Topographic Index Research Articles

Utilizing GIS, remote sensing, and AHP-multi-criteria decision analysis for optimal landfill site selection in Kenitra Province, Morocco: a step towards sustainable development goals

The aim of this study is to identify suitable landfill sites in Kenitra Province, Morocco, using ArcGIS 10.8, Remote Sensing, and the Analytic Hierarchy Process (AHP) to enhance site selection and improve environmental and public health outcomes. Additionally, the study seeks to enhance decision-making through multicriteria analysis, align landfill site selection with Sustainable Development Goals (SDGs) to promote long-term sustainability, and provide a model that can be replicated in other Moroccan regions or countries facing similar waste management challenges. Ten criteria were considered in selecting new landfill sites, including distance from surface water, soil permeability, land use land cover (LULC), topographic wetness index, distance from the shoreline, slope, aspect, proximity to urban areas, transportation networks, and villages. Maps for each criterion were generated, and pairwise comparisons were used to assign weights to these factors. A Weighted Overlay Analysis (WOA) was then applied to create a landfill suitability map, classifying areas into high, moderate, low, and unsuitable categories. The results reveal a limited availability of highly suitable landfill areas within the province. To address this, the study proposes two strategically located waste management sites, A and B, to mitigate land constraints. Establishing these sites as centralized landfills for multiple municipalities aligns with Sustainable Development Goals (SDGs) 3, 6, 14, 15, and 17, promoting public health, preventing water pollution, protecting ecosystems, and fostering partnerships for sustainable waste management. This comprehensive approach emphasizes the need for collaboration in achieving long-term sustainability and environmental conservation.

Flood Risk Assessment and Zoning for Niamey and Lokoja Metropolises in Niger and Nigeria

With the increasing frequency of floods in recent decades, particularly in West Africa, many regions have faced unusual and recurrent flooding events. Communities in flood-prone areas experience heightened insecurity, loss of property, and, in some cases, serious injuries or fatalities. Consequently, flood risk assessment and mitigation have become essential. This comparative study between Niamey and Lokoja employs Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to delineate flood susceptibility, vulnerability, and risk zones. The study utilized a comprehensive range of thematic layers, with weight percentages assigned to each parameter as follows: 29% for elevation, 24% for slope, 15% for the Topographic Wetness Index (TWI), 9% for drainage density, 9% for distance from rivers, 4% for both precipitation and the Normalized Difference Water Index (NDWI), and 2% each for the Normalized Difference Vegetation Index (NDVI) and soil type. To validate these weightings, a consistency ratio was calculated, ensuring it remained below 10%. The findings reveal that 32% of the Niamey study area is at risk of flooding, compared to approximately 15% in Lokoja. The results highlight a very high flood potential, particularly in areas near the Niger River, with this potential decreasing as elevation increases. Given the current prevalence of extreme weather events in West Africa, it is crucial to employ effective tools to mitigate their adverse impacts. This research will assist decision-makers in quantifying the spatial vulnerability of flood-prone areas and developing effective flood risk assessment and mitigation strategies in the region.

Dynamic simulation of landscape ecological risk in Gansu’s farming-pastoral ecotone based on topographic gradients

The farming-pastoral ecotone in Gansu Province, a crucial green barrier in northwestern China, faces a conflict between economic development and environmental fragility. Scientifically assessing the landscape ecological risks in this region is essential for developing an early warning system for ecological security and ensuring the sustainable management of regional ecosystems. This study systematically analyzes the dynamic evolution patterns and clustering characteristics of landscape ecological risk along the topographic gradient in the area, using Land-Use and Land-Cover Change (LUCC) data from five periods (each period representing 10 years) between 1980 and 2020, as well as Digital Elevation Model (DEM) data. Methods such as CA-Markov and the Topographic Position Index (TPI) were employed to assess these risks. Additionally, the study simulates the spatial distribution patterns of ecological risks in future years. The results reveal that from 1980 to 2020, the area of built-up land in the farming-pastoral ecotone of Gansu Province increased by approximately 82%, with roughly 21% of the expansion occurring at the expense of grasslands and 53% from arable land. Ecological risk in the study area exhibits a spatial distribution pattern characterized by lower risk in the southwest and higher risk in the northeast. Overall, ecological risk initially increased and then decreased. The area of high-risk zones has steadily decreased by approximately 50% since 2010, primarily in regions with low topographic gradients and high human activity. In other areas, high-risk levels are directly proportional to the topographic gradient, showing a high degree of spatial clustering. Consequently, land-use planning should be tailored to local conditions, and a long-term monitoring mechanism should be established. This study provides novel insights into the spatiotemporal changes in landscape ecological risk in ecologically vulnerable areas, focusing on topographic gradients.

Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method. The results revealed that the top four models, including both single and ensemble models, demonstrated high accuracy in the tests. The flash flood susceptibility map generated by the best-performing eXtreme Randomized Trees (XRT) model showed that 8.92%, 12.95%, 15.42%, 31.34%, and 31.37% of the study area exhibited very high, high, moderate, low, and very low flash flood susceptibility, respectively, with approximately 74.9% of the historical flash floods occurring in areas classified as moderate to very high susceptibility. The SHAP plot identified topographic factors as the primary drivers of flash floods, with the importance analysis ranking the most influential factors in such descending order as DEM, topographic wetness index, topographic position index, normalized difference vegetation index, and average multi-year precipitation. This study demonstrates the benefits of interpretable machine learning, which can provide guidance for flash flood mitigation.

Analysis of Uneven Settlement of Long-Span Bridge Foundations Based on SBAS-InSAR

Bridge foundation settlement monitoring is crucial for infrastructure safety management, as uneven settlement can lead to stress redistribution, structural damage, and potentially catastrophic collapse. While traditional contact sensors provide reliable measurements, their deployment is labor-intensive and costly, especially for long-span bridges. Current remote sensing methods have not been thoroughly evaluated for their capability to detect and analyze complex foundation settlement patterns in challenging environments with multiple influencing factors. Here, we applied Small Baseline Subsets Synthetic Aperture Radar Interferometry (SBAS-InSAR) technology to monitor foundation settlement of a long-span bridge. Our analysis revealed distinct deformation patterns: uplift in the north bank approach bridge foundation and the left-side main bridge foundation (maximum rate: 36.97 mm/year), concurrent with subsidence in the right-side main bridge foundation and south bank approach bridge foundation (maximum rate: 35.59 mm/year). We then investigated the relationship between these settlement patterns and various environmental factors, including geological conditions, Sediment Transport Index (STI), Topographic Wetness Index (TWI), precipitation, and temperature. The observed settlement patterns were attributed to the combined effects of stratigraphic heterogeneity, dynamic hydrological conditions, and seasonal climate variations. These findings demonstrate that SBAS-InSAR technology can effectively capture complex bridge foundation deformation processes, offering a cost-effective alternative to traditional monitoring methods. This advancement in bridge monitoring technology could enable more widespread and frequent assessment of bridge foundation stability, ultimately improving infrastructure safety management.

Flood risk in mountainous settlements: A new framework based on an interpretable NSGA-II-GB from a point-area duality perspective.

Global climate change has significantly increased the frequency and intensity of extreme precipitation events, thereby heightening flood risks for mountainous settlements. However, due to geographical and socio-economic constraints in these regions, flood-related sample data are generally scarce. This study introduces a Mean Filter (MF) grounded in the point-area duality perspective, combined with a feature selection approach utilizing multi-objective optimization algorithms. Three such algorithms (NSGA-II, SPEA2, and NSPSO) were applied to generate the Pareto front of flood susceptibility features, followed by a comprehensive evaluation using the CRITIC-TOPSIS method. The results show that NSGA-II performed best in terms of Hypervolume, Spacing, Generational Distance, and the number of Pareto solutions. The selected feature subsets demonstrated improvements across multiple model validations, with the GB model achieving the highest overall performance and stability. Notably, half of the retained features are related to buffer zone and watershed characteristics, and the SHAP analysis indicates that the mean Topographic Wetness Index within the 50-m buffer zone is the main contributor to flood risk. This finding validates the effectiveness of the MF strategy based on the point-area duality perspective under limited sample conditions, offering a new approach for flood studies with sparse data in mountainous regions.

Environmental DNA Reveals Habitat Variables Driving Platypus (Ornithorhynchus anatinus) Distribution Across an Urbanised Landscape.

