Assessment of forest fire danger using automatic weather stations and MODIS TERRA satellite datasets for the state Madhya Pradesh, India

Babu, K. V. Suresh; Vanama, V. S. K.; Roy, Arijit; Prasad, Prabhunath

doi:10.1109/icacci.2017.8126118

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…The integration of RS data/techniques and GIS facilitates the efficient and accurate analysis of wildfire dynamics, enabling informed decision-making processes for fire management and mitigation strategies [17,18]. The occurrence of fires, including their severity and duration, is intricately correlated to vegetation conditions [10,17,18], including critical dynamic factors such as Fuel Moisture Content (FMC) and Fuel Temperature (FT) [20,21]. Several studies have been conducted worldwide to classify and map land cover due to its vast and vital role in natural resource management [22], agriculture management [23], and biodiversity conservation [24], among others.…”

Section: Introductionmentioning

confidence: 99%

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Rahimi,

Duarte,

Teodoro

2024

Sustainability

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Annually, the oak forests of the Zagros Mountains chains in western Iran and northeastern Iraq face recurring challenges posed by forest fires, particularly in the Kurdo–Zagrosian forests in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation conditions. Integrating in situ data, Remote Sensing (RS) data, and Geographical Information Systems (GIS) integration presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces and tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies by isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas crucial for the study region’s fire susceptibility assessment. Results demonstrate a substantial overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

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Section: Introductionmentioning

confidence: 99%

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Rahimi,

Duarte,

Teodoro

2024

Sustainability

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Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2

Kacic,

Gessner,

Holzwarth

et al. 2024

Remote Sens Ecol Conserv

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Assessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.

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Forest Fire Detection Using Satellite Images

Lalitha

Veerapandu

2022

Smart Innovation, Systems and Technologies

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Assessment of forest fire danger using automatic weather stations and MODIS TERRA satellite datasets for the state Madhya Pradesh, India

Cited by 5 publications

References 5 publications

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2

Forest Fire Detection Using Satellite Images

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