ABSTRACT Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance. Synthetic Aperture Radar (SAR) images are often used to monitor the characteristics of glaciers based on the backscattering coefficient. However, the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered. This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band SAR data. The investigated regions have different climate patterns. We compare backscatter coefficient distributions for wet snow and firn, derived from maximum likelihood classification under various polarizations, alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn. However, a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer. We furthermore show, by analyzing weather stations on glaciers, that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit. In contrast, firn ratios across different orbits show less variation in winter, and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences. These findings demonstrate glaciers’ sensitivity to temperature fluctuations and the radar wave’s responsiveness to surface characteristics. Consequently, when employing SAR for glacier monitoring, it is crucial to consider the influence of orbit and polarization, in combination with temperature variations, and whether the season is winter or summer.

ABSTRACT With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

ABSTRACT Spatial-temporal dynamics monitoring of Arctic vegetation structure (i.e. distribution range of tundra and forest) is of great significance for evaluating global warming effect. Currently, time-series monitoring of Arctic vegetation structure relies primarily on the Normalized Difference Vegetation Index (NDVI), which is derived from optical remote sensing images. However, because of factors such as the long revisit period of satellites and the impact of climate, optical observations are severely lacking in the Arctic region. This results in NDVI time-series data highly discontinuous and difficult to reflect actual variations in Arctic vegetation structure, and the traditional time-series reconstruction method would usually fail for severe missing conditions. Therefore, this study developed a Time Series Reconstruction method considering Periodic Trend (TSR-PT), which is specifically for alleviating the severe missing observation condition in the Arctic region. It can separate the phenological change and trend change of the incomplete time series NDVI, and borrow the information from the neighboring unchanged years for compensate of the missing observations in current years, based on the learned inter-annual and intra-annual correlation. We explore its usability in monitoring vegetation structure variation in Vorkuta region (transition zone of tundra and taiga in the Arctic Circle) based on MODIS data. It is found that the proposed TSR-PT is able to reconstruct NDVI with reasonable phenological feature even the missing rate reaches over 70%, which is usually falsely constructed by traditional filtering or fitting method, and suppress them by 0.038 in terms of RMSE; besides, we find that since 21-century, the Arctic trees have continued to increase and encroach the original tundra ecosystem, which caused a largely Arctic vegetation structural change, and we believe the proposed method would largely promote the Arctic vegetation research.

ABSTRACT Accurate evaluation of terrestrial carbon balance is essential for designing climate change mitigation policies, and capabilities of remote sensing techniques in monitoring carbon fluxes are widely recognized for their great contributions to regional and global carbon budget accounting. In this review, we synthesized satellite-based data and methodologies to estimate the main flux components of terrestrial carbon balance and their uncertainties over the past two decades. The global gross primary production (GPP) during the period 2001–2022 is 134 ± 14 PgC yr−1, and nearly half of them occurs in tropical forest regions such as South America and Africa. Less than 2% of global GPP is converted into a net carbon sink of 2.28 ± 1.12 PgC yr−1 using satellite-based atmospheric inversion during 2015–2020, and this sink is comparable to the stock change-based estimate (2.49 PgC yr−1) but twice as large as model-based estimate (1.08 ± 0.78 PgC yr−1). By decomposing satellite-derived net carbon balance into different terms including satellite-derived carbon emissions from land-use change and wildfires (3.55 PgC yr−1), we inferred that ~ 43% of global GPP would be respired through soil microbes (57.1 PgC yr−1), but which is higher than the previous bottom-up estimate (39–46 PgC yr−1). We then propose that an accurate remote sensing of terrestrial carbon balance requires to enhance representations of photosynthetic responses to rising CO2 and disturbances, develop satellite-constrained belowground carbon dynamics and separate natural fluxes from anthropogenic CO2 emissions, by integrating multi-source satellite sensors in orbit, revolutionized remote sensing capabilities with focused field campaigns in data-scarce regions.

ABSTRACT Information about trees in forest is essential for the assessment of the quantity and the quality of forest ecosystem services. Recently, Deep Learning (DL) methods were regarded as a new cornerstone of algorithm development. Semantic annotations of 3D forest scenes are fundamental for DL algorithm developments. Its necessity has become more urgent as DL is data-driven and requires large amount of training and verification data. However, high-quality annotated forest datasets are still rare, as trees comprise of irregular structures and small components and pose significantly greater challenges even for manual recognition in comparison with artificial objects. This paper introduces a new open point cloud dataset ForestSemantic for forest semantic studies at both individual tree- and plot-levels. The dataset is based on TLS data with different forest conditions. Manual annotation was carried out to a level of detail 4, i.e., until all visible branches. Semantic information is provided at both plot- and tree-levels, as well as at both object- and point-level. Thus, the dataset supports both instance and semantic studies, such as objects detection and segmentation and classification at both tree- and plot-levels. In addition, the dataset also provides comprehensive structural tree traits as reference for further methodological development and verification. This dataset is expected to facilitate research in new dimensions and benchmarks of different systems and solutions. A few examples are demonstrated in this paper to unveil the potentials of the dataset for various applications. In future, it is also possible to simulate other types of point clouds by down-sampling and deforming, and to transfer the dataset for training and verification of other close-range sensing systems, as the dataset was generated using TLS point clouds that represent the highest spatial resolution and geometric accuracy in all close-range point clouds.