Freshwater waterways, and species that depend on them, are threatened by urbanisation and the consequences of the urban stream syndrome. In south-east Queensland, Australia, little is known about the impacts of the urban stream syndrome on the platypus (Ornithorhynchus anatinus), meaning that populations cannot be adequately managed by conservation practitioners. The aim of this study was to determine how habitat and environmental variables, related to the urban stream syndrome, influenced platypus distribution across this region. We used environmental DNA sampling over a 5-year period (2016-2020) to determine how platypus occurrence (presence or absence) was affected by habitat and the environment. Five scenarios based on key characteristics of the urban stream syndrome were modelled using binomial generalised linear models. The highest-ranked model of platypus occurrence included positive effects of topographic wetness index and coarse organic matter. These findings reflect important waterway connectivity and food resources for platypus, highlighting relationships between platypus, their environment and their tolerance to urban stream threats. For example, platypuses are unlikely to occur in streams where water availability is reduced, and movement will be limited in shallow, dry streams. Therefore, waterway management should focus on water availability and connectivity, especially during drought. Our study can be used to guide management plans for the persistence of platypus and other freshwater species.

Two-tier classification framework for mapping grassland types using multisource earth observation data

ABSTRACT The dynamic changes in grassland types are crucial for conserving grassland biodiversity, conducting comprehensive health assessments, and gaining insights into ecosystem evolution. However, accurately mapping grassland types remains an ongoing challenge, especially over large areas. In response, we developed a hierarchical classification framework using random forest to tackle this task. This framework was structured into two tiers: land cover (LC) classification and grassland-type mapping, with each tier using specific features tailored to its respective objectives. In this framework, LC samples were automatically generated using existing LC products combined with vegetation indices and phenological features. We fused spectral information, phenological features, and habitat factors such as topographic indices, soil, and climate from multisource earth observation (EO) data to enhance grassland-type mapping performance. This framework successfully generated distribution maps of grassland types on the Qinghai Plateau for the years 1990–2020. Our findings revealed the following: (1) Using the two-tier classification framework and fusing multisource features, an ideal distribution map of grassland types was obtained, with a macro-average F1 score (F1) of 91% and an overall accuracy (OA) of 96%; (2) Compared to a one-shot classification framework, the two-tier classification framework achieved higher accuracy, with F1 and OA increasing by 11% and 7%, respectively; (3) Climate, topographical, soil, and phenological features assisted in distinguishing grassland types with similar spectral characteristics, especially for zonal grasslands. Adding these features increased F1 by 21%, 10%, 7%, and 3%, respectively, while OA increased by 9%, 5%, 5%, and 2%, respectively. Among these features, relative humidity, total precipitation from May to September, geographic coordinates, and elevation had the greatest effect on grassland type differentiation. June and July were the optimal phenological periods for mapping; (4) Over the 30-year period, the grasslands on the Qinghai Plateau showed an expanding trend, with extensive areas of alpine steppes transforming into alpine meadows. The results of this study provide the first elucidation of grassland-type changes across the Qinghai Plateau from 1990 to 2020. Moreover, they underscore the potential of hierarchical classification frameworks and the integration of multisource EO data in mapping grassland-type distribution.

Unravelling wetland connectivity: a comparative analysis of landscape structure and connectivity between natural and unnatural (artificial) wetlands in Van Sub-basin

This study compares natural and artificial wetlands in Türkiye’s Van sub-basin, exploring human impacts on landscape connectivity and species diversity. It examines how habitat fragmentation affects species isolation and long-term viability, involving 14 natural and 9 artificial wetlands to improve conservation strategies. A four-stage methodology was used to analyze wetland connectivity. First, focal nodes and resistance surfaces were defined using Corine 2018 data and topographic wetness index (TWI). Second, resistance maps were created. Third, habitat fragmentation was assessed using Fragstats software, analysing metrics like PLAND, and CONNECT. Finally, connectivity was modeled using Circuitscape software. The study revealed that natural wetlands, despite their dominance (PLAND = 89.89% for lakes), did not ensure effective connectivity. Artificial wetlands often served as crucial connectivity points, with a cumulative current value of 0.99. Land cover characteristics significantly impacted connectivity, with agricultural and forest areas promoting better connectivity. The combined analysis showed a slight increase in connectivity value (4.01687) compared to natural wetlands alone (4.01654) but a substantial increase (305%) compared to artificial wetlands alone. Artificial wetlands significantly contribute to landscape connectivity, particularly in areas with heterogeneous agricultural landscapes. Effective conservation should consider both wetland types and focus on enhancing connectivity across landscapes to mitigate habitat fragmentation and support resilient ecosystems. The study highlights the importance of integrated approaches, as the combined natural and artificial wetland network improved overall landscape connectivity by 0.0082% compared to natural wetlands alone.

