Global data-driven prediction of fire activity

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 1, 2025

Abstract Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse novel data types enables transition forecasting fire weather, predicting actual activity. In this study we demonstrate that shift is feasible also within an operational context. Traditional forecasts tend over predict high danger, particularly fuel limited biomes, often resulting false alarms. By using on characteristics, ignitions observed activity, data-driven predictions reduce false-alarm rate high-danger forecasts, enhancing their accuracy. This made possible by quality global datasets evolution detection. We find input more important when improving than complexity ML architecture. While focus justified, our findings highlight importance investing high-quality and, where necessary create it through physical models. Neglecting aspect would undermine gains ML-based approaches, emphasizing essential achieve meaningful progress activity

Language: Английский

---

SARMoistX: temporal forecasting of vegetation moisture using SAR imagery

European Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: 58(1)

Published: April 11, 2025

Language: Английский

---

GWR–ANN modelling for spatiotemporal prediction of land surface temperature in Thanjavur delta: evaluating environmental impacts on climate action and SDGs

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

Language: Английский

---

Analyzing the dihedral scattering component of P-band SAR signals for trunk permittivity estimation – a concept study

Science of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 100236 - 100236

Published: May 1, 2025

Language: Английский

---

Temperature Is Likely an Important Omission in Interpreting Vegetation Optical Depth

Geophysical Research Letters, Journal Year: 2024, Volume and Issue: 51(15)

Published: July 30, 2024

Abstract Vegetation optical depth (VOD) satellite microwave retrievals provide significant insights into vegetation water content and responses to hydroclimatic changes. While VOD variations are commonly linked dry biomass live fuel moisture ( LFMC ), the impact of canopy temperature T c ) remains overlooked in large‐scale studies. Here, we investigated on L‐band (1.4 GHz) X‐band (10.7 at diurnal seasonal timescales. Synthetic benchmark was created using realistic fields , an electromagnetic model. Perturbation experiments revealed that strongly affects both X‐band. Seasonally, while emerges as largest contributor 70% (at X‐band) 90% L‐band) our study region, still play substantial roles. The findings stress importance refining retrieval algorithms distinguish effects for future applications ecohydrology.

Language: Английский

---

Mapping global drought-induced forest mortality based on multiple satellite vegetation optical depth data

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 315, P. 114406 - 114406

Published: Sept. 13, 2024

Language: Английский

---

Towards accurate monitoring of water content in woody tissue across tropical forests and other biomes

Tree Physiology, Journal Year: 2024, Volume and Issue: 44(8)

Published: July 2, 2024

Forest ecosystems face increasing drought exposure due to climate change, necessitating accurate measurements of vegetation water content assess stress and tree mortality risks. Although Frequency Domain Reflectometry offers a viable method for monitoring stem by measuring dielectric permittivity, challenges arise from uncertainties in sensor calibration linked wood properties species variability, impeding its wider usage. We sampled tropical forest trees palms eastern Amazônia evaluate how output differences are controlled density, temperature taxonomic identity. Three individuals per were felled cut into segments within diverse dataset comprising five dicotyledonous three monocotyledonous palm on wide range densities. Water was estimated gravimetrically each segment using temporally explicit wet-up/dry-down approach the relationship with permittivity examined. Woody tissue density had no significant impact calibration, but identity significantly affected readings. The artefact quantitatively important at large differences, which may have led bias daily seasonal dynamics previous studies. established first equation performed well estimating content. Notably, we demonstrated that sensitivity remained consistent across species, enabling creation simplified one-slope accurate, species-independent relative Our serves as general, standard assessing woody tissue, offering valuable tool quantifying responses ecosystems.

Language: Английский

---

Estimating Canopy Interception Water Storage with GNSS-Transmissometry

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Journal Year: 2024, Volume and Issue: unknown, P. 4507 - 4510

Published: July 7, 2024

Language: Английский

---