the complex task of identifying optimal locations for reintroducing Ammotragus lervia in a semi-arid area in the Eastern High Atlas of Morocco, considering three topographical factors. The study assesses the effectiveness of a commonly used machine learning classifier (MLC) in mapping potential areas for introducing these species, which is crucial for promoting and enhancing local biodiversity. To begin with, an extensive inventory of 88 remaining sites where these Barbary sheep still living was conducted, and precise measurements of three topographical parameters were collected at each site. Subsequently, a machine learning algorithm called Bagging was employed to develop a predictive model. The predictive model demonstrated a high level of performance, as evidenced by an area under the curve (AUC) value of 0.929. Bagging effectively identified favorable areas, encompassing around 13.8 % of the study region, which were predominantly located in the western part. These areas were characterized by mountainous terrain, shorter slopes, and higher altitudes. The research findings provide valuable guidance to decision-makers, offering a roadmap to reintroduce these species for enhancing the local biodiversity in the region.

Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years

The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982–2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M-K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982–2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982–2022 trend, and sustainable improvement will dominate in the future; however, 17.69% of vegetation will degrade, that is, NDVI will degrade instead of improvement.. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had > 70% explanatory power (73.5, 79.9, 79.0, and 74.9%, respectively). The explanatory power of edaphic factors was > 30%, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of < 30%. Thus, climatic factors were the dominant factors influencing the spatial variability of NDVI in the study area. The results of the interaction detector analysis showed nonlinear strengthening for any two factors, and the interaction between Rhu and barometric pressure had the highest explanatory power. There were optimal ranges or characteristics of each factor that promoted vegetation growth. This study investigated the differences in the explanatory power of different factors on the NDVI and the optimal range of individual factors to promote vegetation growth, which can provide a basis for the development of vegetation resource management programs.

Modeling of the spatial distribution of species of interest in agriculture for their conservation: case of Punica granatum L.

Abstract Modeling the spatial distribution of species is an important step in biodiversity conservation. The models used can be helpful in predicting the impacts of climate change on the geographical distribution of species and in identifying areas where they are most likely to occur. The purpose of this work was to model the spatial distribution of the pomegranate species (Punica granatum L.) in Morocco according to the principle of maximum entropy (Maxent). This modelling method is widely used in ecology and biogeography because of its ability to work with datasets, and to produce accurate predictions of species distribution. Based on agro-ecological data such as topographical factors and climatic variables and focusing on regions where pomegranate cultivation is significant, these data can be collected at different spatial and temporal scales. They are typically integrated into Geographic Information Systems (GIS) for utilization within the simulation model. The resulting model depicts the potential spatial distribution of pomegranate cultivation throughout Morocco. The model obtained agrees perfectly with the actual distribution of the species in different regions of the country, especially since it is known for its hardiness and its adaptation to variable environmental conditions. Thus, the modeling showed that other geographical areas present favorable conditions for the development of pomegranate cropping. The determination of spatial distribution constitutes a first step to predict possible evolution of the pomegranate cropping according to climate change. The importance of this process for biodiversity conservation lies in several aspects, such as the identification of areas at risk, conservation planning, and the assessment of impacts on ecosystems.

Spatial Analysis of Chronic Kidney Disease of Unknown Aetiology (CKDu) Prevalence in Badulupura Village, Sri Lanka

This study investigated spatial patterns of chronic kidney disease of unknown aetiology (CKDu) patients in Badulupura village. The analysis utilized published locational data for all households, including households affected by CKDu, and all wells used by CKDu-affected patients (CPA wells). The study employed Average Nearest Neighbour (ANN) and Hot Spot Analysis (Getis-Ord Gi*) tools to assess the spatial clustering of CKDu households and CPA wells. The study also explored the impact of topographical factors such as elevation, slope, and distance to watershed boundaries on the spatial distribution of CKDu patients. The ANN analysis revealed a dispersed pattern for all wells, and a random distribution for all households. However, both CKDu households and CPI wells exhibited a clustered pattern, suggesting a location-specific cause for the spatial distribution of the disease. Additionally, overlaying CKDu-affected households on the digital elevation model revealed a concentration of CKDu hot spots in high-elevation areas, while cold spots were observed in low-elevation regions. Furthermore, the t-test was employed to investigate the significance of elevation, slope, and distance to the watershed boundaries in relation to CKDu distribution. The results indicated a significant (p&lt;0.05) prevalence of CKDu in high-elevation areas, while no significant association was found with slope or distance to watershed boundaries. The study revealed the presence of localized CKDu hot spots and cold spots within a small geographical area, highlighting the importance of considering topographical factors and conducting comprehensive hydrogeological and geological studies to gain valuable insights into the spatial distribution of CKDu.

