Guyue Hu

Leaf Area Index (LAI) is an important biophysical parameter in studies of regional and global ecosystems. However, terrain-induced distortion of surface reflectance can reduce the reliability of vegetation indices (VIs) in characterizing the canopy structure, thereby introducing uncertainties in LAI retrieval. In this study, an LAI retrieval method for mountainous areas based on the combination of terrain-corrected VIs and the random forest algorithm was proposed. Typical topographic correction models (Cosine+C, SCS+C, and Statistical–Empirical) were applied to normalize surface reflectance, and the terrain-corrected normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI) were constructed accordingly. Then, random forest regression was used for LAI retrieval, and the proposed method was validated through comparisons of this LAI with the field observations and original VI-based methods. The results showed that topographic correction effectively reduced the radiometric distortions induced by topography, and the NDVI_SCSC-based retrieval method performed well under various terrain conditions (with R² and RMSE of 0.927 and 0.151, respectively). In addition, to investigate the effects of different terrain factors and illumination conditions on LAI retrieval, methods based on the original and terrain-corrected VIs were compared for surfaces with different slopes and aspects. The results revealed that the terrain-corrected VIs can improve the performance of LAI retrieval in areas with terrain-induced reflectance distortion. Finally, the optimal method successfully estimated LAI in the study area. Therefore, the proposed LAI retrieval method for mountainous areas is an effective tool for extracting surface biophysical parameters, and can provide a reliable approach for regional ecological monitoring and evaluation.

The high-precision identification of unstable rock masses in rugged terrain is important for engineering safety and geological hazard prevention. However, shadow occlusion, complex backgrounds, and blurred boundaries caused by rugged terrain often limit the performance of optical feature-based segmentation models, resulting in missed detections and false positives. In this study, to address this issue, we propose a dual-modality deep learning network, named the Global Topography-aware Segmentation Network (GTSNet), that integrates high-resolution unmanned aerial vehicle (UAV) imagery and digital elevation model (DEM) data. The proposed network introduces DEM-derived terrain-semantic information into optical feature modeling through a multi-scale Topography-aware Fusion Module. By using high-level geomorphological context to adaptively recalibrate low-level spatial details, GTSNet improves boundary representation and reduces interference from complex backgrounds. Experiments were conducted on one unstable rock mass dataset compiled from UAV data collected at seven alpine canyon hydropower engineering areas in China. The results show that GTSNet achieved an overall accuracy (Acc) of 90.14%, an F1-score of 75.75%, and an intersection over union (IoU) of 60.97%, showing higher segmentation performance than the six compared semantic segmentation networks under the same RGB + DEM input setting. In addition, GTSNet obtained a Precision of 74.87% and a Recall of 76.66%, indicating a more balanced performance between missed detections and false positives. The results suggest that the integration of RGB imagery and DEM-derived terrain information, together with global context modeling and topography-aware feature fusion, contributes to improved unstable rock mass segmentation in complex canyon environments. This study provides a useful deep learning framework for UAV-based unstable rock mass interpretation in hydropower engineering areas.

Accurately modelling radiation interactions within canopy layers and soil backgrounds is crucial for biophysical variables retrieval across regional or global scales. However, terrain relief introduces significant uncertainties in modeling CR by modulating solar and viewing geometry and exacerbating landscape heterogeneity. Traditional CR models, designed primarily for flat and homogeneous landscapes, often inadequately represent these complex interactions. In this study, we present a canopy reflectance model suitable for heterogeneous structures on sloping terrain. By extending the stochastic radiative transfer theory from flat terrain to sloping terrain and integrating the soil General Spectral Vector, the GSV-SRTS model was introduced. This enables accurate prediction of soil-canopy radiative transfer within subpixel 3D heterogeneous mountain landscapes. The proposed GSV-SRTS model was evaluated against the Discrete Anisotropic Radiative Transfer (DART) model, compared with typical mountain canopy reflectance models, and validated against remote sensing observations at varying spatial resolutions. The results showed that the GSV-SRTS model achieves good accuracy in the comparisons with DART (R²=0.9136 (0.9052) and root-mean-square errors (RMSE)=0.0246 (0.0216) in the red (NIR (Near-Infrared)) band) and performs well in real mountainous areas, particularly with high spatial resolution remote sensing observations (R²=0.9078 (0.9143) and RMSE=0.0201 (0.0212)). Furthermore, the GSV-SRTS model effectively captures the impacts of canopy structure and terrain factors on bidirectional reflectance. This underscores the GSV-SRTS model as a reliable physical tool for simulating radiation regimes over sloping terrain, with the potential to enhance the accuracy of biophysical variable retrieval from remote sensing observations.

