The mapping of tropical rainforest forest structure parameters plays an important role in biodiversity and carbon stock estimation. The current mechanism models based on PolInSAR for forest height inversion (e.g., the RVoG model) are physical process models, and realistic conditions for model parameterization are often difficult to establish for practical applications, resulting in large forest height estimation errors. As an alternative, machine learning approaches offer the benefit of model simplicity, but these tools provide limited capabilities for interpretation and generalization. To explore the forest height estimation method combining the mechanism model and the empirical model, we utilized UAVSAR multi-baseline PolInSAR L-band data from the AfriSAR project and propose a solution of a mechanism model combined with machine learning. In this paper, two mechanism models were used as controls, the RVoG three-phase method and the RVoG phase-coherence amplitude method. The vertical structure parameters of the forest obtained from the mechanism model were used as the independent variables of the machine learning model. Random forest (RF) and partial least squares (PLS) regression models were used to invert the forest canopy height. Results show that the inversion accuracy of the machine learning method, combined with the mechanism model, is significantly better than that of the single-mechanism model method. The most influential independent variables were penetration depth, volume coherence phase center height, coherence separation, and baseline selection. With the precondition that the cumulative contribution of the independent variables was greater than 90%, the number of independent variables in the two study areas was reduced from 19 to 4, and the accuracy of the RF-RVoG-DEP model was higher than that of the PLS-RVoG-DEP model. For the Lope test area, the R² of the RVoG phase coherence amplitude method is 0.723, the RMSE is 8.583 m, and the model bias is −2.431 m; the R² of the RVoG three-stage method is 0.775, the RMSE is 7.748, and the bias is 1.120 m, the R² of the PLS-RVoG-DEP model is 0.850, the RMSE is 6.320 m, and the bias is 0.002 m; and the R² of the RF-RVoG-DEP model is 0.900, the RMSE is 5.154 m, and the bias is −0.061 m. The results for the Pongara test area are consistent with the pattern for the Lope test area. The combined “fusion model” offers a substantial improvement in forest height estimation from the traditional mechanism modeling method.

Even though studies on forest carbon storage are relatively mature, dynamic changes in carbon sequestration have been insufficiently researched. Therefore, we used panel data from 81 Pinus kesiya var. langbianensis forest sample plots measured on three occasions to build an ordinary regression model and a quantile-regression model to estimate carbon sequestration over time. In the models, the average carbon reserve of the natural forests was taken as the dependent variable and the average diameter at breast height (DBH), crown density, and altitude as independent variables. The effects of the DBH and crown density on the average carbon storage differed considerably among different age groups and with time, while the effect of altitude had a relatively insignificant influence. Compared with the ordinary model, the quantile-regression model was more accurate in residual and predictive analyses and removed large errors generated by the ordinary model in fitting for young-aged and over-mature forests. We are the first to introduce panel-data-based modeling to forestry research, and it appears to provide a new solution to better grasp change laws for forest carbon sequestration.

In recent years, the northward migration of elephant herds in China’s Yunnan Province has attracted unprecedented public attention to the conservation of Asian elephants, with habitat fragmentation and human disturbance thought to be key factors. In this study, we used Jiangcheng, a new distribution site for Asian elephants in southwest China, as an example, combined the available remote sensing and monitoring data with the MaxEnt3.4.1 model, to analyze the factors affecting the activities of Asian elephants under the conditions of human disturbance and habitat degradation. The Least Cumulative Resistance model was utilized to construct the potential ecological dispersal corridors, and the key corridors were identified through the gravity model to explore the ecological security pattern of the habitat of Asian elephants in Jiangcheng County. The results show that the habitat of Asian elephants in Jiangcheng County is fragmented, showing a north–south strip distribution, never moving to the northeast. The existing suitable habitat is located in the low-elevation area (<1500 m), which is close to water sources and roads, and there is no nature reserve in this area. The land is mainly occupied by scrub or grassland near mountainous forests, but part of it is also under cultivation, which leads to conflicts between humans and elephants occur frequently. There are 14 ecological source areas, which are mainly distributed in the two regions of Zhengdong and Kangping, and there are 92 ecological corridors, of which 3 are important corridors and 89 are general corridors. This study comprehensively analyzes the current status and connectivity of Asian elephant habitat in Jiangcheng County at the regional scale, which helps to optimize the pattern of suitable habitat, promotes the dispersal of Asian elephants and habitat connectivity, and provides realistic guidance and basic information for the conservation planning of isolated populations of this species and their habitats.

