Dongting Lake, located in Hunan Province, China, is the second-largest freshwater lake in the country and a crucial ecosystem and biodiversity hotspot. Investigating the temporal and spatial evolution of Dongting Lake's water areas is conducive to regional water resource development and provides assurance for regional ecological balance. In this study, Landsat-8 OLI remote sensing images from the years 2017 to 2021 were utilized. The normalized difference water index (NDWI) water threshold value method was employed to extract the water area of Dongting Lake. The study analyzed the temporal and spatial evolution of Dongting Lake, delving into the migration direction of the Dongting Lake basin's centroids, providing a reference basis for the sustainable development of Dongting Lake. The results indicated that the water area of Dongting Lake decreased from 913.65 km² in 2017 to 723.14 km² in 2019, followed by an increase to 866.65 km² in 2021. The centroids of Dongting Lake shifted southwestward from 2017 to 2019 and then east-southeastward from 2019 to 2021.
Climate change is a common problem facing human society, and carbon emissions aggravate global warming. This paper takes Fujian Province as the research area. DMSP/OLS and NPP/VIIRS data were preprocessed, corrected and fused to generate and grow time series data sets. Through the construction of carbon emission estimation model, the spatio-temporal evolution of carbon emission in Fujian from 1992 to 2022 was analyzed at city-county level. The results show that: (1) Temporal dimension: carbon emission in Fujian increased from 37.18 million tons in 1992 to 324.1 million tons in 2022. (2) Spatial dimension: carbon emissions in Fujian showed "high in the east and low in the west" and "high in the south and low in the north". The global spatial distribution is clustering spatial distribution. L-L clustering is mostly in the northern part of Fujian, but not significant in the middle part, and H-H clustering is mainly in the southeast coast. The results of this study can provide data support for promoting the comprehensive green transformation of economic and social development.
The vegetation information system is a key component of the international ecosystem assessment system. It is of great value to construct an environmental assessment system and ecological governance through obtaining plant information from airborne LiDAR data. Focusing on Xiamen University of Technology, this study collected plant point cloud data within the research area using UAV radar technology. It then used the inverse distance weighting interpolation method to construct a plant canopy height model to obtain the appropriate tree height. Meanwhile, the estimated values were compared with actual data points to explore the adaptability of different interpolation calculation methods to extract plant information in the research area.
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