Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods

Tariq, Aqil; Hong, Su Young; Siddiqui, Saima; Munir, Iqra; Sharifi, Alireza; Li, Qingting; Lu, Linlin

doi:10.1007/s11676-021-01354-4

Cited by 73 publications

(33 citation statements)

References 39 publications

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“…The region is not used as pasture and is regularly affected by fires [29]. The climate of the region is subtropical; the average maximum summer temperature is 34.3 • C [4,5,30], and total annual precipitation is, on average, 1200 mm [28,31,32], the majority of which falls in July and August. Accordingly, the fire season occurs between May and July, when the temperatures are warmest and before the beginning of the monsoonal rains [4,5,13,33].…”

Section: Study Areamentioning

confidence: 99%

“…The climate of the region is subtropical; the average maximum summer temperature is 34.3 • C [4,5,30], and total annual precipitation is, on average, 1200 mm [28,31,32], the majority of which falls in July and August. Accordingly, the fire season occurs between May and July, when the temperatures are warmest and before the beginning of the monsoonal rains [4,5,13,33]. Lightning and the hot and dry weather during the summer months combined with the dry conditions in the autumnal months of September and October after the monsoon season are the natural and primary causes for the ignition and spread of wildfires, while anthropogenic causes coming in second place in reports of past fire incidents [34][35][36].…”

Section: Study Areamentioning

confidence: 99%

“…Wildfires are a common natural occurrence in many environments and constitute a vital component for their management. However, fires of high severity that cause devasting impacts over large areas have recently been documented, notably in the United States of America, the Mediterranean region, Australia, and Pakistan [1][2][3][4][5], bringing into question the effectiveness of current management practices, as these severe wildfires lead to an undesirable alteration and degradation of the landscape [6]. Many variables influence the severity of a fire, including elevation, slope and aspect [7], weather and climatic conditions [8], the structural characteristics of the forest, such as the density of trees and their diameter [9], and the history of fires in the area [10].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan

Tariq

Hong

Gagnon

et al. 2021

Forests

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The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan

Tariq

Hong

Gagnon

et al. 2021

Forests

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“…The total mangrove forest cover for all these sites was 981.3 km 2 in this study. The following study was done by Abbas et al [82], who used a NN fuzzy classifier through OBIA to map the mangrove forest belt located at the coastal belt (1580 km 2 study area) of Pakistan. Advanced Land Observation Satellite's (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) sensor high-resolution data were used for 2008-2009.…”

Section: Coastal Mangrove Sites Of Pakistanmentioning

confidence: 99%

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Ahmad

Gilani

et al. 2021

Forests

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This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers' citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

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“…Wildfires are a global agent for environmental change [1,2], and prescribed burning is an important strategy to minimize the harmful impact of wildfires globally [3]. Prescribed burning has proven effective in various regions, including Southern Europe [4], North America [5,6], and Australia [7][8][9]. In order to meet particular environmental protection targets, controlled fires are required for prescribed burning.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data

Tariq

Hong

et al. 2021

Remote Sensing

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Prescribed burning is a common strategy for minimizing forest fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity, and rate of spread. Such conditions deviate from those encountered during the fire season. Prescribed burns mostly affect surface fuels and understory vegetation, an outcome markedly different when compared to wildfires. Data on prescribed burning are crucial for evaluating whether land management targets have been reached. This research developed a methodology to quantify the effects of prescribed burns using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery in the forests of southeastern Australia. C-band SAR datasets were specifically used to statistically explore changes in radar backscatter coefficients with the intensity of prescribed burns. Two modeling approaches based on pre- and post-fire ratios were applied for evaluating prescribed burn impacts. The effects of prescribed burns were documented with an overall accuracy of 82.3% using cross-polarized backscatter (VH) SAR data under dry conditions. The VV polarization indicated some potential to detect burned areas under wet conditions. The findings in this study indicate that the C-band SAR backscatter coefficient has the potential to evaluate the effectiveness of prescribed burns due to its sensitivity to changes in vegetation structure.

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Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods

Cited by 73 publications

References 39 publications

Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan

Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data

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