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This study finds that the livestock environmental regulation (LER) imposed by China has promoted the spatial agglomeration of hog production in the administrative border areas, thereby posing a threat to ecological sustainability. We apply the difference-in-differences-in-differences (DDD) method to a unique dataset in counties along the main stem and 38 important tributaries of the Yangtze River basin in China from 2008 to 2019. Specifically, the slaughter volume and density of hogs in the most downstream border counties of a city are 7.87% and 9.56% higher, respectively, than those of otherwise identical counties within the same municipal jurisdiction. Conversely, in the most upstream border counties, these figures decreased by 12.02% and 15.35%, respectively. Moreover, we find that the fixed capital investment in the most downstream border counties is 22.54% less than that in otherwise identical counties. These findings imply that LER has driven the agglomeration of hog production in downstream administrative border areas, inadvertently shifting the pollution burden to these regions and posing potential challenges to the effectiveness of environmental policies. [ABSTRACT FROM AUTHOR]

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances. [ABSTRACT FROM AUTHOR]

The integration of multi-source, multi-temporal, multi-band optical, and radar remote sensing images to accurately detect, extract, and monitor the long-term dynamic change of coastline is critical for a better understanding of how the coastal environment responds to climate change and human activities. In this study, we present a combination method to produce the spatiotemporal changes of the coastline in the Yellow River Delta (YRD) in 1980–2020 with both optical and Synthetic Aperture Radar (SAR) satellite remote sensing images. According to the measurement results of GPS RTK, this method can obtain a high accuracy of shoreline extraction, with an observation error of 71.4% within one pixel of the image. Then, the influence of annual water discharge and sediment load on the changes of the coastline is investigated. The results show that there are two significant accretion areas in the Qing 8 and Qingshuigou course. The relative high correlation illustrates that the sediment discharge has a great contribution to the change of estuary area. Human activities, climate change, and sea level rise that affect waves and storm surges are also important drivers of coastal morphology to be investigated in the future, in addition to the sediment transport. [ABSTRACT FROM AUTHOR]