.png)
Forest Fire Mapping Using Remote Sensing: Burnt Area Analysis in Telangana with NBR and Landsat
- Shijila P S
- 22 hours ago
- 17 min read
ABSTRACT
The dynamic changes in forest fire regimes are affecting biodiversity and climate, therefore, mapping and monitoring burnt areas have become increasingly important, particularly during predominant fire seasons. Accurate mapping of burned areas is critical for proper resource allocation for combating fire hazards and prioritizing fire management strategies. The current study aimed to systematically map the spatial extent of forest burnt areas in Telangana state using multiple satellite datasets and remote sensing technologies during the fire seasons of 2024 and 2025. A combined sensor, multi-temporal remote sensing approach using the medium resolution of Landsat 8, 9, and AWiFS satellite imagery was ideal for the accurate mapping of burnt areas. The comprehensive burnt area analysis was carried out across the different forest types, and among districts. The Normalized Burn Ratio (NBR) Index was used to map the burnt area. The results showed that this method is promising for burnt area mapping with an accuracy of 92.2% in 2023 and 90.8% in 2024. The total area affected by forest fire during 2023 is 4,182 km² and 4,529.5 km² during 2024. Telangana state, predominantly dominated by dry deciduous forests is highly prone to forest fires and contributed the highest emissions of CO₂ and other trace gases among all vegetation types. This study provides valuable data to assist decision-makers in assessing ecological damage and formulating systematic fire management planning.
INTRODUCTION
Forest fires are a significant environmental hazard, greatly impacting terrestrial ecosystems and serving as a major source of trace gases. Globally, between 2001 to 2023, fires caused the loss of 138 Mha of tree cover. In 2020, the world had 3.5 Gha of natural forest, and in 2024 it lost 26 Mha of natural forest, equivalent to 10 Gt of CO2 (https://www.globalforestwatch.org/dashboards/global/). The tropical regions hold the world's largest forest cover, which is becoming more susceptible to forest fires because of climate change, deforestation, and agricultural practices such as slash-and-burn (Pan et al., 2011; Tyukavina et al., 2022). Historically, forest fires have shaped forest landscapes and natural regeneration processes (Reddy et al., 2013; ISFR, 2021), along with prescribed burning practices to manage hazardous fuel, reduce fire risk, and improve wildlife habitat (Bargali et al., 2024), while some plant and animal species rely on fires for their survival and reproduction (Bowman et al., 2012; Talukdar et al., 2023; Carbone et al., 2024). However, uncontrolled forest fires and prescribed burning have resulted in ecosystem damage. Forests are critical in regulating atmospheric greenhouse gas concentrations, acting as significant carbon sinks, but frequent fires disrupt this balance, contributing to greenhouse gas emissions. Understanding the fire regime (frequency, intensity, extent) and its effect on the ecosystem is crucial. Therefore, the management strategies must be prioritised, according to the severity of the forest fire. Utilizing remote sensing and GIS technologies offers a sophisticated approach to forest fire management, enhancing timely and effective mitigation strategies.
Forest fires are a recurring phenomenon in India, according to the status of the Forest Survey of India, more than 36% of the country’s forest cover has been estimated to be prone to frequent forest fires (ISFR, 2019). Higher instances of fire are noted in March, April, and May because of the abundant presence of dry biomass following the end of winter and during summer. Severe fires are common in various types of forests, especially dry deciduous forests (ISFR, 2015; Reddy et al., 2017). Most forest fires in India are not a natural phenomenon, they are typically low-intensity and surface fires. Traditionally, local communities have been engaging in slash-and-burn or shifting cultivation, leading to widespread annual forest fires. In Central India, the plants in the tropical dry deciduous forests possess some adaptive traits such as thick barks, the capacity to heal up fire scars, seed adaptations, and the ability to reproduce afresh due to frequent fire incidences (Reddy et al., 2013; Kumar et al., 2022; Verma et al., 2017). Forest fires are a significant concern in Telangana state due to dry deciduous forests experiencing a high fire occurrence. The main causes of forest fires in Telangana were burning crop residue in encroached agricultural land, fires set by beedi leaf collectors, Non-Timber Forest Product collection, and activities such as cooking and collecting food materials in forests by tribals and other local people.
