As desertification is one of the most severe ecological and environmental issues worldwide, monitoring desertification and studying its evolution patterns are highly important for its governance and prevention. In this study, a novel desertification monitoring method is developed that combines the three-dimensional desertification index (TDDI) and Gaussian mixture model (GMM). The results of applying this method to desertification monitoring, which is based on historical Google Earth images, in northern China from 2000 to 2020 indicate that the accuracy of desertification classification using the TDDI and GMM algorithms exceeds 82%. Compared with the national desertification survey statistics, the accuracy of classifying areas with different degrees of desertification exceeds 93.4%. In terms of the stability of the monitoring results under different data source and spatial region conditions, TDDIMODISshows a strong correlation and high consistency with TDDILandsatand TDDIsentinel-2. The overall accuracies are greater than 55%. Additionally, the TDDI comprehensively considers the soil moisture level, vegetation coverage, and surface conditions and reflects the complexity of the desertification process more accurately than the NDVI and DDI.

Fine and new DEM of riparian zone is emergently required for hydrological and environmental modeling. With the effects of water fluctuation and submersion, acquiring DEM in the riparian zone is difficult. In this study, a self-adaptive ROC (Receiver Operating Characteristic) method was proposed to generate the DEM of riparian zone with the aid of water fluctuation (called as ROC DEM). The water extent acts as an altimeter to measure the altitude with corresponding water level. The elevation of each pixel was the optimal threshold to determine whether it was covered with water or not. With the fluctuation in water, the time-series labels of land or water and the corresponding water level of each pixel were recorded. Finally, the elevation of each pixel was obtained by acquiring the most optimal classification threshold with the ROC algorithm. The proposed method skillfully transformed the problem of acquiring the elevation of the riparian zone into a 2-class classification problem. The riparian zone in the Dongting Lake was considered as the study area to test the proposed method with the time-series Sentinel-1 SAR images and the corresponding water level. The proposed method is feasible to obtain the DEM and its results is more consistent with the actual topography comparing to other DEM products. The values of R2 between the ROC DEM and GLAS and field altitude reach to 0.7 and 0.9 respectively. This paper presents an alternate method for acquiring the topography in the riparian zone and tidal flat.

Urban green space plays a critical role in contemporary urban planning and ecology as they provide recreational space for residents, promote ecological balance, and enhance the quality of the urban environment. However, the rapid development of urbanization poses increasingly complex challenges to the monitoring and management of these spaces. Previous studies have illustrated that semantic segmentation models based on convolutional neural network (CNN) perform well in classifying urban green space using high-resolution remote sensing images. However, there are still some deficiencies in CNNs model in capturing global information of green space and dealing with complex spatial relationships due to the special nature of urban environments, such as fragmentation of green space. Hence, swin transformer-CNN-fusion-network(Swin-CFNet) was proposed for urban green space classification, which overcomes the limitations of traditional methods in dealing with global green space information and complex spatial relationships by constructing a residual-swin-fusion (RSF) module for fusion of multi-source features. Experimental results demonstrated that the Swin-CFNet outperformed the UNet in urban green space classification, achieving an overall accuracy (OA) of 98.3% and improving the mean intersection over union (mIoU) compared to UNet and SwinUnet by 3.7% and 1%, respectively.

Accurate, detailed urban forest mapping contributes to ecological status monitoring and formulating sustainable development policies in cities worldwide. However, accurate urban forest identification in southern Chinese cities is challenging when samples are insufficient because of high fragmentation and the influence of mountain shadows and cloudy weather. Therefore, this study combined the advantages of transfer, deep, and ensemble learning to propose a VGG16-UNet++&Stacking algorithm for urban forest mapping in heavily urbanized areas based on the Sentinel dataset. Initially, the algorithm mined deep features of an image by pre-training the convolutional layer. Then, the deep feature set was fed into ensemble learning for classification to improve accuracy and robustness. The classification results showed that the VGG16-UNet++&Stacking had an overall accuracy (OA) of 95.87% and a Kappa coefficient of 0.9481. Furthermore, the user accuracy of the forest was 97.94%. The OA of the method in this study was improved by 2.2% and 3.83% compared with that of UNet++ and random forest (RF), respectively. Compared to that of UNet++, the results showed a modest improvement in OA for the VGG16-UNet++&Stacking method; VGG16-UNet++&Stacking is more effective in eliminating cloud influence, a feature that UNet++ lacks.

Mangrove forests are among the most productive of coastal ecosystems, providing a variety of ecological functions and economic value to coastal areas around the world. Accurate identification of mangrove is of great importance for the restoration and conservation of mangrove ecosystems, and for promoting the development of a blue carbon economy and achieving carbon neutral strategies. In this study, a red-edge mangrove index (REMI) was proposed based on Sentinel-2 multispectral images, using red, green, red edge, and SWIR1 bands in the form of a (red edge-red)/(SWIR1-green) combination to highlight the unique green and moisture information of mangrove. Then, the REMI index was combined with the Otsu threshold segmentation algorithm (Otsu) to map the mangrove information in respect of Hainan Island, which has the most abundant mangrove species in China. The results indicate that, when compared with other vegetation indices, such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), mangrove index (MI), normalized difference mangrove index (NDMI), combined mangrove recognition index (CMRI), and mangrove vegetation index (MVI), the REMI showed greater proficiency in distinguishing mangrove from other vegetation. When the REMI was applied to mangrove mapping in Hainan Island, the overall accuracy and kappa coefficient were 95.68% and 0.92, respectively. In addition, the mangrove distribution ranges mapped in this study were compared with existing mangrove products (HGMF_2020 and China National Standard GB/T 7714-2015 (note)), and it was demonstrated that the mangrove distribution ranges identified based on the REMI had high coincidence with the above-mentioned mangrove products. This proves that the REMI has good potential for application in mangrove identification and mapping.