Land use/cover change (LUCC) can impact the provision of ecosystem service values (ESVs), particularly in wetland regions that are subject to frequent and unsustainable land conversions. Exploring the past and future trajectory of LUCC and its effects on ESV has a great significance for wetland management and habitat stability. This study tried to reveal the patterns and magnitude of LUCC on ESV under varying land development scenarios in the Yellow River Delta region, which is a typical region undergoing serious degradation in China. In this study, a combined approach utilizing equivalent coefficients of ecosystem services was employed to determine the ESV of the wetland in relation to the major land use types (LUTs). The Markov–FLUS model was then used to simulate LUTs across multiple scenarios in 2030 and to clarify the relationship of ESV between wetland and other LUTs. The results indicated that the wetland was severely degraded, with a loss in area of 6679.89 ha between 2000 and 2020. Cropland and water body were the main sources of diversion and turnover for the wetland, respectively. Despite the multiple scenario projections revealed, the wetland area exhibited a similar growth rate and a homogeneity in ESV under the natural development (ND), urban construction and development (UCD), and the ecological development (ED) scenarios. The ED scenario was deemed the optimal development strategy for the wetland ecosystem. Our research will improve the comprehension of land development decisions and promote sustainable development in estuarine wetland areas.

Desertification has become a major problem in the field, affecting both the global ecological environment and economy. The effective monitoring of desertified land is an important prerequisite for land desertification protection and governance. With the aim of addressing the problems of spectral confusion as well as the salt and pepper phenomenon concerning the successful extraction of desertification information by utilizing the pixel-based method in the studies, Landsat remote sensing images obtained from the year 2001 to 2021 were selected in this study as the data source, and then, the object-oriented random forest classification method was improved by using different optimal segmentation scale selection techniques and combining multi-thematic index characteristics for measuring the extent of land desertification. Finally, the improved method was applied to study the dynamic changes in desertification in the Mu Us Sandy Land Ecological Function Reserve. The results show that the optimal scale determined by different optimal segmentation scale selection methods is not entirely consistent, and a minor scale should be selected as the optimal scale. Compared with the pixel-based classification method, the overall accuracy of object-oriented classification based on the optimal segmentation scale was improved by 8.06%, the Kappa coefficient increased by 0.1114, and the salt and pepper phenomenon was significantly reduced. From 2001 to 2021, the area of desertified land decreased by 587.12 km² and the area of severely desertified land decreased by 4115.92 km², indicating that the control effect was remarkable. This study can provide effective decision-making evidence and support for the successful governance of desertification.

The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as $Hv$ (high valley value of the 14 May, 26 May, and 7 June SAR data), $Tb$ (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and $Lo$ (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.