Remote sensing with GIS technology is a strong tool for fire management to analyse the area affected by fire and predict areas with a high probability of fire in the future. Fire disturbance is one of the essential climate variables (ECV) and an essential biodiversity variable (EBV) and its monitoring is critical for effective fire management and conservation efforts. The fire disturbance Essential Climate Variable (ECV) products consisting of burned area maps, supplemented by active fires and fire radiative power (FRP) (https://gcos.wmo.int/en/essentialclimate-variables/fire/). These variables are pivotal for assessing and monitoring fire dynamics and their impacts on ecosystems. Mapping of burnt areas and assessment of forest fire is one of the most successful applications of satellite remote sensing (Thariq & Zhao., 2024) because it is a cost and time-effective solution for fire management. Therefore, the spatial mapping of forest burnt area regime is vital for efficient fire management, facilitating preventive measures and post-fire restoration strategies (Reddy et al., 2017; Chuvieco et al., 2019). Burnt areas show distinct spectral behaviour detectable through multi-temporal remote sensing. Multi-temporal approaches have the advantage over single post-fire images by reducing commission errors caused by dark soils, water bodies, topographic shades and cloud shadows. It uses different image processing methods that allow the identification of differences in the spectral signature of the burnt area and other vegetation types between pre- and post-fire (Alganci et al., 2010; Sivrikaya et al., 2024). The combination of near-infrared (NIR) and shortwave-infrared (SWIR) regions of the electromagnetic spectrum can effectively detect burnt areas, as pre-fire vegetation reflects strongly in NIR, whereas post-fire surfaces, dominated by burnt scars, exhibit increased reflectance in SWIR (Key and Benson, 1999; Selvaraj et al., 2022; Saranya et al., 2023).
There are various image classification techniques and vegetation indices are used to detect burnt areas regionally and globally. Image classification methods viz., visual interpretation, spectral vegetation indices, supervised classification, unsupervised classification, and hybrid classification have been employed in distinguishing between burned vs unburned pixels (Gitas et al. 2004). Giglio et al. (2009) developed the Normalized Burn Ratio (NBR) algorithm that records the effects of fire with greater spectral contrast, which uses the SWIR and NIR band combination useful for the detection of post-fire conditions (Alcaras et al., 2022; Pepe & Parente, 2018). Other Spectral indices utilized for burnt area detection are Burnt Area Index,
Normalized Difference Moisture Index, Global Environmental Monitoring Index, Mid-infrared Burn Index, TassCap Brightness, TassCap Greenness and TassCap Wetness (Haeley et al., 2005). There are several satellite-based sensors are now available with high to medium resolution, that are well-suited for burned area mapping (Joseph et al., 2009).
To address the environmental impacts of forest fires, the present study attempts to accurately map and analyze the burnt area from forest fires in Telangana during the fire seasons of 2024 and 2025. A combined sensor multi-temporal remote sensing approach was employed to ensure robust outputs. Integrating 30m Landsat 8 and 9 data with 56m AWiFS data provides significant advantages in detecting small and spatially fragmented burned areas. Furthermore, the 16-day temporal resolution of Landsat 8 can be complemented by AWiFS’s 5-day revisit interval, ensuring that burned areas missed by Landsat 8 are captured by AWiFS. The findings emphasize the need for effective fire management, and the results provided valuable insights into the forest fire damages and emissions in Telangana, India.
STUDY AREA
The study was conducted across the entire forested area of Telangana state, the 12th largest State in India, covers 1,12,077 km2 of area, accounting for 3.41% of the nation's total geographical area. The state lies on the Deccan plateau to the west of the Eastern Ghats ranges between 15°50'N and 19°55'N latitudes, and 77°14'E and 81°19'E longitudes (Reddy et al., 2015), and experiences tropical semi-arid climate conditions. The state is administratively divided into 33 districts, which include several designated tribal districts (FSI, 2019). Geographically, the state has a semi-arid climatic condition, largely experiencing a hot and dry climate with an annual temperature varying from 15℃ to 45℃ (FSI, 2019). The State has a subtropical climate due to its centre location on the Deccan Plateau. The annual rainfall ranges between 1,100 mm to 1,200 mm (FSI, 2019), mostly received during the monsoon season, between June and September (Reddy et al., 2019). The state experiences dry and hot weather during March to May (summer) and very cold weather during December and January. March marks the beginning of the summer season, which lasts until May and the monsoon arrives in the month of June. The State is drained by many rivers which include the Godavari, Krishna, and many other artificial lakes. The protected area of the state covers 5.8% of its total geographical area, which comprises three National Parks and nine Wildlife Sanctuaries.
