Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha⁻¹ or above 50% of 150 Mg·ha⁻¹ are detectable using cross-normalized SAR data.

Leaf area index (LAI) is an important forest structural parameter in the process of photosynthesis. Most studies have investigated the estimation of forest LAI using the spectral information of optical remote sensing images or the height information from LiDAR data. This paper explored the estimation of forest LAI using canopy cover and forest height information extracted from stereo imagery acquired by cameras onboard an unmanned aerial vehicle (UAV). UAV remote sensing has gradually become practical in recent years. Two alternative methods were proposed to extract forest height information. The height indices method extracted forest height indices within each forest plot based on the vertical histogram of the canopy height model derived from stereo imagery, while the segmentation method characterized forest plots using tree numbers and average tree heights based on the individual tree segmentation. The results showed that canopy cover and forest height are complementary in the estimation of forest LAI no matter what method was used. The combined use of canopy cover and forest height information extracted by the segmentation method had a better estimation accuracy of forest LAI with R2 = 0.833 and RMSE = 0.288. This paper demonstrated a new approach to predict forest LAI using UAV optical images.

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R² higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.

Spaceborne stereoscopic systems have been growing in recent years, and the point cloud extracted from spaceborne stereo imagery has been used to measure forest spatial structures. These systems work on different viewing angles and image spatial resolutions, which are two critical factors determining the quality of the derived point cloud. In addition, the complex terrain is also a great challenge for the regional mapping of forest spatial structures using spaceborne stereo imagery. Although several theoretical models for simulating multi-view spectral features of forest canopies have been developed, there is hardly any report of a stereoscopic analysis using these models due to the limited size of the simulated forest scenes and the lack of a geometric sensory model (i.e., physical relationship between two-dimensional image coordinates and three-dimensional georeferenced coordinates). The stereoscopic features (i.e., parallax) are, as important as the spectral features contained in the multi-view images of a targeted area, the basis for the extraction of a point cloud. In this study, a new model, referred to as LandStereo model, has been proposed, which is capable of simulating the stereoscopic features of forest canopies over mountainous areas at landscape scales. The model comprised five parts, including defining the mountainous forest landscapes, setting the sun-senor observation geometry, simulating images, generating ground control points, and building geometric sensor models. The LandStereo model was validated over three different scenes, including flat forest landscapes, bare mountain landscapes, and mountainous forest landscapes. The results clearly demonstrated that the LandStereo model worked well on simulating stereoscopic features of both terrains and forest canopies at landscape scales. The extracted height of a forest canopy top from simulated stereo imagery was highly correlated to the truth (R² = 0.96 and RMSE = 0.99 m) over the flat terrains and (R² = 0.92 and RMSE = 1.15 m) over the mountainous areas. The LandStereo model provided a powerful tool to further our understanding of the relationships between forest spatial structures and point cloud extracted from stereo imagery acquired from different view angles and spatial resolutions under complex terrain conditions.