DETERMINATION OF GROUNDWATER POTENTIAL ZONE USING AHP BASED ON GIS FOR KONYA, TÜRKİYE

Groundwater provides most of the freshwater needs such as drinking, irrigation and industry. As a result of increasing population, urbanization and industrialization, groundwater resources are faced with the risk of decreasing and pollution. In addition, exploited usage of irrigation water from groundwater during agricultural activities causes the formation of sinkholes. For this reason, Konya in Türkiye, having a lot of agricultural activities and common sinkhole formations, was chosen as the study area. This research utilized the analytical hierarchy process (AHP), a type of multi-criteria decision-making analysis, to assess the groundwater potential in Konya, Türkiye. For this purpose, land use, slope, rainfall, topographic wetness index, drainage density, elevation, plan curvature, profile curvature and stream power index thematic maps were created, and their weights were determined with AHP. Groundwater potential map (GWPM) was produced by overlay analysis in GIS environment. The GWPM was validated by comparing it with the groundwater zones (GWZ) map given in the report of “Konya Closed Basin Management Plan” project. As a result of the validation process, the accuracy value was found to be 0.87.

Chlorophyll and topographic patterns demonstrate stress conditions drive the brightness of autumn leaf colour

Abstract Autumn leaf colour brightness is an important cultural ecosystem service. As its spatial patterns and ecophysiological mechanisms remain unclear, we analysed relationships among autumn leaf colour brightness, late summer chlorophyll content, and topographic position in both canopy‐based micro‐scale analysis and site‐based macro‐scale analysis. Multispectral drone observations were made in three Fagus crenata forests at elevations of 300, 600, and 900 m in Niigata Prefecture, Japan. In a drone‐acquired digital surface model of canopy area distribution, we analysed the canopy‐averaged autumn leaf colour brightness, four vegetation indices, topographic position index, and elevation. The macro‐scale pattern showed brighter yellow leaves at lower elevations, suggesting the effect of temperature through productivity or evaporative stress. The micro‐scale pattern showed brighter yellow leaves in canopies that had a low late summer chlorophyll content, or that grew on ridges. The negative correlation between autumn leaf colour and chlorophyll content suggests that low‐chlorophyll trees experience stresses (e.g. irradiance, desiccation, senescence) that induce higher carotenoid content to defend against such stresses. Increased autumn leaf coloration on ridges is consistent with a stress gradient. Although further research is needed to reveal the underlying physiological and ecological mechanisms, autumn leaf colour brightness has different meanings at different scales and thus has potential not only as a cultural ecosystem service but also in forest management through early detection of stress.

Reptile richness and topographic heterogeneity in the 'Uruq Bani Ma'arid Protected Area, southern Saudi Arabia

An important goal of the Kingdom of Saudi Arabia’s Green Initiative is to increase the percentage ofprotected areas. In terms of Saudi Arabian wildlife, reptiles have been identified as an important element for protection,although their status and distributions are not clearly understood. Herein we report the findings from a survey of reptilesconducted in the ’Uruq Bani Ma’arid Protected Area in southern Saudi Arabia. Data were collected using a robust-styledesign and analyzed with multi-species occupancy modelling. Eighteen species were detected, and species occupancyand species richness were positively associated with a topographic heterogeneity index calculated from satellite imagery.Notwithstanding the levels of uncertainty, occupancy by all species and species richness indicated a general proclivity forhigher levels of topographic heterogeneity. In the study area, these associations corresponded to a mixture of rocky andsandy habitats. This is a preliminary study, but the results warrant further investigation of the topographic heterogeneityindex in arid environments to establish any broader generalities. This will be useful, as a relationship between speciesdistribution and easily accessible satellite information can support the identification of possible sites for protection andwill be important for any ongoing management of reptiles within established protected areas.

