Search Results (434)

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 (ALS_FW), 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

LuTan-1(LT-1) is the first Chinese civil L-band satellite constellation for geohazard observation, comprising LT-1A and LT-1B satellites. By employing interferometric altimetry and differential deformation measurement technologies, it achieves high-precision topographic mapping and establishes sub-millimeter-level deformation monitoring capabilities. To meet the high-precision measurement requirements for applications such as topographic surveying and deformation monitoring, this study systematically evaluates four categories of image quality metrics—geometric, radiometric, and polarimetric characteristics, as well as orbital and baseline quality—based on in-orbit test data from the twin satellites. The test results demonstrate that all image quality indicators of the LT-1 SAR satellites meet the design specifications, confirming that the imagery can provide robust spatial technical support for applications including geological hazard monitoring, land resource investigation, earthquake assessment, disaster prevention and mitigation, fundamental surveying and mapping, and forestry monitoring. Full article

Coastal wetlands, situated at the critical land–sea ecotone, play a vital role in sustaining ecological balance and supporting human activities. Currently, these ecosystems face dual stresses from climate change and intensified anthropogenic activities, making the quantitative assessment of ecosystem functions—represented by Gross Primary Productivity (GPP)—essential for their protection and management. However, a knowledge gap remains regarding coastal–urban complex ecosystems, and existing studies on coastal wetlands often overlook macro-environmental drivers beyond sea-level rise. This study leveraged the MOD17A2H V006 dataset to generate a 500 m GPP product for Zhanjiang City. We analyzed the spatiotemporal dynamics of GPP, utilized land use data to examine the evolution of coastal wetlands, and employed the Geodetector model to quantify the contributions of various factors to GPP in Zhanjiang and its coastal wetlands. The results indicate that: (1) GPP in Zhanjiang exhibited an overall steady upward trend, increasing at an average rate of $13.8\mathrm{g}\mathrm{C}·{\mathrm{m}}^{-2}·{\mathrm{yr}}^{-1}$. However, it displayed strong spatial heterogeneity, characterized by higher values in the southwest and lower values in the northern and coastal regions. (2) The land use pattern in Zhanjiang underwent significant transformations over the past two decades. Cropland and impervious surfaces expanded markedly, increasing by 194.6 km² and 290.42 km², respectively, while coastal wetland areas showed a continuous decline, with degraded and newly formed areas of 101.5 km² and 42 km², respectively. (3) The Geodetector results revealed that the q-value of Nighttime Light (NTL) increased from negligible values to over 0.1, emerging as a dominant driving factor. Although the driving force of anthropogenic activity factors on Zhanjiang and its coastal wetlands has steadily increased, natural factors currently remain the dominant forces. These findings unravel the driving mechanisms of natural and anthropogenic factors on GPP in Zhanjiang, providing valuable scientific evidence for the sustainable development of coastal ecosystems. Full article

Forestry environments—such as logging sites, transport trails, and resource monitoring areas—are characterized by rugged terrain and irregularly distributed obstacles, which pose substantial challenges for AGV route planning. This poses challenges for route planning in automated guided vehicles (AGVs) and forestry machinery. To address these challenges, this study proposes a hybrid path optimization method that integrates an improved A* algorithm with the Dynamic Window Approach (DWA). At the global planning level, the improved A* incorporates a dynamically weighted heuristic function, a steering-penalty term, and Floyd-based path smoothing to enhance path feasibility and continuity. In terms of local planning, the improved DWA algorithm employs adaptive weight adjustment, risk-perception factors, a sub-goal guidance mechanism, and a non-uniform and adaptive sampling strategy, thereby strengthening obstacle avoidance in dynamic environments. Simulation experiments on two-dimensional grid maps demonstrate that this method reduces path lengths by an average of 6.82%, 8.13%, and 21.78% for 20 × 20, 30 × 30, and 100 × 100 maps, respectively; planning time was reduced by an average of 21.02%, 16.65%, and 9.33%; total steering angle was reduced by an average of 100°, 487.5°, and 587.5°. These results indicate that the proposed hybrid algorithm offers practical technical guidance for intelligent forestry operations in complex natural environments, including timber harvesting, biomass transportation, and precision stand management. Full article

Building on the thematic foundation established in the first volume of this Special Issue—namely, leveraging spectral technologies (from proximal sensing to unmanned aerial vehicle (UAV) and satellite platforms) to advance precision agriculture and forestry—this second edition further consolidates methodological progress and expands the breadth of applications [...] Full article