Accurately modelling radiation interactions within canopy layers and soil backgrounds is crucial for biophysical variables retrieval across regional or global scales. However, terrain relief can introduce significant uncertainties into forward radiative transfer modeling. Despite the development of numerous canopy reflectance models for sloping terrain, the heterogeneous characteristics of soil-canopy objects have often been overlooked, leading to distortions in the bidirectional reflectance distribution in small-scale landscapes. In this study, we present a canopy reflectance model suitable for heterogeneous structures on sloping terrain. By extending the stochastic radiative transfer theory from flat terrain to sloping terrain and integrating the soil General Spectral Vector, the GSV-SRTS model was introduced. This enables accurate prediction of soil-canopy radiative transfer within subpixel 3-D heterogeneous mountain landscapes. The proposed GSV-SRTS model was evaluated against the Discrete Anisotropic Radiative Transfer (DART) model, compared with typical mountain canopy reflectance models, and validated against remote sensing observations at varying spatial resolutions. The results showed that the GSV-SRTS model achieves good accuracy in the comparisons with DART (R² = 0.9136 (0.9052) and root-mean-square errors (RMSE) = 0.0246 (0.0216) in the red (NIR (Near-Infrared)) band) and performs well in real mountainous areas, particularly with high spatial resolution remote sensing observations (R² = 0.9078 (0.9143) and RMSE = 0.0201 (0.0212)). Furthermore, the GSV-SRTS model effectively captures the impacts of canopy structure and terrain factors on bidirectional reflectance. This underscores the GSV-SRTS model as a reliable physical tool for simulating radiation regimes over sloping terrain, with the potential to enhance the accuracy of biophysical variable retrieval from remote sensing observations.

Accurately modelling radiation interactions within canopy layers and soil backgrounds is crucial for biophysical variables retrieval across regional or global scales. However, terrain relief can introduce significant uncertainties into forward radiative transfer modeling. Despite the development of numerous canopy reflectance models for sloping terrain, the heterogeneous characteristics of soil-canopy objects have often been overlooked, leading to distortions in the bidirectional reflectance distribution in small-scale landscapes. In this study, we present a canopy reflectance model suitable for heterogeneous structures on sloping terrain. By extending the stochastic radiative transfer theory from flat terrain to sloping terrain and integrating the soil General Spectral Vector, the GSV-SRTS model was introduced. This enables accurate prediction of soil-canopy radiative transfer within subpixel 3-D heterogeneous mountain landscapes. The proposed GSV-SRTS model was evaluated against the Discrete Anisotropic Radiative Transfer (DART) model, compared with typical mountain canopy reflectance models, and validated against remote sensing observations at varying spatial resolutions. The results showed that the GSV-SRTS model achieves good accuracy in the comparisons with DART (R² = 0.9136 (0.9052) and root-mean-square errors (RMSE) = 0.0246 (0.0216) in the red (NIR (Near-Infrared)) band) and performs well in real mountainous areas, particularly with high spatial resolution remote sensing observations (R² = 0.9078 (0.9143) and RMSE = 0.0201 (0.0212)). Furthermore, the GSV-SRTS model effectively captures the impacts of canopy structure and terrain factors on bidirectional reflectance. This underscores the GSV-SRTS model as a reliable physical tool for simulating radiation regimes over sloping terrain, with the potential to enhance the accuracy of biophysical variable retrieval from remote sensing observations.

