Fan Xianchuang

As the first marine economic zone, the coastal zone is a complex and active ecosystem, serving as an important resource breeding area. However, during the process of economic development, coastal zone resources have been severely exploited, leading to fragile ecology and frequent natural disasters. Therefore, it is imperative to analyze coastline changes and their correlation with coastal ecological space. Utilizing long-time series high-resolution remote sensing images, Google Earth images, and key sea area unmanned aerial vehicle (UAV) remote sensing monitoring data, this study selected the coastal zone of Ningbo City as the research area. Remote sensing interpretation mark databases for coastline and typical coastal ecological space were established. Coastline extraction was completed based on the visual discrimination method. With the help of the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI) and maximum likelihood classification, a hierarchical classification discrimination process combined with a visual discrimination method was constructed to extract long-time series coastal ecological space information. The changes and the linkage relationship between the coastlines and coastal ecological spaces were analyzed. The results show that the extraction accuracy of ground objects based on the hierarchical classification process is high, and the verification effect is improved with the help of UAV remote sensing monitoring. Through long-time sequence change monitoring, it was found that the change in coastline traffic and transportation is significant. Changes in ecological spaces, such as industrial zones, urban construction, agricultural flood wetlands and irrigation land, dominated the change in artificial shorelines, while the change in Spartina alterniflora dominated the change in biological coastlines. The change in ecological space far away from the coastline on both the land and sea sides has little influence on the coastline. The research shows that the correlation analysis between coastline and coastal ecological space provides a new perspective for coastal zone research. In the future, it can provide technical support for coastal zone protection, dynamic supervision, administration, and scientific research.

In this paper, we present the detection of ore‐forming elements migrated from soil and rock to vegetation leaves using hyperspectral data. The rock, soil (C, B, and A layer), and vegetation (root, stem, and leaf) samples with the measurements of spectra and element concentrations in the vertical section of the abandoned pits were collected. The wavelet approach is applied to analyze the correlations between spectral features and element concentrations. The results show that the significant correlations are found at a lower order mother wavelets such as Haar. Moreover, the significant correlations are negative between Cu and Mo elements and the wavelet energy vector of the soil and rock to vegetation leaves spectrum. The results also show that ore‐forming elements migrated from rock and soil to vegetation leaves can be detected using hyperspectral data in the study area.

Remote sensing technology has shown its irreplaceable advantages in the identification of urban black and odorous water body. However, the universality of the remote sensing recognition algorithm for the black and odorous water body is not clear in different regions. Thus, two typical cities, Shenyang and Nanjing, were selected from northern and southern China as the study areas. Four forms of recognition models of the black and odorous water body are built based on Gaofen-2 images, including the single-band model, difference model, ratio model, and water quality parameter model. Combined with the remote sensing interpretation marks of black and odorous water body, the recognition precision of black and odorous water body is analyzed by the methods of the confusion matrix and Kappa coefficient. The results show that the overall accuracy of the normalized difference black-odorous water index (NDBWI) model is higher than 80% in the northern and southern cities of China, with good consistency of spatial distribution. The accuracy of the black and odorous water index model and G model is higher than 75% only in the special areas of Nanjing or Shenyang. The accuracy of other models is generally low. Research show that the NDBWI model has better universality in the identification of urban black and odorous water body, and it is suitable for promotion and application. The remote sensing interpretation marks of black and odorous water body have shown a good auxiliary identification function. The research provides a technical foundation for large-scale and rapid identification of urban black and odorous water body.