China’s massive wave of urbanization may be threatened by land subsidence. Using a spaceborne synthetic aperture radar interferometry technique, we provided a systematic assessment of land subsidence in all of China’s major cities from 2015 to 2022. Of the examined urban lands, 45% are subsiding faster than 3 millimeters per year, and 16% are subsiding faster than 10 millimeters per year, affecting 29 and 7% of the urban population, respectively. The subsidence appears to be associated with a range of factors such as groundwater withdrawal and the weight of buildings. By 2120, 22 to 26% of China’s coastal lands will have a relative elevation lower than sea level, hosting 9 to 11% of the coastal population, because of the combined effect of city subsidence and sea-level rise. Our results underscore the necessity of enhancing protective measures to mitigate potential damages from subsidence.

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R² = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R² = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R²) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with pre-processing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this study, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and kappa coefficient (93.88%), but ranked second for the root mean square error of the digital terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, our method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics.