Forest Cover Loss and Forest Fire Monitoring Using GIS And GEE Over Similipal Tiger Reserve, Odisha, India

Forest Cover Loss and Forest Fire Monitoring Using GIS And GEE Over Similipal Tiger Reserve, Odisha, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Forest Cover Loss and Forest Fire Monitoring Using GIS And GEE Over Similipal Tiger Reserve, Odisha, India Rasmi Ranjan Das, Debabrata Nandi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3996489/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Similipal Biosphere Reserve is indeed an important natural treasure and holds significance for several reasons as the 7th largest biosphere reserve in India. India’s Mayurbhanj is one among the World’s Greatest Places of 2023 along with the Similipal Biosphere, the Time magazine has included it is the only place in the world where black melanistic tigers were spotted that’s why the researchers all across the globe are curious to explore Mayurbhanj and its rich biodiversity. The Similipal comprises 7% flowering plants, 8% Orchids, 7% Reptiles, 20% birds and 11% mammals. A significant natural resource, forests are crucial to preserving the ecological equilibrium. Forests are now endangered by both man-made and natural forest fires as a result of growing population and civilisation. Basically, there are three different categories into which forest fire causes may be divided: Natural, Intentional/Deliberate, Accidental, and Unintentional are the four categories. In India, human anthropogenic activities that involve slash-and-burn agriculture, deforestation, controlled burning, firewood burning, etc. are to blame for almost 90% of forest fires. Therefore, in nations like India, it is crucial to monitor and manage forest fires. Anthropogenic impact can easily be detected with the satellite data such as Landsat-5, Landsat-8 & MODIS are collected and processed in Arc GIS 10.8 & Google Earth Engine. Forest cover classification Forest fire Change detection Similipal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction India’s Mayurbhanj is one among the World’s Greatest Places of 2023 (TIME Magazine, 2023) along with the Similipal National Park the Time magazine has included it is the only place in the world where black melanistic tigers were spotted that’s why the researchers all across the globe are curious to explore Mayurbhanj and its rich biodiversity. The Biosphere was declared as such in 1994 by the Indian Government, and in 2009, UNESCO recognized the Similipal Biosphere Reserve as part of the World Network of Biosphere Reserves Similipal Biosphere Reserve is the 7th largest biosphere reserve in India. Similipal was officially declared a wildlife sanctuary by the Government of Odisha in 1979 with a 2200 km2 area, and 303 km2 of that sanctuary were recommended as a national park in 1980. Similipal (5569 km2) was designated as a biosphere reserve by the Indian government on June 22, 1994 because of its extensive biodiversity and rich natural history. 42 different species of mammals, 264 bird species, 39 reptile species, 12 amphibian species, 93 orchid species, 300 medicinal plant species (Mishra, 2008 ), and roughly 52 fauna species are on the verge of extinction live in the Similipal Biosphere Reserve. Within the biosphere reserve, there are 1,265 settlements. Tribal people make up around 73% of population. The Erenga-Kharias and the Mankirdias are two tribes that live in the reserve's dense forests and are involved in conventional harvesting practises such the gathering of seeds and wood (Saha & Agarwalla, 2021 ). The tribal communities of this region are directly affected by forest fires and rely on the gathering of non-wood forest products from forests for their survival (Bahuguna & Upadhay, 2002). Forest fire has a drastic socioeconomic impact on Environment (Kala, 2023). Therefore, in nations like India, it is crucial to monitor and manage forest fires. The availability of satellite data from instruments like MODIS, SNPP, and IRS P6-AWiFS (Indian Remote Sensing - Advanced extensive Field Sensor), obtained with excellent temporal repeatability, spectrum variability, and extensive geographical coverage, has made fire monitoring simpler. (Giriraj et al., 2010). According to Forest survey of India report Odisha represents as the largest forest fire state with 1909 fire Point in the year of 2022-23, and Similipal holds a significant stand in forest fire share of Odisha as 7th largest biosphere reserve, but periodic forest fire happening in April to June gradually reduces its forest cover between 2005–2015(SARANYA & REDDY, 2016). Forest fires can have significant impacts on the socioeconomic conditions of affected regions. They often result in the loss of valuable timber resources, agricultural lands, and grazing areas, affecting livelihoods of local communities (Coulibaly & Li, 2020 ). Destruction of forest ecosystems can lead to reduced tourism revenue, affecting businesses and employment opportunities. Fire-induced air pollution can also impact public health, increasing healthcare costs. Forests play a pivotal role in maintaining ecological balance and supporting biodiversity (Kumar et al., 2022; Mina, et al., 2023 ). However, the increasing rate of forest cover loss has become a major concern globally. Concurrently, forest fires have grown in frequency and intensity, posing significant challenges to environmental conservation efforts. This study seeks to shed light on the correlation between these two phenomena and their collective implications on ecosystems, local communities, and climate change. Additionally, post-fire recovery efforts strain government resources, diverting funds from other developmental projects. Long-term consequences include soil erosion, reduced water quality, and disrupted ecosystems, affecting agricultural productivity and food security. Implementing sustainable forest management and fire prevention measures is crucial to mitigating these socioeconomic impacts. Forest fires are a significant cause of forest cover loss worldwide. These fires result in the destruction and degradation of vast areas of forests, leading to the loss of valuable vegetation and biodiversity. The intense heat and flames consume trees, shrubs, and undergrowth, leaving behind charred landscapes. In addition to the immediate loss of forest cover, the ecological impacts can be long-lasting, affecting regeneration and recovery of the forest ecosystems. Deforestation caused by forest fires has a direct impact on climate change since trees are essential for absorbing carbon dioxide. To maintain forest cover and the crucial ecological services that forests provide, it is imperative to prevent and manage forest fires. the conservation and management of Similipal Biosphere Reserve have far-reaching implications for biodiversity conservation, sustainable development, scientific knowledge, and the well-being of both wildlife and local communities (Dash & Behera, 2015 ). Its status as the 7th largest biosphere reserve highlights its scale and importance in the context of India's efforts to protect its natural heritage. 2. Study Area The Similipal Tiger Reserve (Fig. 1 ), which is situated in northeast India, is part of two biogeographical zones: the Chhotanagpur bio geographic region of the Deccan peninsular zone and the Mahanadian east coastal region located within the Oriental realm. Volcanic sedimentary rocks are aligned in three concentric rings and accentuate the area’s geologic formations., India. It is a vast biosphere reserve covering around 4,374 square kilometres, renowned for its rich biodiversity and varied ecosystems. Situated in the northern part of Odisha, it is known for its lush forests and diverse wildlife, including Bengal tigers and Asian elephants. There are 1276 species of vascular plants recorded including 60 species of ferns, 92 species of orchids and two gymnosperms in the area. In this study, we provide an overview of the mapping of the forest cover and forest fires in the Similipal region, which is situated between latitudes 20°17'N and 22°34'N and longitudes 85°40'E and 87°10'E. According to the Sanctuary from 1979, the Similipal Tiger Reserve has an 845 km 2 core area, 2129 km 2 of buffer (Reserved Forest), and 2595 km 2 of transition area. Khairiburu (1,168 metres) is the Similipal hill range's highest mountain. Major rivers like the Budhabalanga, Baitarani, and Subarnarekha receive water from a large number of waterfalls and perennial streams. Rainfall varies between 1200mm to 2000 mm every year. The temperature is between 9° and 33.5°C. 3. Methodology And Data Collection Acquire satellite imagery with multispectral bands, preferably from Landsat and Sentinel-2 satellites, covering the desired time periods. Also obtain additional datasets like forest type maps to aid in the classification. Preprocess the satellite imagery, including radiometric calibration, atmospheric correction, and geometric correction. Use supervised or unsupervised classification methods to classify the pre-processed imagery into different land cover classes, including forest and non-forest classes Compare the classified images of different time periods using various change detection methods, such as post-classification comparison, image differencing, or vegetation indices change detection (Alshari & Gawali, 2021 ). Google Earth Engine provides a convenient platform for processing large-scale satellite imagery and performing forest cover change detection using JavaScript or Python (Guo et al., 2020 ; Agyekum Codjoe & Afrifa Acheampong, 2022 ). Access satellite imagery from Landsat, Sentinel-2, or other suitable datasets available in GEE (OLIVEIRA et al., 2019; Magidi et al., 2021 ) for the desired time periods. Applying necessary preprocessing steps such as cloud masking, radiometric calibration, and geometric correction. Utilize machine learning algorithms available in Google Earth Engine (Piao et al., 2022 ) to perform supervised image classification. Compute change detection algorithms using temporal image differencing, thresholding on vegetation indices. Visualize the change detection results on the GEE platform (Baser Qasimi et al., 2022 ) and interpret the changes to identify forest cover change areas (Fig. 2 ). Landsat 7 and Landsat 5 satellite series are developed by and the USGS (United States Geological Survey). The both dataset is freely available in USGS Earth Explorer website. Experiential meteorological data and satellite imagery of Landsat-5 TM, Landsat-7 and Sentinel-2 were obtained through the free web-based platforms of Survey of India, the USGS's Earth Explorer website and Google Earth Engine (Table 1 , Table 2 , Table 3 ). Table 1 The following datasets Used for this Research paper Satellite and Sensor Year Resolution (m) Repeat Cycle (Day) Landsat 5-TM 2001–2013 30m 16 Landsat 7 2015 30m 16 Sentinel 2 2017–2023 10m 10 Table 2 Landsat 5 Tm Dataset Landsat 5 Bands Bands name Resolution (meters) B1–450-520 Visible blue 30m B2–520-600 Visible green 30m B3–630-690 Visible red 30m B4–760-900 NIR 30m B5–1550-1750 SWIR-1 30m B6–1040–1250 TIR 120m B7–2080-2350 SWIR-2 30m Table 3 Sentinel 2 Dataset Sentinel 2 Bands Band name Resolution (meters) B8–785–900 NIR 10m B11- 1565-1655 SWIR-1 20m B12- 2100-2280 SWIR-2 20m Results And Discussion 3.1 Correlation between Forest Cover Loss and Forest Fire Incidence The research investigated the complex relationship between forest cover loss and forest fire scenarios (Wang et al., 2021 ), aiming to provide a comprehensive understanding of the topic. By analysing spatial and temporal data through Geographic Information Systems (GIS) and incorporating fire incident data, weather patterns, and human activities, the study identified crucial findings with far-reaching implications. Here are the annual forest cover data and annual forest cover change is given (Fig. 3 ), Using forest cover classification and accuracy assessment (Rather et al., 2018 ) done by Geo-spatial algorithms of Google Earth Engine (GEE), we found an unusual forest cover reduction between 2001 to 2020. The above graph shows that 493 hectares of forest land is changed in last decade, and lost its authentic identity and becomes non forest. The above graph shows that forest cover of Similipal is 270832 hectares in 2001 and decreased to 270365 hectares in 2015 which indicates that 467 hectares of forest cover reduced in just 15 years. The above data set is captured from forest cover classification of Similipal using Geo-spatial techniques (Joshi & K.M., 2022; Rakholia et al., 2020 ). Then we go a bit further in forest cover classification and used geo-spatial algorithms that are written in java script and python programming language in Google Earth Engine and we find the forest cover is reduced by 766.4771 hectares from 2000 to 2021 (Kaur et al., 2023 ; Magidi et al., 2021 ). Figure 4. Forest loss area map of Similipal Forest. The analysis revealed a strong positive correlation between the extent of forest cover loss (Fig. 4) and the frequency of forest fires in the study area. Regions experiencing higher rates of forest degradation were found to be more vulnerable to fire outbreaks (Pourtaghi et al.,2014; Urrutia-Jalabert et al.,2018; Shan et al., 2017 ), emphasizing the direct link between these two environmental challenges. The results signify that efforts to address forest cover loss can be instrumental in mitigating the risk of forest fires (Cahoon et al.,1994; Vineetha et al., 2022 ; Tyukavina et al., 2022 ). forest fires further exacerbate the problem of forest cover loss. Intense fires destroy vast areas of vegetation, hindering the regrowth and recovery of forests, thereby perpetuating the cycle of degradation and fire susceptibility. The graph of annual fire points of Similipal is given that shows 843.8% increase in forest fire than 2001 (Fig. 5 ). The last four-year forest fire point maps (Fig. 6 ) and graphs are clearly shows that in last four-year forest fire has a drastic impact and breaks all the past record from 2001. In the above maps shows that the fire points in Similipal biosphere and found that every area of Similipal biosphere has a continuous annual forest fire due to the anthropogenic activities in that biosphere region. Some regions of Similipal has more fire due to more scrub forest and less dense forest, scrub forests are provide natural fuels to the forest fire. The Similipal north has more fire points captured (Parajuli et al.,2020) than the south Similipal that point out that forest cover loss of north Similipal is justified by the periodic forest fire of Similipal. 3.2 Forest Cover Maps of Similipal (Using GIS) Forest cover maps use satellite imagery to classify and quantify different Forest cover types, including dense forest, moderate forests, open forest and scrubs. By comparing LULC maps from different time frames, we can identify areas where forest cover has been lost (Buřivalová et al., 2021), gained, or remained unchanged. The analysis helps monitor deforestation, urban expansion, agricultural encroachment, and other land-use changes affecting forests (Dash et al., 2016 ). It provides valuable insights into the drivers and patterns of deforestation, aiding policymakers and conservationists in making informed decisions to protect and manage forest resources sustainably. Continuous monitoring using forest cover maps ensures timely responses to potential threats to forests, contributing to global efforts to combat deforestation, conserve biodiversity, and mitigate climate change impacts. As Similipal is a large area of natural vegetation is very difficult to find changes from land use land cover classification (Vineetha et al., 2018 ). We find changes from the above satellite imageries when processed with geo-spatial algorithm (Fig. 7 ). We find that there are rapid negative changes for dense forests and positive changes for scrubs since 2000. The dense forest lost by 210.268 Km 2 as well as scrub forest gained by 253.681 Km 2 (Fig. 8 ). 