PERTURBATION IN MEANDER BEHAVIOUR OF A SUBTROPICAL RIVER IN INDIA UNDER VARIABLE NATURAL AND ANTHROPOGENIC CONTROLS

Meander morphology is perturbed worldwide by human interventions to a certain extent in the Anthropocene. To this end, the present study aims to investigate the meander behaviour, including its deformations, of the Kopai River, a subtropical river in India in the last 50 years (1970-2020). A total of 394 loops (134 loops each in 1970 and 1995 and 126 loops in 2020) were selected for the four river reaches – the upper, middle-upper, middle-lower, and lower reaches. The meandering behaviour was assessed using Mueller’s sinuosity index (SI), meander form index, meander shape index, and radius/wavelength ratio. The study found a higher meandering tendency in the lower and upper reaches controlled by fluvial hydraulics, and topographic factors. However, the middle reaches were comparatively stable. The meander loops along the middle reaches regular meander is the dominant meander type, however, more than 50% of meander loops for the lower reaches were intense meander types. Positive extensions were the predominant type of meander deformation in all the reaches. However, few negative extensions are found in the middle-upper and lower reaches, while irregular changes and cut-offs were only found in the lower reach. Thus, the upper and lower reaches exhibited more natural controls dotted with regular meander progression. The middle reaches characterized by higher Bouguer anomaly and steep basement gradient led to channel confinement. Besides, profuse sand mining and the brick kiln industries are perturbing the meander beahviour through human channel straightening, dwarfing the meander evolution in the middle reaches.

GIS and remote sensing-based wildlife habitat suitability analysis for mountain nyala (Tragelaphus buxtoni) species mapping at Bale Mountains National Park, Ethiopia

Ethiopia's Bale Mountains National Park protects the continent's largest remaining alpine environments. This park was first suggested in 1973 to safeguard its great biodiversity, including the endangered Mountain Nyala (Tragelaphus buxtoni) and Red Foxes. Despite these conservation measures, the lack of infrastructure and the enormous area projected have resulted in significant wildlife habitat fragmentation. The purpose of this study is to analyse the habitat appropriateness of the Mountain Nyala in Bale Mountains National Park using GIS and remote sensing technologies in order to inform conservation efforts and assist park managers in making decisions. To identify potential habitats for the Mountain Nyala, the study employed GIS spatial analyst techniques such as the Digital Elevation Model (DEM) and Landsat 9 (OLI/TIRS) data, as well as key environmental factors such as vegetation types, soil types, topographic factors (elevation and slope), climate factors (temperature), and proximity factors (distance to settlements, roads, and rivers). The Weights of these factors was calculated using IDRISI32 Multi-Criteria Evaluation (MCE) with the pair-wise comparison method. These weighted factor maps were then integrated through weighted overlay analysis to model wildlife habitat suitability. The analysis revealed five classes of habitat suitability; of the total land area studied, 1326.7 km2 (60%) was deemed suitable for the Mountain Nyala while 881.3 km2 (40%) was unsuitable. Specifically, 327.4 km2 (15%) was classified as highly suitable, 240.7 km2 (11%) as moderately suitable, 758.6 km2 (34%) as marginally suitable, 352.4 km2 (16%) as currently not suitable, and 528.9 km2 (24%) as permanently not suitable. The majority of suitable habitats are concentrated in the northern part of the park, along the western border, and in the Harrena forest area. This study provides vital insights into habitat suitability that are crucial for the conservation of the Mountain Nyala and the overall management of the park.

