Search Results (1,680)

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Assessing post-fire disturbance in Mediterranean ecosystems is essential for quantifying ecological impacts and guiding restoration strategies. This study evaluates fire severity following an extreme wildfire event (~28,000 ha) in northwestern Spain using vegetation indices (VIs) derived from PRISMA hyperspectral imagery, validated against field-based Composite Burn Index (CBI) measurements at the vegetation, soil, and site levels across three vegetation formations (coniferous forests, broadleaf forests, and shrublands). Hyperspectral VIs were benchmarked against multispectral VIs derived from Sentinel-2. Hyperspectral VIs yielded stronger correlations with CBI values than multispectral VIs. Vegetation-level CBI showed the highest correlations, reflecting the sensitivity of most VIs to canopy structural and compositional changes. Indices incorporating red-edge, near-infrared (NIR), and shortwave infrared (SWIR) bands demonstrated the greatest explanatory power. Among hyperspectral indices, DVIRED, EVI, and especially CAI performed best. For multispectral data, NDRE, CIREDGE, ENDVI, and GNDVI were the most effective. These findings highlight the strong potential of hyperspectral remote sensing for accurate, scalable post-fire severity assessment in heterogeneous Mediterranean ecosystems. Full article

Fractional Vegetation Cover (FVC) dynamics on the Qinghai–Tibetan Plateau (QTP) are critical indicators for assessing ecosystem condition. However, uncertainties persist in the accuracy of existing FVC products over the QTP due to retrieval differences, scale effects, and limited validation data. This study utilized the Google Earth Engine platform to integrate unmanned aerial vehicle (UAV) observations, Sentinel-2, MODIS, climate, and topography datasets, and proposed a comprehensive framework incorporating dual-index screening, machine learning optimization, and multidimensional validation to systematically assess the accuracy of GEOV3, GLASS, and MuSyQ FVC products in the alpine grasslands. The dual-index screening reduced validation uncertainty by improving the spatial representativeness of ground data. To build a high-precision evaluation dataset with limited inter-class coverage, recursive feature elimination and grid search were applied to optimize five ML models, and CatBoost achieved the superior performance (R² = 0.880, RMSE = 0.122), followed by XGBoost, GBM, LightGBM, and RF models. Four validation scenarios were implemented, including direct validation using 250 m UAV plot FVC and multi-scale validation using a 10 m FVC reference aggregated to product grids. Results show that GEOV3 (R² = 0.909–0.925, RMSE = 0.082–0.103) outperformed GLASS (R² = 0.742–0.771, RMSE = 0.138–0.175) and MuSyQ (R² = 0.739–0.746, RMSE = 0.138–0.181), both of which exhibited systematic underestimation. This framework significantly enhances FVC product validation reliability, providing a robust solution for remote sensing product validation in alpine grassland ecosystems. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

This study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines. The workflow integrates 2D image analysis, ExGR-based leaf segmentation, and 3D reconstruction using Structure-from-Motion (SfM). Multi-angle canopy images were collected repeatedly during the growing seasons, and destructive leaf sampling was conducted to quantify true leaf area across multiple vines and years. After removing non-leaf structures with ExGR filtering, the point clouds were voxelized at a 1 cm³ resolution to derive structural occupancy metrics. Voxel-based leaf area showed strong within-vine correlations with destructively measured values (R² = 0.77–0.95), while cross-vine variability was influenced by canopy complexity, illumination, and point-cloud density. In contrast, optical LAI tools (DHP and LAI–2000) exhibited negligible correspondence with true leaf area due to multilayer occlusion and lateral light contamination typical of pergola systems. This expanded, multi-year analysis demonstrates that voxel occupancy provides a robust and scalable indicator of canopy structural density and leaf area, offering a practical foundation for remote-sensing-based phenotyping, yield estimation, and data-driven management in perennial fruit crops. Full article

Moso bamboo, widely distributed in subtropical regions of China, plays an important role in forest management and carbon cycle research. However, accurate estimation of tree density and aboveground biomass (AGB) remains challenging due to the unique characteristics of Moso bamboo forests in their growth and stand structure. This research aims to develop a new procedure for bamboo tree density and AGB estimation based on UAV-LiDAR and sample plots from multiple sites through comparative analysis of the incorporation of two groups of variables—regular point cloud metrics (e.g., height, point density) and layered texture metrics—and three modeling methods—multiple linear regression (MLR), mixed-effects modeling (MEM), and hierarchical Bayesian modeling (HBM). The results showed that incorporating layered texture metrics with regular variables substantially improved the estimation accuracy of both tree density and AGB. Among these models, HBM achieved the highest predictive performance, yielding coefficient of determination (R²) values of 0.54 for tree density and 0.59 for AGB, with corresponding relative root mean square errors (rRMSE) of 21.46% and 17.97%. This study presents a novel and effective method for estimating Moso bamboo tree density and AGB using multi-site UAV-LiDAR and sample plots, offering a scientific basis for precise management and carbon stock assessment. Full article

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R² = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations. Full article

Spectral indices are widely used to assess vegetation fire severity following wildland fires. Although essential, ground-based assessments of how such indices change due to varying fire intensities remain limited, especially with deciduous tree species that exhibit resprouting. In this paper, we evaluate the efficacy of detecting post-fire physiological change and top kill in quaking aspen (Populus tremuloides) saplings using differenced spectral indices. Saplings (n = 64) were burned under controlled conditions over a range of discrete fire intensity levels from 0 to 4.0 MJ m⁻², and reflectance was collected pre-fire and at six post-fire intervals up to 16 weeks. Ten spectral indices (CCI, CSI, MIRBI, NDVIL8, NBR, NBRL8, PRI, SAVI, SW-NIR_ratio, and SW-SW_ratio) were calculated, differenced from pre-fire, and related to the change in net photosynthesis and top kill. Fire intensity most strongly influenced the observed spectral changes at weeks 1–2 post-fire, especially for ΔCSI, ΔCCI, and ΔPRI. Pre- to post-fire change in net photosynthesis was strongly related (Tjur’s R² > 0.5) with ΔCCI, ΔCSI, ΔNBRL8, and the ΔSW–NIR ratio at one week post-fire. Of the spectral indices assessed, ΔCCI and ΔPRI were most effective at predicting top kill. This study illustrates the potential of spectral indices for monitoring vegetation fire severity in deciduous tree species. Full article

Walnut (Juglans regia L.), a critical economic species, experiences substantial declines in fruit quality and yield due to Walnut Leaf Scorch (WLS). This issue is particularly severe in the Xinjiang Uygur Autonomous Region (XUAR)—one of Asia’s leading walnut-producing regions. To mitigate the disease, timely and efficient monitoring approaches for detecting infected trees and quantifying their disease severity are in urgent demand. In this study, we explored the feasibility of developing a predictive model for the precise quantification of WLS severity. First, five 4-mu (1 mu = 0.067 ha) sample plots were established to identify infected individual trees, from which the WLS Disease Index (DI) was calculated for each tree. Concurrently, hyperspectral data of individual trees were acquired via an unmanned aerial vehicle (UAV) platform. Second, DI estimation models were developed based on the Random Forest (RF) and Support Vector Machine (SVM) algorithms, with each algorithm optimized using either Grid Search (GS) or a Genetic Algorithm (GA). Finally, four integrated models (GS-RF, GA-RF, GS-SVM, and GA-SVM) were constructed and systematically compared. The results showed that the Genetic Algorithm-optimized SVM model (GA-SVM) exhibited the highest predictive accuracy and robustness, achieving a coefficient of determination (R²) of 0.6302, a Root Mean Square Error (RMSE) of 0.0629, and a Mean Absolute Error (MAE) of 0.0480. Our findings demonstrate the great potential of integrating UAV-based hyperspectral remote sensing with optimized machine learning algorithms for WLS monitoring, thus offering a novel technical approach for the macroscopic, rapid, and non-destructive surveillance of this disease. Full article