Understanding vegetation phenology responses to climate change is essential for predicting ecosystem dynamics, especially in mountainous transition zones, such as the Qinling Mountains, where climatic and ecological gradients are pronounced. To quantify these complex interactions, we combined high spatiotemporal resolution remote sensing data (30 m, 8-day) with CMFD climate datasets from 2010 to 2020. We leveraged a rigorous analysis of covariance (ANCOVA) framework to simultaneously test the spatial heterogeneity of phenological baselines and the temporal convergence of trends across vegetation types. Results revealed that the spatial pattern of the start of the growing season (SOS) exhibited highly significant heterogeneity (p < 0.001), primarily governed by vegetation composition and altitudinal gradients—a phenomenon we define as a spatial baseline constraint effect. In contrast, the interannual SOS trends (slopes) showed no significant differences among vegetation types (p = 0.685), indicating a temporal convergence effect. This regional synchrony, characterized by a consistent shift toward earlier SOS of approximately −0.8 to −0.9 days yr⁻¹ at low and mid-elevations, was largely driven by rising spring temperatures (R² ≈ 0.20). Crucially, the end of the growing season (EOS) displayed weak climatic sensitivity, revealing an asymmetric phenological response to temperature changes. Our findings demonstrate that vegetation phenology in the Qinling Mountains is jointly controlled by spatial baseline constraint and temporal trend convergence. This dual-mechanism framework provides new insights into the highly structured stability and resilience of mountainous ecosystems under regional warming. Full article

Unmanned Aerial Vehicles (UAVs) have become essential instruments for precision agriculture and forest monitoring, offering rapid, high-resolution data collection over wide areas. This review synthesizes global advances (2015–2024) in UAV-derived vegetation indices (VIs), combining bibliometric and content analyses of 472 peer-reviewed publications. The study identifies key research trends, dominant indices, and technical progress achieved through RGB, multispectral, hyperspectral, and thermal sensors. Results show an exponential growth of scientific output, led by China, the USA, and Europe, with NDVI, NDRE, and GNDVI remaining the most widely applied indices. New indices such as GSI, RBI, and MVI demonstrate enhanced sensitivity for stress and disease detection in both crops and forests. UAV-based monitoring has proven effective for yield prediction, water-stress evaluation, pest identification, and biomass estimation. Despite significant advances, challenges persist regarding illumination correction, soil background influence, and limited forestry applications. The paper concludes that UAV-derived vegetation indices—when integrated with machine learning and multi-sensor data—represent a transformative approach for the sustainable management of agricultural and forest ecosystems. Full article

Rapid and accurate landslide detection is important for minimizing loss of life and property. Supervised machine learning has shown promise for automating landslide mapping, but it often requires thousands of labeled instances, which is impractical for timely emergency responses. Margin sampling active learning (MS) has proven effective for rapid landslide mapping by querying the most “informative” instances. However, it is still unclear how the choice of the landslide modeling algorithm influences the effectiveness of MS. This study assessed MS with four common landslide modeling algorithms, i.e., random forest, support vector machine, a generalized additive model, and an artificial neural network, using an open-source landslide inventory from Iburi, Japan. The results showed that all four combinations obtained > 0.90 the area under the ROC curve (AUROC) with 150 to 400 training instances. In particular, MS integrated with random forest performed best overall, with a mean AUROC of 0.91 and correct delineation of about 60 percent of the mapped landslide area using only 150 training instances. Precision-recall analysis within the ranked susceptibility maps showed that MS integrated with random forest and support vector machine generally outperformed the generalized additive model and artificial neural network. In addition, we developed a graphical user interface using R Shiny that integrates the MS active learning workflow with all four modeling options. Overall, these findings advance machine learning in rapid hazard mapping and provide tools to support decision-makers in emergency response. Full article

Climate change poses significant threats to forest ecosystems, with drought stress being a major factor affecting tree growth and survival. The accurate and early diagnosis of plant water status is, therefore, critical for advancing climate-smart forestry. However, traditional monitoring approaches often rely on single-sensor data or manual field surveys, limiting their capacity to comprehensively capture the complex physiological and structural dynamics of plants under water deficit. To address this gap, this study developed an indoor multi-sensor phenotyping platform, based on a three-axis mobile truss system, which integrates a hyperspectral camera, a thermal infrared imager, and a LiDAR scanner for coordinated high-throughput data acquisition. We further propose a novel hybrid model, the Whale Optimization Algorithm-based Multi-Kernel Extreme Learning Machine (WOA-MK-ELM), which enhances classification robustness by adaptively fusing hyperspectral and thermal features within a dual Gaussian kernel space. We use Perilla frutescens as a model species, achieving an accuracy of 93.03%, an average precision of 93.11%, an average recall of 94.04%, and an F1-score of 0.94 in water stress degree classification. The results demonstrate that the proposed framework not only achieves high prediction accuracy but also provides a powerful prototype and a robust analytical approach for smart forestry and early warning systems. Full article