Spatiotemporal characteristics and socio-ecological drivers of ecosystem service interactions in the Dongting Lake Ecological Economic Zone

Uncovering the spatiotemporal characteristics of ecosystem services and their complex interactions is a prerequisite for managing multiple simultaneously. However, the prior research has predominantly overlooked cultural service objects and neglected the indirect socio-ecological impact pathways on ecosystem services. To delve into the dynamics and developmental trends of ecosystem alterations prior to and following the inception of an Eco-Economic Zone, this study quantitatively assessed four pivotal ecosystem services within the Dongting Lake Ecological Economic Zone spanning from 2000 to 2020. These services encompassed:crop production(CP), carbon sequestration(CS), habitat quality(HQ), and forest recreation (RS). The trade-offs and synergies between them were explored, and the driving mechanism of ecosystem services was explained based on the partial least squares-structural equation model (PLS-SEM). The results revealed that: (1) during the study timeframe, the crop production witnessed a pronounced upward trajectory, whereas the carbon sequestration, habitat quality and forest recreation remained relatively stable. Notably, the low habitat quality area (HQ ≤ 0.3) has been expanding, with a growth rate of 29.4 %; (2) the most trade-offs were observed between crop production and other ecosystem services, among which the trade-off effect between crop production and habitat quality was the strongest, with the highest average proportion of trade-off area, reaching 57.8 %. Among the six ecosystem service pairs, two are in a state of deterioration: the crop production and forest recreation, the carbon sequestration and habitat quality; (3)topographic factors were identified as the primary drivers of crop production, carbon sequestration, and habitat quality, whereas forest recreation was chiefly influenced by landscape configuration factors. Land use intensity and landscape configuration played crucial intermediary roles, with socio-economic elements adversely impacting all four ecosystem services directly, yet significantly contributed to the crop production and carbon sequestration through Suppression Effects. The research endorses the combined utilization of lake area land to improve human welfare, based on the principle of protecting the primary services of various functional sub-regions.

The influence of climate change on the potential distribution of Ageratum conyzoides in China.

Ageratum conyzoides L., an invasive plant originating from South America, is characterized by rapid growth and strong ecological adaptability, posing a threat to China's ecosystems, agricultural industry, and biodiversity. In this study, we optimized the MaxEnt model using the ENMeval package and constructed an ensemble model using the Biomod2 package based on global geospatial distribution data of A. conyzoides and considering climate, soil, and topography factors. We simulated the potential suitable distribution of A. conyzoides in China at present and in the future (2041-2060, 2061-2080). Through multivariate environment similarity surface and most dissimilar variable analysis, we identified the main environmental variables influencing the distribution of A. conyzoides. Additionally, niche analysis elucidated temporal and spatial variations in A. conyzoides' climate niche. Our results demonstrate that the ensemble model, constructed from the top seven single models, outperforms the individual models in predicting the suitable habitat of A. conyzoides. The ensemble model achieved the true skill statistic (TSS) of 0.833 and the area under the subject curve (AUC) of 0.971, indicative of outstanding predictive performance. Presently, the suitable habitat of A. conyzoides in China primarily exists in the region between 18° and 28° N, covering approximately 1.47 million km2. The temperature annual range, precipitation of the wettest month, and mean temperature of the coldest quarter were identified as the primary environmental variables influencing its distribution, while soil and elevation variables had minor roles. Under future climate conditions, the suitable habitat of A. conyzoides is expected to expand northeastward, with the centroid of its habitat shifting northward as the climate warms. The migration speed of A. conyzoides is projected to increase with the degree of warming. Furthermore, the climate niche of A. conyzoides will undergo certain changes and may face both niche expansion and a decrease in niche overlap under different climate conditions.

Methodology for selecting potential CO2 sinks in Macaronesia: The case of Gran Canaria

