It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 10⁴ hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer’s accuracy of 89.97%, user’s accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest.

Assessment of habitat quality for wetland-obligate wildlife and endangered species of waterfowl is essential particularly in the changing environment. In this study, the areas of potential suitable habitat for Siberian cranes in Poyang Lake natural wetland were evaluated using 11 scenes of Landsat TM/ETM+ images. Habitat quality was assessed against six landscape indices. Results indicate that the optimum water level for habitats of Siberian cranes in Poyang Lake wetland would be about 12 m. Potential suitable habitat areas would be reduced from about 2300 km² at 5 m lake level to 530 km² at 15.6 m lake level. Landscape indices revealed that a higher water level would not be suitable for conservation of Siberian cranes. The proposed Poyang Lake Dam should be operated to control water levels for providing a favorable habitat condition for wintering migratory birds. It would create an unfavorable condition to the wintering habitats for Siberian cranes in Poyang Lake wetland if the dam would maintain the lake level higher than 12 m in autumn and winter.

Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R²) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R² = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution.

The Poyang Lake wetland in China is the largest wintering destination for Siberian cranes worldwide. Understanding the spatiotemporal characteristics of crane habitats is of great importance for ecological environment governance and biodiversity protection. The shallow water, grassland, and soft mudflat regions of the Poyang Lake wetland are ideal habitats for wintering Siberian cranes. Based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) remote sensing images, habitat areas were extracted and associated with various water levels taken on multiple dates. Landscape metrics were applied to describe the spatial structural characteristics of the crane habitats, and spatial statistics are used to explore the cold and hot spots of their distribution. Moreover, three indicators including sustainability, stability, and variety were applied to evaluate the vulnerability of the crane habitats under different hydrological conditions. Our findings indicate: (a) The main crane habitats exhibit a gradual decreasing degree of fragmentation in time, an obvious uncertainty of shape complexity and a relatively stable connectivity. (b) The crane habitats have a consistent spatial pattern of highly aggregated distributions associated with various water levels. (c) The hot spots of the habitats formed multiple “sheet” belts centered on the “Lake Enclosed in Autumn” regions, while the cold spots indicate a spatial pattern of axial distributions. (d) The majority of the hot spots of the habitats were distributed in sub-lakes found in the southeast part of the Poyang Lake watershed and the Nanjishan and Wucheng nature reserves, while the cold spots were mainly distributed in the main channels of the basins of Poyang Lake. (e) The sustainable habitats were mainly distributed in the “Lake Enclosed in Autumn” regions and intensively aggregated in two national nature reserves. (f) Under conditions of extremely low to average water levels (5.3–11.46 m), an increase of water level causes a decrease of the stability and variety of the crane habitats and weakens the aggregation structure.

Bamboo forest, especially moso bamboo forest, is very important to human society. However, our ability to detect large-scale moso bamboo with optical remote sensing is still limited due to the spectral similarity with other forest species and the influence of cloud occurrence. In this study, we examined the capability of dense Landsat time series for moso bamboo forest mapping by comparing three different interpretation schemes . For each scheme, two experimental groups were further conducted to investigate the usefulness of gray-level co-occurrence matrix (GLCM) textures. Considering classification accuracy, the full-season compositing strategy was regarded as the most efficient. It was generally beneficial to include GLCM textures as input features, although their usefulness would be partially offset due to noise/correlation issues. We also investigated the roles of 15 types of auxiliary covariates in extracting moso bamboo and found some of them could enhance the classification performance significantly. With the full-season compositing scheme and crucial auxiliary covariates, an improved moso bamboo mapping performance (93.21% in overall accuracy and 73.97% in minimum accuracy) was observed within the study area. Our evaluation results are promising to provide robust guidelines for remote mapping of moso bamboo forest over large areas.