Search Results (1,821)

Tropical rainforests play a vital role in maintaining global ecological balance, carbon cycling, and biodiversity conservation, making research on their biomass dynamics scientifically significant. This study integrates multi-source remote sensing data, including canopy height derived from GEDI and ICESat-2 satellite-borne lidar, Landsat imagery, and environmental variables, to estimate forest biomass dynamics in Hainan’s tropical rainforests at a 30 m spatial resolution, involving a correlation analysis of factors influencing spatiotemporal changes in Hainan Tropical Rainforest biomass. The research aims to investigate the spatiotemporal variations in forest biomass and identify key environmental drivers influencing biomass accumulation. Four machine learning algorithms—Backpropagation Neural Network (BP), Convolutional Neural Network (CNN), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were applied to estimate biomass across five forest types from 2003 to 2023. Results indicate the Random Forest model achieved the highest accuracy (R² = 0.82). Forest biomass and carbon stocks in Hainan Tropical Rainforest National Park increased significantly, with total carbon stocks rising from 29.03 million tons of carbon to 42.47 million tons of carbon—a 46.36% increase over 20 years. These findings demonstrate that integrating multimodal remote sensing data with advanced machine learning provides an effective approach for accurately assessing biomass dynamics, supporting forest management and carbon sink evaluations in tropical rainforest ecosystems. Full article

NASA’s ICESat-2 mission offers potential for coastal monitoring by combining its land/vegetation (ATL08) and nearshore bathymetry (ATL24) products. However, the combined performance of these products in environments where both canopy and bathymetry are present, such as mangroves, has not been explored. This work assesses ATL08 and ATL24 over mangroves using a dual approach: (1) a detailed regional validation in Everglades National Park against high-resolution airborne lidar (ALS), and (2) a global analysis characterizing mangrove structure. The regional validation found strong accuracies, with a root mean square error (RMSE) of 1.63 m for ATL08 canopy height and 0.25 m for ATL24 bathymetry for 10 m segments. In this comparison, using 30 m segments, ICESat-2 (RMSE 1.37 m) demonstrated superior performance to GEDI (RMSE 1.51 m) when measuring the same mangrove canopies. The global analysis confirmed that the majority of mangroves are short-stature (<10 m), a structural range where ICESat-2 demonstrates optimal performance. Despite these strengths, disagreements in photon labels between the ATL08 and ATL24 algorithms limit the ability to identify differences between topography, bathymetry, and water surface in these intertidal areas. While ICESat-2 has potential to accurately measure canopy height and bathymetry in mangroves, the integrated mapping beneath dense canopies is not yet feasible with standard products. Full article

Precise estimation of cotton aboveground biomass (AGB) plays a crucial role in effectively analyzing growth variations and development of cotton, as well as guiding agricultural management practices. Multispectral (MS) sensors mounted on UAVs offer a practical and accurate approach for estimating the AGB of cotton. Many previous studies have mainly emphasized the combination of spectral and texture features, as well as canopy height (CH). However, current research overlooks the potential of integrating spectral, textural features, and CH to estimate AGB. In addition, the accumulation of AGB often exhibits synergistic effects rather than a simple additive relationship. Conventional algorithms, including Bayesian Ridge Regression (BRR) and Random Forest Regression (RFR), often fail to accurately capture the nonlinear and intricate correlations between biomass and its relevant variables. Therefore, this research develops a method to estimate cotton AGB by integrating multiple feature information with a deep learning model. Spectral and texture features were derived from MS images. Cotton CH extracted from UAV point cloud data. Variables of multiple features were selected using Spearman’s Correlation (SC) coefficients and the variance inflation factor (VIF). Convolutional neural network (CNN) was chosen to build a model for estimating cotton AGB and contrasted with traditional machine learning models (RFR and BRR). The results indicated that (1) combining spectral, textural features, and CH yielded the highest precision in cotton AGB estimation; (2) compared to traditional ML models (RFR and BRR), the accuracy of applying CNN for estimating cotton AGB is better. CNN has more advanced power to learn complex nonlinear relationships among cotton AGB and multiple features; (3) the most effective strategy in this study involves combining spectral, texture features, and CH, selecting variables using the SC and VIF methods, and employing CNN for estimating AGB of cotton. The R² of this model is 0.80, with an RMSE of 0.17 kg·m⁻² and an MAE of 0.11 kg·m⁻². This study develops a framework for evaluating cotton AGB by multiple features fusion with a deep learning model. It provides technical support for monitoring crop growth and improving field management. Full article

