Search Results (10)

Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km² (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article

Floods have significant impacts on economic development and cause the loss of both lives and property, posing a serious threat to social stability. Effectively identifying the evolution patterns of floods could enhance the role of flood monitoring in disaster prevention and mitigation. Firstly, in this study, we utilized low-cost multi-source multi-temporal remote sensing to construct an HIS-NSCT fusion model based on SAR and optical remote sensing in order to obtain the best fusion image. Secondly, we constructed a regional growth model to accurately identify floods. Finally, we extracted and analyzed the extent, depth, and area of the farmland submerged by the flood. The results indicated that the HIS-NSCT fusion model maintained the spatial characteristics and spectral information of the remote sensing images well, as determined through subjective and objective multi-index evaluations. Moreover, the regional growth model could preserve the detailed features of water body edges, eliminate misclassifications caused by terrain shadows, and enable the effective extraction of water bodies. Based on multi-temporal remote sensing fusion images of Poyang Lake, and incorporating precipitation, elevation, cultivated land, and other data, the accurate identification of the flood inundation range, inundation depth, and inundated cultivated land area can be achieved. This study provides data and technical support for regional flood identification, flood control, and disaster relief decision-making, among other aspects. Full article

Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF. Full article

Sudden flood disasters cause serious damage to agricultural production. Rapidly extracting information such as the flooding extent of agricultural land and capturing the influence of flooding on crops provides important guidelines for estimating the flood-affected area, promoting post-disaster farmland restoration, and providing an auxiliary decision-making basis for flood prevention and disaster relief departments. Taking the flood event in Henan and Shanxi Provinces as example, based on the characteristics of the variations in radar data and optical data before and after the disaster, we propose an extent information extraction method for the flood inundation area and the flood-affected area of agricultural land. This method consists of change detection, threshold extraction, and superposition analysis, which weakens the negative impact of the radar data speckle noise and cloud contamination of the optical data on the extraction of the agricultural land flooding to a certain extent. The method was developed based on a flood event in Henan Province and validated in Shanxi Province. The results show that the production of this method have a clear boundary and accurate extent, and the overall precisions of the flood inundation area and flood-affected area extraction are 0.87 and 0.92, respectively. The proposed method combines the advantages of both radar and optical remote sensing data in extracting the specific extents of the flood inundation area and the flood-affected area in large spatial scale. Finally, the impact of time window size to the performance of the method is further analyzed. In the application of the proposed method, the Google Earth Engine (GEE) platform provides a low-cost, fast, and convenient way to extract flood information from remote sensing data. The proposed scheme provides a scientific data basis for restoring production of agricultural land after a flood disaster, as well as for national post-disaster damage assessment and disaster relief decision making. Full article

The increase in farm plot size is one of the most apparent and significant trends that have influenced central and eastern European agricultural landscapes since the 1950s. In many countries where the average plot size in traditional land-use systems did not exceed several hectares, present-day plots reach the size of 200 ha or more. In recent times, efforts have been made to reverse this trend to restore important ecosystem functions and to re-establish the aesthetic values of everyday landscapes. Visual landscape quality is becoming a major driving force in the development of agricultural landscapes with known effects on people’s well-being and health, and this quality plays an increasingly important role in agricultural policies. However, no comprehensive research has been carried out to establish the links between perceived visual landscape quality and the scale of the farm plot pattern. The current study was therefore designed to determine whether greater farmland pattern heterogeneity, i.e., smaller farm plot sizes, is consistent with higher visual preferences. The results showed that people preferred a small-scale plot pattern in landscapes characterized by a flat relief and a low proportion of woody vegetation. These homogeneous landscapes were also overall considered significantly less beautiful than more diverse landscapes. However, even a moderate decrease in plot size notably improved these low beauty scores. These preferences were displayed consistently by all respondents, and most strongly by older respondents, respondents with a higher level of education, and those professionally engaged in landscape design or conservation. The high level of consensus among respondents in rejecting further land consolidation in homogeneous landscapes, which form a large proportion of European farmland, underlines that the results of this study provide a valid argument for discussing sustainable agricultural plot sizes as part of agricultural policy-making. Full article

The evaluation of habitat quality and its genesis is of great significance to ecological protection of the watershed. Based on land use data, Digital Elevation Model (DEM), and road network data and population data, the Cellular Automata (CA)-Markov model and InVEST model were used to analyzed the land use change in the Wanhe Watershed, predicting the land use in 2025. Based on this, the degree of the habitat degradation and habitat quality in 2000, 2005, 2010, 2015, 2020, and 2025 were predicted and analyzed, and combined with the particularity of the terrain in the study area, the topography was introduced. Landform relief gradient was used to discuss the relationship between habitat quality and topographic factors in the Wanhe Watershed, and to reveal the distribution law. The result shows that from 2000 to 2025, farmland and forestland are the main land use types in the study area, and the main change is due to the expansion of the construction land, whereby the area increased by 62.86 km², with an increase of 34.41%, mainly from farmland and forestland. From 2000 to 2020, mainly due to the expansion in urban land and the reduction in forestland, the habitat degradation tends to be serious, and the habitat quality generally shows a downward trend, with areas with low habitat quality having had the largest increase from 452.67 km² in 2000 to 526.15 km² in 2025. The topography of the study area affects the distribution of natural landscapes and the intensity of human activities, resulting in significant differences in the landscape pattern of habitat degradation and habitat quality. The western mountains are relatively better. After 2020, due to the implementation of environmental protection policies, the habitat quality has tended to improve. This study can promote the adjustment of land use planning policies in the study area, maintain the biodiversity in the watershed, and realize the coordinated development of environmental benefits and social and economic development. The research results have theoretical significance and practical value for ecological environmental protection and land use layout in Wanhe Watershed. Full article

Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R² = 0.370 and predicted R² = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands. Full article

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE_P) and coefficient of determination of prediction (R²_P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE_P of 3.718 and highest R²_P of 0.391, followed by SVM (RMSE_P = 3.753, R²_P = 0.361) and PLSR (RMSE_P = 4.087, R²_P = 0.283). The SLR model was the worst model, with the lowest R²_P of 0.281 and highest RMSE_P of 3.930. ANN and SVM were better than OK (RMSE_P = 3.727, R²_P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains. Full article

There are potential impacts of Potentially Toxic Elements (PTEs) (e.g., Cd, Cr, Ni, Cu, As, Zn, Hg, and Pb) in soil from the perspective of the ecological environment and human health, and assessing the pollution and risk level of soil will play an important role in formulating policies for soil pollution control. Lingyuan, in the west of Liaoning Province, China, is a typical low-relief terrain of a hilly area. The object of study in this research is the topsoil of farmland in this area, of which 71 soil samples are collected. In this study, research methods, such as the Nemerow Index, Potential Ecological Hazard Index, Ecological Risk Quotient, Environmental Exposure Hazard Analysis, Positive Matrix Factorization Model, and Land Statistical Analysis, are used for systematical assessment of the pollution scale, pollution level, and source of PTEs, as well as the ecological environmental risks and health risks in the study area. The main conclusions are: The average contents of As, Cd, Cr, Cu, Hg, Zn, Ni, and Pb of the soil are 5.32 mg/kg, 0.31 mg/kg, 50.44 mg/kg, 47.05 mg/kg, 0.03 mg/kg, 79.36 mg/kg, 26.01 mg/kg, and 35.65 mg/kg, respectively. The contents of Cd, Cu, Zn, and Pb exceed the background value of local soil; Cd content of some study plots exceeds the National Soil Environmental Quality Standard Value (0.6 mg/kg), and the exceeding standard rate of study plots is 5.63%; the comprehensive potential ecological hazard assessment in the study area indicates that the PTEs are at a slight ecological risk; probabilistic hazard quotient assessment indicates that the influence of PTEs on species caused by Cu is at a slight level (p = 10.93%), and Zn, Pb, and Cd are at an acceptable level. For the ecological process, Zn is at a medium level (p = 25.78%), Cu is at a slight level (19.77%), and the influence of Cd and Pb are acceptable; human health hazard assessment states that the Non-carcinogenic comprehensive health hazard index HI = 0.16 < 1, indicating that PTEs in soil have no significant effect on people’s health through exposure; the PMF model (Positive Matrix Factorization) shows that the contribution rates of agricultural source, industrial source, atmospheric dust source, and natural source are 13.15%, 25.33%, 18.47%, and 43.05%, respectively. Full article

Detailed and accurate information on the spatial variation of soil over low-relief areas is a critical component of environmental studies and agricultural management. Early studies show that the pattern of soil dynamics provides comprehensive information about soil and can be used as a new environmental covariate to indicate spatial variation in soil in low relief areas. In practice, however, data gaps caused by cloud cover can lead to incomplete patterns over a large area. Missing data reduce the accuracy of soil information and make it hard to compare two patterns from different locations. In this study, we introduced a new method to fill data gaps based on historical data. A strong correlation between MODIS band 7 and cumulated reference evapotranspiration has been confirmed by theoretical derivation and by the real data. Based on this correlation, data gaps in MODIS band 7 can be predicted by daily evaporation data. Furthermore, correlations among bands are used to predict soil reflectance in MODIS bands 1–6 from MODIS band 7. A location in northeastern Illinois with a large area of low relief farmland was selected to examine this idea. The results show a good exponential relationship between MODIS band 7 and CET₀^0.5 in most locations of the study area (with average R² = 0.55, p < 0.001, and average NRMSE 10.40%). A five-fold cross validation shows that the approach proposed in this study captures the regular pattern of soil surface reflectance change in bands 6 and 7 during the soil drying process, with a Normalized Root Mean Square Error (NRMSE) of prediction of 13.04% and 10.40%, respectively. Average NRMSE of bands 1–5 is less than 20%. This suggests that the proposed approach is effective for filling the data gaps from cloud cover and that the method reduces the data collection requirement for understanding the dynamic feedback pattern of soil, making it easier to apply to larger areas for soil mapping. Full article