3.3 Soil Moisture Relation Remote sensing data can elucidate the relationship between soil moisture and forest fire occurrences. Through satellite-based observations, changes in soil moisture levels can be monitored over time, revealing areas prone to drought conditions. Dry soil acts as fuel for fires, increasing the likelihood and intensity of forest fire outbreaks (Mhawej et al., 2015 ; Stavi, 2019 ). By analysing remote sensing data, researchers can identify regions with low soil moisture, enabling timely fire risk assessments (Altangerel et al., 2021 ) and proactive fire management strategies to mitigate the impact of forest fires on ecosystems and human communities. The soil moisture map shows that most of the Similipal has an average soil moisture 0.9855 to 1. And the annual soil moisture graph from 2016 to 2021 represents that the soil moisture is low in first quarter of each year which works as a silent fuel and promote the forest fire, hence the soil moisture plays a pivotal role in wildfire scenario (Fig. 9 ). 3.4 Rainfall Relation We established a correlation between rainfall patterns and forest fire occurrences (Xanthopoulos et al., 2006 ). Satellite-based sensors can monitor and quantify rainfall levels over vast regions, providing valuable data on precipitation trends. A strong inverse relationship is often observed between rainfall and forest fires, as regions experiencing prolonged dry spells are more susceptible to fire outbreaks (Webb et al., 2005 ). Through remote sensing, researchers can identify areas with reduced rainfall, indicating higher fire risk potential. Additionally, remote sensing enables the detection of drought-prone regions, where vegetation becomes parched and acts as fuel for wildfires. By analysing historical and real-time rainfall data from remote sensing sources, forest managers can anticipate periods of heightened fire danger, enabling proactive measures to prevent and control fires (Mishra, 2013 ). Moreover, remote sensing assists in post-fire assessments, aiding in the evaluation of fire impacts on ecosystems and guiding post-fire restoration efforts. The precipitation plays a significant role in forest fire reduction and management. The regular periodic rainfall generates green micro vegetation on the lap of the forest that increases the soil moisture and decreases the dryness of the forest by which forest fire is neither occurring nor lasts for more than one day. The average rainfall pattern of Similipal is given in the map (Fig. 10 ) from 1990 to 2022 that represents the south Similipal has more rainfall than the north Similipal and the difference in rainfall has vital role in forest fire of Similipal. Regular precipitation maintains the tiny micro watersheds of Similipal that helps in control of forest fire in summers. 3.5 Tree Height Tree height plays a crucial role in the behaviour and impact of wildfires. Tall trees possess a greater biomass, containing more fuel for fires to spread and intensify. In densely forested areas with tall trees, fires can climb vertically, reaching into the canopy (Tobin et al., 2012) and causing "crown fires," which are highly destructive and challenging to control. Tall trees also facilitate the rapid spread of fires through "fire ladder" effects. Low-hanging branches and foliage act as pathways for fire to move from the forest floor to the tree canopy (Majasalmi & Rautiainen, 2020), creating a continuous chain of burning vegetation. Furthermore, tall trees influence the fire's intensity by elevating flames closer to the atmosphere, where wind speeds are often higher. This can accelerate fire spread and make containment efforts more difficult for firefighting teams. In fire-prone regions, managing tree height through selective thinning and controlled burns (Rabin et al., 2022 ) can reduce the risk of intense crown fires (“A Numerical Study of Crown Forest Fires Behaviour,” 2020) and slow down the rate of fire propagation. Understanding the relationship between tree height and wildfires is crucial for developing effective forest management strategies to mitigate fire risks (Rupasinghe & Chow-Fraser, 2021 ) and protect ecosystems and communities from the devastating impacts of wildfires. Most of the average tree height of Similipal biosphere is from 15.01 to 29 feet which is moderate. In dense forest sun light cannot reach the ground due the high dense forest canopy but the moderate dense forest the tree height has much lesser than the dense forest hence the solar radiation when come to a point and create sparks with the dry leaves of the trees and forest fire occurs (Nabe-Nielsen & Valencia, 2020 ; Pontes-Lopes et al., 2021 ). As we compare forest fire points of Similipal biosphere with tree height we find more forest fire points where the tree height is less than 15 feet and a few less fire points were captured where tree height is more than 30 feet. The wildfire is more prone to the area having low and moderate tree heights (Fig. 11 ). 3.6 Difference Normalized Burn Ratio DNBR is a remote sensing index used to assess burn severity in wildfires (Razavi-Termeh et al., 2020 ; Somashekar et al.,2009). DNBR is a tool used to assess the severity of wildfires after they occur. It calculates the difference between pre-fire and post-fire Normalized Burn Ratio (NBR) to measure ecological impacts. High DNBR values indicate severe burn severity (Fig. 12 ), aiding in fire management and ecological monitoring. In google earth engine we processed the burn sensitivity of Similipal forest that shows the north Similipal is more prone to fire sensitivity hence more wildfires occur in Similipal biosphere. 3.7 Anthropogenic Activities as a Primary Driver of Forest Cover Loss Human activities emerged as the primary drivers of forest cover loss, exacerbating the risk of forest fires (Dong et al., 2005). Uncontrolled activities like illegal logging, land-use changes, and agricultural expansion significantly disrupted the natural ecosystem. The research identified these activities as key factors in amplifying the likelihood of forest fires by creating conditions conducive to ignition and propagation (Chas-Amil et al., 2013; Ganteaume et al., 2021). Addressing and regulating these human-induced activities is imperative to curb both forest degradation and fire outbreaks. To collect primary data and authentic evaluation of anthropogenic impacts on Similipal we collected the ground truth data and the images are given below clearly shows the forest land is converted into agriculture land and the forest cover reduces (Supriya,2020). The new settlements in forest and mis management of land resources plays a significant role in forest cover reduction of Similipal biosphere. Conclusion This research paper provides insights into the complex factors contributing to forest cover loss from forest fires. It explores the roles of soil moisture, rainfall patterns, burn sensitivity, and anthropogenic activities in exacerbating fire frequency and intensity, impacting global forest ecosystems. Through comprehensive analysis of datasets, it highlights the interconnections between these factors and their cumulative effect on forest resilience. Soil moisture's critical role in determining forest susceptibility to fires is emphasized. Reduced soil moisture, whether from natural variability or human influence, fosters wildfire spread. Understanding soil moisture-vegetation-fire dynamics is vital for effective fire management. Rainfall patterns directly correlate with forest fires. Climate change has increased fire frequency and severity due to extreme weather events like prolonged droughts and heavy precipitation. Integrating climate projections into fire risk assessments aids adaptive planning. Burn sensitivity and forest vulnerability insights help prioritize conservation efforts and resource allocation for mitigating fire impacts. Understanding post-fire recovery processes enhances ecosystem regeneration and long-term health. Anthropogenic activities significantly drive forest fires, especially where human encroachment and land-use changes intersect with forests. Deforestation, agriculture, and logging create fire-prone landscapes. Addressing human-induced factors requires comprehensive policies, community engagement, and sustainable land management. In conclusion, a holistic approach is urgently needed to address forest cover loss from fires. Effective fire