Carbon dioxide (CO2) accounts for 80% of the greenhouse gas emissions in the atmosphere. One of the several ways to mitigate CO2 emissions is through afforestation, which prevents catastrophic environmental consequences. The mean average emission per tourist in the Canary Islands on their way to the islands is 0.48 Tn. Like most urban cities, the island of Gran Canaria faces the problem of CO2 emissions due to anthropogenic and human activities. Vegetation coverage significantly influences the distribution of temperature. The correlation between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) of Gran Canaria, using satellite images from Landsat 8 and Sentinel-2, revealed a strong inverse relationship within all land use types, with an R2 value of 0.39. Land suitability analysis is a prerequisite for optimum utilization of available land resources. This study developed a suitability map for afforestation based on land use land cover, topographic, meteorologic, and socio-economic factors. Eight factors, including distance from settlements, land use, distance from the road, distance from water, elevation, slope, precipitation, and temperature, were employed according to previous studies, expert consultation, and land suitability mapping experience. After the criteria decision and data acquisition, maps of each criterion were created and transformed using the Suitability Modeler of ArcGIS Pro. The current study results show that 87% of the total area is suitable for afforestation and reforestation projects in Gran Canaria. Instead of using reactive methods to lessen the effects, the study recommends a proactive approach to climate adaptation through nature-based solutions. The study is part of an umbrella project of the Canary Islands and Spain in general, which considers the contributions of local and institutional stakeholders at different stages of the project. The next stage will be to design a forest afforestation and reforestation project, considering the kind of tree species needed, the methods required to implement it, and the management guidelines about the initial years of installation and growth of the new trees. The most crucial technical choice is which forest species to choose, as it will determine the success of the reforestation effort. The new revegetated space's ability to sequester carbon dioxide will primarily rely on the productivity of the land used for forest reforestation, the species chosen, and the introduced planting density.

Regionalization, Decomposition, and Information Mining for Precipitation: A Novel Framework Based on Multivariate SOM Clustering and Multiscale Features

Abstract Precipitation regionalization, serving as a foundation for extrapolating information from gauged to ungauged sites, contributes to a comprehensive understanding of the spatial distribution of precipitation. However, existing studies have focused mainly on precipitation, neglecting the influence of climate background and meteorological circulation. Moreover, there is a lack of specialized analysis of regionalization results. This study proposes a novel approach to precipitation regionalization that considers covariates in the circulation field and periodic features. The method utilizes Barnett–Preisendorfer canonical correlation analysis coupled with principal component analysis (BPCCA) to select covariates and multivariate self-organizing map (SOM) clustering for preliminary regionalization. Wavelet decomposition is further used for regional feature analysis. The methodology is empirically applied to analyze summer precipitation in Shanxi Province, identifying homogeneous regions characterized by diverse spatiotemporal distributions. The results successfully identified 12 distinct regions of precipitation, effectively capturing the influence of topographic and atmospheric factors. According to the periodic and trend characteristics of each region at different time scales, we merged the division results on the three frequencies and six regions were ultimately differentiated. Specifically, a decreasing trend was observed in the southern and southeastern parts of Shanxi, as well as in the western part of the Lüliang Mountains. They had a significant 4-yr period mode, and the decreasing trend was more significant in the southern region. In contrast, northern Shanxi showed no trend and a significant 8-yr period mode. This proposed method presents an effective strategy for enhancing precipitation regionalization and extracting valuable information from the circulation field and multiple frequencies.

Characteristics of forest understory herbaceous vegetation and its influencing factors in biodiversity hotspots in China

Forest ecosystems, especially within biodiversity hotspots, heavily rely on the often-overlooked understory herbaceous communities (USHC) for their structural integrity and ecological functions. Because of their modest stature, these communities have garnered insufficient attention from researchers. Therefore, in this study attention is focused on USHC, which attempts to fill this research gap. To achieve this, we established 105 sample plots in 2022 within forest ecosystems situated in the biodiversity hotspot of southwest China. Our investigation unveiled a rich biodiversity tapestry, identifying 424 plant species from 108 families and 284 genera. Notable were the 217 herbaceous species identified in the understory layer, representing 67 families and 163 genera. These understory herbaceous species accounted for 51.2 % of the overall species count. Through a detailed analysis, the sample plots were categorized into eight distinct communities using a two-way indicator species analysis. These communities exhibited diverse variations in species composition, diversity, biomass, and nutrient characteristics. Notably, community I (Eucalyptus robusta Smith + Rubus corchorifolius L. f. + Alternanthera philoxeroides (Mart.) Griseb.), V (Camptotheca acuminata Decne + Rubus parkeri Hance + Iris tectorum Maxim), VIII (Platycladus orientalis (L.) Franco + Viburnum dilatatum Thunb. + Cyclosorus interruptus (Willd.) H. Ito) stood out for their exceptional species diversity and productivity. Moreover, correlation analyses highlighted the substantial impact of topographic and soil factors on USHC. Altitude, soil water content, and total carbon content emerged as the primary determinants of species diversity. Biomass demonstrated close associations with total soil carbon and nitrogen. Furthermore, the nutrient characteristics displayed significant correlations with soil nutrient contents. These findings underscore the critical importance of prioritizing USHC attention in forest conservation and management strategies within biodiversity hotspots. The implications strongly advocate for the inclusion of these communities in planning and decision-making processes, emphasizing their pivotal role in maintaining structural and functional diversity in ecosystems.