Current urban green volume quantity and equity evaluations primarily rely on two-dimensional (2D) indicators that capture the planar distribution characteristics but overlook vertical structure variations. This study constructed a three-dimensional (3D) evaluation system for green volume quantity and equity by introducing Lorenz curves and Gini coefficients. Using multi-source data, including a 10 m global vegetation canopy height dataset, land cover, and population distribution data, an automated calculation workflow was established in ArcGIS Model Builder. Focusing on regional and neighborhood scales, this study calculates and analyzes two-dimensional green volume (2DGV) and three-dimensional green volume (3DGV) indicators, along with the spatial equity for 413 Chinese cities and residential and commercial areas of Wuhan, Suzhou, and Bazhong. Meanwhile, a green volume quantity and equity type classification method was established. The results indicated that 3DGV exhibits regional variations, while Low 2DGV–Low 3DGV cities have the highest proportion. Green volume in built-up areas showed a balanced distribution, while park green spaces exhibited 2DGV Equitable Only. At the neighborhood scale, residential areas demonstrated higher green volume equity than commercial areas, but most neighborhood areas’ indicators showed low and imbalanced distribution. The proposed 2DGV and 3DGV evaluation method could provide a reference framework for optimizing urban space. Full article

Conservation of the gallery forest in Berenty Reserve is becoming increasingly urgent. Any deterioration threatens its increasingly rare lemur species. Following a trial planting programme started in 2016 on three plots, with measurement of seedling growth in 2017 and 2018, we returned in 2025 to measure the changes in height, canopy cover and stem diameter. Key insights were that growth had accelerated markedly after 2018. Trees in the forest can be divided into three main species groups—upper canopy, lower canopy and dryland species—but we found scant relationship between species growth and their eventual canopy height, which could have consequences for future planting schemes and management. The plots in the mid-forest showed the highest growth rates. Mortality of seedlings was highest on the riverside plot, but there was also wild recruitment from the forest. The plots by the river and in the mid-forest received the largest number of recruits. The chief problem for the study was that we were only in Berenty for short periods and could not oversee ongoing activities in the plant nursery and in the forest. Consequently, there were problems arising from nursery treatment, unrecorded replanting and difficulties tracking the growth of individuals across years. Future work, based on our results, will focus on identifying and planting species best suited for recovery on the varied sites. Overall, temporal depth is essential for making appropriate restoration decisions based on long-term ecological functioning. Full article

Submerged macrophytes play a pivotal role in the restoration of shallow lakes. Compared to meadow-forming Vallisneria, canopy-forming Myriophyllum spicatum exhibits characteristics that may render it the dominant species. However, M. spicatum may hamper recreational and commercial activities. Herbivorous fish may potentially regulate the biomass and interspecific competition between the two plant species. We conducted an enclosure experiment to elucidate the effects of grass carp (Ctenopharyngodon idella) and Wuchang bream (Megalobrama amblycephala) on the biomass ratio and morphological traits of M. spicatum and V. denseserrulata. Grass carp significantly reduced the biomass, density, and relative growth rate of both plant species, while Wuchang bream had no significant effect on any of these variables. Accordingly, the biomass ratio of M. spicatum to V. denseserrulata was significantly lower in the grass carp treatment than in both the fish-free controls and the Wuchang bream treatment. Wuchang bream significantly decreased the individual height of V. denseserrulata, whereas grass carp substantially reduced the height of both plant species. Our findings suggest that Wuchang bream may be more appropriate for maintaining meadow-forming species such as Vallisneria than grass carp, though it faces challenges in controlling both the biomass and height of canopy-forming species like M. spicatum. Full article

The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R² in the final model remained high (R² = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation. Full article

Maize lodging poses a significant challenge to agricultural production, severely constraining yield improvement and mechanized harvesting efficiency. Under modern agricultural practices characterized by high-density planting and multi-variety intercropping, there is an urgent need for precise and efficient monitoring technologies to address lodging issues. This study utilized unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) to acquire high-precision point cloud data of field maize at full maturity. An innovative method was proposed to automatically identify structural differences induced by lodging by analyzing canopy structural similarity across multiple height thresholds through point cloud stratification. This approach enables automated monitoring of maize lodging in complex field environments. The experimental results demonstrate the following: (1) High-precision point cloud data effectively capture canopy structural differences caused by lodging. Based on the structural similarity change curve, the height threshold for lodging can be automatically identified (optimal threshold: 1.76 m), with a deviation of only 2.3% between the calculated lodging area and the manually measured reference (ground truth). (2) Sensitivity analysis of the height threshold shows that when the threshold fluctuates within a ±5 cm range (1.71–1.81 m), the calculation deviation of the lodging area remains below 10% (maximum deviation = 8.2%), indicating strong robustness of the automatically selected threshold. (3) Although UAV flight altitude influences point cloud quality (e.g., low altitude: 25 m, high altitude: 80 m), the height threshold derived from low-altitude flights can be extrapolated to high-altitude monitoring to some extent. In this study, the resulting deviation in lodging area calculation was only 5.3%. Full article

