Satellite monitoring of forest fire impact and regeneration using NDVI and LST

Digavinti, Jeevalakshmi; Manikiam, B.

doi:10.1117/1.jrs.15.042412

Cited by 8 publications

(6 citation statements)

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“…In contrast, the magnitude of changes in vegetation postfire is large and these changes show clear contrast compared to pre-fire [60]. The results of our study, in the context of the sensitivity of NDVI post and LST post to the fire severity levels, are congruent with other studies in the available literature as these studies report the inversely proportional relationship between NDVI and LST [31], [61], [62], [63]. In healthy vegetation, NDVI values are typically high, indicating abundant and vigorous plant growth [46], [64].…”

Section: Discussionsupporting

confidence: 89%

“…This compact nature contributes to higher storability and transmissivity, consequently affecting the forest type, resulting in dry deciduous forests. Such forests often accumulate dead leaves, branches, Climatological factors, such as temperature, precipitation, relative humidity, wind, seasonal variation, topography, and fuel moisture content all are known to have a role in influencing forest fires in deciduous forests [31], [50], [51], [52]. Elevated temperatures can increase the likelihood of forest fires by drying out vegetation and increasing evaporation rates.…”

Section: Discussionmentioning

confidence: 99%

“…Research efforts aimed at identifying fire-affected areas have been conducted in various regions of India, demonstrating remarkable accuracy in predicting the spatial extent of forest fire impacts and assessing changes in post-fire vegetation using proxies such as the Normalized Difference Vegetation Index (NDVI). For instance, researchers observed a significant change between 2014 and 2018, indicating a 35.8% shift in average NDVI and a 14.69% variation in mean temperatures observed during the forest fire season in Tirupati, India [31].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Forest Fire-affected Areas Using Advanced Machine Learning Techniques in Damoh District of Central India

Moharir,

Manpreet Singh,

Pande

et al. 2024

Kbes

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Forest fires can cause calamitous damage to the forest ecosystems and may affect climatic parameters, such as temperature, evapotranspiration, and precipitation by catalyzing changes in the local ecology of the region. The main research objective of this work was to quantify the different indices as well as to identify the changes in years 2011 and 2020 in pre- and post-fire, these parameters, such as Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Normalized Burn Ratio Index (NBR), associated with the severity of forest fires in the Damoh district of Madhya Pradesh. An attempt has been also made to understand the interrelationship of these parameters to gauge how these may correlate to determine the sensitivity to forest fires. In this context, we have incorporated advanced GIS methods for the identification of the pre-post fires for 2011 and 2020 year from Landsat 5 and Landsat 8 OLI/TIRS level -1 and Random Forest (RF) model in the Google Earth Engine (GEE) platform. Land Use Land Cover (LULC) map was categorized into five classes based on the satellite data sets. Our findings indicate elevated Land Surface Temperature (LST) values in the Northern and Central regions of the study area, reaching 32.0°C before the fire event. Subsequently, following the fire incident in the year 2011, LST escalated to 39.0°C. Similarly, in the southern and south-eastern regions of the Damoh district, LST peaked at approximately 43.0°C coinciding with the onset of a forest fire in 2020. Furthermore, our analysis revealed a negative correlation between the Normalized Difference Vegetation Index (NDVI) and LST, whereas the Normalized Burn Ratio (NBR) displayed a positive correlation with LST. These results underscore the impact of LST on forest vegetation dynamics, with LST nearing 39.0°C indicating an increased risk of forest fires. The results of this study can be used by local administration to devise an efficient policy related to forest fire management.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping Forest Fire-affected Areas Using Advanced Machine Learning Techniques in Damoh District of Central India

Moharir,

Manpreet Singh,

Pande

et al. 2024

Kbes

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“…the imagery from the Sentinel‐2 satellite). To train the classifier, sections of known features were used to create libraries (Gorelick et al, 2017 ), producing an overlay map that delineated the new land cover classifications using normalised difference vegetation index (NDVI) bands (Digavinti & Manikiam, 2021 ). For this study, two land cover types were defined: ‘agriculture’ and ‘urbanisation’ (Table S2 ).…”