management must consider soil moisture, rainfall patterns, burn sensitivity, and anthropogenic activities. Combining scientific knowledge, policies, and community involvement is vital for forest protection and preserving ecosystem services. Forest fires threaten global ecosystems and biodiversity. Advanced technologies, like forest cover mapping, play a pivotal role in monitoring and managing disasters. Using Google Earth Engine, the study identified 766.4771 hectares of forest cover lost in Similipal from 2000 to 2021. Satellite imagery and remote sensing enable proactive conservation and restoration. Integrating forest cover mapping into environmental management empowers us to safeguard forests, mitigate fire risks, and foster sustainable coexistence with nature. In sum, this research highlights the urgency of safeguarding forests and their ecosystems through informed strategies and technological tools. Declarations Data availability: Data will be provided on request. Declaration of Competing Interest We don’t have any conflict of interest regarding the publication of this research paper. They have conducted this study and reported the findings objectively and without bias. There is no financial or personal relationship that could influence the research or its interpretation. The authors have solely focused on providing accurate and reliable information related to fire prediction in the Baripada Forest. The research has been carried out with scientific integrity and adherence to ethical standards, ensuring the transparency and credibility of the reported results. Ethics approval The authors of the study affirm that all accepted ethical guidelines were followed in conducting this study. Funding No particular grant or financial assistance from funding organizations was provided for this study. Author Contribution The study's inception and design were assisted by all of the authors. There are no additional individuals who satisfy the standards for authorship; all mentioned authors have read and approved the manuscript. The authors individual contributions are listed here, Introduction, Conceptualization, Data analysis: [Debabrata Nandi], Data collection, Methodology, Formatting tables and figures, Writing: [Rashmi Ranjan Das], Writing- Review and Editing, critically revised the work: [Rashmi Ranjan Das, Debabrata Nandi]. References Bahuguna V, Upadhay A (2002, June) Forest fires in India: policy initiatives for community participation. 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Land , 9 (12), 470. https://doi.org/10.3390/land9120470 Ganteaume A, Barbero R, Jappiot M, Maillé E (2021, March) Understanding future changes to fires in southern Europe and their impacts on the wildland-urban interface. J Saf Sci Resil 2(1):20–29. https://doi.org/10.1016/j.jnlssr.2021.01.001 Chas-Amil M, Touza J, García-Martínez E (2013, September) Forest fires in the wildland–urban interface: A spatial analysis of forest fragmentation and human impacts. Appl Geogr 43:127–137. https://doi.org/10.1016/j.apgeog.2013.06.010 Nabe-Nielsen J, Valencia R (2020), October 19 Canopy structure and forest understory conditions in a wet Amazonian forest—No change over the last 20 years. Biotropica, 52(6), 1121–1126. https://doi.org/10.1111/btp.12872 Pontes-Lopes A, Silva CVJ, Barlow J, Rincón LM, Campanharo WA, Nunes CA, de Almeida CT, Silva Júnior CHL, Cassol HLG, Dalagnol R, Stark SC, Graça PMLA, Aragão LE (2021), May 19 O. C. Drought-driven wildfire impacts on structure and dynamics in a wet Central Amazonian forest. Proceedings of the Royal Society B: Biological Sciences, 288(1951), 20210094. https://doi.org/10.1098/rspb.2021.0094 Mishra AK (2013) July). A New Technique to Estimate Precipitation at Fine Scale Using Multifrequency Satellite Observations Over Indian Land and Oceanic Regions. IEEE Trans Geosci Remote Sens 51(7):4349–4358. https://doi.org/10.1109/tgrs.2012.2226733 Stavi I (2019), May 20 Wildfires in Grasslands and Shrublands: A Review of Impacts on Vegetation, Soil, Hydrology, and Geomorphology. Water, 11(5), 1042. https://doi.org/10.3390/w11051042 Mhawej M, Faour G, Adjizian-Gerard J (2015) December 8). Wildfire Likelihood’s Elements. Literature Rev Challenges 6(2):282–293. https://doi.org/10.3390/challe6020282 Alshari EA, Gawali BW (2021), June Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2(1), 8–17. https://doi.org/10.1016/j.gltp.2021.01.002 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3996489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275591140,"identity":"e9feba71-45f5-4092-968d-41b4ca70abb4","order_by":0,"name":"Rasmi Ranjan 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15:52:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293723,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology Chart.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/e2e43556df86dcf4d1c2fc71.jpg"},{"id":51960872,"identity":"d1ac7323-d9b7-48a7-bf63-5dd44b33f2ec","added_by":"auto","created_at":"2024-03-04 15:44:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153283,"visible":true,"origin":"","legend":"\u003cp\u003eForest Cover Graph of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/22518258265be301c4517d3d.jpg"},{"id":51960868,"identity":"234b7ab6-6b4e-4e95-8f0f-c1da87ede213","added_by":"auto","created_at":"2024-03-04 15:44:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":131358,"visible":true,"origin":"","legend":"\u003cp\u003eForest loss area map of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/d47a1a0a2601f63785c7c785.jpg"},{"id":51960870,"identity":"1f115767-5f3e-4e67-bf2b-1a30e18a0342","added_by":"auto","created_at":"2024-03-04 15:44:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":146817,"visible":true,"origin":"","legend":"\u003cp\u003eForest Fire Graph of Similipal forest from 2001 to 2023.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/e32780c7cb659b6e49fa1019.jpg"},{"id":51961147,"identity":"73109c90-1f12-484f-aefa-eaef09949f98","added_by":"auto","created_at":"2024-03-04 15:52:32","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":278279,"visible":true,"origin":"","legend":"\u003cp\u003eForest Fire point map of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/519ed068a27e3d123a2a17a7.jpg"},{"id":51961148,"identity":"04ca8861-757d-4a24-9e7a-a0ef0e583d49","added_by":"auto","created_at":"2024-03-04 15:52:33","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":410520,"visible":true,"origin":"","legend":"\u003cp\u003eForest cover maps of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/3a764664eecc33bee8989a01.jpg"},{"id":51960876,"identity":"b47123cf-bd6d-4ea1-a139-de50b57f860c","added_by":"auto","created_at":"2024-03-04 15:44:24","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":146056,"visible":true,"origin":"","legend":"\u003cp\u003eForest Cover Changes from 2000 to 2023 in Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/e6478214fcd9a198ee39870c.jpg"},{"id":51960867,"identity":"00a3f9d0-79b5-49ed-af8b-d3f0036e4e34","added_by":"auto","created_at":"2024-03-04 15:44:24","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":128446,"visible":true,"origin":"","legend":"\u003cp\u003eSoil moisture map of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/968d5ec10fbaa62f6493f29d.jpg"},{"id":51960873,"identity":"df10ac19-7f1d-4716-af72-40ac93b3d517","added_by":"auto","created_at":"2024-03-04 15:44:24","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":140389,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Rainfall map of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/dbd9c8e7ad33e77055e1db9a.jpg"},{"id":51961144,"identity":"592eb078-eedd-477e-8c56-cc8530e37818","added_by":"auto","created_at":"2024-03-04 15:52:25","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":164437,"visible":true,"origin":"","legend":"\u003cp\u003eTree Height map of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/853b065feb851f278ce4d34f.jpg"},{"id":51960878,"identity":"edebff5f-b296-4552-830d-557f2d7e5cf0","added_by":"auto","created_at":"2024-03-04 15:44:24","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":202075,"visible":true,"origin":"","legend":"\u003cp\u003eBurn-Ratio map of Similipal Forest.