Satellite-based mapping of canopy fuels at the pan-European scale DOI Creative Commons
Erico Kutchartt, José Ramón González‐Olabarria, Antoni Trasobares

и другие.

Geo-spatial Information Science, Год журнала: 2024, Номер unknown, С. 1 - 29

Опубликована: Дек. 20, 2024

Canopy base height (CBH) and canopy bulk density (CBD) are forest fuel parameters that key for modeling the behavior of crown wildfires. In this work, we map them at a pan-European scale year 2020, producing new dataset consisting two raster layers containing both variables an approximate resolution 100 m. Spatial data from Earth observation missions derived down-stream products were retrieved processed using artificial intelligence to first estimate aboveground biomass (AGB). Allometric models then used spatial distribution CBH values as explanatory CBD AGB values. Ad-hoc allometric defined study. Data provided by FIRE-RES project partners acquired through field inventories was validating final independent 804 ground-truth sample plots. The maps have, respectively, following accuracy regarding specific metrics reported procedures: (i) coefficient correlation (R) 0.445 0.330 (p-value < 0.001); (ii) root mean square error (RMSE) 3.9 m 0.099 kg m−3; (iii) absolute percentage (MAPE) 61% 76%. Regarding CBD, improved in closed canopies (canopy cover > 80%) R = 0.457, RMSE 0.085, MAPE 59%. short, believe degree is reasonable resulting maps, datasets support fire mitigation simulations.

Язык: Английский

A multi-sensor approach allows confident mapping of forest canopy fuel load and canopy bulk density to assess wildfire risk at the European scale DOI Creative Commons
Elena Aragoneses, Mariano Garcı́a, Hao Tang

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 318, С. 114578 - 114578

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

1

Impact of fire severity on forest structure and biomass stocks using NASA GEDI data. Insights from the 2020 and 2021 wildfire season in Spain and Portugal DOI Creative Commons
Juan Guerra-Hernández, José M. C. Pereira, Atticus Stovall

и другие.

Science of Remote Sensing, Год журнала: 2024, Номер 9, С. 100134 - 100134

Опубликована: Май 16, 2024

Wildfires have been progressively shrinking the C sink capacity of forest accelerating climate change effects on biodiversity, especially where megafires are recurrent and increased frequency such as in Mediterranean. Data from The Global Ecosystem Dynamics Investigation (GEDI) mission can inform changes structure to fire impacts vegetation. In this study, we assessed performance GEDI at measuring biomass structural wildfires using 2020/21 summer seasons Spain Portugal. hybrid-inference method was used calculate mean total pre- post-fire stages, while footprint data further explain severity classes derived optical data. Our results showed increasing impact stocks ecological metrics by severity. More than over stocks, severe fires substantially altered trends plant area volume density. integration had an accuracy 52% considering five 69% when three main classes: unburned, moderate high. Structural be improve optical-based estimates globally evaluate potential based time-series tracks showcased but also measure recovery between seasons. extension is a major support for wildfire mapping efforts, integrated approaches capture biodiversity monitoring carbon stocks.

Язык: Английский

Процитировано

5

Combining Sentinel-2 and diverse environmental data largely improved aboveground biomass estimation in China’s boreal forests DOI Creative Commons
Pan Liu, Chunying Ren,

Xiutao Yang

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 11, 2024

Accurately mapping aboveground biomass (AGB) in China's boreal forests is crucial for assessing global carbon stock and formulating forest management strategies but remains challenging as the environmental heterogeneity complicates AGB estimation. Here, we investigated relative gains of integrating Sentinel-2 data, well synthetic aperture radar (SAR) images to map forests. We used two machine learning algorithms, random gradient boosting regression (GBR), four dataset combinations develop models, then evaluated by carrying on uncertainty analysis comparing it with existing products. Results showed that GBR model based data presented best estimation capability (R

Язык: Английский

Процитировано

4

Hoping the best, expecting the worst: Forecasting forest fire risk in Algeria using fuzzy logic and GIS DOI Creative Commons
Louiza Soualah, Abdelhafid Bouzekri, Haroun Chenchouni

и другие.

