Sustainable forest management relies on the acquisition of timely (change detection) and accurate structural information of forest landscapes. Light detection and ranging (lidar) remote sensing platforms enable rapid, three-dimensional (3-D), structural data collection with a high spatial resolution. This study explores a functional carbon model applied to a dense, closed deciduous forest. Data are collected by manned airborne systems and unmanned aerial system, producing both lidar and structure-from-motion (SfM) 3-D mapping. A hybrid approach combining cost-effective SfM-generated data with lidar-derived digital elevation models also is explored, since the SfM fails to produce adequate terrain returns. Carbon modeling results are comparable to those achieved by the initial developers (r²  =  0.64 versus r²  =  0.72), despite the challenging uneven-aged forest environment. Vertical profiles, mapped utilizing a volumetric point density from the manned airborne lidar, are leveraged to train a binary classifier for disturbance detection. Producer’s accuracy, user’s accuracy, and Kappa statistic for disturbance detection are 94.1%, 92.2%, and 89.8%, respectively, showing a high likelihood of detecting disturbances (harvesting). The results bode well for the use of unmanned aerial system (UAS) systems, and either lidar or SfM, to assess forest stocking. Although disturbance detection is successful, further study is required to validate the use of UAS, and especially SfM, for this task.

Forest canopy height is a very important forest structural attribute. LiDAR and SAR are able to penetrate the forest canopy to obtain information on the understory and canopy vertical structure. But the single data of LiDAR or SAR has its own shortcomings in forest height extraction. We jointly use LiDAR and ALOS PALSAR data to retrieve forest canopy height. First, the extinction degree of the canopy is extracted using airborne LiDAR. The canopy is assumed to be uniform, and the extinction degree is divided by the canopy height to obtain the average extinction coefficient. Then, the extinction coefficient is substituted into random volume over ground (RVoG), and the forest canopy height is obtained. Experimental results showed that the collaborative inversion algorithm based on RVoG model proposed in this paper improves the accuracy of forest canopy height retrieval.

To better assess spatial variabilities in subtropical secondary forest biomass, the goal of the present study was to employ small-footprint, discrete-return light detection and ranging and unmanned aerial vehicles, integrated with structure from motion (UAV-SFM) data, to accurately estimate the stand characteristic parameters of subtropical forests. The Lorey’s height, density, basal area, volume, and aboveground biomass (AGB) of 30 plots (Ø10  m) were modeled using an array of SFM point cloud metrics and image color metrics (forestry vegetation coverage and color index set). Further, the individual models developed using stepwise multiregression analysis and a branch-and-bound algorithm were employed to examine the best models. Overall, the results indicated that the coefficient of determination (R²) for Lorey’s height (R²  =  0.58 to 0.95), volume (R²  =  0.29 to 0.71), and AGB (R²  =  0.27 to 0.64) were significantly enhanced compared with the density (R²  =  0.18 to 0.48), and basal area (R²  =  0.24 to 0.52). Utilizing independent stand-level data from ground inventory, our results revealed that, overall, the model fit was significant for most stand characteristic parameters, with relationships close to a 1:1 line, thereby indicating no significant bias [relative root mean square error   (  rRMSE  )    =  5.32  %   to 27.04%]. Our image metrics had a statistically significant association with Lorey’s height (rRMSE decreased by 0.08%), volume (rRMSE decreased by 0.58%), and AGB (rRMSE decreased by 3.11%), which provided additional explanatory power in the regression analysis. This research demonstrated the potential of UAV-SFM as a technology to accurately assess subtropical forest carbon while providing an improved elucidation of the positive effects of image color metrics.

Traditional field measurement methods are usually time-consuming and costly. With recent developments in remote sensing, precise estimation of some key forest parameters is becoming a close reality. One of the robust technologies being utilized for this aim is light detection and ranging (LiDAR). We used LiDAR to derive tree height and canopy density metrics using pixel values, texture features, and vegetation indices obtained from WorldView-3 imagery to estimate volume per hectare, mean height, dominant height, and number of trees per hectare. A total of 58 sample plots, predominantly composed of Pinus brutia, which is one of the most abundant tree species in Turkey, were analyzed. The adjusted R² values obtained by the best regression models, using LiDAR-derived metrics alone, were 0.66, 0.73, 0.83, and 0.83 for the volume per hectare, number of trees per hectare, and average and dominant heights, respectively. After integrating LiDAR-derived metrics with WorldView-3 imagery band values and subsequently with vegetation indices, higher adjusted R² values of 0.70 and 0.77, respectively, were obtained for the volume per hectare. In contrast, incorporating texture features besides other parameters had no positive effect on the accuracy of estimation.

Sun-induced chlorophyll fluorescence (SIF), now observed from space on a global scale, has been shown to be a powerful proxy for photosynthetic activity. Long-term in situ field canopy SIF measurements are improving, enabling better understanding of SIF signal and support to satellite missions. Since SIF retrievals rely on the absolute irradiance radiance measurements, accurate outdoor radiometric calibration is necessary to provide accurate radiance data, especially for long-term continuous measurements on forest stands. However, standard laboratory calibration methods are not practical to be performed for outdoor long-term measurements, and several in-field alternative methods are generally used, which may result in some uncertainties. Here we evaluate the effects of different radiometric calibration methods on SIF retrievals and its relation with gross primary productivity (GPP) at the canopy level. Three widely used methods are used, i.e., well-established laboratory methods with integrating sphere, laboratory-calibrated reference spectrometer (ASD FieldSpec Pro), and a light calibration source with a white reference panel. Our results indicate that different radiometric calibrations have marginal effects on vegetation indices but have significant effects on the SIF absolute value, and have slight effects on diurnal and seasonal patterns of SIF. Moreover, the relationships between GPP and SIF retrieved using different calibration coefficients are similar at both diurnal and seasonal scales, but with different regression slopes. Therefore, we recommend that a standard laboratory radiometric calibration is conducted using an integrating sphere before installation in the field and a regular in-field calibration with a well-calibrated spectrometer and white reference, especially for long-term observation above a forest canopy. Our findings have strong implications for the ongoing and future ground canopy SIF measurements and suggest the need for a consistent radiometric calibration method, especially for cross-site comparison studies.