Section: Methodsmentioning

confidence: 99%

The combined effects of treated sewage discharge and land use on rivers

Albini,

Lester,

Sanders

et al. 2023

Global Change Biology

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Freshwater ecosystems are increasingly threatened by multiple anthropogenic stressors. Release of treated sewage effluent and pollution from agricultural or urban sources can independently reduce water quality with implications for ecological communities. However, our knowledge of the combined effects of these stressors is limited. We performed a field study to quantify the combined effect of treated sewage discharge and land use on nutrient concentrations, sewage fungus presence and communities of macroinvertebrates and benthic algae. Over three seasons in four rivers we found that a model which included an interaction between sewage pollution and time of the year (i.e. months) was the best predictor of nutrient concentrations and the abundance of algae and sewage fungus. Both macroinvertebrate and algae communities shifted downstream of sewage input. Specifically, more tolerant groups, such as cyanobacteria and oligochaetes, were more abundant. The EPT (Ephemeroptera, Plecoptera and Tricoptera) water quality score was best explained by an interaction between month and agriculture in the surrounding landscape. Overall, our results show that sewage discharge has a significant impact on water quality and benthic riverine communities, regardless of the surrounding land uses. Agricultural inputs, however, could be more important than treated sewage discharge in reducing the abundance of sensitive invertebrate taxa. We need both improvements to wastewater treatment processes and reductions in agricultural pollution to reduce threats to vulnerable freshwater communities.

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“…

is the actual surface temperature;

is the radiance brightness of the black body at temperature

; K 1 and K 2 are constant values with the value of 774.89

and 1321.08 K , which are provided by the metadata file of the Landsat 8 image;

represents the vegetation coverage, which is calculated based on

index;

and

represent the

value of pure vegetation pixels and bare soil or no vegetation coverage pixels. In this study, the

and

values are 0.70 and 0.05, respectively [ 40 , 41 ]; When the

value at certain pixel is greater than 0.70, the value of

equals 1.00, while the

value at a certain pixel is less than 0.05, the value of

equals 0.00.

and

represent the surface emissivity of the water pixel, natural surface pixel, and urban area pixel, respectively.…”

Section: Methodsmentioning

confidence: 99%

Study on the Relationship between Urban Street-Greenery Rate and Land Surface Temperature Considering Local Climate Zone

Wang

Ding

et al. 2023

IJERPH

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Relationship exploration between the street-greenery rate (SGR) of different street types and land surface temperature (LST) is of great significance for realizing regional sustainable development goals. Given the lack of consideration of the local climate zone concept (LCZ), Chongqing’s Inner Ring region was selected as a case to assess the relationship between SGR and LST. Firstly, the LST was retrieved based on Landsat 8 imagery, which was calibrated by the atmospheric correction method; next, the street-greenery rates of different streets were calculated based on the semantic segmentation method; finally, street types were classified in detail by introducing LCZ, and the relationship between SGR and LST was investigated. The results showed that: (1) The LST spatial distribution pattern was closely related to human activity, with the high-temperature zones mainly concentrated in the core commercial areas, dense residential areas, and industrial cluster areas; (2) The average SGR values of expressways, main trunk roads, secondary trunk roads, and branch roads were 21.70%, 22.40%, 24.60%, and 26.70%, respectively. The level of SGR will decrease when the street width increases; (3) There is a negative correlation between the SGR and the LST in most streets. Among them, the LST of secondary trunk roads in low-rise and low-density built-up areas with a south-north orientation had a strong negative correlation with the SGR. Moreover, the wider the street, the higher the cooling efficiency of plants. Specifically, the LST of streets in low-rise and low-density built-up areas with south-north orientation may decrease by 1°C when the street-greenery rate is increased by 3.57%.

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Satellite monitoring of forest fire impact and regeneration using NDVI and LST

Cited by 8 publications

References 0 publications

Mapping Forest Fire-affected Areas Using Advanced Machine Learning Techniques in Damoh District of Central India

Mapping Forest Fire-affected Areas Using Advanced Machine Learning Techniques in Damoh District of Central India

The combined effects of treated sewage discharge and land use on rivers

Study on the Relationship between Urban Street-Greenery Rate and Land Surface Temperature Considering Local Climate Zone

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