PolInSAR is an active remote sensing technique that is widely used for forest canopy height estimation, with the random volume over ground (RVoG) model being the most classic and effective forest canopy height inversion approach. However, penetration of microwave energy into the forest often leads to a downward shift of the canopy phase center, which leads to model underestimation of the forest canopy height. In addition, in the case of sparse and low forests, the canopy height is overestimated, owing to the large ground-to-volume amplitude ratio in the RVoG model and severe temporal decorrelation effects. To solve this problem, in this study, we conducted an experiment on forest canopy height estimation with the RVoG model using L-band multi-baseline fully polarized PolInSAR data obtained from the Lope and Pongara test areas of the AfriSAR project. We also propose various RVoG model error correction methods based on penetration depth by analyzing the model’s causes of underestimation and overestimation. The results show that: (1) In tall forest areas, there is a general underestimation of canopy height, and the value of this underestimation correlates strongly with the penetration depth, whereas in low forest areas, there is an overestimation of canopy height owing to severe temporal decorrelation; in this instance, overestimation can also be corrected by the penetration depth. (2) Based on the reference height RH100, we used training sample iterations to determine the correction thresholds to correct low canopy overestimation and tall canopy underestimation; by applying these thresholds, the inversion error of the RVoG model can be improved to some extent. The corrected R² increased from 0.775 to 0.856, and the RMSE decreased from 7.748 m to 6.240 m in the Lope test area. (3) The results obtained using the infinite-depth volume condition p-value as the correction threshold were significantly better than the correction results for the reference height, with the corrected R² value increasing from 0.775 to 0.914 and the RMSE decreasing from 7.748 m to 4.796 m. (4) Because p-values require a true height input, we extended the application scale of the method by predicting p-values as correction thresholds via machine learning methods and polarized interference features; accordingly, the corrected R² increased from 0.775 to 0.845, and the RMSE decreased from 7.748 m to 6.422 m. The same pattern was obtained for the Pongara test area. Overall, the findings of this study strongly suggest that it is effective and feasible to use penetration depth to correct for RVoG model errors.

The study on PolInSAR forest canopy height(FCH) inversion is an essential branch within the SAR field, where the temporal decorrelation(TD) inherent in the interferometric complex coherence of repeat-pass Polarization Interferometric Synthetic Aperture Radar (PolInSAR) data significantly impacts the precision of FCH estimation.This study is grounded in airborne multi-baseline PolInSAR data, employing Random Volume over Ground (RVoG) + VTD and Random Motion over Ground(RMoG) as the FCH inversion models. The classical RVoG three-stage method(TS) process was used to simplify the solution of RVoG+VTD and RMoG, and a novel, more efficient method was attempted to further streamline the solution of the RVoG+VTD model(RVoG+VTDs). The results demonstrate that the RVoG+VTDs model obtains the most satisfactory results, and the efficiency of the RVoG+VTDs model is 23 times more efficient than that of the RVoG+VTD; 19 times more efficient than that of the RMoG model, and 22 times more efficient than that of the RVoG model, respectively; and the inversion time consumed by the four models is 1.5 min, 35 min, 28 min, and 33 min, respectively. In the accuracy validation, the estimation accuracy of RVoG+VTDs still keeps the best accuracy, with corresponding R² values for the four methods (RVoG+VTDs, RVoG+VTD, RMoG, and RVoG) equal to 0.813, 0.774, 0.804, and 0.770, respectively. The RMSE values are 6.626 m, 7.277 m, 6.774 m, and 7.335 m.The study’s conclusions illustrate the feasibility of solving the RVoG+VTD and RMoG model by fixing the extinction coefficients in the model, with RVoG+VTDs taking precedence over the model selection, which can provide a valuable reference for the application of spaceborne PolInSAR data.

Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R² value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R² and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R² value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates.