Rainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented method (OOA) has been developed to recognize loess landslides by combining spectral, textural, and morphometric information with auxiliary topographic parameters based on high-resolution multispectral satellite data (GF-1, 2 m) and a high-precision DEM (5 m). The quality percentage (QP) values were all greater than 0.80, and the kappa indices were all higher than 0.85, indicating good landslide detection with the proposed approach. We quantitatively analyze the spectral, textural, morphometric, and topographic properties of loess landslides. The normalized difference vegetation index (NDVI) is useful for discriminating landslides from vegetation cover and water areas. Morphometric parameters, such as elongation and roundness, can potentially improve the recognition capacity and facilitate the identification of roads. The combination of spectral properties in near-infrared regions, the textural variance from a grey level co-occurrence matrix (GLCM), and topographic elevation data can be used to effectively discriminate terraces and buildings. Furthermore, loess flows are separated from landslides based on topographic position data. This approach shows great potential for quickly producing accurate results for loess landslides that are induced by extreme rainfall events in the hilly and gully regions of the Loess Plateau, which will help decision makers improve landslide risk assessment, reduce the risk from landslide hazards and facilitate the application of more reliable disaster management strategies.

Habitat suitability assessment is critical for wildlife population conservation and management planning. The MaxEnt model is widely used in species habitat suitability modeling. In order to investigate the habitat status of the black-necked crane in the Zoige grassland wetland ecological function area, this study evaluated the habitat suitability of the black-necked crane using the MaxEnt model with 152 occurrence records and 13 environmental variables. Based on the Akaike information criterion corrected for the small sample size, the best optimal parameter combination (feature class LQPHT, regularization multiplier 3.0) was selected. The results show that the Maxent model had good accuracy with an area under the curve (AUC) value of 0.895. Distance to roads, average summer precipitation, distance to lakes, and altitude are the dominant environmental variables affecting the potential distribution of black-necked cranes, with the contribution rates of 31.6%, 29.8%, 14.3%, and 8.2%, respectively. The suitable habitat area of black-necked cranes is mainly distributed in and around the Zoige Wetland National Nature Reserve, accounting for 46.49% of the Zoige Grassland Wetland National Key Ecological Function Zone. The potential distribution area has a tendency to spread to Hongyuan County in the south of the functional zone, and the unsuitable habitat is mainly distributed in the high-altitude area in the southwest of the functional zone. This study recommends focusing on the distribution area of black-necked cranes around Zoige Wetland National Nature Reserve and Hongyuan County to improve conservation strategies and strengthen protection efforts.

Coastal and estuarine areas are often polluted by heavy metals that result from industrial production and agricultural activities. In this study, we investigated the concentration trait and vertical pattern of trace elements, such as As, Cd, Ni, Zn, Pb, Cu, and Cr, and the relationship between those trace elements and the soil properties in coastal wetlands using 28 profiles that were surveyed across the Diaokouhe Nature Reserve (DKHNR). The goal of this study is to investigate profile distribution characteristics of heavy metals in different wetland types and their variations with the soil depth to assess heavy metal pollution using pollution indices and to identify the pollution sources using multivariate analysis and sediment quality guidelines. Principal component analysis, cluster analysis, and pollution level indices were applied to evaluate the contamination conditions due to wetland degradation. The findings indicated that the concentration of trace elements decreased with the soil depth, while Cd increases with soil depth. The As concentrations in reed swamps and Suaeda heteroptera surface layers were slightly higher than those in other land use types. All six heavy metals, i.e., Ni, Cu, As, Zn, Cr, and Pb, were strongly associated with PC1 (positive loading) and could reflect the contribution of natural geological sources of metals into the coastal sediments. PC2 is highly associated with Cd and could represent anthropogenic sources of metal pollution. Most of the heavy metals exhibited significant positive correlations with total concentrations; however, no significant correlations were observed between them and the soil salt and soil organic carbon. Soil organic carbon exhibited a positive linear relationship with Cu, Pb, and Zn in the first soil layer (0–20 cm); As, Cr, Cu, Ni, Pb, and Zn in the second layer (20–40 cm); and As, Cr, Cu, Ni, Pb, and Zn in the third layer (40–60 cm). Soil organic carbon exhibited only a negative correlation with Cd (P < 0.05), in the second and third layers. As, Cr, Cu, Ni, Pb, and Zn exhibited lower geoaccumulation index values (I _geo values), which averaged less than 0 in the three soil layers, this finding indicates that the soils have remained unpolluted by these heavy metals. The mean concentrations of these trace elements were lower than Class I criteria. The degradation wetland restoration suggestions have also been provided in such a way as to restore the reserved flow path of the Yellow River. The results that are associated with trace element contamination would be helpful in providing scientific directions to restore wetlands across the world.