\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/0c22b4006c2d9656be3cde3b.jpg"},{"id":57770183,"identity":"87f60f82-cb33-44eb-b4ed-e84777bbe878","added_by":"auto","created_at":"2024-06-05 12:02:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2825214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996489/v1/af4ebddb-b15e-46df-a18a-a10b30bf0049.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forest Cover Loss and Forest Fire Monitoring Using GIS And GEE Over Similipal Tiger Reserve, Odisha, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIndia\u0026rsquo;s Mayurbhanj is one among the World\u0026rsquo;s Greatest Places of 2023 (TIME Magazine, 2023) along with the Similipal National Park the Time magazine has included it is the only place in the world where black melanistic tigers were spotted that\u0026rsquo;s why the researchers all across the globe are curious to explore Mayurbhanj and its rich biodiversity. The Biosphere was declared as such in 1994 by the Indian Government, and in 2009, UNESCO recognized the Similipal Biosphere Reserve as part of the World Network of Biosphere Reserves Similipal Biosphere Reserve is the 7th largest biosphere reserve in India. Similipal was officially declared a wildlife sanctuary by the Government of Odisha in 1979 with a 2200 km2 area, and 303 km2 of that sanctuary were recommended as a national park in 1980. Similipal (5569 km2) was designated as a biosphere reserve by the Indian government on June 22, 1994 because of its extensive biodiversity and rich natural history. 42 different species of mammals, 264 bird species, 39 reptile species, 12 amphibian species, 93 orchid species, 300 medicinal plant species (Mishra, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and roughly 52 fauna species are on the verge of extinction live in the Similipal Biosphere Reserve. Within the biosphere reserve, there are 1,265 settlements. Tribal people make up around 73% of population. The Erenga-Kharias and the Mankirdias are two tribes that live in the reserve's dense forests and are involved in conventional harvesting practises such the gathering of seeds and wood (Saha \u0026amp; Agarwalla, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The tribal communities of this region are directly affected by forest fires and rely on the gathering of non-wood forest products from forests for their survival (Bahuguna \u0026amp; Upadhay, 2002). Forest fire has a drastic socioeconomic impact on Environment (Kala, 2023).\u003c/p\u003e \u003cp\u003eTherefore, in nations like India, it is crucial to monitor and manage forest fires. The availability of satellite data from instruments like MODIS, SNPP, and IRS P6-AWiFS (Indian Remote Sensing - Advanced extensive Field Sensor), obtained with excellent temporal repeatability, spectrum variability, and extensive geographical coverage, has made fire monitoring simpler. (Giriraj et al., 2010). According to Forest survey of India report Odisha represents as the largest forest fire state with 1909 fire Point in the year of 2022-23, and Similipal holds a significant stand in forest fire share of Odisha as 7th largest biosphere reserve, but periodic forest fire happening in April to June gradually reduces its forest cover between 2005\u0026ndash;2015(SARANYA \u0026amp; REDDY, 2016). Forest fires can have significant impacts on the socioeconomic conditions of affected regions. They often result in the loss of valuable timber resources, agricultural lands, and grazing areas, affecting livelihoods of local communities (Coulibaly \u0026amp; Li, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Destruction of forest ecosystems can lead to reduced tourism revenue, affecting businesses and employment opportunities. Fire-induced air pollution can also impact public health, increasing healthcare costs. Forests play a pivotal role in maintaining ecological balance and supporting biodiversity (Kumar et al., 2022; Mina, et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the increasing rate of forest cover loss has become a major concern globally. Concurrently, forest fires have grown in frequency and intensity, posing significant challenges to environmental conservation efforts. This study seeks to shed light on the correlation between these two phenomena and their collective implications on ecosystems, local communities, and climate change. Additionally, post-fire recovery efforts strain government resources, diverting funds from other developmental projects. Long-term consequences include soil erosion, reduced water quality, and disrupted ecosystems, affecting agricultural productivity and food security.\u003c/p\u003e \u003cp\u003eImplementing sustainable forest management and fire prevention measures is crucial to mitigating these socioeconomic impacts. Forest fires are a significant cause of forest cover loss worldwide. These fires result in the destruction and degradation of vast areas of forests, leading to the loss of valuable vegetation and biodiversity. The intense heat and flames consume trees, shrubs, and undergrowth, leaving behind charred landscapes. In addition to the immediate loss of forest cover, the ecological impacts can be long-lasting, affecting regeneration and recovery of the forest ecosystems. Deforestation caused by forest fires has a direct impact on climate change since trees are essential for absorbing carbon dioxide. To maintain forest cover and the crucial ecological services that forests provide, it is imperative to prevent and manage forest fires. the conservation and management of Similipal Biosphere Reserve have far-reaching implications for biodiversity conservation, sustainable development, scientific knowledge, and the well-being of both wildlife and local communities (Dash \u0026amp; Behera, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Its status as the 7th largest biosphere reserve highlights its scale and importance in the context of India's efforts to protect its natural heritage.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eThe Similipal Tiger Reserve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is situated in northeast India, is part of two biogeographical zones: the Chhotanagpur bio geographic region of the Deccan peninsular zone and the Mahanadian east coastal region located within the Oriental realm. Volcanic sedimentary rocks are aligned in three concentric rings and accentuate the area\u0026rsquo;s geologic formations., India. It is a vast biosphere reserve covering around 4,374 square kilometres, renowned for its rich biodiversity and varied ecosystems. Situated in the northern part of Odisha, it is known for its lush forests and diverse wildlife, including Bengal tigers and Asian elephants. There are 1276 species of vascular plants recorded including 60 species of ferns, 92 species of orchids and two gymnosperms in the area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, we provide an overview of the mapping of the forest cover and forest fires in the Similipal region, which is situated between latitudes 20\u0026deg;17'N and 22\u0026deg;34'N and longitudes 85\u0026deg;40'E and 87\u0026deg;10'E. According to the Sanctuary from 1979, the Similipal Tiger Reserve has an 845 km\u003csup\u003e2\u003c/sup\u003e core area, 2129 km\u003csup\u003e2\u003c/sup\u003e of buffer (Reserved Forest), and 2595 km\u003csup\u003e2\u003c/sup\u003e of transition area. Khairiburu (1,168 metres) is the Similipal hill range's highest mountain. Major rivers like the Budhabalanga, Baitarani, and Subarnarekha receive water from a large number of waterfalls and perennial streams. Rainfall varies between 1200mm to 2000 mm every year. The temperature is between 9\u0026deg; and 33.5\u0026deg;C.