ABSTRACT Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

ABSTRACT True Digital Orthophoto Maps (TDOMs) have high geometric accuracy and rich image characteristics, making them essential geographic data for national economic and social development. Complex terrain and artificial structures, automatic distortion elimination and occluded area recovery in TDOM generation pose significant challenges. Hence, the need for further improvements in both mapping accuracy and automation is highlighted. In this paper, we present an approach for generating a TDOM based on a Neural Radiance Field (NeRF) without utilizing prior three-dimensional geometry information called an Ortho Neural Radiance Field (Ortho-NeRF). The Ortho-NeRF divides a large-scale scene into small tiles, implicitly reconstructing each tile by selecting pixels on posed images, and individually generate TDOMs of all tiles using a true-ortho-volume rendering before mosaicking. Additionally, the Ortho-NeRF uses a strategy to skip empty spaces and adaptively set the spatial resolution of a voxel grid, improving the generated TDOM quality with fewer computational resources. Many experiments showed that our approach outperforms ContextCapture, Metashape, Pix4DMapper, and Map2DFusion, especially in challenging areas. Owing to its global consistency and continuous nature, Ortho-NeRF was able to effectively reconstruct the geometry information and details, generating TDOMs without distortion or misalignment. Eight ground control points were randomly selected to evaluate the geometric accuracy of the TDOMs, with an average median error of 0.267 m. The length between two points on a plane was also measured for quantitative evaluation, with a mean absolute error of 0.08 m and a mean relative error of 0.14%. Compared with the NeRF efficiency, that of the Ortho-NeRF increased 104 times in training and about 1000 times in rendering.

ABSTRACT The accurate three-dimensional wind field obtained from a Doppler lidar not only helps to comprehend the refined structure of complex airflow but also provides important and valuable solutions for many fields. However, the underlying homogeneity assumption of the typical wind retrieval methods, such as Doppler Beam Swinging (DBS) and Velocity Azimuth Display (VAD) based on a single-lidar, will introduce the measurement uncertainty in complex terrain. In this paper, a new design of a wind measurement campaign involving seven lidars was carried out, which contained the three-lidar-based Virtual Tower (VT) using a time-space synchronization technique and four single-lidars with different elevation angles. This study investigates the performance of VT and VAD measurements under various conditions and evaluates the sensitivity of wind measurement uncertainty of VAD to the horizontal spatial- and probe volume-average effects associated with elevation angles of the laser beam. The inter-comparison results between VT and four VADs show consistent trends with small relative errors under neutral atmospheric conditions with weak wind velocity. Under convective or high Turbulence Intensity (TI) conditions, the relative errors between VT and VAD become larger and more fluctuant. Moreover, it is found that the measurement uncertainty of VAD increases at a given elevation angle with the increasing measurement heights, which is caused by the horizontal homogeneity associated with the conical scanning area. Additionally, the simulated and measured results of four VADs indicate that a larger elevation angle corresponds to a lower measurement uncertainty for a given height.

ABSTRACT Accurate estimation of forest aboveground biomass is essential for the assessment of regional carbon cycle and the climate change in the terrestrial ecosystem. Currently, ensemble learning algorithms and cross-validation methods have been widely applied to estimate regional forest Above Ground Biomass (AGB). However, the effects of ensemble learning algorithms, validation methods, and their interactions on forest AGB estimation were rarely investigated. Based on Landsat 8 Operational Land Imager (OLI) imagery, Airborne Laser Scanning (ALS) data and China’s National Forest Continuous Inventory data, this study explored the effects of five ensemble learning algorithms, including Simple Averaging (SA), Weighted Averaging (WA), Stacked Generalization (SG), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and two validation approaches (i.e. 10-fold and leave-one-out cross-validation) on the AGB estimation of the Natural Secondary Forests (NSFs) in northeast China. The results revealed that the ensemble learning algorithms that combine heterogenous-based models (i.e. SA, WA, SG) generally produced higher accuracy than the base models (i.e. Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Convolutional Neural Network (CNN)). Among all ensemble learning algorithms, the SG algorithm has the highest accuracy whereas the XGBoost algorithm has the lowest accuracy. Although prediction models considerably impact the accuracy of AGB estimation, the validation approach also plays a non-negligible role in AGB estimation. The leave-one-out cross-validation produced much higher accuracy than the 10-fold cross-validation using the same prediction model and tends to generate over-optimistic AGB estimates compared to 10-fold cross-validation, especially for the averaging and stacking ensemble learning algorithms (i.e. SA, WA, SG). This study highlights the potential challenges of applying a leave-one-out cross-validation approach and provides a scientific foundation for the feasibility of different ensemble learning algorithms and cross-validation approaches for accurate AGB estimation.

ABSTRACT The novel coronavirus 2019 (COVID-19)-imposed restrictions in 2020 and 2021 led to a notable reduction in human activity, providing an opportunity to study the impact of human activity on global vegetation productivity. The impacts on vegetation productivity are of particular interest, as vegetation carbon sinks serve as one of the main pathways for carbon neutrality. This study investigated the impacts of the COVID-19 pandemic restrictions on global vegetation productivity in 2020 and 2021 by leveraging remotely sensed big data and model data. This study revealed reduced atmospheric emissions and increased radiation reaching the surface in these two years. Compared to the time period from 2017 to 2019, global vegetation productivity increased by 1.95% and 1.15% in 2020 and 2021, respectively, with a majority of countries hit by the COVID-19 pandemic showing enhanced vegetation productivity. This study conclude that a sudden reduction in human activities due to COVID-19 restrictions plays a positive role in global vegetation productivity and carbon neutrality. The widely implemented COVID-19 control measures at the global scale allow scholars to observe the responding mechanism of vegetation productivity, greatly benefiting the rethinking of existing sustainable development strategies.