Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove forests. In this study, we compared the performance of UAV Rikola hyperspectral images, WorldView-2 (WV-2) satellite-based multispectral images, and a fusion of data from both in the classification of mangrove species. We first used recursive feature elimination‒random forest (RFE-RF) to select the vegetation’s spectral and texture feature variables, and then implemented random forest (RF) and support vector machine (SVM) algorithms as classifiers. The results showed that the accuracy of the combined data was higher than that of UAV and WV-2 data; the vegetation index features of UAV hyperspectral data and texture index of WV-2 data played dominant roles; the overall accuracy of the RF algorithm was 95.89% with a Kappa coefficient of 0.95, which is more accurate and efficient than SVM. The use of combined data and RF methods for the classification of mangrove species could be useful in biomass estimation and breeding cultivation.

As the boundary between the sea and the land, information on the location and type of coastline constantly changes with environmental changes in coastal zones. Monitoring of coastline changes in long time series becomes important for the monitoring and assessment of the coastal zone environment. In this study, Landsat series images from five time periods (1990, 2000, 2010, 2015, and 2020) were selected for monitoring and analyzing the changes in coastline length, sea–land pattern, the index of coastline diversity, and fractal dimension characteristics. Our conclusions are as follows: (1) The lengths of the entire coastline and the artificial coastline of mainland China increased from 30,041.22 km and 10,022.49 km in 1990 to 32,977.34 km and 17,660.84 km in 2020, with annual change rates of 97.87 km/year and 254.61 km/year, respectively. From 1990 to 2020, the rate of natural coastline decreased from 66.68% to 42.29%, and the artificial coastline increased from 33.32% to 57.71%. (2) The length of natural coastline decreased from 20,018.73 km to 15,316.5 km; among the types of natural coastline, the length of sandy coastline and bedrock coastline decreased the most, at 2062.95 km and 1815.8 km, respectively. (3) The coastal zone of mainland China had a significant increase in land area, with a net increase of about 10,902.55 km². (4) The index of coastline diversity continued to decrease, and the coastline structure tended to be simple. The fractal dimension of the mainland coastline was consistent with the trend of the length of the coastline, which basically shows an increasing trend. Therefore, the length of the mainland coastline and artificial coastline displayed an upward trend between 1990 and 2020, which also led to simpler coastline diversity and more complex coastline shapes. Since the first year of the SDGs (2015), the growth rate of the artificial coastline has decreased by 158.32 km/year compared with that between 2010 and 2015. In recent years, China has enacted a number of laws, regulations, and action plans to protect its coastline, and it has proposed that by 2020, the proportion of natural coastline will be no less than 35%. The rapid development of China’s coastal areas drives the construction of coastal zone cities but also creates a variety of challenges for the ecological environment of the coastal zone, and the management and sustainable use of the mainland coastline resources should be further strengthened.

Water is essential for the survival of plants, animals, and human beings. It is imperative to effectively manage and protect aquatic resources to sustain life on Earth. Small tributaries are an important water resource originating in mountain areas, they play an important role in river network evolution and water transmission and distribution. Snow and cloud cover cast shadows leading to misclassification in optical remote sensing images, especially in high-mountain regions. In this study, we effectively extract small and open-surface river information in the Upper Yellow River by fusing Sentinel-2 with 10 m resolution optical imagery corresponding to average discharge of the summer flood season and the 90 m digital elevation model (DEM) data. To effectively minimize the impact of the underlying surface, the study area was divided into five sub-regions according to underlying surface, terrain, and altitude features. We minimize the effects of cloud, snow, and shadow cover on the extracted river surface via a modified normalized difference water index (MNDWI), revised normalized difference water index (RNDWI), automated water extraction index (AWEI), and Otsu threshold method. Water index calculations and water element extractions are operated on the Google Earth Engine (GEE) platform. The river network vectors derived from the DEM data are used as constraints to minimize background noise in the extraction results. The accuracy of extracted river widths is assessed using different statistical indicators such as the R-square (R²) value, root mean square error (RMSE), mean bias error (MBE). The results show the integrity of the extracted small river surface by the RNDWI index is optimal. Overall, the statistical evaluation indicates the accuracy of the extracted river widths is satisfactory. The effective river width that can be accurately extracted based on satellite images is three times the image resolution. Sentinel-2 MSI images with a spatial resolution of 10 m are used to find that the rivers over 30 m wide can be connectedly, accurately extracted with the proposed method. Results of this work can enrich the river width database in the northeast Tibetan Plateau and its boundary region. The river width information may provide a foundation for studying the spatiotemporal changes in channel geometry of river systems in high-mountain regions. They can also supplement the necessary characteristic river widths information for the river network in unmanned mountain areas, which is of great significance for the accurate simulation of the runoff process in the hydrological model.

The Ice, Cloud, and land Elevation Satellite (ICESat)-2 is the next generation of National Aeronautics and Space Administration (NASA)'s ICESat mission launched in September 2018. The new photon-counting LiDAR onboard ICESat-2 introduces new challenges to the estimation of forest parameters and their dynamics, the greatest being the abundant photon noise appearing in returns from the atmosphere and below the ground. To identify the potential forest signal photons, we propose an approach by using a local outlier factor (LOF) modified with ellipse searching area. Six test data sets from two types of photon-counting LiDAR data in the USA are used to test and evaluate the performance of our algorithm. The classification results for noise and signal photons showed that our approach has a good performance not only in lower noise rate with relatively flat terrain surface but also works even for a quite high noise rate environment in relatively rough terrain. The quantitative assessment indicates that the horizontal ellipse searching area gives the best results compared with the circle or vertical ellipse searching area. These results demonstrate our methods would be useful for ICESat-2 vegetation study.

The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.