Topographic Factors Research Articles

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.

Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland.

Landscape positioning of Neolithic mustatil stone structures along the margins of the Nefud Desert, Saudi Arabia

Monumental rectangular stone structures called mustatils are an important emerging feature of the Holocene archaeological record of northwestern Arabia. To date, few have been excavated, with available radiocarbon dates suggesting an age range of ca. 5400–4200 BC. Here we present a rigorous spatial analysis to identify the patterning and landscape context of 169 mustatils in the southern and western margins of the Nefud Desert. This included: (1) a systematic survey of satellite imagery to identify mustatils; (2) viewshed analysis to examine location and landscape visibility; (3) a point process model to understand how diverse environmental and landscape variables affect mustatil locations; (4) mark correlation function to assess spatial patterning of mustatils based on their size. Results indicate that mustatil locations are determined most by proximity to water (likely locations of enhanced surface water occurrence under the enhanced humidity of the Mid-Holocene), on east facing slopes, close to rocky areas, at elevations between 880 and 950 masl, and on or near topographic ridges (positive topographic position index). Viewshed analysis showed that mustatils are preferentially located in areas that have good views, but not the best that are available, indicating complex landscape positioning that balances a range of topographic and behavioural factors. Using a rank permutation method with size (length) of mustatils as a proxy for labour mobilization we show that mustatils within clusters are not arranged hierarchically based on size, and were likely built by non-stratified groups of people. Our analyses show that people were choosing multiple factors when deciding where to build mustatils, and that the distribution of mustatils may relate to different groups of people resulting in the construction of complex ritual landscapes. As one of the earliest examples of large-scale monumental stone structure construction in global prehistory, understanding mustatils can enlighten us on human-environment interaction during the Neolithic.

The dynamic evolution characteristics of PM2.5 concentrations and health risk assessment during typical forest fires in China

In the context of climate change, increasingly frequent wildfire events are exacerbating the air quality crisis in China and have become a significant source of atmospheric PM2.5. This study selected 12 major forest wildfire events in China from 2018 to 2023. Using atmospheric pollutant and component concentration data, as well as meteorological data, the study employed MK trend analysis, forward trajectory simulation, potential source contribution function (PSCF), and health risk indices to investigate the evolution patterns of PM2.5 concentrations before and after each wildfire event. The study explored the driving role of meteorological factors in this evolution process and quantitatively analyzed the spatial distribution characteristics of health risks in surrounding cities post-wildfire. The results indicated that: 1) PM2.5 levels changed significantly before and after each wildfire event, although the proportion of components remained relatively stable. 2) PM2.5 concentrations exhibited varying characteristics within different buffer zones of the wildfire areas. In the forest wildfire events in Foshan, Guangdong Province, and Jinzhong, Shanxi Province, the average PM2.5 concentrations within the 50 km buffer zones reached approximately 90 μg/m3, about 4–5 times the multi-year average for these areas. 3) Wildfire emissions were mainly influenced by meteorological and topographical factors. Wind field dependence diagrams showed that PM2.5 tended to accumulate in all wind directions, particularly at lower wind speeds of 0–5 m/s 4) Potential source analysis revealed that PM2.5 emissions from wildfires posed significant risks to surrounding areas. Multiple events had high-risk areas (WPSCF ≥0.8), and among the cities in these high-risk areas, the health risk value (ΔM) for Ya'an City reached an extremely high 85.1. This study provides a theoretical basis for policymakers to develop locally tailored wildfire management policies, ensuring the protection of the public's right to breathe clean air.

Spatial–Temporal Variations and Driving Factors of the Albedo of the Qilian Mountains from 2001 to 2022

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.

Comparing Current and Future Land Suitability for Growing Rainfed Corn (Zea mays) in Georgia, USA.

Land suitability (LS) classifications are essential for efficient and sustainable agricultural land use. With climate change, future LS classifications are necessary to ensure that crop growth remains sustainable and prevents land degradation. This study develops a current LS classification for rainfed corn (Zea mays) growth in the state of Georgia, USA, which is validated using historical census data on yield, acres planted, and corn crop lost. Significant (p < 0.05) differences were found between yield, acres planted, and crop loss percentage across LS classes for many years. Soil factors (Ph and soil texture) showed significant differences in fewer years compared to climate and topography factors, as soil factors can be altered by management practices such as liming and irrigation. Future LS classes determined by climate factors indicated a shift to the northwest of 150-300 km by the year 2100 based on the RCP4.5 or RCP8.5 emissions scenarios. The northwards shift in more suitable land due to rising maximum temperatures is expected to limit rainfed corn growth in Georgia in the future. As urban areas become more suitable for corn growth, farmers may need to plant crops earlier, irrigate, or switch to different crops. These results have important implications for agricultural planning and policy in the state of Georgia.

