Land Cover Change Dynamics and their Impacts on Thermal Environment of Dadri Block, Gautam Budh Nagar, India

Shastri, Sushma; Singh, Prafull; Verma, Parul; Kumar, Prabhat; Singh, Ajit Pratap

doi:10.2478/jlecol-2020-0007

Cited by 19 publications

(3 citation statements)

References 42 publications

(20 reference statements)

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“…In this study, the scaled NDVI values are calculated. These scaled values range from 0 to 1 and are known as vegetation coverage index [5], green vegetation fraction [28], proportion of vegetation [29], or VCI [30], which can be derived by: VCI = (NDVI -NDVI min ) / (NDVI max -NDVI min ) (2) where NDVI min and NDVI max are the Minimum and Maximum values of the NDVI, respectively. Fig.…”

Section: B Vegetation Condition Index (Vci)mentioning

confidence: 99%

Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping

Khalil,

Khan,

Hasan

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Temporal-vegetation mapping bearing temporal-related features is important because it helps to understand the global climate changes that drive resource management and habitat conservation. This paper presents a Supervised Normalized Difference Vegetation Index (SNDVI) approach for mapping the vegetation cover in arid environment regions. The NDVI is used to extract features to classify land as a vegetation cover, water body, or bare soil. Through the use of Normalized Difference Vegetation Index (NDVI), regions can be categorized as dry or sandy, based on the soil reflectance values. NDVI is the most commonly deployed index for accurate vegetation cover estimates. The NDVI values lie in a range from -1 to +1, depending on the environmental region and vegetation conditions. It is difficult to assign a specific threshold value to distinguish between vegetation and non-vegetation for all the eco-regions under a specific landscape and ecological conditions. The proposed approach is based on the quantitative verification of the samples as well as the supervised classification method followed to categorize the images. The SNDVI approach has been applied to three different locations in three different seasons in arid ecoregions to extract features for vegetation mapping. The results disclose that SNDVI is a very reliable parameter in extracting true vegetation cover in arid regions. An accuracy evaluation matrix has been performed for each case study and the overall obtained accuracy value ranged from 82% to 100%, depending on the season of the area under investigation. The utility of the proposed method is determined by bench-marking the results with those of the techniques recently utilized by contemporary researchers.

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Section: B Vegetation Condition Index (Vci)mentioning

confidence: 99%

Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping

Khalil,

Khan,

Hasan

et al. 2024

Eng. Technol. Appl. Sci. Res.

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“…More specifically, there is insufficient literature on aerosol monitoring in the highly urbanized and significantly polluted National Capital Region (NCR) of India, due to the lack of AERONET surface stations in the region and the use of MAIAC AOD in only a few studies to understand the influence of aerosols on climate (Sharma et al, 2022;Shastri et al, 2020;Chowdhury et al, 2019a;Dey et al, 2020). In such a region with limited data, it is necessary to estimate the uncertainty associated with new retrieval algorithm.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India

Sharma

Ghosh

Kumari

et al. 2022

Journal of Landscape Ecology

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Aerosol monitoring is the emerging application field of satellite remote sensing. As a satellite-based indicator of aerosol concentration, aerosol optical depth (AOD) can aid in assessing the crucial effects of aerosols on the global environment. Among various satellite-based aerosol product, Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6), Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol product (1 km resolution) has still untapped potential in Indian regions. Considering the importance of regional validation of such high-resolution aerosol product, the present study attempts to fill this gap by validating MAIAC aerosol estimates (AODMAIAC) in highly polluted districts (Faridabad, Ghaziabad, Gautam Budh Nagar, Gurugram) of National Capital Region (NCR) with heavy aerosol loading using limited AErosol RObotic NETwork (AERONET) observations obtained from AERONET sites at Amity University (AU) and Gual Pahari (GP). Such evaluation of satellite-retrieved aerosol product with ground data confirms its practicality based on retrieval errors (Expected Error (EE) values (EE = 0.05 + 15 %*AOD) (EE: 78.85 % at AU, 73.58 % at GP), root mean square error (RMSE) values (RMSE: 0.15 at AU, 0.24 at GP), and correlation coefficient (R) values (R: 0.86 at AU, 0.73 at GP). The seasonal variation in AOD over the study area from 2010-2019 reveals increasing trend of AOD in the monsoon and post-monsoon season due to natural and anthropogenic factors. In addition to contributing to a holistic assessment of MAIAC aerosol estimates as a recent, high-resolution aerosol product, present results provide a basis for further research into NCR aerosols.

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“…[11], [15] and [16] posited that it is the detrimental effects that have been of concern because of their negative influence on the welfare and well-being of man. Land use / land cover (LULC) has been considered as one of the important bio-physical parameters and have significant effect on local environmental change, particularly increasing anthropogenic temperature [18], [17]. As the impacts of anthropogenic climate change have become…”

Section: Introductionmentioning

confidence: 99%

Mapping Spatiotemporal Dynamics of Akure Industrial Layout for Sustainable Development

Ogunlade¹

2021

ijcsrr

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This research aimed at mapping the spatiotemporal dynamics of the Industrial Layout located in Akure Ondo State Nigeria. The dataset used are the administrative map of Ondo State, Akure Industrial Layout Boundary,various Landsat imageries of 32m resolution which are Thematic Mapper (TM) of 1986 & 1991, Enhanced Thematic Mapper Plus (ETM)+ of 2002, Operational Land Imager / Thermal Infrared Sensor (OLI/TIRS) of 2014, 2017, 2020; and Worldview 3 image 2020 of 1.24m resolution. The Landsat data were used to extract the different Land use/Land cover (LULC) within the study area. GPS receiver and Worldview 3 image were used to obtain the coordinates of the different LULC classes, which aided in the classification of image, and also for accuracy assessment of the classified image. All the Landsat standard data products were processed, to ascertain that they are free of radiometric and geometric errors using the Level 1 Product Generation System (LPGS) and extracted to obtain the landsat image bands. The extracted Landsat images (bands) were used in the processing and calculating the Normalized Difference Vegetation Index (NDVI) and calculation of LULC changes. Evaluation the accuracy of the results produced from the land cover classification was carried out by comparing the results of ground coordinates with the coordinates obtained from a higher resolution image (Worldview 3 image) in order to determine the accuracy of the land cover classification in the study area. The trend of changes of land cover in these areas was assessed and also, the prediction for the future condition both in terms of development was determined based on the results obtained from the initial results. Results from various maps produced and numerical data generated showed that Akure Industrial Layout was mainly dominated by shrub and grass land in 1986 and has in 34 years experienced transformation of 604% in the built environment (18% /year), 119% of Bareland (3.5%/year), and -29% of Grassland (0.9%/year), -66% of Shrub (2%/year). The forecast of the probable spatial extent for the years 2025 and 2030 were estimated to be 175.3Ha and 214.8Ha respectively, which shows there will be a continuous increase in the future development in Akure Industrial layout. The research recommended a proactive action from the government and end-users that will ensure a sustained manageability of the layout.

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Land Cover Change Dynamics and their Impacts on Thermal Environment of Dadri Block, Gautam Budh Nagar, India

Cited by 19 publications

References 42 publications

Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping

Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping

Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India

Mapping Spatiotemporal Dynamics of Akure Industrial Layout for Sustainable Development

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