Within the last few decades, ALS-enhanced two-phase forest inventory has emerged as viable alternative to standard inventory designs. As a relatively new and compound method, there still remains significant potential for its optimisation. One key aspect concerns the design of the second-phase sampling. Apart from well-known designs such as random, systematic, or stratified sampling—which often involve some degree of uncertainty regarding their realisations—there are relatively less common, structurally guided sampling designs (SGS), which can facilitate the unambiguous allocation of balanced and well-optimised samples. Unlike traditional stratification, the SGS design does not rely on fixed divisions, which may induce additional errors due to pre-defined and potentially non-representative strata. Instead of geographical (spatial) sample deployment, the SGS uses the multidimensional space of covariates, e.g., ALS metrics, to optimise sample allocation. SGS can be powered by different engines. While some algorithms for SGS, such as the cube method or local pivotal method, have been briefly tested in recent studies, no thorough attention has yet been paid to data clustering algorithms. Therefore, this study compares the performance of several popular data clustering algorithms for structurally guided sampling to train the model for growing stock volume estimation in a two-phase ALS-enhanced forest inventory design. The results showed that hierarchical clustering was competitive with other methods but outperformed them in terms of the highest stability of estimates, even at lower sampling intensity levels. The use of data clustering methods can ensure unambiguous yet more optimal sample distribution, minimising sampling variation or estimation error caused by the randomness of other sampling methods or the inflexibility of pre-defined strata. Full article

Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and conducting forest inventories due to its complex stand structure and overlapping crowns. To determine the most effective ITS approach for P. euphratica, we benchmarked six commonly used tree segmentation approaches for terrestrial laser scanning (TLS) data: canopy height model segmentation (CHMS), point cloud segmentation (PCS), comparative shortest-path algorithm (CSP), stem location seed point segmentation (SPS), deep-learning trunk-based segmentation (TBS), and leaf–wood separation-based segmentation (LWS). All methods followed a unified preprocessing and tuning protocol. We evaluated these methods based on tree-count accuracy, crown delineation, and structural attributes such as tree height (H), diameter at breast height (DBH), and crown diameter (CD). The results indicated that the TBS and LWS methods performed the best, achieving a mean tree-count accuracy of 98%, while the CHMS method averaged only 46%. These two methods provide the basic branch structure within the tree crown, reducing the likelihood of incorrect segmentation. Validation against field-measured values for H, DBH, and CD showed that both the TBS and LWS methods achieved accuracies exceeding 80% (RMSE = 0.8 m), 86% (RMSE = 0.02 m), and 73% (RMSE = 0.7 m), respectively. For TLS data in P. euphratica desert riparian forests, these two methods provide the most reliable results, facilitating rapid plot-scale inventory and monitoring. These findings establish a practical basis for conducting high-accuracy inventories of Euphrates poplar desert riparian forests. Full article

Forest canopy height is essential for ecosystem process modeling and carbon stock assessment. However, most prediction approaches rely on sparse or interpolated LiDAR labels, leading to uncertainties in heterogeneous forests where laser footprints are limited or unevenly distributed. To address these issues, this study proposes a multi-source ensemble learning framework that uses airborne PolInSAR-derived continuous canopy height as training labels for accurate forest height prediction. The framework features two key innovations: (1) a hybrid baseline selection strategy (PROD+ECC) within the PolInSAR inversion, significantly improving the quality and stability of initial labels; (2) a dual-layer ensemble learning model that integrates machine learning and deep learning to interpret multi-source features (Landsat-8, GEDI, DEM, and kNDVI), enabling robust upscaling from local inversion to regional prediction. Independent validation in Gabon’s Akanda National Park achieved R² = 0.748 and RMSE = 5.873 m, reducing RMSE by 43.6% compared with existing global products. This framework mitigates sparse supervision and extrapolation bias, providing a scalable paradigm for high-accuracy canopy height mapping in complex tropical forests and offering an effective alternative to LiDAR-based approaches for global carbon assessment. Full article