Optimizing Jatropha curcas bioenergy plantations in Pakistan: A geospatial suitability analysis using advanced spatial modeling

Bioenergy from energy plants, an alternative fuel, is projected to increasingly meet future worldwide energy demands as well as those of a developing nation like Pakistan. Jatropha curcas (JC) has been found to possess many desirable qualities, including high oil content seeds (27–40 %), rapid growth, ease of growing, tolerance to drought, ability to grow on poor soil and wasteland, needing less nutrient input and management, and not interfering with current food crops, insects, or pest resistance. Hence, growing JC in highly appropriate barren lands could reduce the global reliance on fossil fuels. However, an evaluation of the suitability of the land would be required for tree establishment to be effective. In this study, we integrated an Analytical Hierarchy Process (AHP) with the ArcGIS tool for assessing suitable available sites to cultivate JC across Pakistan. Using geospatial technologies, this study intended to assess and map possible areas appropriate for JC bioenergy plantations with the necessary climate, soil type, and topography factors. Pakistan was classified into 3 levels of suitability for JC plantations based on the results of a spatial analysis that integrated various data sets and varied evaluation criteria: Most Suitable, Moderately Suitable, and Less Suitable. Our results showed that out of the total area of Pakistan, around 54,392,075 hectares (57 %), 22,516,700 hectares (23 %), and 19,594,100 hectares (20 %) were identified as highly suitable, moderately suitable, and less suitable, respectively, for JC bioenergy plantations. We conclude that the methods used in this study provided a considerably reliable estimate of suitable sites for JC production in Pakistan. It can assist smallholder-based initiatives to promote JC cultivation on farmer-owned to enhance their living circumstances. Multi-criteria decision analysis (MCDA) was found to be a beneficial tool in breaking down the issues of identifying and classification of the locations for afforestation in the management of forests.

Interactions of climate, topography, and soil factors can enhance the effect of a single factor on spring phenology in the arid/semi-arid grasslands of China

Understanding the interactions among climate, topography, and soil factors on vegetation spring phenology can help reveal the feedback mechanisms of vegetation ecosystems in response to environmental conditions and climate change. Nonetheless, the coupling effects of these environmental elements on vegetation start of season (SOS) are still poorly understood. We took Xinjiang Province, a typical arid/semi-arid region, as the research area and quantitatively analyzed the explanatory power and interactions of climate, topography, and soil factors on grassland SOS through the geographical detector model based on the data extracted by Timesat 3.3 software. The results showed that: (1) From 2001 to 2020, early SOS predominantly occurred from 75 to 105 days in Northern Xinjiang. While later SOS was widespread in Southern Xinjiang from 105 to 135 days. The area of the SOS advanced region was larger than that of the delayed region; (2) There was obvious spatial hierarchical heterogeneity between SOS and climate, topography, and soil factors. Altitude, maximum temperature, and soil type were the primary drivers of SOS changes in the study area (P < 0.001); (3) The interactions among environmental factors strengthened the explanatory power of individual factors, either through mutual reinforcement or non-linear effects. The interactions between elevation and climate factors were particularly significant, with an explanatory power above 0.3. This study highlighted the importance of the coupling effects of environmental variables on SOS and suggested that drives should be considered more comprehensively in future analyses.

Modeling and Application of Drought Monitoring with Adaptive Spatial Heterogeneity Using Eco–Geographic Zoning: A Case Study of Drought Monitoring in Yunnan Province, China

Drought, characterized by frequent occurrences, an extended duration, and a wide range of destruction, has become one of the natural disasters posing a significant threat to both socioeconomic progress and agricultural livelihoods. Large-scale geographical environments often exhibit obvious spatial heterogeneity, leading to significant spatial differences in drought’s development and outcomes. However, traditional drought monitoring models have not taken into account the impact of regional spatial heterogeneity on drought, resulting in evaluation results that do not match the actual situation. In response to the above-mentioned issues, this study proposes the establishment of ecological–geographic zoning to adapt to the spatially stratified heterogeneous characteristics of large-scale drought monitoring. First, based on the principles of ecological and geographical zoning, an appropriate index system was selected to carry out ecological and geographical zoning for Yunnan Province. Second, based on the zoning results and using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, the vegetation condition index (VCI), the temperature condition index (TCI), the precipitation condition index (TRCI), and three topographic factors including the digital elevation model (DEM), slope (SLOPE), and aspect (ASPECT) were selected as model parameters. Multiple linear regression models were then used to establish integrated drought monitoring frameworks at different eco–geographical zoning scales. Finally, the standardized precipitation evapotranspiration index (SPEI) was used to evaluate the monitoring effects of the model, and the spatiotemporal variation patterns and characteristics of winter and spring droughts in Yunnan Province from 2008–2019 were further analyzed. The results show that (1) compared to the traditional non-zonal models, the drought monitoring model constructed based on ecological–geographic zoning has a higher correlation and greater accuracy with the SPEI and (2) Yunnan Province experiences periodic and seasonal drought patterns, with spring being the peak period of drought occurrence and moderate drought and light drought being the main types of drought in Yunnan Province. Therefore, we believe that ecological–geographic zoning can better adapt to geographical spatial heterogeneity characteristics, and the zonal drought monitoring model constructed can more effectively identify the actual occurrence of drought in large regions. This research finding can provide reference for the formulation of drought response policies in large-scale regions.

