Reply on RC1 DOI Creative Commons
Lilian Vallet

Published: June 21, 2023

Abstract. The frequency and intensity of summer droughts heat waves in Western Europe have been increasing, raising concerns about the emergence fire hazard less prone areas. This exposure old-growth forests hosting unadapted tree species may cause disproportionately large biomass losses compared to those observed frequently burned Mediterranean ecosystems. Therefore, analyzing seasons from perspective exposed areas alone is insufficient, we must also consider impacts on loss. In this study, focus exceptional 2022 season France use very high-resolution (10 m) satellite data calculate area, height at national level, subsequent ecological impact based loss during fires. Our high resolution semi-automated detection estimated 42,520 ha 66,393 by European automated remote sensing system (EFFIS), including 48,330 actually occurring forests. We show that had a lower than previous years, whereas there was drastic increase area over Atlantic pine temperate High were driven (28,600 vs. 494 yr−1 2006–2021 period) but mitigated low mostly located intensive management Conversely, abnormally due both 15-fold years (3,300 216 which burned. Overall, (i.e. wood dry weight) 0.25 Mt shrublands, 1.74 forest, 0.57 forests, amounting total 2.553 Mt, equivalent 17 % average natural mortality all French as reported inventory. A comparison between our estimates global biomass/burned indicates higher improves identification small patches, reduces commission errors with more accurate delineation perimeter each fire, increases affected. study paves way for development low-latency, high-accuracy assessment patch contours deliver informative impact-based characterization year.

Language: Английский

Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes DOI
Rui Chen, Binbin He,

Yanxi Li

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 351, P. 120005 - 120005

Published: Jan. 5, 2024

Language: Английский

Citations

8
The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests DOI Creative Commons
Yiru Zhang, Binbin He, Rui Chen

et al.

GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)

Published: April 26, 2024

Accurate assessments of forest biomass carbon are invaluable for managing resources, evaluating effects on ecological protection, and achieving goals related to climate change sustainable development. Currently, the integration optical synthetic aperture radar (SAR) data has been extensively utilized in estimating aboveground (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture interannual dynamic growth seasonal variations photosynthetic phenology forests, can sufficiently describe characteristics. However, there remains a gap research focusing utilizing satellite-based time-series AGC estimation, especially SAR sensors. This study investigated potential AGC. Here, we undertook nine quantitative experiments estimation from Landsat 8 Sentinel-1 tested several regression algorithms (including multiple linear (MLR), random forests (RF), artificial neural network (ANN), extreme gradient boosting (XGBoost)) explore contributions spatiotemporal features estimation. The results suggested that XGBoost algorithm was suitable with explanatory solid power stable performance. temporal representing trends periodic characteristics (such as coefficients continuous wavelet transform) were more valuable than spatial both sensor types, accounting around 40% ~50% variance compared 17% ~25%. combination produced best performance (R2 = 0.814, RMSE 18.789 Mg C/ha, rRMSE 26.235%), when or alone (optical: R2 0.657 35.317%; SAR: 0.672 34.701%). Feature importance analysis also verified vegetation indices, SWIR 1/2 bands, backscatter VV polarization most critical variables Furthermore, incorporating into modeling illustrated be effective reducing saturation within high-biomass forests. demonstrated superiority While applicability this methodology only evergreen coniferous may provide viable approach needed make full use increasingly better free satellite estimate high accuracy, supporting policy making management

Language: Английский

Citations

4
Forest Fire Driving Factors and Fire Risk Zoning Based on an Optimal Parameter Logistic Regression Model: A Case Study of the Liangshan Yi Autonomous Prefecture, China DOI Creative Commons

Fuhuan Zhang,

Bin Zhang, Jun Luo

et al.

