Science of Remote Sensing最新文献

{"title":"Quantifying change in urban tree cover in the city of Lubbock, Texas, using LiDAR and NAIP imagery fusion","authors":"Mukti Subedi ,&nbsp;Carlos Portillo-Quintero","doi":"10.1016/j.srs.2025.100240","DOIUrl":"10.1016/j.srs.2025.100240","url":null,"abstract":"<div><div>Urban tree cover provides important ecosystem services, such as the maintenance of biodiversity, the conservation of water, and human health. An accurate estimate of the canopy cover of urban trees is essential to quantify spatial variations, monitor changes, and provide spatial prioritization for the expansion of urban tree cover. Mapping canopy cover in semi-arid urban landscapes using general-purpose light detection and ranging (LiDAR) presents significant challenges due to the effect of seasonality and aridity on tree crown structures and leaf phenology. We present a locally adapted method based on data fusion and variable window filtering. We fused airborne LiDAR (2011 and 2019) with near-concurrent National Agriculture Imagery Analysis (NAIP: four bands) orthophotos and developed a segmentation model [(Canopy height model: CHM &gt;2m) &amp; (Normalized difference vegetation index: NDVI &gt;0.3)] to account for changes in urban tree canopy cover in Lubbock City (320.75 km²), Texas, United States. Our results indicate a 35.4 % increase in city canopy cover between 2011 and 2019, ∼3.86 % yr⁻¹, with the Northwest and Northeast quadrants showing the largest gains (45.2 % and 42.7 %, respectively). The validation of the resultant tree canopy map was performed using Wayback images from the Earth System Research Institute (ESRI). Canopy delineation improved with increased LiDAR pulse density and an increase in resolution of NAIP imagery in 2019. Our results demonstrate that LiDAR-NAIP fusion can capture canopy change in heterogeneous, semi-arid landscapes and highlight the need for spatially and temporally aligned LiDAR and NAIP acquisitions to support reliable tree inventories and ecosystem service assessments.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100240"},"PeriodicalIF":5.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A century of Sudd wetland’s water storage dynamics using reconstructed and downscaled GRACE/GRACE-FO Data","authors":"Dinuka Kudagama ,&nbsp;Joseph Awange ,&nbsp;Jielong Wang ,&nbsp;Ayalsew Zerihun ,&nbsp;Leidy Luna","doi":"10.1016/j.srs.2025.100232","DOIUrl":"10.1016/j.srs.2025.100232","url":null,"abstract":"<div><div>Although the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have provided valuable Total Water Storage Anomaly (TWSA) observations for understanding the stored water changes in the Ramser Sudd wetland, their coarse resolution (<span><math><mrow><mo>∼</mo><mn>300</mn></mrow></math></span> km) and relatively short records (<span><math><mo>&lt;</mo></math></span>30 years) limit their applicability to study long-term water storage variations over this wetland, one of the largest (tropical) wetlands in the world that has so far received little attention. Random Forest (RF) modeling is used to downscale the Sudd wetland’s TWSA from 0.5°to 0.1°for the period 2003–2023 for the evaluation of the short-term TWSA dynamics, whereas the centenary precipitation-driven TWSA product is employed to elucidate the long-term climate impacts on the Sudd wetland’s TWSA. Our findings show that the downscaled TWSA exhibits a significant correlation with the Global Land Data Assimilation System (correlation coefficient of 0.72) and the WaterGAP Global Hydrology Model (0.65), indicating its potential utility in climate variability analysis. Time series analysis of precipitation/potential evapotranspiration with the downscaled TWSA reveals a higher influence of precipitation-driven runoff from the upstream sub-basins. Our analysis also shows that El Niño–Southern Oscillation (ENSO) is the predominant climate driver, influencing the Sudd’s TWSA in both short-term and long-term periods; however, Indian Ocean Dipole (IOD) is found to augment the effect of ENSO over the wetland. Wavelet coherence analysis identifies significant coherence between climate patterns and the Sudd wetland’s TWSA over periods of 8–16 months and 4–7 years, indicating the recurrent and cyclic nature of the ENSO/IOD and their influence on TWSA in the Sudd over time.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100232"},"PeriodicalIF":5.