Spatial Estimation of Soil Organic Carbon Content Utilizing PlanetScope, Sentinel-2, and Sentinel-1 Data

The accurate prediction of soil organic carbon (SOC) is important for agriculture and land management. Methods using remote sensing data are helpful for estimating SOC in bare soils. To overcome the challenge of predicting SOC under vegetation cover, this study extracted spectral, radar, and topographic variables from multi-temporal optical satellite images (high-resolution PlanetScope and medium-resolution Sentinel-2), synthetic aperture radar satellite images (Sentinel-1), and digital elevation model, respectively, to estimate SOC content in arable soils in the Wuling Mountain region of Southwest China. These variables were modeled at four different spatial resolutions (3 m, 20 m, 30 m, and 80 m) using the eXtreme Gradient Boosting algorithm. The results showed that modeling resolution, the combination of multi-source remote sensing data, and temporal phases all influenced SOC prediction performance. The models generally yielded better results at a medium (20 m) modeling resolution than at fine (3 m) and coarse (80 m) resolutions. The combination of PlanetScope, Sentinel-2, and topography factors gave satisfactory predictions for dry land (R2 = 0.673, MAE = 0.107%, RMSE = 0.135%). The addition of Sentinel-1 indicators gave the best predictions for paddy field (R2 = 0.699, MAE = 0.114%, RMSE = 0.148%). The values of R2 of the optimal models for paddy field and dry land improved by 36.0% and 33.4%, respectively, compared to that for the entire study area. The optical images in winter played a dominant role in the prediction of SOC for both paddy field and dry land. This study offers valuable insights into effectively modeling soil properties under vegetation cover at various scales using multi-source and multi-temporal remote sensing data.

Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran’s I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran’s I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems.

Effects of check dam construction on sediment production and topographic changes in a gully in the Loess Plateau: A rainfall simulation experiment

AbstractTo maintain a reasonable sediment regulation system in the Loess Plateau, it is critical to determine the effects of check dam construction on sediment production and topographic changes. An indoor simulation experiment was conducted to investigate sediment production at the outlet section of the gully and micro‐topographic changes within the channel before and after dam construction. The results showed that check dam significantly affected the run‐off and sediment transport processes in the watershed. Specifically, the cross‐sectional morphology index (η) and the width‐depth ratio increased by 10.23% and 40.44%, respectively, while sediment content and particle size decreased by 39.29% and 18.58%, respectively. Additionally, the relative importance of section parameters and micro‐topographic parameters that affect sediment production rate and particle size was ranked using the random forest algorithm. The roughness after check dam construction was identified as a relatively important topographic factor affecting sediment production and particle selection by erosion. These findings provide valuable information for future check dam construction and development in the Loess Plateau region.

ESTIMATION OF LENGTH AND SLOPE FACTOR (LS) AS EROSION PREDICTION IN WAY PUBIAN SUB WATERSHED

Erosion caused by land degradation is one of the main challenges to soil quality around the world. One way to prevent erosion is by analyzing the length and slope factor (LS). The increasing length of the slope causes the higher amount of cumulative runoff and also the increasing steepness of the soil slope causes the higher speed of runoff that contributes to erosion. The purpose of this research is to analyze the LS factor value of Moore and Burch (1986) method and LS Nomograph for Way Pubian Subwatershed. The research method was done with Moore and Burch method and Nomograf LS. Moore and Burch (1986) method used the flow accumulation on DEM data as the main input in ArcGIS and the result shows the LS value ranged from 0 to 765.625 with an average (mean) value of 4.427. Meanwhile, the nomograph LS method resulted in high LS values and is not compatible with the Jakarta RTL-RLKT Preparation Implementation Guidelines (1986). Slope conditions affect the amount of surface flow so that it also affects the contribution of erosion and LS value. Way Pubian sub-watershed also classified as steep, resulting in a high LS value. Based on the method of Moore and Burch (1986) using ArcGIS software, we can conclude that the results of LS factor values can be used in Indonesia compared to the nomograph LS method which the results are less suitable due to topographic factors. As a result, the LS value from Moore and Burch (1986) method can be used in erosion prediction calculation.