The Recorded Forest Area (RFA) is 26,904 km2 (FSI, 2019). Telangana's geographical location, climate, and topography facilitate the growth of tropical deciduous forests in this region. The forests in Telangana can be generally classified into three main categories: tropical moist deciduous, tropical dry deciduous, and tropical thorn forests. In these forest types, tropical dry deciduous is the predominant one (Reddy et al., 2008), which are particularly vulnerable to more forest fire incidents. Topography is an important physiographic factor related to wind behaviour and hence affects the fire proneness of the area (Erten et al., 2004). The fires in Telangana State are mainly ground fires, that are deliberate or incidental, causing widespread damage to the ground flora and fauna. Therefore, control of forest fires is a prerequisite for a healthy state of the forest (Telangana State Forest Report, 2015).
MATERIALS AND METHODS
Forest burnt area assessment:
The medium resolution multi-temporal cloud-free IRS-R2 AWiFS (56m spatial resolution) and orthorectified Landsat-8/9 Leval-2 OLI datasets (30m spatial resolution) were acquired from the webpage of Bhoonidhi and USGS Earth Explorer
(https://bhoonidhi.nrsc.gov.in/bhoonidhi/index.html, https://earthexplorer.usgs.gov/). These data has been used and validated in forest fire research and studies globally, including Telangana state (Reddy et al., 2017; Chaudhary et al., 2022; Sedano et al., 2012). The satellite images selected between Telangana's predominant fire seasons (February, March, April) during 2024 and 2025 were used for mapping of burnt areas. All the non-vegetation and agricultural land classes were masked out from the satellite imagery using NRSC’s classified land cover data of 2024. This study used the selected True Colour Composite images using the combination of SWIR, NIR, and Red spectral bands to effectively highlight fire plumes, active fire and burnt pixels. Burnt vegetation can be distinguished from unburnt vegetation by its higher reflectance in the SWIR region than in the NIR region of the electromagnetic spectrum, thereby enabling its identification through this spectral contrast. Normalized burn ratio index combined with visual interpretation technique was used to finalize the burnt area product.
Normalized Burn Ratio Index for burnt area mapping
The spectral analysis, which used Normalized Burn Ratio (NBR) Index algorithm for the extraction of burnt area. It was derived from the ratio between NIR and SWIR values.
NBR= (NIR−SWIR) / (NIR+SWIR)
NBR provides the values ranging from -1 to 1 (Alcaras et al., 2022). In the aftermath of a forest fire, burnt areas show low reflectance in the NIR band and high reflectance in the SWIR band, where lower NBR values indicate more severe damage shows burnt vegetation and positive NBR values indicate less damage and may show healthy green vegetation productivity (Chaudhary et al., 2022). Following classification, a 3 × 3 matrix was applied for postclassification smoothing. The classification results were interpreted visually and subjected to spatial GIS analysis. Burnt area map were overlaid on forest cover, different vegetation types and districts to evaluate the areas affected by forest fires. Burnt area frequency over the two years (2024 &2025) has been analysed using spatial overlay to understand the fire distribution and occurrence.
Accuracy Assessment
Accuracy assessment is an important procedure used to quantify the reliability of a classified image. This process involves comparing the classified burnt area map with reference data obtained from the ground truth data like satellite imagery. Then the confusion matrix table compares the classified data to the reference data. The stratified random sampling procedure was adopted for the data sampling and assessing the overall accuracy. This method randomly places a minimum number of sample points in each class. From the error matrix, several measures of classification accuracy can be calculated; overall accuracy (the percentage of correctly classified samples), producer’s accuracy (the probability that a reference sample is correctly classified, indicating omission errors), user’s accuracy (the probability that a classified sample is correct, reflecting commission errors) and kappa coefficient. Kappa coefficient is a measure of chance agreement that compares the overall performance of the classified map with the reference data (Landis & Koch, 1977). It provides a more robust evaluation of classification accuracy.
Kappa coefficient can be calculated as;
RESULT AND DISCUSSION
Estimation of forest burnt area
The results provide valuable information about the spatial area extent of fire damages. Fig. 3 and 4 shows the extent of forest burnt area in 2024 and 2025. Classified burnt area maps revealed that the Telangana forest is severely affected by fires.