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the dihedral scattering component of P-band SAR signals for trunk permittivity estimation – a concept study","authors":"Anke Fluhrer ,&nbsp;Hamed Alemohammad ,&nbsp;Thomas Jagdhuber","doi":"10.1016/j.srs.2025.100236","DOIUrl":"10.1016/j.srs.2025.100236","url":null,"abstract":"<div><div>A new retrieval method, based on a hybrid decomposition technique and the extended (x-) Fresnel model, is proposed for estimating trunk permittivity from polarimetric P-band SAR observations. P-band SAR observations of NASA's Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission campaign are employed to test the proposed retrieval method at individual measuring stations across the U.S. between 2013 and 2015. In order to test the feasibility of the x-Fresnel model for such analyses and its sensitivity to required input parameters, a detailed sensitivity study revealed that at P-band frequencies there is a need to account for scattering losses, phase differences, as well as potential depolarization effects due to surface roughness. The decomposed dihedral scattering component increases with increasing vegetation cover from barren land at one station (control station) to homogeneously forested stations (target stations). Overall, no clear correlation between the amount of dihedral scattering and estimated trunk permittivity could be found, which is expected due to the architecture of the employed method. With the proposed approach, the estimated trunk permittivity varies between 2.4 and 59.7 [-], where the barren land and less dense forested stations show lower trunk permittivity. At these stations, the dihedral scattering is not the dominant scattering mechanism within the total SAR signal, which violates the physics of the proposed approach. At stations with dominant dihedral scattering, reasonable correlations (with <span><math><mrow><mi>r</mi></mrow></math></span> ranging from ±0.1 to ±0.64) between estimated trunk permittivity and AMSR2 relative water content (RWC), MODIS evapotranspiration (ET), in-situ measured relative humidity (RH), and air temperature (T_air) could be found. These parameters are used for analyzing the feasibility of the proposed approach as no in-situ trunk moisture measurements are available for the investigated stations and years. Hence, P-band SAR observations that exhibit sufficiently high dihedral scattering portions can be used for estimating trunk permittivity and extend the potential applications of remote sensing for climate research.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100236"},"PeriodicalIF":5.7,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic eddy detection in the MIZ based on YOLO algorithm and SAR images","authors":"Nikita Sandalyuk ,&nbsp;Eduard Khachatrian","doi":"10.1016/j.srs.2025.100228","DOIUrl":"10.1016/j.srs.2025.100228","url":null,"abstract":"<div><div>The automatic detection and analysis of oceanic eddies within the marginal ice zone using synthetic aperture radar present significant challenges, yet are crucial for both scientific research and practical applications. Thus, we explored the feasibility of automating the eddy detection process by applying YOLOv8, a state-of-the-art computer vision model, to high-resolution synthetic aperture radar data, specifically targeting the dynamic region of the Fram Strait. We specifically aim to distinguish between two distinct classes of eddies, based on their rotational direction: cyclonic and anticyclonic. The accurate identification of these eddy types is particularly important for collecting extensive statistical datasets, which are vital for understanding long-term oceanographic patterns and their impact on the Arctic climate. By fine-tuning of YOLOv8 model on an accurately labeled dataset, we achieved robust detection results with minimal training data. The performance of the different architectures within the model was evaluated using various metrics, and the best-performing one was selected through visual inspection and quantitative analysis. Experiments demonstrated the model’s robustness and precision in reliably identifying and distinguishing between cyclonic and anticyclonic eddies with different scales and in different sensing conditions. This work represents a significant advancement in automated eddy detection within the marginal ice zone, offering new insights into the dynamics of polar oceanography.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100228"},"PeriodicalIF":5.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A modified METRIC model for high resolution partitioning and mapping of evapotranspiration over irrigated agricultural landscapes in Morocco","authors":"Jamal Elfarkh ,&nbsp;Bouchra Ait Hssaine ,&nbsp;Salah Er-Raki ,&nbsp;Jamal Ezzahar ,&nbsp;Matthew F. McCabe ,&nbsp;Abdelghani Chehbouni","doi":"10.1016/j.srs.2025.100233","DOIUrl":"10.1016/j.srs.2025.100233","url":null,"abstract":"<div><div>Partitioning of evapotranspiration (ET) into soil evaporative (E) and plant transpiration (T) components remains challenging in flux modeling that has particular relevance to crop water use management. Here, we develop an approach to modify the Mapping EvapoTranspiration at high Resolution and with Internalized Calibration