Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features.

To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas.

Urbanization is the natural trend of human social development, which leads to various changes in vegetation conditions. Analyzing the dynamics of landscape patterns and vegetation coverage in response to urban expansion is important for understanding the ecological influence of urban expansion and guiding sustainable urban development. However, existing studies on the effects of urbanization on vegetation conditions in severe cold regions are limited. Here, taking Harbin, China as an example, the study explored the evolution of the urban expansion process and adopted landscape metrics to derive landscape pattern changes from 2005 to 2020. Based on the fraction of vegetation coverage (FVC) derived from Landsat satellite observations during 2004–2020, we investigated the spatiotemporal change characteristics of FVC. By separating the direct and indirect effects of urbanization on vegetation growth, we quantified the impacts of urbanization on vegetation. The results show that the urban area increased by 70.37%, and urban expansion exhibited a compact sprawl pattern. Cropland and water were the major resources converted for urban expansion. The distribution of FVC exhibits a pattern that the urban fringe region is significantly higher than the central region. The FVC trend is decreased overall, but the changes are not significant with regional variation. Moreover, the average observed FVC decreased with increasing urban intensity. By contrast, the indirect impact is increased along the intensity gradient, with growth enhancement offsetting about 2.26%~2.71% of direct vegetation loss. The study further shows that vegetation growth responses to urbanization vary according to urbanization levels. Our findings provide detailed information and reveal the relationship between urban intensity and vegetation coverage, which could help to manage urban vegetation for planners and stakeholders.

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring.

The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) provides highly dense full waveform LiDAR data for global carbon monitoring. However, in forest environments, the complex canopy structure and terrain introduce significant background noise, making effective signal extraction challenging. To address this issue, an adaptive waveform denoising framework is proposed in this study. Bayesian optimization (BO) is first employed to adaptively determine the effective signal threshold for each waveform by minimizing structural volatility within background noise segments, thereby achieving reliable separation between effective signals and background noise. Subsequently, a structure-aware adaptive Gaussian filtering (SAAF) method is developed. Residual noise in the effective signal is suppressed by dynamically adjusting the Gaussian kernel width based on structural factors, including notch density, flat peak characteristics, peak skewness, and multipeak structures. The results indicate that, compared to the official TECIS parameter extraction (TPE) method, the BO method removes an additional 5.14% of redundant background noise energy, yielding a cleaner, more physically realistic effective signal. Furthermore, relative to manually annotated effective signal start and end points, the localization accuracy of the extracted start and end points is improved by 77.5% and 78.3%, respectively. For effective signal smoothing, SAAF maintains high precision across varying slopes and forest types, with peak signal-to-noise ratio (PSNR) consistently exceeding 44 dB and the correlation coefficient (CC) remaining above 0.96. This framework provides reliable, structurally complete waveform data for subsequent inversion of forest structure parameters.