Accurately modelling radiation interactions within canopy layers and soil backgrounds is crucial for biophysical variables retrieval across regional or global scales. However, terrain relief can introduce significant uncertainties into forward radiative transfer modeling. Despite the development of numerous canopy reflectance models for sloping terrain, the heterogeneous characteristics of soil-canopy objects have often been overlooked, leading to distortions in the bidirectional reflectance distribution in small-scale landscapes. In this study, we present a canopy reflectance model suitable for heterogeneous structures on sloping terrain. By extending the stochastic radiative transfer theory from flat terrain to sloping terrain and integrating the soil General Spectral Vector, the GSV-SRTS model was introduced. This enables accurate prediction of soil-canopy radiative transfer within subpixel 3-D heterogeneous mountain landscapes. The proposed GSV-SRTS model was evaluated against the Discrete Anisotropic Radiative Transfer (DART) model, compared with typical mountain canopy reflectance models, and validated against remote sensing observations at varying spatial resolutions. The results showed that the GSV-SRTS model achieves good accuracy in the comparisons with DART (R² = 0.9136 (0.9052) and root-mean-square errors (RMSE) = 0.0246 (0.0216) in the red (NIR (Near-Infrared)) band) and performs well in real mountainous areas, particularly with high spatial resolution remote sensing observations (R² = 0.9078 (0.9143) and RMSE = 0.0201 (0.0212)). Furthermore, the GSV-SRTS model effectively captures the impacts of canopy structure and terrain factors on bidirectional reflectance. This underscores the GSV-SRTS model as a reliable physical tool for simulating radiation regimes over sloping terrain, with the potential to enhance the accuracy of biophysical variable retrieval from remote sensing observations.

Accurately modelling radiation interactions within canopy layers and soil backgrounds is crucial for biophysical variables retrieval across regional or global scales. However, terrain relief can introduce significant uncertainties into forward radiative transfer modeling. Despite the development of numerous canopy reflectance models for sloping terrain, the heterogeneous characteristics of soil-canopy objects have often been overlooked, leading to distortions in the bidirectional reflectance distribution in small-scale landscapes. In this study, we present a canopy reflectance model suitable for heterogeneous structures on sloping terrain. By extending the stochastic radiative transfer theory from flat terrain to sloping terrain and integrating the soil General Spectral Vector, the GSV-SRTS model was introduced. This enables accurate prediction of soil-canopy radiative transfer within subpixel 3-D heterogeneous mountain landscapes. The proposed GSV-SRTS model was evaluated against the Discrete Anisotropic Radiative Transfer (DART) model, compared with typical mountain canopy reflectance models, and validated against remote sensing observations at varying spatial resolutions. The results showed that the GSV-SRTS model achieves good accuracy in the comparisons with DART (R² = 0.9136 (0.9052) and root-mean-square errors (RMSE) = 0.0246 (0.0216) in the red (NIR (Near-Infrared)) band) and performs well in real mountainous areas, particularly with high spatial resolution remote sensing observations (R² = 0.9078 (0.9143) and RMSE = 0.0201 (0.0212)). Furthermore, the GSV-SRTS model effectively captures the impacts of canopy structure and terrain factors on bidirectional reflectance. This underscores the GSV-SRTS model as a reliable physical tool for simulating radiation regimes over sloping terrain, with the potential to enhance the accuracy of biophysical variable retrieval from remote sensing observations.

