Underutilized Feature Extraction Methods for Burn Severity Mapping: A Comprehensive Evaluation DOI Creative Commons
Linh Nguyen Van, Giha Lee

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4339 - 4339

Published: Nov. 20, 2024

Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial assessing wildfire damage; however, incorporating many can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used address this issue, its effectiveness relative other feature extraction (FE) methods in BSM remains underexplored. This study aims enhance ML classifier accuracy by evaluating various FE techniques that mitigate multicollinearity among indices. Using composite index (CBI) data from the 2014 Carlton Complex fire United States as a case study, we extracted 118 seven Landsat-8 spectral bands. We applied compared 13 different techniques—including linear nonlinear such PCA, t-distributed stochastic neighbor embedding (t-SNE), discriminant (LDA), Isomap, uniform manifold approximation projection (UMAP), factor (FA), independent (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally (LLE), (SE), neighborhood components (NCA). The performance of these was benchmarked against six classifiers determine their improving Our results show alternative outperform computational efficiency. Techniques like LDA NCA effectively capture relationships critical BSM. contributes existing literature providing comprehensive comparison methods, highlighting potential benefits underutilized

Language: Английский

Fostering Post-Fire Research Towards a More Balanced Wildfire Science Agenda to Navigate Global Environmental Change DOI Creative Commons
João Gonçalves, Ana Paula Portela, Adrián Regos

et al.

Fire, Journal Year: 2025, Volume and Issue: 8(2), P. 51 - 51

Published: Jan. 26, 2025

As wildfires become more frequent and severe in the face of global environmental change, it becomes crucial not only to assess, prevent, suppress them but also manage aftermath effectively. Given temporal interconnections between these issues, we explored concept “wildfire science loop”—a framework categorizing wildfire research into three stages: “before”, “during”, “after” wildfires. Based on this partition, performed a systematic review by linking particular topics keywords each stage, aiming describe one quantify volume published research. The results from our identified substantial imbalance landscape, with post-fire stage being markedly underrepresented. Research focusing is 1.5 times (or 46%) less prevalent than that “before” 1.8 77%) “during” stage. This discrepancy likely driven historical emphasis prevention suppression due immediate societal needs. Aiming address overcome imbalance, present perspectives regarding strategic agenda enhance understanding processes outcomes, emphasizing socioecological impacts management recovery multi-level transdisciplinary approach. These proposals advocate integrating knowledge-driven burn severity ecosystem mitigation/recovery practical, application-driven strategies policy development. supports comprehensive spans short-term emergency responses long-term adaptive management, ensuring landscapes are better understood, managed, restored. We emphasize critical importance “after-fire” breaking negative planning cycles, enhancing practices, implementing nature-based solutions vision “building back better”. Strengthening balanced focused will ability close loop involved improve alignment international agendas such as UN’s Decade Ecosystem Restoration EU’s Nature Law. By addressing can significantly restore ecosystems, resilience, develop suited challenges rapidly changing world.

Language: Английский

Citations

1
Effect of phosphorus fractions on benthic chlorophyll-a; Insight from the machine learning models DOI Creative Commons
Yuting Wang,

Sangar Khan,

Zongwei Lin

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: 85, P. 102990 - 102990

Published: Jan. 5, 2025

Language: Английский

Citations

0
Forest Disturbance and Restoration in China's North-South Transition Zone: A Case from the Funiu Mountains DOI Open Access
Qifan Wu, Jingming Hou, Shiwen Wu

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 269 - 269

Published: Feb. 4, 2025

Accurate monitoring and assessment of forest disturbance recovery dynamics are essential for sustainable management, particularly in ecological transition zones. This study analyzed patterns China’s Funiu Mountains from 1991 to 2020 by integrating the LandTrendr algorithm with space-time cube analysis. Using Landsat time series data Geodetector method, we examined both spatiotemporal characteristics driving factors change across three periods. The results showed that (1) between 2020, area experienced 131.19 km2 495.88 recovery, processes most active during 1990s; (2) analysis revealed were predominantly characterized cold spots, suggesting relatively stable conditions despite localized changes; (3) human activities primary drivers early period, while was consistently influenced combined effects topographic precipitation. Additionally, fires emerged as an important factor affecting after 2010. These findings enhance our understanding zones provide empirical support regional management strategies. also highlight importance considering spatial temporal dimensions when long-term changes.