\u003c/p\u003e"},{"header":"3. Methodology And Data Collection","content":"\u003cp\u003eAcquire satellite imagery with multispectral bands, preferably from Landsat and Sentinel-2 satellites, covering the desired time periods. Also obtain additional datasets like forest type maps to aid in the classification. Preprocess the satellite imagery, including radiometric calibration, atmospheric correction, and geometric correction. Use supervised or unsupervised classification methods to classify the pre-processed imagery into different land cover classes, including forest and non-forest classes Compare the classified images of different time periods using various change detection methods, such as post-classification comparison, image differencing, or vegetation indices change detection (Alshari \u0026amp; Gawali, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGoogle Earth Engine provides a convenient platform for processing large-scale satellite imagery and performing forest cover change detection using JavaScript or Python (Guo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Agyekum Codjoe \u0026amp; Afrifa Acheampong, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Access satellite imagery from Landsat, Sentinel-2, or other suitable datasets available in GEE (OLIVEIRA et al., 2019; Magidi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the desired time periods. Applying necessary preprocessing steps such as cloud masking, radiometric calibration, and geometric correction. Utilize machine learning algorithms available in Google Earth Engine (Piao et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to perform supervised image classification. Compute change detection algorithms using temporal image differencing, thresholding on vegetation indices. Visualize the change detection results on the GEE platform (Baser Qasimi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and interpret the changes to identify forest cover change areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLandsat 7 and Landsat 5 satellite series are developed by and the USGS (United States Geological Survey). The both dataset is freely available in USGS Earth Explorer website. Experiential meteorological data and satellite imagery of Landsat-5 TM, Landsat-7 and Sentinel-2 were obtained through the free web-based platforms of Survey of India, the USGS's Earth Explorer website and Google Earth Engine (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe following datasets Used for this Research paper\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite and Sensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolution (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRepeat Cycle (Day)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 5-TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSentinel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandsat 5 Tm Dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 5 Bands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBands name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolution (meters)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u0026ndash;450-520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisible blue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u0026ndash;520-600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisible green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3\u0026ndash;630-690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisible red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4\u0026ndash;760-900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB5\u0026ndash;1550-1750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB6\u0026ndash;1040\u0026ndash;1250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7\u0026ndash;2080-2350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSentinel 2 Dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSentinel 2 Bands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBand name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolution (meters)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB8\u0026ndash;785\u0026ndash;900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB11- 1565-1655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB12- 2100-2280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Correlation between Forest Cover Loss and Forest Fire Incidence\u003c/h2\u003e \u003cp\u003eThe research investigated the complex relationship between forest cover loss and forest fire scenarios (Wang et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), aiming to provide a comprehensive understanding of the topic. By analysing spatial and temporal data through Geographic Information Systems (GIS) and incorporating fire incident data, weather patterns, and human activities, the study identified crucial findings with far-reaching implications. Here are the annual forest cover data and annual forest cover change is given (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e),\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Using forest cover classification and accuracy assessment (Rather et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) done by Geo-spatial algorithms of Google Earth Engine (GEE), we found an unusual forest cover reduction between 2001 to 2020. The above graph shows that 493 hectares of forest land is changed in last decade, and lost its authentic identity and becomes non forest. The above graph shows that forest cover of Similipal is 270832 hectares in 2001 and decreased to 270365 hectares in 2015 which indicates that 467 hectares of forest cover reduced in just 15 years. The above data set is captured from forest cover classification of Similipal using Geo-spatial techniques (Joshi \u0026amp; K.M., 2022; Rakholia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Then we go a bit further in forest cover classification and used geo-spatial algorithms that are written in java script and python programming language in Google Earth Engine and we find the forest cover is reduced by 766.4771 hectares from 2000 to 2021 (Kaur et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Magidi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;4.\u003c/b\u003e Forest loss area map of Similipal Forest.\u003c/p\u003e \u003cp\u003eThe analysis revealed a strong positive correlation between the extent of forest cover loss (Fig.\u0026nbsp;4) and the frequency of forest fires in the study area. Regions experiencing higher rates of forest degradation were found to be more vulnerable to fire outbreaks (Pourtaghi et al.,2014; Urrutia-Jalabert et al.,2018; Shan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), emphasizing the direct link between these two environmental challenges. The results signify that efforts to address forest cover loss can be instrumental in mitigating the risk of forest fires (Cahoon et al.,1994; Vineetha et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tyukavina et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). forest fires further exacerbate the problem of forest cover loss. Intense fires destroy vast areas of vegetation, hindering the regrowth and recovery of forests, thereby perpetuating the cycle of degradation and fire susceptibility. The graph of annual fire points of Similipal is given that shows 843.8% increase in forest fire than 2001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe last four-year forest fire point maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and graphs are clearly shows that in last four-year forest fire has a drastic impact and breaks all the past record from 2001. In the above maps shows that the fire points in Similipal biosphere and found that every area of Similipal biosphere has a continuous annual forest fire due to the anthropogenic activities in that biosphere region. Some regions of Similipal has more fire due to more scrub forest and less dense forest, scrub forests are provide natural fuels to the forest fire. The Similipal north has more fire points captured (Parajuli et al.,2020) than the south Similipal that point out that forest cover loss of north Similipal is justified by the periodic forest fire of Similipal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Forest Cover Maps of Similipal (Using GIS)\u003c/h2\u003e \u003cp\u003eForest cover maps use satellite imagery to classify and quantify different Forest cover types, including dense forest, moderate forests, open forest and scrubs. By comparing LULC maps