model (METRIC) that allows improved partitioning to landscape-scale flux components. Referred to herein as METRIC-2S, the approach introduces a two-source scheme into the original one source model, using soil and vegetation temperatures to drive the partitioning process. These temperatures are used by METRIC to calculate two ET components, one for the soil and another for the vegetation, which are subsequently weighted by the fractional vegetation cover (fc) to compute E and T. Soil and vegetation temperatures are estimated using the hourglass method, which is driven by the surface temperature and fc. ET estimates from the original METRIC and revised METRIC-2S models are intercompared and validated against eddy covariance measurements over three agricultural sites, including an olive orchard, wheat field and a mixed wheat/olive plantation. Overall, METRIC-2S provides considerable improvements in accuracy relative to the original METRIC model over the three sites, with observed decreases in RMSE from 141 to 63 W/m² at the olive site, 102 to 83 W/m² over the wheat field and from 180 to 78 W/m² at the mixed site. To evaluate the performance of the partitioning scheme, transpiration estimates are compared against available sap flow measurements over the olive orchard site for selected dates that coincide with a Landsat overpass, with an RMSE from this reduced sample of approximately 22.3 W/m². While additional verification and assessment of component values are required, results suggest that the METRIC-2S approach represents a good trade-off between simplicity and improved accuracy.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100233"},"PeriodicalIF":5.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in soil salinity diagnosis for mangrove swamp rice production in Guinea Bissau, West Africa","authors":"Gabriel Garbanzo ,&nbsp;Jesus Céspedes ,&nbsp;Marina Temudo ,&nbsp;Maria do Rosário Cameira ,&nbsp;Paula Paredes ,&nbsp;Tiago Ramos","doi":"10.1016/j.srs.2025.100231","DOIUrl":"10.1016/j.srs.2025.100231","url":null,"abstract":"<div><div>Rice is one of the most important crops in many West African countries and has a direct impact on food security. Mangrove swamp cultivation is the most productive rice system in this area but is highly vulnerable to changes in rainfall patterns due to soil salinity. Diagnosing and identifying areas of high salinity concentration are essential strategies for adapting to climate change and mitigating its impacts. The aim of this study is to provide a methodological approach to identify the causes of soil salinity and map the spatial distribution of hypersaline areas, focusing on three case studies in Guinea Bissau. At three study sites in the north, center, and south of the country, 382 soil samples were collected under initial conditions before rice cultivation. Indices derived from spectral bands and soil texture raster of the Planet Scope project were used to calibrate the three machine learning based models: Random Forest (RF), Support Vector Machine, and Convolutional Neural Networks. Chemical analysis of the soil revealed that Mg²⁺ and Na⁺ were the extractable cations with the highest concentration in all three study sites. The RF showed the highest accuracy for salinity prediction (ρ = 0.90, R² = 0.80, MAE = 15.41 dS m⁻¹, RMSE = 25.49 dS m⁻¹, NRMSE = 51 %, BIAS = 0.18, PBIAS = 0.36 %, RPIQ = 2.25), with normalized difference salinity index (RNDSI, calculated with red edge). Silt raster, normalized salinity index (NDSI), and normalized difference water index (NDWI) were the main contributors in the predicted data for soil electrical conductivity of the saturation paste extract (EC_e, dS m⁻¹). This approach produced a reliable approximation during validation for the three study sites (ρ = 0.84 to 0.90, R² = 0.68 to 0.78, MAE = 11.74 dS m⁻¹ to 24.85 dS m⁻¹, RMSE = 17.26 dS m⁻¹ to 38.98 dS m⁻¹, NRMSE = 42 %–54 %, BIAS = −2.25 to 2.24, PBIAS = −5.49 %–7.01 %, RPIQ = 2.01 to 2.43), each exhibiting unique edaphoclimatic characteristics. This study highlights the critical importance of diagnosing hypersaline sites to improve agronomic management practices by introducing improved water management infrastructures, conserving mangrove forests, and promoting regional ecological resilience.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100231"},"PeriodicalIF":5.