Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote sensing approach. Therefore, we developed a method to extract the spatial extent of the UHI during the period from 2000 to 2021 in Nanjing, China, and explored the impact of urban two- and three-dimensional expansion on UHI spatial extent and UHI intensity. After cropland effects (i.e., bare soil) were eliminated, our proposed method combines the Getis-Ord-Gi* and the standard deviation of the normalized difference vegetation index (NDVI STD) to extract the UHI area from Landsat 5 and Landsat 8 images using land surface temperature (LST) spatial autocorrelation characteristics and the seasonal variation of vegetation. Our results show the following: (1) Bare farmland has a large influence on the extraction results of UHI—combined with the seasonal variation characteristics of NDVI STD, the impact of bare soil on UHI extraction was highly reduced, strongly improving the accuracy of UHI extraction. (2) The dynamics of the UHI area are consistent with the changes in the built-up area in Nanjing at both spatial and temporal scales, but with the increase of the urban green ratio, the UHI area of mature urban areas trends to decrease due to the cooling effect of green space. (3) The accumulation of population and GDP promote the vertical expansion of urban buildings. When the two-dimensional expansion of the city reaches saturation, the UHI intensity is primarily affected by three-dimensional urban expansion.

The dynamics of shallow water areas of inland lakes is closely related to the regional ecology and economy. However, it is still a challenge to extract the natural shallow water area for inland lakes using satellite images due to their rapid changes and various human demands. Therefore, we developed a new remote sensing-based method applied in Hongze Lake (one of the largest freshwater lakes in China) to first delineate the lake from the SWIR1 band of Landsat OLI imagery using cold spots in the LISA method, and then distinguish deep and shallow water areas from the G band of Landsat OLI images using hot spots with LISA after masking the lake out, and finally extracting the natural shallow water area by masking aquatic farms out from shallow water areas using farm ridge classification from NDWI images and aggregating points of farm ridges. The results show that (1) the method of this study is efficient in extracting the natural shallow water area with limited effects from aquatic vegetation; (2) water inflow (upstream water supply and precipitation) and the area of aquatic farms, the two dominant factors for the temporal changes in natural shallow water area, contributed 38.3% (positively) and 42.2% (negatively) to the decrease in the natural shallow water area during 2013–2022 in Hongze Lake; (3) the natural shallow water area of Hongze Lake decreased significantly every April as paddy rice farms withdrew a large amount of irrigation water from Hongze Lake. Our research provides a new approach to extract the natural shallow water areas of inland lakes from satellite images and demonstrates that the upstream water supply, precipitation, and agriculture demands are the three main reasons for seasonal and temporal variations in natural shallow water areas for inland lakes.