from different time frames, we can identify areas where forest cover has been lost (Buřivalov\u0026aacute; et al., 2021), gained, or remained unchanged. The analysis helps monitor deforestation, urban expansion, agricultural encroachment, and other land-use changes affecting forests (Dash et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It provides valuable insights into the drivers and patterns of deforestation, aiding policymakers and conservationists in making informed decisions to protect and manage forest resources sustainably. Continuous monitoring using forest cover maps ensures timely responses to potential threats to forests, contributing to global efforts to combat deforestation, conserve biodiversity, and mitigate climate change impacts. As Similipal is a large area of natural vegetation is very difficult to find changes from land use land cover classification (Vineetha et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We find changes from the above satellite imageries when processed with geo-spatial algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). We find that there are rapid negative changes for dense forests and positive changes for scrubs since 2000. The dense forest lost by 210.268 Km\u003csup\u003e2\u003c/sup\u003e as well as scrub forest gained by 253.681 Km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Soil Moisture Relation\u003c/h2\u003e \u003cp\u003eRemote sensing data can elucidate the relationship between soil moisture and forest fire occurrences. Through satellite-based observations, changes in soil moisture levels can be monitored over time, revealing areas prone to drought conditions. Dry soil acts as fuel for fires, increasing the likelihood and intensity of forest fire outbreaks (Mhawej et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stavi, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By analysing remote sensing data, researchers can identify regions with low soil moisture, enabling timely fire risk assessments (Altangerel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and proactive fire management strategies to mitigate the impact of forest fires on ecosystems and human communities. The soil moisture map shows that most of the Similipal has an average soil moisture 0.9855 to 1. And the annual soil moisture graph from 2016 to 2021 represents that the soil moisture is low in first quarter of each year which works as a silent fuel and promote the forest fire, hence the soil moisture plays a pivotal role in wildfire scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Rainfall Relation\u003c/h2\u003e \u003cp\u003eWe established a correlation between rainfall patterns and forest fire occurrences (Xanthopoulos et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Satellite-based sensors can monitor and quantify rainfall levels over vast regions, providing valuable data on precipitation trends. A strong inverse relationship is often observed between rainfall and forest fires, as regions experiencing prolonged dry spells are more susceptible to fire outbreaks (Webb et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Through remote sensing, researchers can identify areas with reduced rainfall, indicating higher fire risk potential. Additionally, remote sensing enables the detection of drought-prone regions, where vegetation becomes parched and acts as fuel for wildfires. By analysing historical and real-time rainfall data from remote sensing sources, forest managers can anticipate periods of heightened fire danger, enabling proactive measures to prevent and control fires (Mishra, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, remote sensing assists in post-fire assessments, aiding in the evaluation of fire impacts on ecosystems and guiding post-fire restoration efforts. The precipitation plays a significant role in forest fire reduction and management. The regular periodic rainfall generates green micro vegetation on the lap of the forest that increases the soil moisture and decreases the dryness of the forest by which forest fire is neither occurring nor lasts for more than one day. The average rainfall pattern of Similipal is given in the map (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e) from 1990 to 2022 that represents the south Similipal has more rainfall than the north Similipal and the difference in rainfall has vital role in forest fire of Similipal. Regular precipitation maintains the tiny micro watersheds of Similipal that helps in control of forest fire in summers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Tree Height\u003c/h2\u003e \u003cp\u003eTree height plays a crucial role in the behaviour and impact of wildfires. Tall trees possess a greater biomass, containing more fuel for fires to spread and intensify. In densely forested areas with tall trees, fires can climb vertically, reaching into the canopy (Tobin et al., 2012) and causing \"crown fires,\" which are highly destructive and challenging to control. Tall trees also facilitate the rapid spread of fires through \"fire ladder\" effects. Low-hanging branches and foliage act as pathways for fire to move from the forest floor to the tree canopy (Majasalmi \u0026amp; Rautiainen, 2020), creating a continuous chain of burning vegetation. Furthermore, tall trees influence the fire's intensity by elevating flames closer to the atmosphere, where wind speeds are often higher.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis can accelerate fire spread and make containment efforts more difficult for firefighting teams. In fire-prone regions, managing tree height through selective thinning and controlled burns (Rabin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) can reduce the risk of intense crown fires (\u0026ldquo;A Numerical Study of Crown Forest Fires Behaviour,\u0026rdquo; 2020) and slow down the rate of fire propagation. Understanding the relationship between tree height and wildfires is crucial for developing effective forest management strategies to mitigate fire risks (Rupasinghe \u0026amp; Chow-Fraser, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and protect ecosystems and communities from the devastating impacts of wildfires. Most of the average tree height of Similipal biosphere is from 15.01 to 29 feet which is moderate. In dense forest sun light cannot reach the ground due the high dense forest canopy but the moderate dense forest the tree height has much lesser than the dense forest hence the solar radiation when come to a point and create sparks with the dry leaves of the trees and forest fire occurs (Nabe-Nielsen \u0026amp; Valencia, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pontes-Lopes et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As we compare forest fire points of Similipal biosphere with tree height we find more forest fire points where the tree height is less than 15 feet and a few less fire points were captured where tree height is more than 30 feet. The wildfire is more prone to the area having low and moderate tree heights (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Difference Normalized Burn Ratio\u003c/h2\u003e \u003cp\u003eDNBR is a remote sensing index used to assess burn severity in wildfires (Razavi-Termeh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Somashekar et al.,2009). DNBR is a tool used to assess the severity of wildfires after they occur. It calculates the difference between pre-fire and post-fire Normalized Burn Ratio (NBR) to measure ecological impacts. High DNBR values indicate severe burn severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e), aiding in fire management and ecological monitoring. In google earth engine we processed the burn sensitivity of Similipal forest that shows the north Similipal is more prone to fire sensitivity hence more wildfires occur in Similipal biosphere.