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.

Terrestrial ecosystems provide a variety of benefits for human life and production, and are a key link for achieving sustainable development goals (SDGs). The basin ecosystem is one type of terrestrial ecosystem. Ecological security (ES) assessments are an important component of the overall strategy to achieve regional sustainable development. The Huaihe River Basin (HRB) has the common characteristics of most basins, such as high population density, a rapidly developing economy, and many environmental problems. This study constructed an ES evaluation system by applying a pressure-state-response framework as an assessment method for the sustainable development of basins. Taking the HRB as an example, this study determined the ES status of the region from 2001 to 2019 and analyzed crucial factors for any variation observed by combining remote sensing and climate data, relevant policies, and spatial information technology. The results highlight the importance of reserves and the negative impact of urban expansion on ES. Additionally, the enactment of policies had a positive impact on ES, whereas precipitation had a negative effect on ES in most areas of the HRB. Based on these results, the government should strengthen the protection of forests, grasslands, and wetlands and improve water conservation facilities. This study provides guidance for the subsequent economic development, environmental protection, and the achievements of SDG 15 in the HRB.

Evergreen forest provides essential ecosystem services such as maintaining the balance in carbon and oxygen cycles and air purification. However, under cloudy and rainy weather conditions, it is difficult to obtain optical remote sensing images with high spatial resolution and complete time series. In addition, surfaces underlying the forest canopy can be complex and fragmented. To solve this problem, we developed a new approach (NDVI _CV -LS) for mapping urban evergreen forest at the subpixel scale. In order to capture more accurate growth characteristics of evergreen forest, we harmonized Landsat-8 and Sentinel-2 images with cloud cover less than 10% acquired within 1 year to denser the time-series dataset. In view of the time series fluctuation stability of evergreen forest, the coefficient of variation (CV) of the normalized difference vegetation index (NDVI) was used to distinguish evergreen forest from other vegetation. Meanwhile, the annual minimum NDVI (NDVI _ann-min ) was used as the parameter in a dimidiate pixel model for estimating fractional evergreen forest cover (FVC _ever ). Hefei, a cloudy and rainy subtropical city in China, was selected as a case study to evaluate the validity of the model. The verification results revealed that harmonizing Landsat-8 and Sentinel-2 time-series images to extract evergreen forest improved the overall accuracy by 8% compared with using Landsat-8 images alone, indicating that the NDVI _CV -LS model can improve the accuracy of FCV _ever estimation, especially for areas with complex underlying surfaces under cloudy and rainy conditions.

Cropland retirement is a widespread phenomenon across the world. The conversion of inefficient cropland to forest or grassland is a policy aimed at restoring ecology, improving the environment, and promoting economic development. However, in most developing countries, the results of cropland retirement and land restoration are characterized by spatial fragmentation, and there are significant temporal differences as a result of poor agricultural intensification, human interference, and regional environmental differences. This substantially increases the difficulty of information extraction and reduces the extraction accuracy of remote sensing methods. In this paper, we developed a new phenology-based cropland retirement remote sensing (PCRRS) model to detect the extent and timing of cropland retirement. Considering the characteristic growth of crops, the normalized difference vegetation index (NDVI), at the start, middle, and end of the growth cycle, is the phenological metric to distinguish cropland from other vegetation types. In addition, the interannual variation of phenological metrics are significant after cropland retirement, which is the key to effectively identify retired cropland. High-resolution Google Earth images were used to verify the accuracy of the algorithm. The results suggested that the overall accuracy of our algorithm exceeded 85%, and was more suitable for sloping cropland. In comparison with other cropland retirement extraction methods, the PCRRS model had high sensitivity and stability. We found it was common for sloping cropland to be retired earlier, and we also identified the existing inter-planting phenomena between crops and shrubs in areas with gentle slopes. Overall, this study provided a basis for understanding the drivers of cropland retirement and evaluating their environmental effects.