The total forest cover of Telangana was estimated to be 20,655.4 km² based on the vegetation type map (2024) of NRSC (Fig.5.). The total burnt has been estimated as 4,182 km², which accounts for 20.2% of the total forest cover of the state during 2024. A slight increase in the burnt area was observed from 2024 to 2025, with the burnt area reaching 4,529.5 km² in 2025, accounting for 21.9% of the total forest cover. It was also determined that, 42.2% of the total forest cover in Telangana, was affected by fires over the two-year period (2024 and 2025). A national study by Reddy et al. (2017) identified Telangana as one of the top five states with the highest burnt area relative to its total forest cover. Furthermore, the primary cause of fires in the Telangana forest is linked to human activities, along with the significant record high temperature during 2024 and 2025, which accelerates the spread of surface head fires. The geospatial analysis shows that annually, a certain percentage of the forest area is regularly impacted by fires.
The classification accuracy of burnt area maps was computed and the Cohen's Kappa value for the 2024 burnt area map is 0.86, indicating an overall accuracy of approximately 92.2%. For the 2025 burnt area map, the Cohen's Kappa value is 0.81, with an overall accuracy of about
90.8%.
Forest-Type Wise Burnt Area Assessment
The burnt area under different vegetation types is extracted using forest and land cover map (2024) of Telangana, sourced from NRSC. The natural vegetation cover (forest, scrub, grassland, and plantation) was estimated to be 30,611 km². The total burnt area across natural vegetation types was estimated at 4115.7 km² in 2023 and 4413.9 km² in 2025.
The findings indicate that all forest types in Telangana have experienced fire events. Dry deciduous forest is the most abundant forest type in Telangana which comprises of 18,462.8 km², followed by moist deciduous of 1,237.9 km² and thorn forests of 954.8 km² (Table 1). The highest spatial extent of burnt area were reported in dry deciduous forests (Reddy et al., 2012), making them the most vulnerable to fire due to their high amount of combustible material. The study area's semi-arid climatic condition, along with the prevalence and high fire susceptibility of dry deciduous forests, has led to a large-scale fire occurrence in these forest type. Also the dry deciduous forests area getting very less rainfall compared to other forest types. In 2024, the burnt area of dry deciduous forests was found to be 3,532.2 km² (85.8% of the total burnt area), and 3,796 km² (86% of the total burnt area) in 2025. The estimates show that 19.1% of the total area of dry deciduous forests was impacted by fires in 2024, and this percentage increased to 20.6% in 2024, reflecting a 1.4% rise in the affected area.
Following dry deciduous forests, the moist deciduous and thorn forests are affected by fire incidents. Specifically, the burnt area in moist deciduous forests, with reference to the total burnt area, was 162.6 km² (3.9%) in 2023 and 128 km² (2.9%) in 2024. And for thorn forest, it accounts for 127.6 km² (3.1%) in 2024 and 152.2 km² (3.4%) in 2025. Only a smaller amount of fires are located among the non-forest types. Table 1 shows the estimates of forest type-wise assessment of the burnt area in Telangana.