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatially remotely sensed evapotranspiration estimates in Sahel region using an ensemble contextual model with automated heterogeneity assessment","authors":"Nesrine Farhani ,&nbsp;Jordi Etchanchu ,&nbsp;Gilles Boulet ,&nbsp;Philippe Gamet ,&nbsp;Albert Olioso ,&nbsp;Alain Dezetter ,&nbsp;Ansoumana Bodian ,&nbsp;Nanée Chahinian ,&nbsp;Kanishka Mallick ,&nbsp;Chloé Ollivier ,&nbsp;Olivier Roupsard ,&nbsp;Aubin Allies ,&nbsp;Jérôme Demarty","doi":"10.1016/j.srs.2025.100229","DOIUrl":"10.1016/j.srs.2025.100229","url":null,"abstract":"<div><div>Water scarcity and the inter-annual variability of water resources in semi-arid areas are limiting factors for agricultural production. The characterization of plant water use, together with water stress, can help us to monitor the impact of drought on agrosystems and ecosystems, especially in the Sahel region. Indeed, this region is identified as a ”hot spot” for climate change. In-situ measurements often are insufficient for accounting for spatial variability at large scales (<span><math><mrow><mo>&gt;</mo><mn>100</mn></mrow></math></span> km) due to the scarcity of gauge networks. To tackle this issue, remotely sensed evaporation is often used. In this study, estimates using thermal infrared and visible data from MODIS/TERRA and AQUA are used. Spatially distributed estimates of the daily actual evapotranspiration (ETd) are simulated using the EVASPA S-SEBI Sahel (E3S) ensemble contextual method over a mesoscale area (145x145 km) in central Senegal. E3S uses a set of different methods in order to identify the dry and wet edges of the surface temperature/albedo scatterplot and therefore estimate the evaporative fraction (EF). However, contextual approaches assume the simultaneous presence of sufficient fully wet and fully dry pixels within the same satellite image. This assumption of heterogeneity does not always hold, especially in the Sahel, which is characterized by the alternation of dry and wet seasons due to the monsoon-influenced climate. To tackle this issue, E3S uses different sets of methods depending on the season, based on local knowledge. The present study thus aims at generalizing the approach by proposing a new version of E3S called ”E3S-V2”. This latter allows an automatic detection of different heterogeneity conditions. Therefore, a sensitivity analysis examining the effect of using different EF estimation methods over different spatial coverages was performed. It made it possible to identify relevant normalized indicators to determine the heterogeneity level, as well as to discriminate among the most adapted EF determination methods for each situation. From this analysis, an automated procedure of method selection according to the heterogeneity conditions is proposed. A local-scale evaluation was performed using eddy-covariance measurements in the Senegal Groundnut Basin. A spatialized evaluation was also performed using GLEAM and ERA5-Land, which are proven reference ETd products over the area. ”E3S-V2” simulations yield comparable performances with in-situ and reference products in our study area.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100229"},"PeriodicalIF":5.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating aufeis detection methods using Landsat imagery: Comparative assessment and recommendations","authors":"Julian Dann ,&nbsp;Simon Zwieback ,&nbsp;Paul Leonard ,&nbsp;W. Robert Bolton","doi":"10.1016/j.srs.2025.100230","DOIUrl":"10.1016/j.srs.2025.100230","url":null,"abstract":"<div><div>In the continuous permafrost environment of the North Slope of Alaska, extensive aufeis fields develop each winter on river floodplains, primarily via discharge from perennial springs. Currently, changes to the spatial and temporal distribution of large aufeis fields are predominantly monitored using optical satellite imagery. However, existing detection methods struggle to distinguish between snow and ice surfaces.</div><div>This study compares the accuracy of two techniques for identifying aufeis in a dataset comprising 515 Landsat optical images across four aufeis fields on the North Slope of Alaska. The first method involves empirical thresholding on snow and ice indices (2FT), while the second applies random forest (RF) machine learning methods. We evaluate their performance on multiple training and test datasets with pixel-, image-, and site-based stratification. Additionally, we evaluate the utility of additional bands and indices in aufeis detection using a grid-search for the top features (3FT) and feature importance metrics.</div><div>The more complex RF classifier, which relies on an extensive training dataset, outperforms both feature thresholding methods across all validation datasets with an average F1 score of 0.967±0.029. Feature importance metrics indicate that the near-infrared is effective for distinguishing between snow and ice surfaces. These findings demonstrate that machine learning approaches significantly enhance aufeis detection capabilities in snow-affected scenes and improve the retrieval of the annual maximum aufeis extent. While scaling challenges remain for these techniques, the results provide a foundation for improving our ability to monitor regional aufeis dynamics and their role in hydrologic and permafrost systems.