Fire, Journal Year: 2023, Volume and Issue: 6(9), P. 336 - 336

Published: Aug. 26, 2023

Planning the analyses of spatial distribution and driving factors forest fires regionalizing fire risks is an important part management. Based on Landsat-8 active dataset Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes optimal parameter logistic regression (OPLR) model, conducts risk zoning research under analysis scale model parameters, establishes a prediction model. The results showed that unit in study area was 5 km accuracy OPLR about 81%. climate main factor fires, while temperature had greatest influence probability fires. According mapping zoning, which medium- high-risk 6021.13 km2, accounted for 9.99% area. contribute better understanding management based local environmental characteristics provide reference related prevention control

Language: Английский

Citations

10
Multi-Decision Vector Fusion Model for Enhanced Mapping of Aboveground Biomass in Subtropical Forests Integrating Sentinel-1, Sentinel-2, and Airborne LiDAR Data DOI Creative Commons
Wenhao Jiang,

Linjing Zhang,

Xiaoxue Zhang

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1285 - 1285

Published: April 3, 2025

The accurate estimation of forest aboveground biomass (AGB) is essential for effective resource management and carbon stock assessment. However, the accuracy AGB often constrained by scarce in situ measurements limitations using a single data source or retrieval model. This study proposes multi-source integration framework Sentinel-1 (S-1) Sentinel-2 (S-2) along with eight predictive models (i.e., multiple linear regression—MLR; Elastic-Net; support vector regression (with kernel polynomial kernel); k-nearest neighbor; back-propagation neural network—BPNN; random forest—RF; gradient-boosting tree—GBT). With airborne light detection ranging (LiDAR)-derived as reference, three-stage optimization strategy was developed, including stepwise feature selection (SFS), hyperparameter optimization, multi-decision fusion (MDVF) model construction. Initially, optimal subsets each were identified SFS, followed through grid search strategy. Finally, evaluated, MDVF implemented to integrate outputs from top-performing models. results revealed that LiDAR-derived demonstrated strong performance (R2 = 0.89, RMSE 20.27 Mg/ha, RMSEr 15.90%), validating its effectiveness supplement field measurements, particularly subtropical forests where traditional inventories are challenging. SFS could adaptively select variable different models, effectively alleviating multicollinearity. Satellite-based yielded robust 0.652, 31.063 20.4%) synergy S-1 S-2, R2 increasing 4.18–7.41% decreasing 3.55–5.89% compared four (BPNN, GBT, RF, MLR) second stage. aims provide cost-effective precise large-scale spatially continuous mapping, demonstrating potential integrating active passive satellite imagery LiDAR enhance mapping further ecological monitoring accounting.

Language: Английский

Citations

0
High-resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, contextualizing the 2022 fire season distinctiveness in France DOI Creative Commons
Lilian Vallet, Martin Schwartz, Philippe Ciais

et al.

Biogeosciences, Journal Year: 2023, Volume and Issue: 20(18), P. 3803 - 3825

Published: Sept. 20, 2023

Abstract. The frequency and intensity of summer droughts heat waves in Western Europe have been increasing, raising concerns about the emergence fire hazard less fire-prone areas. This exposure old-growth forests hosting unadapted tree species may cause disproportionately large biomass losses compared to those observed frequently burned Mediterranean ecosystems. Therefore, analyzing seasons from perspective exposed areas alone is insufficient; we must also consider impacts on loss. In this study, focus exceptional 2022 season France use very high-resolution (10 m) satellite data calculate area, height at national level, subsequent ecological impact based loss during fires. Our semi-automated detection estimated 42 520 ha 66 393 by European automated remote sensing system (EFFIS), including 48 330 actually occurring forests. We show that had a lower than previous years, whereas there was drastic increase area over Atlantic pine temperate High were driven (28 600 vs. 494 yr−1 2006–2021 period) but mitigated low mostly located intensive management Conversely, abnormally high due both 15-fold years (3300 216 which burned. Overall, (i.e., wood dry weight) 0.25 Mt shrublands, 1.74 forest, 0.57 forests, amounting total 2.553 Mt, equivalent 17 % average natural mortality all French as reported inventory. A comparison between our estimates global biomass/burned indicates higher resolution improves identification small patches, reduces commission errors with more accurate delineation perimeter each fire, increases affected. study paves way for development low-latency, high-accuracy assessment patch contours deliver informative impact-based characterization year.

Language: Английский

Citations

7
Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation DOI Creative Commons
Chunquan Fan, Binbin He, Jianpeng Yin

et al.

GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)

Published: March 5, 2024

Dead fuel moisture content (DFMC) is essential for assessing wildfire danger, fire behavior, and consumption. Several process-based models have been proposed to estimate DFMC. Previous studies employed DFMC, solely relying on meteorological data obtained from stations. Satellite can offer higher spatial resolution compared data, with the potential enhance DFMC estimates. Within this content, we aimed improve estimates by consideration of geostationary satellite-derived key variable (relative humility, RH) into Fuel Stick Moisture Model (FSMM). The RH was derived Himawari-8 satellite other variables required FSMM were Global Forecast System (GFS). As comparison, an equilibrium (EMC) model, Simard, random forest regression also used field measurement southwest China validate these three models. Results show that estimated reached a reasonable accuracy (R2 = 0.73, RMSE 3.60%, MAE 2.69%). comparison between two confirmed superior performance model. A case over region continuous decreasing trends until outbreak, highlighting applicability our approach in contributing risk assessment.

Language: Английский

Citations

2
Incorporating fire spread simulation and machine learning algorithms to estimate crown fire potential for pine forests in Sichuan, China DOI Creative Commons
Rui Chen, Binbin He,

Yanxi Li

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 132, P. 104080 - 104080

Published: Aug. 1, 2024

Accurate estimation of crown fire potential (CFP) can improve guidance on control and management. However, robust simulations behavior are still challenging, limiting the accuracy regional-scale CFP mapping. This study aims to incorporate spread simulation machine learning algorithms mapping at a regional scale. First, we built dataset using from FARSITE model, as well multi-source data, including fuel, weather, topography variables. Fuel model parameters were optimized with four metaheuristic for simulations. Then, hybrid models (TBA-ML) established by coupling transfer AdaBoost (TrAdaBoost) algorithm three (ML) algorithms, i.e., Bayesian Network (BN), Random Forest (RF), Support Vector Machine (SVM), estimate danger assessment spatially. Results showed that TBA-BN performed best in estimating higher (AUC>0.9 F1 score > 0.8) than RF- SVM-based models. The variable importance causal analysis fuel variables have major contributions occurrence. Finally, mapped monthly average passive active scales qualitatively demonstrated our time-series products successfully captured dynamic change danger. above results suggest integrating accurately

Language: Английский

Citations

2
High resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, seizing the 2022 fire season distinctiveness in France DOI Creative Commons
Lilian Vallet, Martin Schwartz, Philippe Ciais

et al.

Published: April 5, 2023

Abstract. The frequency and intensity of summer droughts heat waves in Western Europe have been increasing, raising concerns about the emergence fire hazard less prone areas. This exposure old-growth forests hosting unadapted tree species may cause disproportionately large biomass losses compared to those observed frequently burned Mediterranean ecosystems. Therefore, analyzing seasons from perspective exposed areas alone is insufficient, we must also consider impacts on loss. In this study, focus exceptional 2022 season France use very high-resolution (10 m) satellite data calculate area, height at national level, subsequent ecological impact based loss during fires. Our high resolution semi-automated detection estimated 42,520 ha 66,393 by European automated remote sensing system (EFFIS), including 48,330 actually occurring forests. We show that had a lower than previous years, whereas there was drastic increase area over Atlantic pine temperate High were driven (28,600 vs. 494 yr−1 2006–2021 period) but mitigated low mostly located intensive management Conversely, abnormally due both 15-fold years (3,300 216 which burned. Overall, (i.e. wood dry weight) 0.25 Mt shrublands, 1.74 forest, 0.57 forests, amounting total 2.553 Mt, equivalent 17 % average natural mortality all French as reported inventory. A comparison between our estimates global biomass/burned indicates higher improves identification small patches, reduces commission errors with more accurate delineation perimeter each fire, increases affected. study paves way for development low-latency, high-accuracy assessment patch contours deliver informative impact-based characterization year.

Language: Английский

Citations

5
A Novel Method for Estimating Fine Fuel Loads in Vertical Forest Layers with Airborne Lidar DOI
Trung Hiếu Nguyễn, Simon Jones, Karin Reinke

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

0
Prediction and Mapping of Boreal Forest Fire Fuel Loads Using High-Resolution Satellite Stereo Imagery DOI
Ranjith Gopalakrishnan, Lauri Korhonen, Matti Maltamo

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0