Cargando…

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simul...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Jia-Hua, Yao, Feng-Mei, Liu, Cheng, Yang, Li-Min, Boken, Vijendra K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166733/
https://www.ncbi.nlm.nih.gov/pubmed/21909297
http://dx.doi.org/10.3390/ijerph8083156
_version_ 1782211176843706368
author Zhang, Jia-Hua
Yao, Feng-Mei
Liu, Cheng
Yang, Li-Min
Boken, Vijendra K.
author_facet Zhang, Jia-Hua
Yao, Feng-Mei
Liu, Cheng
Yang, Li-Min
Boken, Vijendra K.
author_sort Zhang, Jia-Hua
collection PubMed
description Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.
format Online
Article
Text
id pubmed-3166733
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher Molecular Diversity Preservation International (MDPI)
record_format MEDLINE/PubMed
spelling pubmed-31667332011-09-09 Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview Zhang, Jia-Hua Yao, Feng-Mei Liu, Cheng Yang, Li-Min Boken, Vijendra K. Int J Environ Res Public Health Review Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status. Molecular Diversity Preservation International (MDPI) 2011-08 2011-07-28 /pmc/articles/PMC3166733/ /pubmed/21909297 http://dx.doi.org/10.3390/ijerph8083156 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Review
Zhang, Jia-Hua
Yao, Feng-Mei
Liu, Cheng
Yang, Li-Min
Boken, Vijendra K.
Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
title Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
title_full Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
title_fullStr Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
title_full_unstemmed Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
title_short Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
title_sort detection, emission estimation and risk prediction of forest fires in china using satellite sensors and simulation models in the past three decades—an overview
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166733/
https://www.ncbi.nlm.nih.gov/pubmed/21909297
http://dx.doi.org/10.3390/ijerph8083156
work_keys_str_mv AT zhangjiahua detectionemissionestimationandriskpredictionofforestfiresinchinausingsatellitesensorsandsimulationmodelsinthepastthreedecadesanoverview
AT yaofengmei detectionemissionestimationandriskpredictionofforestfiresinchinausingsatellitesensorsandsimulationmodelsinthepastthreedecadesanoverview
AT liucheng detectionemissionestimationandriskpredictionofforestfiresinchinausingsatellitesensorsandsimulationmodelsinthepastthreedecadesanoverview
AT yanglimin detectionemissionestimationandriskpredictionofforestfiresinchinausingsatellitesensorsandsimulationmodelsinthepastthreedecadesanoverview
AT bokenvijendrak detectionemissionestimationandriskpredictionofforestfiresinchinausingsatellitesensorsandsimulationmodelsinthepastthreedecadesanoverview