Reciprocal regulation between rural settlement expansion and human-elephant conflict in China's wild elephant range

Human-wildlife conflict (HWC) and its socioeconomic impacts are a pressing global issue. Accurately quantifying HWCs and their interaction with residential development is crucial for rural revitalization and biodiversity conservation efforts. This study investigates the interplay between rural residential expansion (RRE) with human-elephant conflict (HEC) in southern Yunnan Province using high-accuracy yearly land use/land cover data and Asian elephant accident data. A piecewise regression along with several metrics, including expansion intensity and rate of rural residential land, and residential density, were employed to analyze the spatial-temporal change characteristics of RRE. Then, a geographical detector and a bivariate spatial autocorrelation model were used to reveal the driving mechanisms of RRE, with particular emphasis on the spatial relations between RRE and HECs. The results indicate that HECs had a significant negative impact on RRE, exhibiting higher expansion intensity and rate of rural residential land in non-HEC areas than in HEC areas. High spatiotemporal consistency between accelerated RRE and intensified HECs occurred from 2010 onwards, which aligns with the year when the trend of settlement area expansion changed. RRE activities and ensuing land use conversions led to increased occurrences of HECs, which negatively affected the RRE. Compared to HECs, topography and locational factors exhibited a secondary effect on RRE activities. The findings underscore reciprocal feedback mechanisms between RRE and HECs and the elevated risk of adverse interactions between humans and elephants within the range of China's wild elephants, providing theoretical support for coordinating conservation initiatives for Asian elephants with rural revitalization in the border areas of Southwest China.

Variation Patterns and Affecting Factors of Plant Alpha Diversity, Beta Diversity and Its Components in Restoration Grasslands on Loess Plateau

ABSTRACTUnderstanding spatiotemporal variation in diversity and identifying key external affecting factors are essential for biodiversity conservation. However, community assembly and species diversity in natural grassland (NG) restoration on the Loess Plateau remain unclear. In this study, we examined α diversity (species richness), β diversity (βtotal), and its components (βrepl and βrich) across 89 grassland communities, which were categorized into five restoration stages: recent grassland (RG), early restoration (ER), middle restoration (MR), later restoration (LR), and NG. Linear mixed models (LMM) were used to analyze the effects of topographic, soil, and landscape factors on diversity. Results showed that α diversity followed an increasing‐decreasing trend across restoration stages (7–21 species m−2), peaking at the MR stage. In contrast, β diversity and its two components declined with duration, with βtotal and βrepl being notably higher in ER than in LR and NG. Differences in βtotal primarily resulted from species replacement (βrepl, 61%–70%), with a smaller contribution from species gain and loss (βrich, 30%–39%). The LMM also revealed that restoration stage was the most important factor affecting plant diversity, explaining 59.3%, 68.4%, 53.5%, and 58.8% of richness, βtotal, βrepl, and βrich, respectively. In comparison, landscape had weaker effects on diversity (17.2%, 24.2%, 46.5%, 39.8%), while topography and soil factors had the least effects. In summary, deterministic processes (restoration stage) dominate natural restoration, but substantial differences persist between restoration grassland and NG. This study provides valuable insights for assessing restoration progress, prioritizing conservation areas, and informing future grassland management in semi‐arid and fragmented regions.

Spatial distribution and topographic gradient effects of habitat quality in the Chang-Zhu-Tan Urban Agglomeration, China