Table 1: Burnt area statistics across forest and land cover categories (2024 and 2025)
Forest / Vegetation Type | Area (km²) | Burnt Area 2024 (km²) | Burnt Area 2024 (%) | Burnt Area 2025 (km²) | Burnt Area 2025 (%) |
Dense Moist Deciduous Forest | 884.5 | 113.1 | 2.7 | 79.9 | 1.8 |
Open Moist Deciduous Forest | 353.4 | 49.4 | 1.2 | 48.1 | 1.1 |
Dense Dry Deciduous Forest | 8703.8 | 1782.7 | 43.3 | 1862.4 | 42.2 |
Open Dry Deciduous Forest | 9759.0 | 1749.5 | 42.5 | 1933.3 | 43.8 |
Dense Thorn Forest | 224.0 | 20.7 | 0.5 | 31.7 | 0.7 |
Open Thorn Forest | 730.8 | 106.9 | 2.6 | 120.5 | 2.7 |
Scrub | 7314.1 | 216.9 | 5.3 | 248.5 | 5.6 |
Grassland | 2280.5 | 66.0 | 1.6 | 77.5 | 1.8 |
Plantations | 361.3 | 10.4 | 0.3 | 12.0 | 0.3 |
Grand Total | 30611.3 | 4115.7 | 100.0 | 4413.9 | 100.0 |
District Wise Forest Burnt Area
Administratively, Telangana has 33 districts. The spatial coverage of burnt area across districts indicates that during the years 2024-2025, Mulugu district was severely affected by continuous fires, followed by Nagarkurnol, Komram bheem Asifabad, Mahabubabad, Nirmal and Bhadradri Kothagudem. Among the 33 districts in Telangana, 10 districts have experienced more than 100 km² of burnt area in both years. In that, a higher extent of the burnt area was found in Mulugu district with 713 km² (17.0% of total area burnt), in 2024 and it increased to 897.7 km² (19.8% of total area burnt) in 2025. Following that, In 2024, Nagarkurnol, Komram Bheem Asifabad, Mahabubabad, Nirmal, and Bhadradri Kothagudem were also evidenced with large-scale fire, with affected areas estimated as 15.7%, and 10.7%, 7.6%, 7.2%, 6.4% of the total burnt area, respectively (Table 2). In 20255, these same districts continued to witness substantial forest fire incidents, with affected areas estimated as 16.9%, 6.3%, 7.9%, 7.4%, and 6.4% respectively. Mulugu and Nagarkurnol districts exhibited the highest burnt areas in both years (Fig. 6).
Mulugu and Nagarkurnol districts have shown the highest burnt areas in both years (Fig. 6).
The year 2025, shows a 2.8% increase in the burnt area in Mulugu and a 1.5% increase in Nagarkurnol compared to 2024. These observations indicate heightened human pressure and a potential for significant forest cover loss in these regions.
The persistent occurrence and intensification of fire incidents in these areas indicate a significant human influence in the forest environment, which is contributing to the rise in the frequency and scale of forest fires.
Table 2: District wise spatial extent of burnt area in Telangana state (km²)
CONCLUSION
The study aims to assess the status of the forest fire in Telangana using multi-temporal remote sensing data. The study developed a burnt area map for the years 2023 and 2024 using the AWiFS, Landsat 8, and 9 datasets. The areas of higher fire incidents and trace gas emissions from forests were presented for the formulation of long-term action plans. A proper management strategy needs to be taken in the districts with higher burnt areas, especially in the pre-monsoon season to avoid the potential consequences of forest fire. In particular, the dry deciduous forest region with high fire incidence should given special attention. The elevated atmospheric temperatures in recent years have intensified forest fire occurrences, thereby increasing the emission of trace gases, therefore, fire management strategies must account for the impacts of climate change. The spatial data generated from our study is useful in this context as a valuable addition to ongoing efforts to develop conservation strategies, aiding in the mitigation of climate change impacts resulting from forest fires.
REFERENCE
1) Alcaras, E., Costantino, D., Guastaferro, F., Parente, C., & Pepe, M. (2022). Normalized Burn Ratio Plus (NBR+): a new index for Sentinel-2 imagery. Remote Sensing, 14(7), 1727. https://doi.org/10.3390/rs14071727
2) Alganci, U., Sertel, E., & Ormeci, C. (2010). Forest fire damage estimation using remote sensing and GIS.
https://www.researchgate.net/publication/256454355_Forest_Fire_Damage_Estimation_ Using_Remote_Sensing_and_GIS
3) Bargali, H., Pandey, A., Bhatt, D., Sundriyal, R. C., & Uniyal, V. P. (2024). Forest fire management, funding dynamics, and research in the burning frontier: A comprehensive review. Trees Forests and People, 16 (100526). https://doi.org/10.1016/j.tfp.2024.100526
4) Bhoonidhi. (n.d). https://bhoonidhi.nrsc.gov.in/bhoonidhi/index.html
5) Bowman, D. M. J. S., Wood, S. W., Neyland, D., Sanders, G. J., & Prior, L. D. (2012). Contracting Tasmanian montane grasslands within a forest matrix is consistent with cessation of Aboriginal fire management. Austral Ecology, 38(6), 627–638. https://doi.org/10.1111/aec.12008
6) Carbone, L. M., Tavella, J., Marquez, V., Ashworth, L., Pausas, J. G., & Aguilar, R.