This study quantifies forest aboveground biomass (AGB) using integrated remote sensing features from high-resolution GaoFen-7 (GF-7) satellite imagery. We combined texture features, vegetation indices, and RGB spectral bands to improve estimation accuracy. Three machine learning algorithms—Random Forest (RF), Gradient Boosting Tree (GBT), and XGBoost—were compared with a stacking ensemble model using five-fold cross-validation on forest plots in Beijing’s Daxing District. Feature importance was evaluated through SHAP to identify key predictive variables. Results show that texture features exhibit scale-dependent predictive power, while visible-band vegetation indices strongly correlate with AGB. The Stacking ensemble achieved optimal performance (R² = 0.62, RMSE = 57.34 Mg/ha, MAE = 39.99 Mg/ha), outperforming XGBoost (R² = 0.59), RF (R² = 0.58), and GBT (R² = 0.57). Compared to the best individual model, Stacking improved R² by 5.1% and effectively mitigated over- and underestimation biases. These findings demonstrate the effectiveness of ensemble learning for forest AGB estimation and suggest potential for regional-scale carbon monitoring applications. Full article

This study aimed to develop a thinning tree selection system by applying genetic algorithms based on precisely estimated tree-level forest structural parameters derived from LiDAR data. Conventional thinning tree selection methods have limitations due to their dependence on subjective judgement and field experience of operators, resulting in inconsistency and variations according to skill levels. To address these issues, tree positions, diameters at breast height (DBH), and tree heights were extracted by integrating terrestrial laser scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS) data, forming a Multi-Platform LiDAR dataset. The derived DBH and Hegyi competition index were utilized as indicators for thinning tree selection. Optimization of tree selection was performed using a genetic algorithm, with an objective function designed to maximize the average DBH and minimize the average competition index of the remaining trees, and the system’s performance was compared with results obtained by forestry experts. The results showed that tree detection accuracy exceeded 99%, DBH estimation exhibited an RMSE of 0.74 cm, and tree height estimation showed an RMSE of approximately 2 m, demonstrating the construction of precise forest structural parameters. Compared to expert driven selection, the Genetic Algorithm-based thinning system produced a higher average DBH (30.06 cm vs. 29.26 cm) and a lower Hegyi competition index (1.31 vs. 1.41) under Scenario 3. This indicates superior performance in competition alleviation and growing space allocation among individual trees. Spatial statistical analysis revealed that while expert selection maintained the existing spatial clustering pattern of stand structure (Global Moran’s I = 0.16), the machine learning system achieved an almost random distribution (Global Moran’s I = −0.04) under Scenario 3. This study demonstrates the potential of overcoming the limitations of conventional thinning practices dependent on subjective judgement by introducing an objective, consistent, data-driven quantitative decision support system for precision forest management. Full article

The demand for forest materials necessitates advancements in forest management and inventory practices. We explore the integration of Unmanned Aerial Vehicles (UAVs) equipped with LiDAR sensors as a cost-effective alternative for precise forest monitoring. It evaluates the impact of varying point cloud densities on the accuracy of individual tree height estimation in Eucalyptus benthamii within Crop–Livestock–Forestry systems (15.9 ha and 357 individuals·ha⁻¹). We use a DJI M600 Pro UAV with a Velodyne 32c Ultra Puck LiDAR sensor at the Center for Technological Innovation in Agriculture (NITA) in Brazil. The resulting point clouds were processed to generate Digital Terrain Models and Canopy Height Models at densities ranging from 5 to 2000 points per square meter (pts·m⁻²). Statistical analyses, including Pearson correlation, root mean square error, and bias, were conducted to compare UAV-LiDAR-derived heights with field measurements. We found that reduced point densities, particularly around 100 pts·m⁻², maintained high accuracy in height estimation (RMSE = 17.129%, BIAS = −7.889%), with more than 90% in trees’ detection. UAV-LiDAR systems with optimized point cloud densities offer a viable solution for forest monitoring. 100 pts·m⁻² is an optimal density, promoting faster data collection, lower battery consumption, and reduced computational costs on trees’ height estimates. Full article