An evaluation of corneal topographic changes in a large series of patients with vernal keratoconjunctivitis.

To evaluate corneal topographic indices and the prevalence of keratoconus in patients with vernal keratoconjunctivitis (VKC) and to compare these with normal individuals. One-hundred twelve consecutive patients with VKC and 112 age- and sex-matched healthy individuals were included in this prospective study. Demographic characteristics were recorded, and complete ophthalmic examinations were performed for all participants. A Sirius topographer was used to assess all participants. Data regarding keratoconus and corneal topographic indices were compared between the two groups. Sixty-five of the 224 eyes of the 112 patients with VKC were of the tarsal type, 101 were of the limbal type, and 58 were of the mixed type of VKC. Central corneal thickness was significantly lower in the VKC group than in the control group (P<0.004). Symmetry index back, keratoconus vertex front, keratoconus vertex back, Baiocchi Calossi Versaci (BCV) (front and back indices), and the root mean square of higher-order aberrations (RMS/A) (anterior and back indices) values were significantly higher in the VKC group than in the control group (P<0.004). There were no significant differences between the two groups in terms of corneal volume, symmetry index front values, simulation keratometry1, or simulation keratometry2 (P>0.05). Patients with VKC assessed using keratoconus screening indices exhibited more abnormal corneal topographic changes and higher rates of keratoconus compared to the control group. Topographic analysis may be useful for evaluating corneal changes and for the early diagnosis of keratoconus in patients with VKC.

Investigating soil erosion vulnerable zones based on clustered geoinformatics approach: a case study of Tyume River Catchment, Eastern Cape, South Africa

Rigorous field surveys, environmental specificity, and data paucity hamper detailed soil erosion assessments, model selection, ecological monitoring, and prioritization against soil erosion. To address this in a topographically complex environment, the present study presents a novel selection of physiographic factors integrated geospatially with the land use/cover and geology data to prioritize the soil erosion vulnerable areas within a watershed, using Tyume River Catchment, Eastern Cape, South Africa as a case study. A quantitative morphometric analysis involving parameters such as the drainage density, topographic wetness index, terrain ruggedness index, topographic position index, and vector roughness measure was computed using a digital elevation model based on their inference of watershed's morphogenetic response to anthropic factors and pluviometric processes. Based on expert judgment for thematic ranking and weightage, the soil erosion prioritization area map was generated through weighted overlay analysis of the morphometric parameters with land use land cover and surficial lithology themes. The results depicted a catchment-scale soil erosion vulnerability map, classified into very high (40 km2), high (135 km2), medium (209 km2), low (186 km2), and non-vulnerable (113 km2) zones. Using Google Earth image analyses through the coefficient of determination (R2 = 0.563) and Receiver Operating Characteristics Curve (AUC = 0.899), the model corroboration indicated that the soil erosion vulnerability assessment is reliable and highly predictive. The study identified free-range animal operation and hillslope overgrazing, especially in riparian zones, as the environmental practices aggravate the catchment's terrain susceptibility to soil erosion. The assessment showed that some of the selected morphometric parameters could be used to improve the validated soil erosion models in mountainous regions. Due to the high precision of the engaged approach and the identified environmental concerns, the method can be adopted in similar environments.