Trees Forests and People, Год журнала: 2024, Номер 17, С. 100614 - 100614

Опубликована: Июнь 24, 2024

Forest fires pose severe threats to ecosystems and communities globally, especially in vulnerable semi-arid regions like North Africa. Understanding the key factors influencing forest fire dynamics is essential for effective management mitigation. This study aims comprehensively analyze risk patterns Djebel El Ouahch's massive (Algeria), focusing on integrating bioclimatic, fuel, geomorphological, human through advanced fuzzy logic geographic information system (GIS) techniques. Climatic station data, satellite imagery, GIS were employed map bioclimatic parameters, land cover, geomorphological features. Fuzzy systems applied integrate these factors, assigning appropriate weights based their significance. The resulting prediction model was defuzzified generate predictive maps indicating varying vulnerability levels within area. Predictive delineated areas of low high risk. Low-risk zones characterized by sparse vegetation, while high-risk featured densely vegetated slopes near settlements. identified critical vulnerability, emphasizing impact climate, terrain, activities. Urgent attention directed toward areas, necessitating tailored prevention measures strategic urban planning minimize human-induced risks. results underscored complex interaction natural anthropogenic shaping susceptibility. facilitates evidence-based policymaking, enhancing preparedness, biodiversity preservation, community safety. Additionally, emphasized need continuous research incorporating real-time climate data socio-economic refine models. provided valuable insights into massive, serving as a foundation targeted strategies. By bridging gap between theoretical knowledge practical application, this contributes significantly sustainable disaster mitigation efforts importance proactive safeguarding communities.

Язык: Английский

Процитировано

3

Using Structural Class Pairing to Address the Spatial Mismatch Between GEDI Measurements and NFI Plots DOI Creative Commons
Nikola Bešič, Sylvie Durrieu, Anouk Schleich

и другие.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 12854 - 12867

Опубликована: Янв. 1, 2024

ou non, émanant des établissements d'enseignement et de recherche français étrangers, laboratoires publics privés.

Процитировано

3

Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions DOI Creative Commons
Christopher R. Hakkenberg, Matthew L. Clark, Theodore N. Bailey

и другие.

Communications Earth & Environment, Год журнала: 2024, Номер 5(1)

Опубликована: Ноя. 20, 2024

Drivers of forest wildfire severity include fuels, topography and weather. However, because only fuels can be actively managed, quantifying their effects on has become an urgent research priority. Here we employed GEDI spaceborne lidar to consistently assess how pre-fire fuel structure affected across 42 California wildfires between 2019–2021. Using a spatial-hierarchical modeling framework, found positive concave-down relationship GEDI-derived severity, marked by increasing with greater loads until decline in the tallest most voluminous canopies. Critically, indicators canopy volumes (like biomass height) became decoupled from patterns extreme topographic weather conditions (slopes >20°; winds > 9.3 m/s). On other hand, vertical continuity metrics like layering ladder more predicted – especially where sparse understories were uniformly associated lower levels. These results confirm that estimates overcome limitations optical imagery airborne for interactive drivers severity. Furthermore, these findings have direct implications designing treatment interventions target versus entire canopies delineating risk conditions. Wildfire is such as rather than total range conditions, according analysis data fires

Язык: Английский

Процитировано

3

Aboveground Biomass and Tree Mortality Revealed Through Multi-Scale LiDAR Analysis DOI Creative Commons
Inácio Thomaz Bueno, Carlos Alberto Silva, Kristina J. Anderson‐Teixeira

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 796 - 796

Опубликована: Фев. 25, 2025

Accurately monitoring aboveground biomass (AGB) and tree mortality is crucial for understanding forest health carbon dynamics. LiDAR (Light Detection Ranging) has emerged as a powerful tool capturing structure across different spatial scales. However, the effectiveness of predicting AGB depends on type instrument, platform, resolution point cloud data. We evaluated three distinct LiDAR-based approaches in 25.6 ha North American temperate forest. Specifically, we following: GEDI-simulated waveforms from airborne laser scanning (ALS), grid-based structural metrics derived unmanned aerial vehicle (UAV)-borne lidar data, individual detection (ITD) ALS Our results demonstrate varying levels performance approaches, with ITD emerging most accurate modeling median R2 value 0.52, followed by UAV (0.38) GEDI (0.11). findings underscore strengths approach fine-scale analysis, while used to analyze showed promise broader-scale monitoring, if more uncertainty acceptable. Moreover, complementary scales each method may offer valuable insights management conservation efforts, particularly dynamics informing strategic interventions aimed at preserving mitigating climate change impacts.

Язык: Английский

Процитировано

0

Multitemporal Sentinel and GEDI data integration for overstory and understory fuel type classification DOI
Pegah Mohammadpour, D. X. Viegas, Alcides Pereira

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104455 - 104455

Опубликована: Март 20, 2025

Язык: Английский

Процитировано

0

National-scale calibrated GEDI AGBD models for effective assessment of growth conditions across forest strata DOI Creative Commons

Hantao Li,

Xiaoxuan Li, Tomomichi Kato

и другие.

Forest Ecology and Management, Год журнала: 2025, Номер 585, С. 122657 - 122657

Опубликована: Март 22, 2025

Язык: Английский

Процитировано

0

Changes in GEDI-based measures of forest structure after large California wildfires relative to pre-fire conditions DOI Creative Commons
Matthew L. Clark, Christopher R. Hakkenberg, Theodore N. Bailey

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 323, С. 114718 - 114718

Опубликована: Апрель 4, 2025

Язык: Английский

Процитировано

0