This study aimed to analyze spatio-temporal changes in habitat quality in Chang-Zhu-Tan Urban Agglomeration during the 2000–2020 period and explore its topographic gradient effects. Using land use data from this period, the InVEST model was employed to assess the spatio-temporal variations in habitat quality. The bivariate spatial autocorrelation model was used to analyze the spatial correlation characteristics between habitat quality and various topographical factors. Additionally, terrain factor analysis was utilized to study the terrain gradient effects on habitat quality in the study area. The results reveal that: (1) The primary land use changes in the study area from 2000 to 2020 predominantly involve substantial arable land and forest conversions into urban development. (2) The average habitat quality indices for 2000, 2010, and 2020 in the Chang-Zhu-Tan Urban Agglomeration stand at 0.651, 0.622, and 0.606, respectively, indicating a consistent declining trend in habitat quality. The distribution of habitat quality grades demonstrates a spatial pattern of “lower in the central surroundings, higher in the surroundings.” (3) The Chang-Zhu-Tan Urban Agglomeration shows significant positive correlations between habitat quality and topographical gradients. Spatial aggregation tendencies between habitat quality and topographical gradients primarily exhibit “high-high” and “low-low” clustering. (4) The habitat quality of the Chang-Zhu-Tan urban agglomeration exhibits a significant topographic gradient effect, primarily characterized by an increase in habitat quality with the rise of the topographic gradient. The study outcomes contribute to unveiling the spatiotemporal variations in habitat quality within the Chang-Zhu-Tan Urban Agglomeration. Moreover, leveraging different habitat types’ distinctive terrain distribution characteristics, it proposes targeted habitat conservation measures, thereby offering theoretical support for biodiversity conservation and territorial spatial planning in the study area.

Anthropogenic impacts drive habitat suitability in South Asian bats

AbstractDespite their diversity and importance as ecological indicators and ecosystem service providers, the macroecology of bats in South Asia is poorly understood, and until recently studies on the ecological niches of these species have been rare. This study analyses the ecogeographic predictors of habitat suitability in South Asian bats by conducting ensemble ecological niche modelling using four algorithms (random forests, artificial neural networks, multivariate adaptive regression splines, and maximum entropy) to define suitability envelopes for 48 selected bat species, based on topographic, hydrographic, land-use, land-cover, and other anthropogenic impact factors. Anthropogenic impact variables showed high importance with Median Night-time Light being the biggest driver of habitat suitability for most of the study species with generally lower suitability of brighter areas. Projected suitable areas for individual species covered between 6.28% and 22.98% of the study area. Regions such as the Thar desert of northwestern India were consistently identified to have low suitability. The Western Ghats in India, the Himalayas in Bhutan, northern India, and Nepal, and Sri Lanka were identified as suitability hotspots for more than half the studied species overlapping with human-impacted habitats. This study offers insight into the impacts of anthropogenic pressure on the macroecology of bats in a megadiverse region and stresses the importance of analysing ecogeographic effects on ecological niches and habitat suitability, which can be vital to inform conservation planning and policymaking in the future.

Unsupervised machine learning with different sampling strategies and topographic factors for distinguishing between landslide source and runout areas to improve landslide inventory production

This study derived 12 topographical and hydrological factors related to landslides from a 10-m digital elevation model. Three unsupervised machine learning algorithms were employed to distinguish between the features of landslide sources and runout areas for Typhoon Morakot. Two sampling strategies were designed in this study. The first strategy involved creating a data set by pairing landslide sources and runout areas on the basis of the size of polygonal areas recorded in the inventory and then exploring the effectiveness of feature separation with k-means++ as the primary method and EM (expectation maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) as supplementary methods. Because of practical challenges associated with the inability to determine whether remote sensing results correspond to landslide sources or runout areas, the first sampling strategy was used to test the feasibility of the adopted algorithms. In the second sampling strategy, the effectiveness of feature separation was tested by dividing polygon samples into several intervals on the basis of their sizes, thereby verifying the practicability of the adopted algorithms in real-world operations. This study verified that combining unsupervised machine learning algorithms with topographic factors facilitates the effective separation of landslide sources and runout areas, thereby enhancing the quality of landslide inventories and reducing the cost of landslide inventory creation. Moreover, it indicated that meter-scale topographic data yield classification results similar to those obtained from manually created landslide inventories based on centimeter-scale stereo aerial photos. The proposed method effectively balances the cost and quality of landslide inventories.