Detection of planted tree seedlings at the individual level is crucial for monitoring forest ecosystems and supporting silvicultural management. The combination of deep learning (DL) object detection algorithms and remote sensing (RS) data from unmanned aerial vehicles (UAVs) offers efficient and cost-effective solutions. However, current methods predominantly rely on unimodal RS data sources, overlooking the multi-source nature of RS data, which may result in an insufficient representation of target features. Moreover, there is a lack of multimodal frameworks tailored explicitly for detecting planted tree seedlings. Consequently, we propose a multimodal object detection framework for this task by integrating texture information from digital orthophoto maps (DOMs) and geometric information from digital surface models (DSMs). We introduce alternate scanning fusion (ASF), a novel multimodal fusion module based on state space models (SSMs). The ASF can achieve global feature fusion while maintaining linear computational complexity. We embed ASF modules into a dual-backbone YOLOv5 object detection framework, enabling feature-level fusion between DOM and DSM for end-to-end detection. To train and evaluate the proposed framework, we establish the planted tree seedling (PTS) dataset. On the PTS dataset, our method achieves an AP50 of 72.6% for detecting planted tree seedlings, significantly outperforming the original YOLOv5 on unimodal data: 63.5% on DOM and 55.9% on DSM. Within the YOLOv5 framework, comparative experiments on both our PTS dataset and the public VEDAI benchmark demonstrate that the ASF surpasses representative fusion methods in multimodal detection accuracy while maintaining relatively low computational cost. Full article

Extreme weather increasingly damages forest ecosystems, and affected areas are often remote or inaccessible, limiting field surveys. In such contexts, remote sensing can complement damage assessment. This study presents a regional case study evaluating established multi-temporal optical change detection for windthrow mapping in Triglav National Park (Slovenia) using Sentinel-2 and PlanetScope imagery. Bitemporal index differencing and fixed thresholds were applied, with accuracy quantified via a block bootstrap to account for spatial autocorrelation. Within-sample overall accuracy was 69.2% (95% CI: 67.4–71.2%) for Sentinel-2 and 72.9% (95% CI: 71.2–74.6%) for PlanetScope. Detection was strongly size-dependent: gaps greater than 0.5 ha were consistently detected, whereas gaps smaller than 0.1 ha were frequently omitted, particularly with Sentinel-2. Linking satellite-derived change maps with forest stand data enabled parcel-level estimates of damaged timber volume; this linkage was examined on a small, non-probability set of parcels and is therefore preliminary. We position the work strictly as a case study documenting within-sample performance in alpine terrain. Broader generalisation will require probability-based validation across additional events and forest types, and wider access to parcel-level official records. Full article

In recent years, ecosystems in Saudi Arabia have experienced severe degradation due to factors such as hyperaridity, overgrazing, climate change, urban expansion, and an increase in uncontrolled wildfires. Among these, wildfires have emerged as the second most significant threat to forests after urban expansion. This study aims to map wildfire susceptibility in southwestern Saudi Arabia by identifying key driving factors and evaluating the performance of several machine learning models under conditions of limited and imbalanced data. The models tested include Maxent, logistic regression, random forest, XGBoost, and support vector machine. In addition, an NDVI-based phenological approach was applied to assess seasonal vegetation dynamics and to compare its effectiveness with conventional machine learning-based susceptibility mapping. All methods generated effective wildfire risk maps, with Maxent achieving the highest predictive accuracy (AUC = 0.974). The results indicate that human activities and dense vegetation cover are the primary contributors to wildfire occurrence. This research provides valuable insights for wildfire risk assessment in data-scarce regions and supports proactive fire management strategies in non-traditional fire-prone environments. Full article