Mapping Forest-Fire Potentiality Using Remote Sensing and GIS, Case Study: Kurdistan Region-Iraq

Rahimi, Iraj; Azeez, Salim Neimat; Ahmed, Imran H.

doi:10.1007/978-3-030-21344-2_20

Cited by 19 publications

(18 citation statements)

References 32 publications

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“…Oak forests, specifically Q. aegilops, like the rest of the natural resources, have been degrading due to several factors by shifting cultivation and land use/land cover changes, population increase, civil war, and inadequate forest management policy (Nasser 1984). Wildfires (natural or man-made) during the scorching hot summer has also been reported (Chapman 1948;Rahimi et al 2020). Furthermore, the economic sanctions by the United Nations Security Council between 1990 and 2000 significantly affected the oak forests in the KRI.…”

Section: Introductionmentioning

confidence: 99%

Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

Khwarahm

2020

Ecol Process

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Background The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species, Q. aegilops forest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI, Q. aegilops has been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions of Q. aegilops is minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records of Q. aegilops, and environmental variables. Results The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges of Q. aegilops would be reduced by 3.6% (1849.7 km2) and 3.16% (1627.1 km2), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km2) and 1.7% (848.0 km2), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes. Conclusions The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.

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Section: Introductionmentioning

confidence: 99%

Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

Khwarahm

2020

Ecol Process

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“…It's also proven that change in Land Use and Land Cover (LULC) is considered as heart of the sustainable development debate and it considered as one of the most important factors of the global-scaled environmental transformations [3] . There are many contributing factors, both natural-based and human-based, which may result in changes in LULC [4] . Many formal reports and researches show that human-based activities are the main driving force that change the land covers, as well as, climate changes [4,5] .…”

Section: Introductionmentioning

confidence: 99%

“…There are many contributing factors, both natural-based and human-based, which may result in changes in LULC [4] . Many formal reports and researches show that human-based activities are the main driving force that change the land covers, as well as, climate changes [4,5] . These changes are proved to have substantially changed about 80% of the Earth's land area over the recent centuries [6] .…”

Section: Introductionmentioning

confidence: 99%

Integration of Field Data and Online Satellite Images to Map Urban Change Pattern from 2003 to 2013, Case Study: Darbandikhan city, Kurdistan Region, Iraq.

Amin¹,

Azeez

Ahmad

2022

PJBAS

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Remote Sensing accompanied with GIS is a powerful way of mapping land-use/cover change at different spatial and temporal resolutions. This study aims to map the urban land use change of the small city of Darbandikhan, in the west of Kurdistan Region-Iraq using Google Earth time series images, as well as, field statistical data, from 2003 to 2013 which is considered as the most prosperous economic period within the study area. Results showed that the city has extended by 2.7 times, from almost 1.9 km 2 in 2003 to 5.3 km 2 in 2013, mainly to the North and West of the city considering the potentials of the area. During that period residents have doubled mainly because of the rural movement toward urban areas as the result of economic flourishment caused by the economic evolvement in 2003. Also, the developed map has revealed the fact that the distribution of the facilities, except for schools, is not organized. It has been located in such a way around the city center exclusively. The developed map, also revealed, that the distribution of facilities, except schools, not well distributed all over the town and mainly maintain around the city center. Furthermore, the roads, as showed on the map, are mostly (90%) less than 15m wide which rarely met the transportation demands of the city.

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“…With satellite images delivering accurate data at high temporal resolution and with such high spatial precision that the need for conducting field surveys has been significantly obliterated. This has resulted in a plethora of mapping studies such as landslide susceptibility mapping (Bragagnolo et al 2020a, b;Hu et al 2020;Sameen et al 2020;Sansare and Mhaske 2020;Tang et al 2020;Van Dao et al 2020;Wang et al 2020;Wu et al 2020), flood susceptibility mapping (Bui et al 2020;Chen et al 2020a;Costache and Bui 2020;Feloni et al 2020;Feng et al 2020;Mishra and Sinha 2020;Pourghasemi et al 2020;Sansare and Mhaske 2020;Sarkar and Mondal 2020), and forest fire susceptibility mapping (Abedi Gheshlaghi et al 2020;Ç olak and Sunar 2020;Rahimi et al 2020;Sevinc et al 2020;Venkatesh et al 2020), mineral potential mapping (de Quadros et al 2006) etc., employing RS for obtaining data for regions that were traditionally considered inaccessible. These studies have focused on generating zonation maps delineating the zones on the basis of their relative potential/susceptibility/vulnerability/proneness using a variety of statistical techniques such as weight-of-evidence (Mastere 2020;Zaheer et al 2020;Rahmati et al 2016;Kayastha et al 2012;Ozdemir 2011;Corsini et al 2009;de Quadros et al 2006;Lee and Choi 2004), frequency ratio (Sarkar and Mondal 2020;Rahmati et al 2016;Naghibi et al 2015;…”

Section: Introductionmentioning

confidence: 99%

Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

Sachdeva

Kumar

2020

Stoch Environ Res Risk Assess

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In the drought prone district of Dholpur in Rajasthan, India, groundwater is a lifeline for its inhabitants. With population explosion and rapid urbanization, the groundwater is being critically over-exploited. Hence the current groundwater potential mapping study was undertaken to ascertain the areas that are more likely to yield a larger volume of groundwater against those areas that have poor groundwater potential and accordingly perpetuate the much needed damage control. Thematic layers for 14 groundwater influencing factors were considered for the study region, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), geology, soil, land use, normalized difference vegetation index (NDVI), surface temperature, precipitation, distance from roads, and distance from rivers. These were then subjected to an overlay operation, with the groundwater inventory which comprised of the locations of observational groundwater wells. The resulting geospatial database was then used to train two decision tree based ensemble models: gradient boosted decision trees (GBDT) and random forest (RF). The predictive performance of these models was then compared using various performance metrics such as area under curve (AUC) of receiver operating characteristics (ROC), sensitivity, accuracy, etc. It was found that GBDT (AUC: 0.79) outperformed RF (AUC: 0.71). The validated GBDT model was then used to construct the groundwater potential zonation map. The generated map showed that about 20.2% of the region has very high potential, while 22.6% has high potential to yield groundwater, and approximately 19.9-17.5% of the study region has very low to low groundwater potential.

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Mapping Forest-Fire Potentiality Using Remote Sensing and GIS, Case Study: Kurdistan Region-Iraq

Cited by 19 publications

References 32 publications

Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

Integration of Field Data and Online Satellite Images to Map Urban Change Pattern from 2003 to 2013, Case Study: Darbandikhan city, Kurdistan Region, Iraq.

Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

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