Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids’ labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement.

Deep learning-based landslide detection in optical remote sensing images has been extensively studied. However, several challenges remain. Over time, factors such as vegetation cover and surface weathering can weaken the distinct characteristics of landslides, leading to blurred boundaries and diminished texture features. Furthermore, obtaining landslide samples is challenging in regions with low landslide frequency. Expanding the acquisition range introduces greater variability in the optical characteristics of the samples. As a result, deep learning models often struggle to achieve accurate landslide identification in these regions. To address these challenges, we propose a multi-scale target attention YOLO model (MSTA-YOLO). First, we introduced a receptive field attention (RFA) module, which initially applies channel attention to emphasize the primary features and then simulates the human visual receptive field using convolutions of varying sizes. This design enhances the model’s feature extraction capability, particularly for complex and multi-scale features. Next, we incorporated the normalized Wasserstein distance (NWD) to refine the loss function, thereby enhancing the model’s learning capacity for detecting small-scale landslides. Finally, we streamlined the model by removing redundant structures, achieving a more efficient architecture compared to state-of-the-art YOLO models. Experimental results demonstrated that our proposed MSTA-YOLO outperformed other compared methods in landslide detection and is particularly suitable for wide-area landslide monitoring.

A precise estimation of the heavy metal concentrations in soils using multispectral remote sensing technology is challenging. Herein, Landsat8 imagery, a digital elevation model, and geochemical data derived from soil samples are integrated to improve the accuracy of estimating the Cu, Pb, and As concentrations in topsoil, using the Daxigou mining area in Shaanxi Province, China, as a case study. The relationships between the three heavy metals and soil environmental factors were investigated. The optimal combination of factors associated with the elevated concentrations of each heavy metal was determined combining correlation analysis with collinearity tests. A back propagation network optimised using a genetic algorithm was trained with 80% of the data for samples and subsequently employed to estimate the heavy metal concentrations in the area. The validation results show that the RMSE of the proposed model is lower than those of the existing linear model and rule-based M5 model tree. From the spatial distribution map of the three metals concentrations using the proposed method, there are findings that high concentrations of the heavy metals studied occur in the mining area, across the slag storage area, on the sides of the road used for transporting ore materials, and along the base of slopes in the area. These findings are consistent with the survey results in the field. The validation and findings validate the effectiveness of the proposed method.

This letter proposes an associative hierarchical conditional random field (AHCRF) model to improve the classification accuracy of high-resolution remote sensing images. It considers segmentation quality of superpixels, avoids a time-consuming selection of optimal scale parameters, and alleviates the problem of classification accuracy sensitive to undersegmentation errors that is present in traditional object-oriented classification methods. The model is built on a graph hierarchy, including the pixel layer as a base layer and multiple superpixel layers derived from a mean shift presegmentation. It extracts clustered features of pixels for superpixels at each layer and then defines the potentials of the AHCRF model. We suggest a weighted version of the interlayer potential using the size of a superpixel as a measure to reflect segmentation quality. In this way, erroneously labeled pixels of a superpixel are penalized. Experiments are presented using a part of the downsampled Vaihingen data from the ISPRS benchmark data set. Results confirm that our model shows more than 80% overall classification accuracy and is superior to the original AHCRF model and comparable to other models. It also alleviates the choosing of suitable segmentation parameters.

Monitoring environmental pollution sources is an ongoing issue that must be addressed to reduce risks to public health, food safety, and the environment. However, retrieving topsoil heavy metal content at a low cost for environmental monitoring in mining areas is challenging. Therefore, this study proposes a network model based on transfer learning theory and a back propagation (BP) network optimized by a genetic algorithm (GA), taking the Daxigou mining area in Shaanxi Province, China, as a case study. Firstly, visible and near-infrared spectrum data from Landsat8 satellite images, digital elevation models, and geochemical data from field-collected soil samples were used to extract environmental factor candidates indicating the content and spatial distribution of certain heavy metals, including copper (Cu) and lead (Pb). Secondly, each element was correlated with environmental factors and a multicollinearity test was performed to determine the optimal factor set. Then, the BP network optimized by GA was pre-trained with sample data collected in 2017 and retrained with minimal sample data from 2019 using the parameter transfer learning method, allowing spatial distribution mapping of the Cu and Pb content in topsoil of the Daxigou mining area in 2019. From the validation results using field-collected data, the root mean square error (RMSE) and mean relative error (MRE) values using the proposed model, respectively, reduced by 4.688 mg/kg and 1.533 mg/kg for Cu and reduced by 1.586 mg/kg and 1.232 mg/kg for Pb compared to the traditional GA-BP model. Thus, conclusions can be drawn that our proposed Tr-GA-BP network performs well, requiring 16 training samples collected in 2019. In addition, the content of Cu is the highest; Pb is the second highest in the study area. Both of them were spatially distributed mainly in the exploitation, slag stacking, roadside, etc., consistent with field investigation results.

The Jinghe River Basin is an important grain-producing area in northwest China; yet, serious soil erosion and water shortage problems have greatly limited the development of agriculture in the region. Evapotranspiration (ET) is a key component of rational water resources planning and water cycle mechanism exploration; yet, studies in this region have mainly focused on rainfall and runoff sand transport, neglecting the role of ET. Therefore, to understand the characteristics of ET in the Jinghe River basin, we estimated the ET during the vegetation growing season (April to October) in 2018 using the surface energy balance system (SEBS) based on remote sensing images and meteorological data, and analyzed the characteristics of its spatio-temporal distribution and its relationship with environmental factors. The results showed that (1) SEBS has good applicability in the study area, with R1 between 0.57 and 0.77, RMSE between 0.98 and 1.15 mm / d, and MRE between 24% and 36% as verified with the lysimeter and ET products; (2) on the basin scale, the average daily ET showed a unimodal distribution with time, with variations ranging from 1.6 to 4.1 mm while spatially it showed that the mountainous forest and river catchment areas were overall higher than the loess plateau and hilly gully areas; (3) on the landscape scale, the vegetation with the highest average daily ET was broad-leaved forest, and the lowest was grassland. The vegetation with the highest ecological water demand was cultivated vegetation, and the lowest was meadow; (4) correlation analysis showed that rainfall, temperature, sunshine hours, and vegetation cover were positively correlated with ET. Among them, rainfall and temperature have the strongest correlation with ET and maybe the main factors affecting ET in the Jinghe River Basin.