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, Jiahua; Yao, Fengmei; Liu, Cheng; Yang, Limin; Boken, Vijendra K.

doi:10.3390/ijerph8083156

Cited by 48 publications

(28 citation statements)

References 79 publications

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“…In recent years, earth observation using sensors on board space-borne satellites has provided useful raw data to detect and monitor active fires and extract burned land patches, not only at national or larger geographic scales but also at the continental and global scales [9][10][11]. The availability of free-of-charge, global scale active fire (AF) and burned area (BA) products such as the MODIS derived products [12] has significantly increased the interest of global community end-users in their adoption for regional to local applications, especially in areas where ground data are lacking or are not publicly available [9].…”

Section: Introductionmentioning

confidence: 99%

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

2017

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An increasing number of end-users looking for ground data about fire activity in regions where accurate official datasets are not available adopt a free-of-charge global burned area (BA) and active fire (AF) products for applications at the local scale. One of the pressing requirements from the user community is an improved ability to detect small fires (less than 50 ha), whose impact on terrestrial environments is empirically known but poorly quantified, and is often excluded from global earth system models. The newest generation of BA algorithms combines the capabilities of both the BA and AF detection approaches, resulting in a general improvement of detection compared to their predecessors. Accuracy assessments of these products have been done in several ecosystems; but more complex ones, such as regions that are characterized by frequent small fires and steep terrain has never been assessed. This study contributes to the understanding of the performance of global BA and AF products with a first assessment of four selected datasets: MODIS-based MCD45A1; MCD64A1; MCD14ML; and, ESA's Fire_CCI in a mountainous region of northwest Yunnan; P.R. China. Due to the medium to coarse resolution of the tested products and the reduced sizes of fires (often smaller than 50 ha) we used a polygon intersection assessment method where the number and locations of fire events extracted from each dataset were compared against a reference dataset that was compiled using Landsat scenes. The results for the two sample years (2006 and 2009) show that the older, non-hybrid products MCD45A1 and, MCD14ML were the best performers with Sørensen index (F1 score) reaching 0.42 and 0.26 in 2006, and 0.24 and 0.24 in 2009, respectively, while producer's accuracies (PA) were 30% and 43% in 2006, and 16% and 47% in 2009, respectively. All of the four tested products obtained higher probabilities of detection when smaller fires were excluded from the assessment, with PAs for fires bigger than 50 ha being equal to 53% and 61% in 2006, 41% and 66% in 2009 for MCD45A1 and MCD14ML, respectively. Due to the technical limitations of the satellites' sensors, a relatively low performance of the four products was expected. Surprisingly, the new hybrid algorithms produced worse results than the former two. Fires smaller than 50 ha were poorly detected by the products except for the only AF product. These findings are significant for the future design of improved algorithms aiming for increased detection of small fires in a greater diversity of ecosystems.

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Section: Introductionmentioning

confidence: 99%

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

2017

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“…The findings of earlier studies suggest that the risk of forest fire incidence is strongly linked to the annual volume of precipitation (Zhang et al 2011;Mohammadi et al 2014). Moreover, only distance to roads had a strong positive association with longevity of fire across all the variables.…”

Section: Insertmentioning

confidence: 86%

Monitoring Forest Fire using Geo-Spatial Information Techniques and Spatial Statistics: One Case Study of Forest fire in Margalla Hills, Islamabad, Pakistan

Tariq

Hong

Siddiqui

2020

Preprint

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Background Understanding the spatial patterns of forest ﬁres is of key importance for ﬁre risk management with ecological implications. Fire occurrence, which may result from the presence of an ignition source and the conditions necessary for a fire to spread, is an essential component of fire risk assessment. Methods The aim of this research was to develop a methodology for analyzing spatial patterns of forest ﬁre danger with a case study of tropical forest fire at Margalla Hills, Islamabad, Pakistan. A geospatial technique was applied to explore inﬂuencing factors including climate, vegetation, topography, human activities, and 299 ﬁre locations. We investigated the spatial extent of burned areas using Landsat data and determined how these factors inﬂuenced the severity rating of ﬁres in these forests. The importance of these factors on forest ﬁres was analyzed and assessed using logistic and stepwise regression methods. Results The findings showed that as the number of total days since the start of fire has increased, the burned areas increased at a rate of 25.848 ha / day (R 2 = 0.98). The average quarterly mean wind speed, forest density, distance to roads and average quarterly maximum temperature were highly correlated to the daily severity rating of forest fires. Only the average quarterly maximum temperature and forest density affected the size of the burnt areas. Fire maps indicate that 22% of forests are at the high and very high level (> 0.65), 25% at the low level (0.45-0.65), and 53% at the very low level (0.25 – 0.45). Conclusion Through spatial analysis, it is found that most forest fires happened in less populated areas and at a long distance from roads, but some climatic and human activities could have influenced fire growth. Furthermore, it is demonstrated that geospatial information technique is useful for exploring forest fire and their spatial distribution.

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“…In recent years, the availability of data from space satellites with different spatial, temporal and spectral resolutions has allowed access to raw data useful for detecting and monitoring active fires and extracting burnt debris not only at national or local level, but also at global scales [15]. These technologies allow comparison with other methodological approaches because of their accuracy, repeatability and speed of data acquisition, longer historical series and ease of combination with other thematic data [16].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal patterns of wildfires in the Niassa Reserve –Mozambique, using remote sensing data

Nhongo

Fontana

Guasselli

2020

Preprint

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Wildfires are among the biggest factors of ecosystem change. Knowledge of fire regime 12 (fire frequency, severity, intensity, seasonality, and distribution pattern) is an important factor in 13 wildfire management. This paper aims to analyze the spatiotemporal patterns of fires and burned 14 areas in the Niassa Reserve between 2002-2015 using MODIS data, active fire product (MCD14ML) 15 and burned area product (MCD64A1). For this, the annual and monthly frequencies, the trend of 16 fires and the frequency by types of forest cover were statistically analyzed. For the analysis of the 17 spatial dynamics of forest fires we used the Kernel density (Fixed Method). The results show a 18 total of 20.449 forest fires and 171.067 km² of burned areas in the period 2002-2015. Fire incidents 19 were highest in 2015, while the largest burned areas were recorded in 2007. The relationship 20between increased fires and burned areas is not linear. There was a tendency for fires to increase, 21 while for burnt areas there was stabilization. Forest fires start in May and end in December. 22August-October are the most frequent period, peaking in September. Fires occur predominantly 23 in deciduous forests and mountain forests because of the type of vegetation and the amount of dry 24 biomass. There is a monthly spatial dynamics of wildfires from east to west in the reserve. This 25 behavior is dependent on vegetation cover type, fuel availability, and senescence. 26

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

Cited by 48 publications

References 79 publications

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

Monitoring Forest Fire using Geo-Spatial Information Techniques and Spatial Statistics: One Case Study of Forest fire in Margalla Hills, Islamabad, Pakistan

Spatio-temporal patterns of wildfires in the Niassa Reserve –Mozambique, using remote sensing data

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