Accurately modelling radiation interactions within canopy layers and soil backgrounds is crucial for biophysical variables retrieval across regional or global scales. However, terrain relief can introduce significant uncertainties into forward radiative transfer modeling. Despite the development of numerous canopy reflectance models for sloping terrain, the heterogeneous characteristics of soil-canopy objects have often been overlooked, leading to distortions in the bidirectional reflectance distribution in small-scale landscapes. In this study, we present a canopy reflectance model suitable for heterogeneous structures on sloping terrain. By extending the stochastic radiative transfer theory from flat terrain to sloping terrain and integrating the soil General Spectral Vector, the GSV-SRTS model was introduced. This enables accurate prediction of soil-canopy radiative transfer within subpixel 3-D heterogeneous mountain landscapes. The proposed GSV-SRTS model was evaluated against the Discrete Anisotropic Radiative Transfer (DART) model, compared with typical mountain canopy reflectance models, and validated against remote sensing observations at varying spatial resolutions. The results showed that the GSV-SRTS model achieves good accuracy in the comparisons with DART (R² = 0.9136 (0.9052) and root-mean-square errors (RMSE) = 0.0246 (0.0216) in the red (NIR (Near-Infrared)) band) and performs well in real mountainous areas, particularly with high spatial resolution remote sensing observations (R² = 0.9078 (0.9143) and RMSE = 0.0201 (0.0212)). Furthermore, the GSV-SRTS model effectively captures the impacts of canopy structure and terrain factors on bidirectional reflectance. This underscores the GSV-SRTS model as a reliable physical tool for simulating radiation regimes over sloping terrain, with the potential to enhance the accuracy of biophysical variable retrieval from remote sensing observations.

Rubber tree phenotyping is transitioning from labor-intensive manual techniques toward high-throughput intelligent sensing platforms. However, the advancement of high-throughput phenotyping remains hindered by complex canopy architectures and pronounced seasonal morphological variations. To address these challenges, this paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems. The proposed approach begins by extracting stable wooden structures through a region-of-interest (ROI) segmentation process. Repeatable WSKs are then generated using a newly proposed wood structure significance (WSS) score, which quantifies and identifies salient regions across multi-view data. For transformation estimation, descriptor matching, WSS constraints, and geometric consistency optimization are integrated into a fast global registration (FGR) pipeline. Extensive evaluation across 25 plots covering 5 sites at the National rubber plantation base in Danzhou, Hainan, China, demonstrates that the method achieves a mean co-registration accuracy of 9 cm. Further analysis under varying seasonal canopy complexities confirms its robustness and critical role in enabling high-precision rubber tree phenotyping.

Accurately modelling radiation interactions within canopy layers and soil backgrounds is crucial for biophysical variables retrieval across regional or global scales. However, terrain relief can introduce significant uncertainties into forward radiative transfer modeling. Despite the development of numerous canopy reflectance models for sloping terrain, the heterogeneous characteristics of soil-canopy objects have often been overlooked, leading to distortions in the bidirectional reflectance distribution in small-scale landscapes. In this study, we present a canopy reflectance model suitable for heterogeneous structures on sloping terrain. By extending the stochastic radiative transfer theory from flat terrain to sloping terrain and integrating the soil General Spectral Vector, the GSV-SRTS model was introduced. This enables accurate prediction of soil-canopy radiative transfer within subpixel 3-D heterogeneous mountain landscapes. The proposed GSV-SRTS model was evaluated against the Discrete Anisotropic Radiative Transfer (DART) model, compared with typical mountain canopy reflectance models, and validated against remote sensing observations at varying spatial resolutions. The results showed that the GSV-SRTS model achieves good accuracy in the comparisons with DART (R² = 0.9136 (0.9052) and root-mean-square errors (RMSE) = 0.0246 (0.0216) in the red (NIR (Near-Infrared)) band) and performs well in real mountainous areas, particularly with high spatial resolution remote sensing observations (R² = 0.9078 (0.9143) and RMSE = 0.0201 (0.0212)). Furthermore, the GSV-SRTS model effectively captures the impacts of canopy structure and terrain factors on bidirectional reflectance. This underscores the GSV-SRTS model as a reliable physical tool for simulating radiation regimes over sloping terrain, with the potential to enhance the accuracy of biophysical variable retrieval from remote sensing observations.