(2024). Fire effects on pollination and plant reproduction: a quantitative review. Annals of Botany. https://doi.org/10.1093/aob/mcae033
7) Chaudhary, S. K., Pandey, A. C., & Parida, B. R. (2022). Forest fire characterization using Landsat-8 satellite data in Dalma Wildlife Sanctuary. Remote Sensing in Earth Systems
Sciences, 5(4), 230–245. https://doi.org/10.1007/s41976-022-00076-3
8) Chuvieco, E., Mouillot, F., van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., ... & Giglio, L. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45-64. https://doi.org/10.1016/j.rse.2019.02.013
9) Erten, E., Kurgun, V., & Musaoglu, N. (2004). Forest fire risk zone mapping from satellite Imagery and GIS: a case study. International Society for Photogrammetry and Remote Sensing. https://www.researchgate.net/publication/290482402_Forest_fire_risk_zone_mapping_fr om_satellite_imagery_and_GIS_A_case_study_XXth_Congress_of_the_International_S ociety_for_Photogrammetry_and_Remote_Sensing_Istanbul
10) Faseela, V,S., Reddy, C. S., Abraham, G. A., Joseph, A., S, S., Sabu, M. M., & R, J. (2020). Assessment and monitoring of deforestation and forest fires in Bangladesh using remote sensing data. NeBIO, 11(4), 1-8,
https://www.researchgate.net/publication/375925664_Assessment_and_monitoring_of_d eforestation_and_forest_fires_in_Bangladesh_using_remote_sensing_data
11) FSI. (2015). Indian State of Forest Report 2015. https://fsi.nic.in/forest-report-2015
12) FSI. (2019). Indian State of Forest Report 2019. https://fsi.nic.in/forest-report-2019
13) FSI. (2021). Indian State of Forest Report 2021. https://fsi.nic.in/forest-report-2021details
14) Giglio, L., Loboda, T., Roy, D.P., Quayle, B. & Justice, C.O. (2009). An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment. 113, 408–420. 10.1016/j.rse.2008.10.006
15) Gitas, I. Z., Mitri, G. H., & Ventura, G. (2004). Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery. Remote
Sensing of Environment, 92(3), 409-413. 10.1016/j.rse.2004.06.006
16) Global Forest Watch. (n.d). https://www.globalforestwatch.org/dashboards/global/
17) Healey, S. P., Cohen, W. B., Zhiqiang, Y., & Krankina, O. N. (2005). Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote sensing of environment, 97(3), 301-310. 10.1016/j.rse.2005.05.009
18) Joseph, S., Anitha, K., & Murthy, M. S. R. (2009). Forest fire in India: a review of the knowledge base. Journal of Forest Research, 14(3), 127–134. https://doi.org/10.1007/s10310-009-0116-x
19) Key, C.H. & Benson, N.C. (1999). Measuring and remote sensing of burn severity. In Proceedings joint fire science conference and workshop. 2 (284). https://www.researchgate.net/publication/241687936_Measuring_and_remote_sensing_o f_burn_severity_the_CBI_and_NBR
20) Kumar, S., & Kumar, A. (2022). Hotspot and trend analysis of forest fires and its relation to climatic factors in the western Himalayas. Natural Hazards, 114(3), 3529–3544. https://doi.org/10.1007/s11069-022-05530-5
21) A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A., Sitch, S. & Hayes, D. (2011). A large and persistent carbon sink in the world’s forests. Science, 333(6045), 988–993. 10.1126/science.1201609
22) Pepe, M., & Parente, C. (2018). Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case study of Sorrento Penisola, Italy. Journal of Applied Engineering Science, 16(2), 225–232. https://doi.org/10.5937/jaes1617249
23) Reddy, C. S., Alekhya, V. V. L. P., Saranya, K. R. L., Athira, K., Jha, C. S., Diwakar, P.
G., & Dadhwal, V. K. (2017). Monitoring of fire incidences in vegetation types and Protected Areas of India: Implications on carbon emissions. Journal of Earth System
Science, 126(11). https://doi.org/10.1007/s12040-016-0791-x
24) Reddy, C. S., Jha, C. S., Ganjam, M., Alekhya, P. V., Pasha, V,S., Sathish, K, V.,
Diwakar, P. G., & Dadhwal, V. K. (2017). Nationwide Assessment of Forest Burnt area in India using ResourcesAT-2 AWIFS data. Current Science, 112(7), 1521. https://doi.org/10.18520/cs/v112/i07/1521-1532
25) Reddy, C. S., Krishna, P. H., Anitha, K., & Joseph, S. (2012). Mapping and inventory of forest fires in Andhra Pradesh, India: Current status and conservation needs. ISRN Forestry, 2, 1–10. 10.5402/2012/380412
26) Reddy, C. S., Pasha, S. V., Jha, C. S., & Dadhwal, V. K. (2015). Geospatial characterization of deforestation, fragmentation and forest fires in Telangana state, India: conservation perspective. Environmental Monitoring and Assessment, 187(7). https://doi.org/10.1007/s10661-015-4673-5
27) Reddy, C. S., Pujar, G. S., Sudhakar, S., Shilpa, B., Sudha, K., Trivedi, S., Gharai, B., & Murthy, M. S. R. (2008). Mapping the vegetation types of Andhra Pradesh, India using remote sensing. Proceedings Andhra Pradesh Akademi of Sciences, 12(1&2), 14–23. https://www.researchgate.net/publication/262007710_Mapping_the_Vegetation_Types_ of_Andhra_Pradesh_India_using_Remote_Sensing
28) Reddy, K. K., Sahadevan, D. K., Gupta, H., & Reddy, D. V. (2019). GIS-based prediction of groundwater fluoride contamination zones in Telangana, India. Journal of Earth System Science, 128(5). https://doi.org/10.1007/s12040-019-1151-4
29) Saranya, K., Nair, M. S., & Reddy, C,S., (2023). From Detection to Management: Insights and Applications from Satellite Remote Sensing of Forest Fires. In book: Exploring
Emerging Techniques in Plant Sciences, 17, 182-194.
Insights_and_Applications_from_Satellite_Remote_Sensing_of_Forest_Fires
30) Sedano, F., Kempeneers, P., Strobl, P., McInerney, D., & Miguel, J. S. (2012). Increasing spatial detail of burned scar maps using IRS‐AWIFS data for Mediterranean Europe. Remote Sensing, 4(3), 726–744. https://doi.org/10.3390/rs4030726
31) Selvaraj, V., Babu, R., Anoop, V., Sivakumar, C., & Kumar, S,V. (2022). Satellite-based Assessment of Forest Fire Burnt Area using the Sentinel Data in Periyar Tiger. Reserve.
Indian Journal of Space Science, 13(1), 60-64.
based_Assessment_of_Forest_Fire_Burnt_Area_using_the_Sentinel_Data_in_Periyar_Ti ger_Reserve_Kerala_India
32) Sivrikaya, F., Günlü, A., Küçük, Ö., & Ürker, O. (2024). Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices. Ecological Informatics, 79(102461). https://doi.org/10.1016/j.ecoinf.2024.102461
33) Talukdar, N. R., Ahmad, F., Goparaju, L., Choudhury, P., Qayum, A., & Rizvi, J. (2023). Forest fire in Thailand: Spatio-temporal distribution and future risk assessment. Natural Hazards Research, 4(1), 87–96. https://doi.org/10.1016/j.nhres.2023.09.002
34) Thariq, A., & Zhao, N. (2024). Application of remote sensing in forest fire. https://www.mdpi.com/topics/06S2F7D819
35) Tyukavina, A., Potapov, P., Hansen, M. C., Pickens, A. H., Stehman, S. V., Turubanova, S., Parker, D., Zalles, V., Lima, A., Kommareddy, I., Song, X., Wang, L., & Harris, N. (2022). Global trends of forest loss due to fire from 2001 to 2019. Frontiers in Remote Sensing, 3. https://doi.org/10.3389/frsen.2022.825190
36) Telangana Forest Department Government of Telangana, Hyderabad. (2015). Telangana State of Forest Report, 2015. https://forests.telangana.gov.in/Docs/SFR2015.pdf 37) USGS EarthExplorer. (n.d). https://earthexplorer.usgs.gov/
38) Verma, S., Singh, D., Mani, S., & Jayakumar, S. (2017). Effect of forest fire on tree diversity and regeneration potential in a tropical dry deciduous forest of Mudumalai Tiger
Reserve, Western Ghats, India. Ecological Processes, 6(1). https://doi.org/10.1186/s13717-017-0098-0
39) WMO GCOS. (n.d). Fire. https://gcos.wmo.int/en/essential-climate-variables/fire
Comments