Language: Английский

Citations

0
Mapping spatiotemporal mortality patterns in Spruce mountain forests using Sentinel-2 data and environmental factors DOI Creative Commons
Marcin Kluczek, Bogdan Zagajewski

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103074 - 103074

Published: Feb. 1, 2025

Language: Английский

Citations

0
Vegetation coverage patterns in the “mountain–basin” system of arid regions: Driving force contribution, non-stationarity, and threshold effects DOI Creative Commons
Rou Ma, Zhengyong Zhang,

Lin Liu

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103084 - 103084

Published: Feb. 1, 2025

Language: Английский

Citations

0
Assessing and Forecasting Natural Regeneration in Mediterranean Landscapes After Wildfires DOI Creative Commons

Paraskevi Oikonomou,

Vassilia Karathanassi, Vassilis Andronis

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 897 - 897

Published: March 4, 2025

Forest ecosystems in the Mediterranean basin are significantly affected by summer wildfires. Drought, extreme temperatures, and strong winds increase fire risk Greece. This study explores potential of NDVI for assessing forecasting post-fire regeneration burnt areas Peloponnese (2007) Evros (2011). data from Landsat 7 9 were analyzed to identify stages process dominant vegetation species at each stage. Comparing pre-fire values highlighted recovery rate, while trendline slope indicated rate. combined analysis forms a methodology that allows drawing conclusions about type prevails after fire. Validation was conducted using photointerpretation techniques CORINE land cover data. The findings suggest sclerophyllous regenerate faster, fir forests recover slowly may be replaced sclerophylls. To predict regrowth, two time series models (ARMA, VARIMA) machine learning-based ones (random forest, XGBoost) tested. Their performance evaluated comparing predicted actual numerical values, calculating error metrics (RMSE, MAPE), analyzing how patterns align with observed ones. results showed overperformance multivariate need introduce additional variables, such as soil characteristics effect climate change on weather parameters, improve predictions.

Language: Английский

Citations

0
Vegetation dynamics in Mainland Southeast Asia: Climate and anthropogenic influences DOI
Dafang Zhuang,

Chenxi Cui,

Zhanpeng Liu

et al.

Land Use Policy, Journal Year: 2025, Volume and Issue: 153, P. 107546 - 107546

Published: March 29, 2025

Language: Английский

Citations

0
Post-fire vegetation dynamic patterns and drivers in greater Hinggan Mountains: Insights from long-term remote sensing data analysis DOI Creative Commons
Bohan Jiang, Wei Chen, Yuan Zou

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 83, P. 102850 - 102850

Published: Oct. 9, 2024

Language: Английский

Citations

3
Underutilized Feature Extraction Methods for Burn Severity Mapping: A Comprehensive Evaluation DOI Creative Commons
Linh Nguyen Van, Giha Lee

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4339 - 4339

Published: Nov. 20, 2024

Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial assessing wildfire damage; however, incorporating many can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used address this issue, its effectiveness relative other feature extraction (FE) methods in BSM remains underexplored. This study aims enhance ML classifier accuracy by evaluating various FE techniques that mitigate multicollinearity among indices. Using composite index (CBI) data from the 2014 Carlton Complex fire United States as a case study, we extracted 118 seven Landsat-8 spectral bands. We applied compared 13 different techniques—including linear nonlinear such PCA, t-distributed stochastic neighbor embedding (t-SNE), discriminant (LDA), Isomap, uniform manifold approximation projection (UMAP), factor (FA), independent (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally (LLE), (SE), neighborhood components (NCA). The performance of these was benchmarked against six classifiers determine their improving Our results show alternative outperform computational efficiency. Techniques like LDA NCA effectively capture relationships critical BSM. contributes existing literature providing comprehensive comparison methods, highlighting potential benefits underutilized

Language: Английский

Citations

1