Hybrid delineation of landforms: Case of Bystrytsia-Pidbuzka drainage basin

Landforms, together with geological substrate and geomorphic processes, are essential for delineation of geoecosystems, which are indispensable for environmental management at landscape and regional levels. Geomorphometry is a geographic information system technology, which affords quantitative land-surface analysis and landform delineation using digital elevation models (DEM). Application of geomorphometry has been fostered by the emergence of free global high-resolution DEMs. However, automated geomorphometric extraction of landforms for flat areas, like wide river valleys, may be problematic owing to insufficient accuracy of the DEM. We selected the Bystrytsia-Pidbuzka drainage basin of 500 km², which has a transitional location between low flysch External Carpathian Mountains and wavy denudation-alluvial plains of the Fore-Carpathian Upland in Lviv Oblast (Ukraine), to test a hybrid methodology of landform delineation – manual and automated geomorphometric. We considered regional landforms as orotectonic units (morphostructures), which are used to characterize ecoregions, and morpholithotopes as the smallest local landforms – mesorelief elements together with surface deposits and current geomorphic processes. FABDEM V1-2 with a resolution of 30*30 m was used as a primary geodataset of elevation data. Ecoregions and morpholithotopes of wide flat valley bottoms were delineated via manual interpretation of the DEM and ancillary data, while the morpholithotopes of the hilly and mountainous interfluves were delineated automatedly using three topographic variables: topographic position index, slope, and flow accumulation. Within the study area, we singled-out six microecoregions and four mesoecoregions: Upper Dnister Depression, Upper Dnister Upland, Marginal Beskydy, and Dnister Beskydy. There are 21 classes of morpholithotopes distinguished belonging to flat valley bottoms with alluvial and lacustrine deposits, narrow valleys and big gullies, as well as lower concave and upper convex slopes formed by colluvial and eluvial-colluvial deposits respectively, divided into four inclination categories, and two lithological groups. Each morpholithotope class is attributed with a drainage status and probable current geomorphic processes. The obtained dataset is oriented on further ecological application. Key words: morpholithotope; ecoregion; geomorphometry; FABDEM; the Carpathian Mountains; the Fore-Carpathian Upland.

Assessment of urban flood susceptibility based on a novel integrated machine learning method.

Flood susceptibility assessment is the premise and foundation to prevent flood disaster events effectively. To accurately assess urban flood susceptibility (UFS), this study first analyzes the advantages and disadvantages of multi-layer perceptron (MLP), and light gradient boosting machine (LightGBM), and designs a new integrated machine learning method by combining logistic regression (LR) method, i.e., LG-MLP-LR. Then, we verify the performance of LG-MLP-LR by taking the flood disaster events in Fuzhou from 2013 to 2016 as example and analyze the contribution of flood conditioning factors by calculating the SHapley Additive exPlanations values. Finally, the assessment results are compared with MLP, LightGBM, XG-MLP-LR, and CB-MLP-LR. The results show that (1) the selected flood conditioning factors can accurately depict the UFS of the study area; (2) compared with MLP, LightGBM, XG-MLP-LR, and CB-MLP-LR, the assessment results by LG-MLP-LR have higher average accuracy (94.950%) and higher average AUC (98.813%); (3) the factors affecting the occurrence and damage degree of flood disaster events in Fuzhou from 2013 to 2016 were elevation, topographic wetness index, maximum one-day rainfall, and stream power index, respectively. This study provides a new idea and method for the effective prevention and control of flood disasters in cities.

Niche based distribution modelling and mapping of Brutian pine in the Gölhisar district

The purpose of this work was to elucidate the fundamental characteristics and application areas of various modelling techniques that are widely employed in current ecological modelling research. Using five distinct distribution modelling techniques, possible species distribution modelling and mapping of the Brutian pine species in the Gölhisar district were conducted. The data was collected from Brutian pine species in 400 sampling plots in the area. The variables used in the models were elevation, slope, aspect, radiation index, heat index, topographic position index and bedrock types. Logistic regression, classification tree, random forest, generalized additive model and maximum entropy were used as the species distribution modelling methods. Receiver Operating Characteristics (ROC) curves were created and the performance of the species distribution models was evaluated with the Area Under the ROC curve (AUC). The statistical analyses revealed that the best models were generalized additive model, random forest, classification tree, maximum entropy and logistic regression, respectively. Elevation and bedrock types had the highest contribution to the Brutian pine distribution models. The outputs of the generalized additive model technique that had the highest AUC value were mapped. Some ecological and statistical differences were found between the models and their reasons were presented. Compared to the methods commonly used in species distribution modelling studies, generalized additive model technique has a specific smoothing function which ensures both fittings between the envirenvironmental changes and explanatory curves and more accurate ecological interpretation of the models obtained.