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100230"},"PeriodicalIF":5.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning","authors":"Shahab Aldin Shojaeezadeh ,&nbsp;Abdelrazek Elnashar ,&nbsp;Tobias Karl David Weber","doi":"10.1016/j.srs.2025.100227","DOIUrl":"10.1016/j.srs.2025.100227","url":null,"abstract":"<div><div>Crop phenology describes the physiological development stages of crops from planting to harvest which is valuable information for decision makers to plan and adapt agricultural management strategies. In the era of big Earth observation data ubiquity, attempts have been made to accurately detect crop phenology using Remote Sensing (RS) and high resolution weather data. However, most studies have focused on large scale predictions of phenology or developed methods which are not adequate to help crop modeler communities on leveraging Sentinel-1 and Sentinal-2 data and fusing them with high resolution climate data, using a novel framework. For this, we trained a Machine Learning (ML) LightGBM model to predict 13 phenological stages for eight major crops across Germany at 20 m scale. Observed phenologies were taken from German national phenology network (German Meteorological Service; DWD) between 2017 and 2021. We proposed a thorough feature selection analysis to find the best combination of RS and climate data to detect phenological stages. At national scale, predicted phenology resulted in a reasonable precision of R² &gt; 0.43 and a low Mean Absolute Error of 6 days, averaged over all phenological stages and crops. The spatio-temporal analysis of the model predictions demonstrates its transferability across different spatial and temporal context of Germany. The results indicated that combining radar sensors with climate data yields a very promising performance for a multitude of practical applications. Moreover, these improvements are expected to be useful to generate highly valuable input for crop model calibrations and evaluations, facilitate informed agricultural decisions, and contribute to sustainable food production to address the increasing global food demand.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100227"},"PeriodicalIF":5.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling multi-layer fine fuel loads in temperate eucalypt forests using airborne LiDAR and inventory data","authors":"Trung H. Nguyen ,&nbsp;Simon Jones ,&nbsp;Karin J Reinke ,&nbsp;Mariela Soto-Berelov","doi":"10.1016/j.srs.2025.100226","DOIUrl":"10.1016/j.srs.2025.100226","url":null,"abstract":"<div><div>Wildfires are increasing in intensity and frequency due to climate change and land-use changes, posing critical threats to ecosystems, economies, and human safety. Fine fuels (&lt;6 mm, such as leaves and twigs) are known key drivers of wildfire ignition and spread, particularly in temperate forests where high flammability increases wildfire hazard. Accurately quantifying fine fuel loads (FFL) across vertical forest layers is essential for understanding and predicting wildfire behaviour, yet past studies using Airborne Laser Scanning (ALS) have been limited to canopy fuels, overlooking surface and understorey layers that play a key role in wildfire propagation. This study addresses this gap by developing an ALS-based modelling approach to estimate FFL across four vertical layers: canopy, elevated (or ladder), near-surface, and surface. The study was conducted in eucalypt-dominated forests in Victoria, southeastern Australia. We stratified ALS point clouds into distinct layers (overstorey, intermediate, shrub, and herb), computed layer-specific structural metrics, and trained Random Forest models to predict multi-layer FFL. The models performed well, with the highest accuracy for canopy FFL (R² = 0.74, relative RMSE = 49.42 %) and moderate accuracy for elevated, near-surface, and surface FFL (R² = 0.42–0.56, relative RMSE = 59.77–77.57 %). Model interpretation revealed that integrating ALS metrics from multiple forest layers maximised accuracy and highlighted the complex role of vertical forest structure in predicting FFL. Prediction maps captured horizontal and vertical FFL variations across landscapes, reflecting differences in forest structure. Furthermore, pre-fire FFL, especially in surface and canopy layers, showed statistically significant associations with wildfire-induced forest loss. This study advances multi-layer FFL estimation using ALS data, offering a more comprehensive fuel information for wildfire hazard assessment and forest management. Future research should explore the scalability of this method by integrating satellite-derived data to extend FFL mapping at broader spatial scales.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100226"},"PeriodicalIF":5.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}