Unveiling spatial pattern in slope units-based landslides using a data-driven GAMM and InSAR techniques

ABSTRACT Spatial slope deformation pattern is crucial for landslide early warning and management. Quantitative modelling of the effects of multiple driving factors on the landslide spatial pattern has not yet been fully explored. To address this, this study utilizes data-driven models and Interferometric Synthetic Aperture Radar (InSAR) techniques to unveil the spatial pattern of landslides based on SUs (slope units). Specifically, we propose a methodology that establishes a correlation between InSAR deformation and topographic/meteorological factors by constructing a Generalized Additive Mixed Model (GAMM). Using this method, we analysed ALOS-2 ascending SAR images from April 2020 to September 2021 and successfully obtained cumulative minimum deformation during the period. Through the GAMM, we linked the observed cumulative minimum deformation in each SU to various factors, including topographic and meteorological factors. In terms of accuracy, there is an excellent agreement between the observed and fitted deformation, with a Pearson Correlation Coefficient (PCC) of 0.83. In terms of interpretability, our method can well explain the contribution of each factor to landslide deformation. We conclude that using the data-driven GAMM and InSAR techniques can effectively reveal the spatial pattern of landslides based on SUs. Furthermore, based on the method proposed in this study, it is possible to predict landslide deformation in the future.

Prediction of Forest-Fire Occurrence in Eastern China Utilizing Deep Learning and Spatial Analysis

Forest fires are a major natural calamity that inflict substantial harm on forest resources and the socio-economic landscape. The eastern region of China is particularly susceptible to frequent forest fires, characterized by high population density and vibrant economic activities. Precise forecasting in this area is essential for devising effective prevention strategies. This research utilizes a blend of kernel density analysis, autocorrelation analysis, and the standard deviation ellipse method, augmented by geographic information systems (GISs) and deep-learning techniques, to develop an accurate prediction system for forest-fire occurrences. The deep-learning model incorporates data on meteorological conditions, topography, vegetation, infrastructure, and socio-cultural factors to produce monthly forecasts and assessments. This approach enables the identification of spatial patterns and temporal trends in fire occurrences, enhancing both the precision and breadth of the predictions. The results show that global and local autocorrelation analyses reveal high-incidence areas mainly concentrated in Guangdong, Fujian, and Zhejiang provinces, with cities like Jiangmen exhibiting distinct concentration characteristics and a varied spatial distribution of fire occurrences. Kernel density analysis further pinpoints high-density fire zones primarily in Meizhou, Qingyuan, and Jiangmen in Guangdong Province, and Dongfang City in Hainan Province. Standard deviation ellipse and centroid shift analysis indicate a significant northward shift in the fire-occurrence centroid over the past 20 years, with an expanding spatial distribution range, decreasing flattening, and relatively stable fire-occurrence direction. The model performs effectively on the validation set, achieving an accuracy of 80.6%, an F1 score of 81.6%, and an AUC of 88.2%, demonstrating its practical applicability. Moreover, monthly fire zoning analysis reveals that high-incidence areas in spring and winter are mainly concentrated in Guangdong, Fujian, Zhejiang, and Hainan, while autumn shows widespread medium-incidence areas, and summer presents lower fire occurrences in most regions. These findings illustrate the influence of seasonal climate variations on fire occurrences and highlight the necessity for enhanced fire monitoring and prevention measures tailored to different seasons.

Susceptibility assessment of glacier-related debris flow on the southeastern Tibetan Plateau using different hybrid machine learning models

The southeastern Tibetan Plateau (SETP) is a construction area of several key infrastructure projects in China, such as the Sichuan-Tibet Railway and hydropower developments, which has historically faced the threat of glacier-related debris flows. However, a robust assessment of such debris flow susceptibility is a challenge due to the complex and variable climate, terrain and glacial environment. In this study, we used the hybrid models that combine statistical techniques (certainty factors, CF) with machine learning methods (logistic regression, LR; random forest, RF; extreme gradient boosting, XGBoost) to more accurately identify debris flow susceptible (DFS) areas. Topography, geology, and hydrological factors including glaciers and snow cover were used in these models to assess the DFS. Results show that 21 % to 42 % of the study area is very high susceptible to debris flows, particularly from Ranwu to Bomi and around Namcha Barwa. The hybrid models effectively enhance the accuracy of the DFS assessments. The CF-RF model showed the greatest improvement, with an 8.4 % increase in accuracy compared to the single model, the DFS spatial distribution of which aligns closely with field survey results. The glacial area ratio and annual snowmelt positively impact DFS accuracy, ranking 2nd and 9th in the factor importance, respectively. The results of this research could provide valuable assistance and guidance in mitigating glacier-related debris flow hazards.