Against the backdrop of accelerating population aging, urban green spaces have become primary venues for elderly daily activities, with their winter thermal comfort emerging as a critical determinant of senior wellbeing. However, existing studies lack quantitative guidelines on how plant characteristics affect thermal comfort, limiting age-friendly design. Thirty representative landscape space sites (waterfront, square, dense forest, and sparse forest) in Beijing’s Zizhuyuan and Taoranting Parks were analyzed through microclimate measurements, 716 questionnaires, and scoring evaluations, supplemented by PET field data and ENVI-met simulations. A scoring system was developed based on tree density, plant traits (height, crown spread), and spatial features (canopy closure, structure, enclosure, and evergreen coverage). Key findings: (1) Sparse forests showed the best overall thermal comfort. Square building spaces were objectively comfortable but subjectively poor, while waterfront spaces showed the opposite. Dense forests performed worst in both aspects. (2) Wind speed and humidity were key drivers of both subjective and objective thermal comfort, and differences in plant configurations and landscape space types shaped how these factors were perceived. (3) Differentiated optimal scoring thresholds exist across the four landscape space types: waterfront (74 points), square building (52 points), sparse forest (61 points), and dense forest (88 points). (4) The landscape space design prioritizes sparse forest spaces, with moderate retention of waterfront and square areas and a reduction in dense forest zones. Optimization should proceed by first controlling enclosure and shading, then adjusting canopy closure and evergreen ratio, and finally refining tree traits to improve winter thermal comfort for the elderly. This study provides quantitative evidence and optimization strategies for improving both subjective and objective thermal comfort under diverse plant configurations. Full article

Unmanned aerial vehicle (UAV)-based spraying is transforming precision agriculture by enabling targeted, uniform agrochemical application. This study evaluates four nozzle types across three flight heights for rice crop canopy, analyzing spray metrics including canopy coverage (CA%), droplet density (DD), volume median diameter (VMD), and swath width (SW). Statistical analysis identified nozzle N-1 at 3 m and N-3 at 2.5 m as optimal configurations, maximizing coverage and droplet uniformity. Results support evidence-based nozzle–height selection to enhance spraying efficiency and reduce environmental impact. The findings promote sustainable UAV spraying strategies, especially for smallholder rice farmers in Northern India. 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

To address the challenges of narrow, confined spaces in traditional closed-canopy orchards, where complex terrain between and within rows hinders the operation of large and medium-sized mowers. A self-propelled intra-plant obstacle-avoiding oscillating mower was developed. Its core innovation is an integrated oscillating mechanism that achieves one-pass, full-coverage operation by coordinating a 110° fan-shaped cutting path for inter-row areas with an adaptive flipping contour-cutting action for intra-plant areas. The power and transmission systems were optimized according to the shear and bending forces of three common weed species. The integrated prototype was then built and subjected to field tests. The results showed that the shear and bending forces of all three weed species peaked at 30 mm from the root and stabilized beyond 50 mm. Field tests demonstrated a 100% intra-plant obstacle passage rate, 96.9% cutting width utilization rate, 92.07% stubble height stability coefficient, and a 1.66% missed-cutting rate, which meets the operational requirements of closed-canopy orchards. Full article

Weeds pose a ubiquitous challenge to researchers as a source of unintended variation on crop yield and other metrics in designed experiments, creating a need for practical and spatially comprehensive techniques for weed detection. To that end, imagery acquired using unmanned aerial systems (UASs) and classified using pixel-based, object-based, or neural network-based approaches provides researchers a promising avenue. However, in scenarios where spectral differences cannot be used to distinguish between crop and weed foliage, where physical overlap between crop and weed foliage obstructs object-based detection, or where large datasets are not available to train neural networks, alternative methods may be required. For instances where there is a consistent difference in height between crop and weed plants, a mask can be applied to a canopy height model (CHM) such that pixels are determined to be weed or non-weed based on height alone. The CHM Mask (CHMM) approach, which produces a measure of weed area coverage using UAS-acquired, red–green–blue imagery, was used to detect Palmer amaranth in Sweetpotato with an overall accuracy of 86% as well as explain significant variation in sweetpotato yield (p < 0.01). The CHMM approach contributes to the diverse methodologies needed to conduct weed detection in different agricultural settings. Full article

Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems. Full article

Urban heat islands intensify heat stress and degrade air quality in densely built areas, yet the physical processes governing near-surface thermal variability remain poorly quantified. This study applies the coupled MOLOCH and PALM model system 6.0 (PALM-4U) over Bologna (Italy) during a summer 2023 heatwave to resolve meter-scale atmospheric dynamics within the Urban Canopy Layer and Roughness Sublayer at 2 m horizontal resolution. The coupled configuration was validated against in situ meteorological observations and Landsat-8 LST data, showing improved agreement in air temperature and wind speed compared to standalone mesoscale simulations. Results reveal pronounced diurnal and vertical variability of wind speed, turbulent kinetic energy, and friction velocity, with maxima between two/three times the median building height (h_c). Distinct surface-dependent contrasts emerge: asphalt and roofs act as strong daytime heat sources (Bowen ratio β_asphalt ≈ 4.8) and nocturnal heat reservoirs at pedestrian level (z ≈ 0.07 h_c), while vegetation sustains daytime latent heat fluxes (β_vegetation ≈ 0.6÷0.8) and cooler surface and near-surface air (Temperature anomaly of surface ΔT_s ≈ −9 °C and air ΔT_air ≈ −0.3 °C). Thermal anomalies decay with height, vanishing above z ≈ 2.5 h_c due to turbulent mixing. These findings provide insight into fine-scale energy exchanges driving intra-urban thermal heterogeneity and support climate-resilient urban design. Full article