Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

Accurate and efficient estimation of forest volume or biomass is critical for carbon cycles, forest management, and the timber industry. Individual tree detection and segmentation (ITDS) is the first and key step to ensure the accurate extraction of detailed forest structure parameters from LiDAR (light detection and ranging). However, ITDS is still a challenge to achieve using UAV-LiDAR (LiDAR from Unmanned Aerial Vehicles) in broadleaved forests due to the irregular and overlapped canopies. We developed an efficient and accurate ITDS framework for broadleaved forests based on UAV-LiDAR point clouds. It involves ITD (individual tree detection) from point clouds taken during the leaf-off season, initial ITS (individual tree segmentation) based on the seed points from ITD, and improvement of initial ITS through a refining process. The results indicate that this new proposed strategy efficiently provides accurate results for ITDS. We show the following: (1) point-cloud-based ITD methods, especially the Mean Shift, perform better for seed point selection than CHM-based (Canopy Height Model) ITD methods on the point clouds from leaf-off seasons; (2) seed points significantly improved the accuracy and efficiency of ITS algorithms; (3) the refining process using DBSCAN (density-based spatial clustering of applications with noise) and kNN (k-Nearest Neighbor classifier) classification significantly reduced edge errors in ITS results. Our study developed a novel ITDS strategy for UAV-LiDAR point clouds that demonstrates proficiency in dense deciduous broadleaved forests, and this proposed ITDS framework could be applied to single-phase point clouds instead of the multi-temporal LiDAR data in the future if the point clouds have detailed tree trunk points.

Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote sensing approach. Therefore, we developed a method to extract the spatial extent of the UHI during the period from 2000 to 2021 in Nanjing, China, and explored the impact of urban two- and three-dimensional expansion on UHI spatial extent and UHI intensity. After cropland effects (i.e., bare soil) were eliminated, our proposed method combines the Getis-Ord-Gi* and the standard deviation of the normalized difference vegetation index (NDVI STD) to extract the UHI area from Landsat 5 and Landsat 8 images using land surface temperature (LST) spatial autocorrelation characteristics and the seasonal variation of vegetation. Our results show the following: (1) Bare farmland has a large influence on the extraction results of UHI—combined with the seasonal variation characteristics of NDVI STD, the impact of bare soil on UHI extraction was highly reduced, strongly improving the accuracy of UHI extraction. (2) The dynamics of the UHI area are consistent with the changes in the built-up area in Nanjing at both spatial and temporal scales, but with the increase of the urban green ratio, the UHI area of mature urban areas trends to decrease due to the cooling effect of green space. (3) The accumulation of population and GDP promote the vertical expansion of urban buildings. When the two-dimensional expansion of the city reaches saturation, the UHI intensity is primarily affected by three-dimensional urban expansion.

Chongming Island, the third largest island in China, has experienced dramatic shoreline changes due to erosion, river deposits, and human activities. While previous studies have shown the capacity of Landsat series images to extract shoreline dynamics, the spatial variation of shoreline dynamics and their corresponding driving factors remain unclear. Therefore, we established a method to monitor the shoreline dynamics of Chongming Island from 1984 to 2020 and to evaluate the driving factors of shoreline changes using a novel approach to Landsat image analysis. The method, based on the LISA (local indicator of spatial autocorrelation) concept, automatically extracted the shoreline from Landsat imagery. The results show that the LISA method, based on the SWIR1 band, has a high capacity for shoreline extraction in Chongming Island. By distinguishing the responses of the eastern and northern shorelines to upstream sediment loads and comprehensively analyzing the driving factors of eastern and northern dynamics, we found that: (i) although upstream sediment loads decreased dramatically, the shoreline of Chongming Island is still expanding due to human activities (i.e., reclamation and an estuary project) and sediment re-suspension from near-shore or cross-shore currents; (ii) the expansion of Chongming Island was initially due to the dynamics at the eastern shoreline, but the expansion of the eastern shoreline slowed after 2008 as upstream sedimentation slowed, less construction of cofferdams took place, and the Qingcaosha Reservoir was constructed; (iii) the northern shoreline of Chongming Island expanded rapidly after 1999, due to the merger of Xinlongsha, Xincunsha, and Chongming Island, and the transport of coastal and offshore sediments by hydrodynamic processes; and (iv) the main driving factors of eastern shoreline movement on Chongming Island are cofferdam reclamation and coastal engineering, and the changes at the northern shoreline are mainly affected by reclamation projects, offshore sediment supplies, and upstream sediment inflow. The results of this study provide theoretical fundamentals for land reclamation and future urban planning for Chongming Island.

Canopy closure (CC), a useful biophysical parameter for forest structure, is an important indicator of forest resource and biodiversity. Light Detection and Ranging (LiDAR) data has been widely studied recently for forest ecosystems to obtain the three-dimensional (3D) structure of the forests. The components of the Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) are similar to those of the airborne LiDAR, but with higher pulse density, which reveals more detailed vertical structures. Hemispherical photography (HP) had proven to be an effective method for estimating CC, but it was still time-consuming and limited in large forests. Thus, we used UAV-LiDAR data with a canopy-height-model-based (CHM-based) method and a synthetic-hemispherical-photography-based (SHP-based) method to extract CC from a pure poplar plantation in this study. The performance of the CC extraction methods based on an angular viewpoint was validated by the results of HP. The results showed that the CHM-based method had a high accuracy in a 45° zenith angle range with a 0.5 m pixel size and a larger radius (i.e., k = 2; $R^2$ = 0.751, $RMSE$ = 0.053), and the accuracy declined rapidly in zenith angles of 60° and 75° ($R^2$ = 0.707, 0.490; $RMSE$ = 0.053, 0.066). In addition, the CHM-based method showed an underestimate for leaf-off deciduous trees with low CC. The SHP-based method also had a high accuracy in a 45° zenith angle range, and its accuracy was stable in three zenith angle ranges ($R^2$: 0.688, 0.674, 0.601 and $RMSE$ = 0.059, 0.056, 0.058 for a 45°, 60° and 75° zenith angle range, respectively). There was a similar trend of CC change in HP and SHP results with the zenith angle range increase, but there was no significant change with the zenith angle range increase in the CHM-based method, which revealed that it was insensitive to the changes of angular CC compared to the SHP-based method. However, the accuracy of both methods showed differences in plantations with different ages, which had a slight underestimate for 8-year-old plantations and an overestimate for plantations with 17 and 20 years. Our research provided a reference for CC estimation from a point-based angular viewpoint and for monitoring the understory light conditions of plantations.