Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVI_ann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVI_ann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVI_ann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

Cellular Automata models are employed for simulating geographical distributions, while Markov-Chain models are utilized for simulating temporal changes. This study aims to forecast the dynamics of land use and land cover (LULC) change and land surface temperature (LST) using a Cellular Automata (CA) and Markov-Chain model for the period 1992-2022 in Islamabad, Pakistan. This research innovatively predicts future LULC and LST and examines their correlations with various vegetation indices. LULC maps were generated from time series data of Landsat satellite images (Landsat 5, 7, and 8) for the years 1992, 2002, 2012, and 2022, utilizing the Random Forest (RF) algorithm. Five satellite indices were employed: Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Soil Adjusted Vegetation Index (SAVI). Through the CA-Markov-Chain analysis, the results showed significant new findings, such as a rise in the built-up area of Islamabad, which increased from 105.63 km2 (11.66%) in 1992 to 447.39 km2 (49.38%) in 2022. The study predicted that the built-up area would further increase to 531.82 km2 (55.38%) by 2042. The study analyzed the enhancement of LST, which was about 2.40°C from 1992 to 2022, ultimately because of the expansion of uncontrolled urban areas. The correlation of LST with the vegetation indexes NDVI, BSI, NDWI, NDBI, and SAVI was also analyzed through regression analysis. Furthermore, the surface temperature was predicted well by the Urban Index (UI), a non-vegetation index, demonstrating the positive correlation of R2 = 0.87 with respect to retrieved surface temperature. Using the Urban Index (UI) as a predictor of LST, our projections indicate that regions with temperatures ranging from 20–24°C and 25–28°C will decrease in coverage from 4.63% to 3.85% and from 25.33% to 21.08%, respectively, between 2032 and 2042.

Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (RMSE) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with RMSEs of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB.