MODIS based forest fire hotspot analysis and its relationship with climatic variables

Kumari, Binita; Pandey, Arvind Chandra

doi:10.1007/s41324-019-00275-z

Cited by 43 publications

(31 citation statements)

References 45 publications

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“…The forest cover of Jharkhand accounts for 29% of its total geographical area and is prone to degradation due to forest fire hazard leading to loss of green cover and biodiversity (Kumari and Pandey 2019). Palamau Tiger Reserve is situated between latitude 23°25′-23°55′ N and longitude 83°50′-84°36′ E in the Jharkhand state covering the total area of 1222.43 km 2 at an elevation from 200 to 1100 m above sea level (ASL; Fig.…”

Section: Study Areamentioning

confidence: 99%

“…Reddy et al (2017) reported that in India, large-scale deforestation and degradation occurred in the forest dominated by dry deciduous and moist deciduous forests. This is primarily occurring due to extensive cultivation, increasing population, rapid urbanization, and rising stresses on agriculture land (Kumari and Pandey 2019). As per State of Forest Reports, India's total forest cover was 633, 397 km 2 (19.27%) in 1997 (Forest Survey of India 1997), which was insignificantly increased to 637,293 km 2 (19.39%) in 1999, 675,538 km 2 (20.55%) in 2001, and 692, 027 km 2 (21.05%) in 2011 (FSI 1999(FSI , 2001(FSI , 2011.…”

Section: Introductionmentioning

confidence: 99%

“…The Forest Survey of India in the biannual State of Forest Reports (SFR) reported the net annual loss of 99, 850 ha of forest in Jharkhand during 2007-2009(FSI 2009, 2011. Also, due to high level of urbanization in the Ranchi district, significant rate of deforestation (− 0.95%) was observed during 1996-2008 (Kumari and Pandey 2019). The forest cover in Jharkhand was 21,692 km 2 in 1997, which was decreased slightly to 22,637 km 2 in 2001.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing approach to evaluate anthropogenic influences on Forest Cover of Palamau Tiger Reserve, Eastern India

2020

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Background: Tropical forests have been experiencing remarkable rates of transformation over the past century as they are getting degraded or decimated to a great extent by anthropogenic activities. This study aims at investigating the long-term forest cover transformation in Palamau Tiger Reserve (PTR), Jharkhand, India, using Landsat TM, ETM + , and OLI satellite images during 1975-2015. The forest cover was delineated utilizing various keys of visual interpretation techniques. Results: The forest cover was primarily decreased in the northeastern and northwestern parts in PTR. In order to identify the anthropogenic disturbance in the forest reserve, human settlement density was mapped using highresolution Google Earth imagery. The results showed a positive correlation between human population density and settlement density. Five major affected sites with an outer buffer of 2 km were demarcated in order to deduce the anthropogenic influences in major non-forested sites in PTR. It was observed that the forest change was maximum at site 3 (Ranidah, area 61.06 km 2 , − 6.47% change) followed by site 2 (Saidup, area 124.38 km 2 , − 7.65% change), where settlement units were also high (2638 and 2621 settlement units, respectively). At site 1 (Barkheta, area 81.59 km 2), − 1.99% change was observed, and at site 4 (Samadh Tola, area 9.15 km 2), 1.03% change was observed having moderate settlement units (2422 and 1892 settlement units, respectively). Areas with the low level of human settlements (1038 settlement units) observed the least change, i.e., at site 5 (Netarhat, area 48.52 km 2), 0.58% change was observed mainly during the years. Conclusions: The forest cover exhibited an overall decrease of 14.55 km 2 (− 1.34% change) with episodic variation during 1975-2015 in PTR, Eastern India. A significant forest disturbance occurred primarily in the northeastern and northwestern parts of PTR along the forest fringe due to the high population and settlement density. The study highlighted the potential use of freely available multi-temporal satellite observations in forest management.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote Sensing approach to evaluate anthropogenic influences on Forest Cover of Palamau Tiger Reserve, Eastern India

2020

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“…Further the precision of the FRZI model may also be increased by adding additional explanatory variables in the analysis such as climate data. Many researchers have found that climate change occurs gradually and can directly impact length and severity of the forest fires (Kumari and Pandey, 2020). Globally forest fires have many causes of emergence, with climatic drought directly indicating forest fire risk.…”

Section: Fire Risk Zone Mappingmentioning

confidence: 99%

Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey

Gülçin¹,

Deniz²

2020

Turkish Journal of Forestry | Türkiye Ormancılık Dergisi

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The aim of this research is to map the potential forest fire risk zones using various landscape analysis techniques in Manisa province, Turkey. Forest fire, which is defined as an ecological disaster caused by natural processes or as a result of human activities, causes environmental degradation and fragmentation of the landscape. Therefore, it is very important to produce a fire risk zone map that can be used to minimize the frequency of fire, to prevent damage, to provide a prediction for the problems that may cause fire and to form a decision mechanism for the solution methods. This research utilized CORINE 2018 produced under the framework of the Copernicus Program which is the European Union's Earth Observation Programme coordinated and managed by the European Commission, ASTER Global DEM digital elevation model data obtained from the website of NASA Earthdata, fire archive records based on MODIS satellite images, digital stand map displaying the spatial distribution of tree species, and the OpenStreetMap (OSM) which were used for mapping the existing road network. Vegetation cover, slope, aspect, elevation, distance to settlement, and distance to road variables were used to determine risk zones. The specific weights were assigned to each thematic map layer according to their capacity on fire ignition. The slope, aspect, and elevation maps were generated from the digital elevation model. The distance to settlement map was generated from the CORINE database while the distance to road map was produced from OSM. The Fire Risk Zone Index (FRZI) was utilized to determine forest fire risk zones. According to the generated final fire risk map, almost 25.8% of the study area was predicted to be under very high and highrisk zones. The final forest fire risk model was validated with past fire incidents data that was acquired from MODIS images as fire points. The result of this research showed that out of 149 fire incidents in Manisa between 2001 and 2018, 97 incidents had occurred in very high and highrisk areas. This finding supports that the presented methodology based on RS and GIS techniques is reliable and can be effectively used in the process of delineation of the forest fire risk zones.

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“…Forest in the study area is affected by fragmentation [24] and connectivity weakening [25]. The forest of Jharkhand comprised 29% of its total geographical area, and it is prone to degradation due to many natural and anthropogenic factors including forest re hazard and drought, which lead to loss of green cover [26]. This study examines the usefulness of sentinel 2A imagery for retrieving leaf chlorophyll content in the forest.…”

Section: Introductionmentioning

confidence: 99%

Chlorophyll Changes Using Neural Network and Vegetation Indices in Tropical deciduous forest

Gupta

Pandey

2020

Preprint

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Background: Ongoing climate and Earth’s atmosphere changes create profound effect on distribution and composition of forest, as well as on the fauna that depends on forest. The Sentinel-2A satellite data eases the mapping of Leaf Chlorophyll Content (LCC) at higher spatial and temporal resolution. In the present study, the temporal dimension of LCC was evaluated as an indicator of plant stress. LCC was retrieved using the inversion of the radiative transfer model based on an artificial neural network. The data used for Spatio-temporal modelling of LCC was Landsat data.Result: From the Sentinel imagery derived vegetation indices, it was found that the narrowband indices having high correlation with LCC were pigment specific simple ratio and normalized difference index (45) (R2 > 0.7; p < 0.001) centred at 665 nm, 705 nm, and 740 nm. Landsat 8 infrared percentage vegetation index had a strong relationship with LCC (R2 =0.8). The Spatio-temporal (1997 to 2017) plant stress were detected using changes in LCC through an equation of correlation. The negative changes and deterioration of LCC were seen in the forest during the year 1997 to 20I7(rate = -1.2 µgcm-2year-1) showing higher rate of forest health decline. Conclusion: The 33% of plant stress increased currently in the protected forest mainly because of anthropogenic influences. These vast decline in the chlorophyll gives rise to various photosynthetic vulnerabilities in forest ecosystem and indirectly affects human including wildlife.

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MODIS based forest fire hotspot analysis and its relationship with climatic variables

Cited by 43 publications

References 45 publications

Remote Sensing approach to evaluate anthropogenic influences on Forest Cover of Palamau Tiger Reserve, Eastern India

Remote Sensing approach to evaluate anthropogenic influences on Forest Cover of Palamau Tiger Reserve, Eastern India

Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey

Chlorophyll Changes Using Neural Network and Vegetation Indices in Tropical deciduous forest

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