Quantifying the contributions of precipitation, topography and human activity and their coupling to the development of permanent gully

Gully erosion poses greater threat to ecological and food security globally. The gully development rate and the contributions of primary influencing factors have not been thoroughly understood, primarily due to the complicated environmental conditions characterized by climate, soil, topography, and human activities. Therefore, this study was conducted to explore the gully development and contributions of its driving factors from 2013 to 2021 based on historical Google Earth images and field investigation in a typical rolling hilly watershed of northeast China. The climatic factors reflected by precipitation, topographical factors related to gully, and anthropological factors reflected by tillage ridge orientation and linear anthropological factors (LAF) including roads and shelterbelts were obtained. The tillage effect index (TEI), LAF effect index (LEI) and the distance from gully to LAF (DL) were calculated to analyze the influence of anthropological factors on gully development. The results showed that the linear, areal and volumetric gully development rates (RL, RA and RV) showed great variabilities in different periods with the average of 5.69 m year−1, 137.37 m2 year−1 and 428.54 m3 year−1, respectively. The accumulated RL, RA and RV increased exponentially or linearly with accumulated precipitation amount (P), erosive rainfall duration (RD), erosive rainfall (ER), maximum 30-min rainfall intensity (I30) and rainfall erosivity (RE) increased (P < 0.001). The slope of the gully head (HS) was identified as the primary topographic factor influencing RL. Both RA and RV showed positive power relationships with the drainage area of the gully (CA) and the upstream gully head (HA). The human activity reflected by tillage ridge orientation, unpaved road and shelterbelts had different effects on RL and RA. The increased orientation angle between gully and tillage ridge (ROA) mitigated headcut retreat for normal precipitation year but it would aggravate headcut retreat in the periods of continuous heavy rainfall. Gully bank with the ROA of 30−90° was more prone to areal development. The TEI, LEI and DL had significantly positive linear, power or exponential relations with gully development rates. The PLSR and VPA analysis indicated that topography, the interaction between topography and human activity, and the coupled effects of precipitation, topography and human activity were the main contributor of RL, RA and RV, respectively, and accounted for 34 %, 50 %, and 40 % of total variability of RL, RA and RA, respectively. This study can provide guidance for the control of gully erosion and the optimization of land use structure in the gully catchment.

Ecosystem services in mountain pastures: A complex network of site conditions, climate and management

Mountain pastures offer a multitude of ecosystem services (ES) such as fodder for ruminants, habitat for pollinators, climate change mitigation, aesthetic landscape for recreation, and biodiversity conservation. We aimed at analysing to which extend these ES are influenced by small-scale gradients of climate, site conditions and management – and to disentangle relationships among ES and the factors influencing them. Therefore, we quantified ES on six mountain summer farms in two contrasting regions in Switzerland: the Northern Alpine Foothills (lower elevation, higher precipitation, calcareous bedrock) and the Eastern Central Alps (higher elevation, lower precipitation, silicious bedrock). We measured six ES indicators (forage quantity, forage quality, carbon storage, colour abundance, resources for pollinators, plant diversity) and related them to explanatory factors of climate (temperature and precipitation), site conditions (soil fertility, soil acidity, terrain slope) and management (local grazing intensity, remoteness) in 66 study plots, i.e., 11 per farm. A holistic picture of the complex relationships among these factors was drawn by various statistical approaches: allometric line fitting, variance partitioning, and structural equations modelling. We found a huge heterogeneity of ES indicators and explanatory factors on each farm: the variability within farms was even higher than between regions. Variance partitioning and structural equations modelling demonstrated strongest influence of climate and site conditions and revealed trade-offs among ES indicators: High forage quantity and quality were associated with low plant diversity and grassland aesthetics, whereas diversity, aesthetics and pollinator resources were positively correlated with each other. ES indicators were explained by a range of climatic and topographic factors: High precipitation reduced plant diversity, whereas temperature increased forage quantity and quality; slope reduced soil fertility, forage quantity, forage quality and carbon storage; soil fertility in turn increased forage quantity; the farther away a pasture was from the main farm building, the lower was the forage quantity and the higher the plant diversity. Although allometric relations among local grazing intensity and ES indicators were strong, the direct influence of the management factors measured on ES was surprisingly small: Cattle preferred areas of high forage quantity and quality, and carbon storage was higher in these areas. On the other hand, places less visited by cattle offered more pollinator resources, and showed higher aesthetics and plant diversity. Trade-offs among ES prevent the realisation of all ES at the same place, but heterogeneity of mountain pastures allows to realise a broad bundle of contrasting ES on each individual summer farm.