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Anthropogenic Activities as a Primary Driver of Forest Cover Loss\u003c/h2\u003e \u003cp\u003eHuman activities emerged as the primary drivers of forest cover loss, exacerbating the risk of forest fires (Dong et al., 2005). Uncontrolled activities like illegal logging, land-use changes, and agricultural expansion significantly disrupted the natural ecosystem. The research identified these activities as key factors in amplifying the likelihood of forest fires by creating conditions conducive to ignition and propagation (Chas-Amil et al., 2013; Ganteaume et al., 2021). Addressing and regulating these human-induced activities is imperative to curb both forest degradation and fire outbreaks. To collect primary data and authentic evaluation of anthropogenic impacts on Similipal we collected the ground truth data and the images are given below clearly shows the forest land is converted into agriculture land and the forest cover reduces (Supriya,2020). The new settlements in forest and mis management of land resources plays a significant role in forest cover reduction of Similipal biosphere.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eThis research paper provides insights into the complex factors contributing to forest cover loss from forest fires. It explores the roles of soil moisture, rainfall patterns, burn sensitivity, and anthropogenic activities in exacerbating fire frequency and intensity, impacting global forest ecosystems. Through comprehensive analysis of datasets, it highlights the interconnections between these factors and their cumulative effect on forest resilience. Soil moisture's critical role in determining forest susceptibility to fires is emphasized. Reduced soil moisture, whether from natural variability or human influence, fosters wildfire spread. Understanding soil moisture-vegetation-fire dynamics is vital for effective fire management. Rainfall patterns directly correlate with forest fires. Climate change has increased fire frequency and severity due to extreme weather events like prolonged droughts and heavy precipitation. Integrating climate projections into fire risk assessments aids adaptive planning. Burn sensitivity and forest vulnerability insights help prioritize conservation efforts and resource allocation for mitigating fire impacts. Understanding post-fire recovery processes enhances ecosystem regeneration and long-term health. Anthropogenic activities significantly drive forest fires, especially where human encroachment and land-use changes intersect with forests. Deforestation, agriculture, and logging create fire-prone landscapes. Addressing human-induced factors requires comprehensive policies, community engagement, and sustainable land management. In conclusion, a holistic approach is urgently needed to address forest cover loss from fires. Effective fire management must consider soil moisture, rainfall patterns, burn sensitivity, and anthropogenic activities. Combining scientific knowledge, policies, and community involvement is vital for forest protection and preserving ecosystem services. Forest fires threaten global ecosystems and biodiversity. Advanced technologies, like forest cover mapping, play a pivotal role in monitoring and managing disasters. Using Google Earth Engine, the study identified 766.4771 hectares of forest cover lost in Similipal from 2000 to 2021. Satellite imagery and remote sensing enable proactive conservation and restoration. Integrating forest cover mapping into environmental management empowers us to safeguard forests, mitigate fire risks, and foster sustainable coexistence with nature. In sum, this research highlights the urgency of safeguarding forests and their ecosystems through informed strategies and technological tools.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData will be provided on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe don\u0026rsquo;t have any conflict of interest regarding the publication of this research paper. They have conducted this study and reported the findings objectively and without bias. There is no financial or personal relationship that could influence the research or its interpretation. The authors have solely focused on providing accurate and reliable information related to fire prediction in the Baripada Forest. The research has been carried out with scientific integrity and adherence to ethical standards, ensuring the transparency and credibility of the reported results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of the study affirm that all accepted ethical guidelines were followed in conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo particular grant or financial assistance from funding organizations was provided for this study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe study's inception and design were assisted by all of the authors. There are no additional individuals who satisfy the standards for authorship; all mentioned authors have read and approved the manuscript. The authors individual contributions are listed here, Introduction, Conceptualization, Data analysis: [Debabrata Nandi], Data collection, Methodology, Formatting tables and figures, Writing: [Rashmi Ranjan Das], Writing- Review and Editing, critically revised the work: [Rashmi Ranjan Das, Debabrata Nandi].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBahuguna V, Upadhay A (2002, June) Forest fires in India: policy initiatives for community participation. 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Literature Rev Challenges 6(2):282\u0026ndash;293. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/challe6020282\u003c/span\u003e\u003cspan address=\"10.3390/challe6020282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlshari EA, Gawali BW (2021), June Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2(1), 8\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gltp.2021.01.002\u003c/span\u003e\u003cspan address=\"10.1016/j.gltp.2021.01.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Forest cover classification, Forest fire, Change detection, Similipal","lastPublishedDoi":"10.21203/rs.3.rs-3996489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3996489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSimilipal Biosphere Reserve is indeed an important natural treasure and holds significance for several reasons as the 7th largest biosphere reserve in India. India\u0026rsquo;s Mayurbhanj is one among the World\u0026rsquo;s Greatest Places of 2023 along with the Similipal Biosphere, the Time magazine has included it is the only place in the world where black melanistic tigers were spotted that\u0026rsquo;s why the researchers all across the globe are curious to explore Mayurbhanj and its rich biodiversity. The Similipal comprises 7% flowering plants, 8% Orchids, 7% Reptiles, 20% birds and 11% mammals. A significant natural resource, forests are crucial to preserving the ecological equilibrium. Forests are now endangered by both man-made and natural forest fires as a result of growing population and civilisation. Basically, there are three different categories into which forest fire causes may be divided: Natural, Intentional/Deliberate, Accidental, and Unintentional are the four categories. In India, human anthropogenic activities that involve slash-and-burn agriculture, deforestation, controlled burning, firewood burning, etc. are to blame for almost 90% of forest fires. Therefore, in nations like India, it is crucial to monitor and manage forest fires. Anthropogenic impact can easily be detected with the satellite data such as Landsat-5, Landsat-8 \u0026amp; MODIS are collected and processed in Arc GIS 10.8 \u0026amp; Google Earth Engine.\u003c/p\u003e","manuscriptTitle":"Forest Cover Loss and Forest Fire Monitoring Using GIS And GEE Over Similipal Tiger Reserve, Odisha, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-04 15:44:19","doi":"10.21203/rs.3.rs-3996489/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d41d0a2c-d20a-41a0-a783-224a770b0466","owner":[],"postedDate":"March 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-05T11:54:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-04 15:44:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3996489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3996489","identity":"rs-3996489","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}