Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving vegetation parameters, is constrained by spectral saturation problems and cloud cover. On the other hand, LiDAR data, which have been extensively used to estimate forest structure attributes, cannot provide sufficient spectral information of vegetation canopies. Thus, this study aimed to develop a novel synergistic approach to estimating biomass by integrating LiDAR data with Landsat 8 imagery through a deep learning-based workflow. First the relationships between biomass and spectral vegetation indices (SVIs) and LiDAR metrics were separately investigated. Next, two groups of combined optical and LiDAR indices (i.e., COLI1 and COLI2) were designed and explored to identify their performances in biomass estimation. Finally, five prediction models, including K-nearest Neighbor, Random Forest, Support Vector Regression, the deep learning model, i.e., Stacked Sparse Autoencoder network (SSAE), and multiple stepwise linear regressions, were individually used to estimate biomass with input variables of different scenarios, i.e., (i) all the COLI1 (ACOLI1), (ii) all the COLI2 (ACOLI2), (iii) ACOLI1 and all the optical (AO) and LiDAR variables (AL), and (iv) ACOLI2, AO and AL. Results showed that univariate models with the combined optical and LiDAR indices as explanatory variables presented better modeling performance than those with either optical or LiDAR data alone, regardless of the combination mode. The SSAE model obtained the best performance compared to the other tested prediction algorithms for the forest biomass estimation. The best predictive accuracy was achieved by the SSAE model with inputs of combined optical and LiDAR variables (i.e., ACOLI1, AO and AL) that yielded an R² of 0.935, root mean squared error (RMSE) of 15.67 Mg/ha, and relative root mean squared error (RMSE_r) of 11.407%. It was concluded that the presented combined indices were simple and effective by integrating LiDAR-derived structure information with Landsat 8 spectral data for estimating forest biomass. Overall, the SSAE model with inputs of Landsat 8 and LiDAR integrated information resulted in accurate estimation of forest biomass. The presented modeling workflow will greatly facilitate future forest biomass estimation and carbon stock assessments.

Building change detection (BCD) is essential for urban dynamic measurement. Deep learning has demonstrated significant potential in image processing, providing powerful feature extraction capabilities for BCD tasks. However, existing methods do not adequately mine multiscale feature information and ignore the importance of multiscale feature alignment, leading to an inadequate representation of the internal structure. Therefore, we propose a hybrid attention-aware transformer network (HATNet) designed to effectively extract and interact with multiscale context information. Specifically, HATNet first incorporates a hybrid attention-aware feature extractor (HAFE) module that integrates self-attention (SA) and coordinate-attention (CA) to effectively extract complementary multiscale features. The SA establishes long-range dependencies between multiscale features, while the CA captures spatial dependencies and preserves positional details. Then, we devise a building saliency detection enhancement (BSDE) module that utilizes three independent channels to facilitate the identification and localization of changed buildings, fostering a symbiotic relationship between unchanged and changed areas. Furthermore, we adopt a coarse-to-fine feature interaction (CFFI) module to progressively fuse multiscale features using a hierarchical strategy. In order to better locate the changed detail features, we introduce a global feature alignment (GFA) module to achieve global multiscale feature alignment. HATNet surpasses eight BCD methods on LEVIR-CD, WHU-CD, and S2Looking-CD datasets. The experimental results provide compelling evidence of our method to precisely detect and measure building changes. The codes are available at https://github.com/yzygit1230/HATNet .

Unmanned aerial vehicle (UAV) photogrammetry without ground control points (GCPs) can effectively improve production efficiency and reduce production costs; this method is especially advantageous in areas that are difficult for people to reach. However, there are a series of problems in UAV photogrammetry without GCPs. One of the main problems is that the accurate camera parameters cannot be obtained through the on-the-job calibration method; furthermore, the inaccurate principal distance will have a serious impact on the elevation accuracy of object points. The other one is that the projection deformation and earth curvature also have impacts on the elevation accuracy, when the mapping task is carried out in the map projection frame. This article explains the specific reasons of elevation errors and proposes an effective solution. First, the camera self-calibration is performed in a geocentric frame with control strips. Then, the exterior orientation elements of the images are calculated in the map projection frame without control strips. Finally, the elevation errors that are caused by the map projection deformation and the earth’s curvature are corrected. The experimental results show that the proposed method can achieve accurate mapping, and the elevation accuracy has been significantly improved.

This paper proposes a novel adjustment model for mosaicking low-overlap sweeping images captured by medium-altitude unmanned aerial vehicle (UAV) with a long focal length. Commonly used methods are not suitable for this type of data. Three innovations are proposed to make this possible: first, building a new error model denoted by an error homograph matrix, which includes the camera parameter error, perspective center error, image attitude error, and the error of projection plane; second, the error homograph matrix is considered an unknown variable and is optimized, instead of optimizing the exterior orientation element in commonly used photogrammetry method; and third, the proposed algorithm is a global optimization, avoiding the cumulative error that appeared in traditional homographic-based methods. The results reveal that the proposed adjustment model can effectively eliminate the misalignments in the seam lines, compared to the direct homograph transformation. The numerical experiment results also demonstrate that the algorithm has perfect convergence and stability. In addition, this method is also suitable for low-altitude UAV images when the covered area can be regarded as a plane.

Unmanned aerial vehicle (UAV) hyperspectral images are endowed with abundant spectral information and spatial texture details, which is crucial for the precise classification and monitoring of terrestrial features. Despite the imagery offers high spatial and spectral resolution along with exceptional mobility, UAV-borne hyperspectral images exhibit intricate intraclass spectral variability and high spatial heterogeneity among features, which consequently poses a significant obstacle to the accurate classification. Simultaneously, the high spectral resolution of UAV-borne hyperspectral images, constrained by sensor payload, often results in a relatively low signal-to-noise ratio, posing challenges for traditional deep learning models in effectively extracting features for fine classification tasks. To address the obstacles presented, this paper innovatively proposes a multi-feature fusion attention residual hybrid model for UAV-borne hyperspectral imagery classification. The proposed network initially employs an innovative sequential mechanism that leverages multi-scale filters and attention residuals, and followed by the fusion of a multi-cascade 2D-3D parallel structure to achieve a semantic-spectral-spatial multi-feature fusion, aimed at enhancing the fine classification of UAV-borne hyperspectral imagery. Experimental results using HySpex data highlight the superiority of the proposed approach, achieving an Overall Accuracy of 80.40%, which represents a significant Average Accuracy improvement of 5.22% over existing advanced classification methods. Furthermore, the proposed network demonstrates robust generalization and stability across UAV-borne hyperspectral imagery captured by diverse sensors with different spatial and spectral resolutions.