Response of Topsoil Organic Carbon in the Forests of Northeast China Under Future Climate Scenarios

Soil organic carbon (SOC) serves as a highly sensitive indicator of climate change and plays a crucial role in terrestrial carbon cycles. Evaluating the impact of regional land use changes on SOC stocks is essential for assessing ecological and environmental effects. In this study, we utilized 157 soil samples and 11 environmental variables—including soil properties, topographic factors, and climatic conditions—to develop boosted regression tree (BRT) and random forest (RF) models to estimate topsoil SOC stocks for the year 2015. We used a 10-fold cross-validation approach, along with four validation metrics, to assess model performance. The BRT model demonstrated superior accuracy, with a higher R2 and Lin’s consistency correlation coefficient and a lower mean absolute error and root mean square error compared to the RF model. The key environmental factors influencing SOC stock variability in the BRT model included mean annual temperature, elevation, mean annual precipitation, the topographic wetness index (TWI), and catchment area. Based on this, we employed the space-for-time substitution approach and BRT model to forecast the spatial distribution of soil organic carbon (SOC) stocks in Northeast China’s forested regions under future climate scenarios for the 2050s and 2090s. Our findings indicate that, compared to the 2015 levels, the forecast indicates that SOC stocks will decrease by 122 Tg carbon and 123 Tg carbon under two different future scenarios, SSP245 and SSP585, respectively, by the 2050s. By the 2090s, these figures are expected to decrease further by 127 Tg C and 126 Tg C, respectively. Throughout both future periods, SOC stocks will predominantly be concentrated in the northwest region. This research highlights the necessity of thoroughly considering climatic factors in future studies of regional SOC stock dynamics. Moreover, the high-resolution maps produced in this study offer a scientific foundation for enhancing the implementation of ecological management practices in the forested regions of Northeast China, fostering environmental improvement and bolstering SOC and soil management strategies in response to future climate change.

Contrasting topography-vegetation relationships at natural and human-influenced mountain treelines in the Peruvian Andes

ContextThe tropical Andes have a long history of human land use, which has affected vegetation patterns especially at high elevations. However, reference patterns to quantify these effects are missing due to lacking data from mountain ranges unaffected by humans.ObjectivesWe compared landscape-scale patterns of forest distribution in the largest tropical Andean mountain range without high-elevation human occupancy, the Cordillera de Vilcabamba, and a nearby range with a long land-use history, the Cordillera de San Miguel, in Peru.MethodsWe quantified topographical positions (exposition, slope, wetness index) of 12.5 × 12.5-m2 pixels with and without forest cover in 100-m elevational bands and compared the study regions.ResultsIn the undisturbed range (Vilcabamba), forest consistently occurred on steeper slopes than non-forest, differing little in other topographic metrics except a slight preference for the less sunny South- and West-facing slopes. In the disturbed range (San Miguel), forest showed little preference for any landscape position at elevations below 3500 m, but at higher elevations patterns were very clear: forest was more restricted to valley bottoms, as indicated consistently by higher topographic wetness and lower slope and topographic position indices, and to the less sunny South-facing slopes.ConclusionsLand-use has a strong influence on landscape-level forest distribution in this part of the tropical Andes. Similar effects are likely in other tropical mountains, although no reference areas are available in most of them. The patterns described here can support the use of remote sensing to detect minimally disturbed tropical alpine-treeline ecotones, which should be focal points for conservation and research.

A hybrid approach: remote sensing, analytic hierarchy process and geographic information system for groundwater potential mapping in Dediapada, India.

In recent years, the overexploitation of groundwater accompanied by a significant climatic shift has placed a burden on the world's groundwater supply. This has generated a rising need for potential groundwater zones. In recent year the ever-increasing demand for potable water across the world for various groundwater exploration investigation. The present study explores the utility of remote sensing (RS) and geographic information system (GIS) to identify potential water zones in the dry deciduous forest area of Dediapada Taluka, Narmada District, Gujarat, India. Twelve thematic layers of different environmental variables including geology, geomorphology, land use/land cover (LULC), drainage density, lineament density, rainfall, soil, slope, roughness, topographic wetness index (TWI), topographic position index (TPI), and curvature were integrated. GIS mode was subjected to weight analysis using Saaty's 9-point scale. The result was normalized using analytic hierarchy process (AHP), and five distinct groundwater potential zones (GWPZs) were identified namely very high, high, moderate, low, and very low. The area occupied by each category was 6.52%, 3.1%, 60.1%, 14.32%, and 15.99%. The output aids in validation assessment with the existing well data. The groundwater potential zones maps generated using AHP technique can prove beneficial for designing strategies for water management for the present taluka and can be extended to the different talukas also.