Accurate estimation of carbon sequestration at the single-tree scale is essential for understanding forest carbon dynamics and supporting precision forestry under global carbon-neutral goals. Traditional allometric models often neglect environmental variability, while data-driven machine learning approaches suffer from limited interpretability. To bridge this gap, we developed a hybrid prediction framework that integrates a Structural Causal Model (SCM) with an Enhanced Long Short-Term Memory (LSTM) network. Using 47-year observation data (1975–2022) of Mongolian oak (*Quercus mongolica* Fisch. ex Ledeb.) from the Laoyeling Ecological Station, the SCM was applied to infer causal relationships among growth and environmental factors, while the Enhanced-LSTM combined multiscale convolution and self-attention modules to capture nonlinear temporal dependencies. Results showed that the proposed SCM-Enhanced-LSTM achieved the highest predictive performance (R² = 0.944, RMSE = 0.079 kg, MAE = 0.064 kg), outperforming Bi-LSTM and XGBoost models by over 20% in accuracy and maintaining robustness under noise perturbations. Causal analysis identified soil moisture and stem diameter as the dominant drivers of carbon increment. This study provides a transparent, interpretable, and high-precision framework for single-tree carbon sequestration prediction, offering methodological support for fine-scale forest carbon accounting and sustainable management strategies. Full article

Cationic polyelectrolytes, characterized by positively charged functional groups, play an essential role in industries ranging from food solutions, water treatment, medical, cosmetic, textiles and agriculture due to their electrostatic interactions, biocompatibility, and functional versatility. This paper critically examines the transition from petroleum-based synthetic polymers such as poly(diallyldimethylammonium chloride) and cationic polyacrylamides to sustainable natural alternatives derived from agri-forestry resources like starch derivatives and cellulose. Through a cradle-to-gate life cycle assessment, we highlight the superior renewability, biodegradability, and lower carbon footprint of bio-based polycations, despite challenges in agricultural sourcing and processing. This study examines cationization processes by comparing the environmental limitations of traditional chemical methods, such as significant waste production and limited scalability, with those of second-generation reactive extrusion (REX), which enables solvent-free and rapid modification. REX also allows for adjustable degrees of substitution and ensures uniform charge distribution, thereby enhancing overall functional performance. Groundbreaking research and optimization achieved through the integration of artificial intelligence and machine learning for parameter regulation and targeted mechanical energy management underscore REX’s strengths in precision engineering. By methodically addressing current limitations and articulating future advancements, this work advances sustainable innovation that contributes to a circular economy in materials science. Full article

Urban tree detection is fundamental to effective forestry management, biodiversity preservation, and environmental monitoring—key components of sustainable smart city development. This study introduces a deep learning framework for urban tree crown segmentation that exclusively leverages high-resolution satellite imagery from GeoEye-1, WorldView-2, and WorldView-3, thereby eliminating the need for additional data sources such as LiDAR or UAV imagery. The proposed framework employs a Residual U-Net architecture augmented with Attention Gates (AGs) to address major challenges, including class imbalance, overlapping crowns, and spectral interference from complex urban structures, using a custom composite loss function. The main contribution of this work is to integrate data from three distinct satellite sensors with varying spatial and spectral characteristics into a single processing pipeline, demonstrating that such well-established architectures can yield reliable, high-accuracy results across heterogeneous resolutions and imaging conditions. A further advancement of this study is the development of a hybrid ground-truth generation strategy that integrates NDVI-based watershed segmentation, manual annotation, and the Segment Anything Model (SAM), thereby reducing annotation effort while enhancing mask fidelity. In addition, by training on 4-band RGBN imagery from multiple satellite sensors, the model exhibits generalization capabilities across diverse urban environments. Despite being trained on a relatively small dataset comprising only 1200 image patches, the framework achieves state-of-the-art performance (F1-score: 0.9121; IoU: 0.8384; precision: 0.9321; recall: 0.8930). These results stem from the integration of the Residual U-Net with Attention Gates, which enhance feature representation and suppress noise from urban backgrounds, as well as from hybrid ground-truth generation and the combined BCE–Dice loss function, which effectively mitigates class imbalance. Collectively, these design choices enable robust model generalization and clear performance superiority over baseline networks such as DeepLab v3 and U-Net with VGG19. Fully automated and computationally efficient, the proposed approach delivers cost-effective, accurate segmentation using satellite data alone, rendering it particularly suitable for scalable, operational smart city applications and environmental monitoring initiatives. Full article