Assessing Land Use–Land Cover Change and Its Impact on Land Surface Temperature Using LANDSAT Data: A Comparison of Two Urban Areas in India

Mukherjee, Falguni; Singh, Deepika

doi:10.1007/s41748-020-00155-9

Cited by 113 publications

(51 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Generally, a strong inverse relationship was observed between the NDVI and LST and between EVI and LST. An inverse relationship between NDVI and LST was also noted in studies by Li et al (2010), Ferrelli et al (2018), Mukherjee and Singh (2020) and Guha et al (2020). The strongest positive correlations were observed between NDVI and EVI over the 35-year data span.…”

Section: Analysis Of Relationship Between Parameterssupporting

confidence: 62%

“…The regression analysis between the parameters and the time period presented in Table 10 shows a negative linear relationship between the LST and the VIs over the years. Several authors have also noted this inverse relationship, for example, Dewan and Corner (2012), Ferrelli et al (2015), Guha et al (2020) and Mukherjee and Singh (2020). This means that the LST increased as the NDVI and EVI decreased over time.…”

Section: Analysis Of Relationship Between Parametersmentioning

confidence: 85%

“…Statistical analyses are commonly applied to establish relationships between parameters, for example, Land cover-LST (Dewan, Corner 2012), LST-NDVI (Ferrelli et al 2018, Hamoodi et al 2019, Malik et al 2019, Ullah et al 2019, Guha et al 2020, Mukherjee, Singh 2020, NDVI-EVI (Chen et al 2006b, Matsushita et al 2007, Li et al 2010, Uyeda et al 2017 and LST-EVI (Phompila et al 2015). In this study, an inventory of the parameters was created for the study area.…”

Section: Relationships Change Detection and Trend Analysismentioning

confidence: 99%

See 2 more Smart Citations

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

Alademomi

Okolie

Daramola

et al. 2020

Quaestiones Geographicae

View full text Add to dashboard Cite

The Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson's correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.

show abstract

Section: Analysis Of Relationship Between Parameterssupporting

confidence: 62%

Section: Analysis Of Relationship Between Parametersmentioning

confidence: 85%

Section: Relationships Change Detection and Trend Analysismentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

Alademomi

Okolie

Daramola

et al. 2020

Quaestiones Geographicae

View full text Add to dashboard Cite

show abstract

“…The estimated land surface temperature is compared with the actual air temperature data from meteorological stations to validate LST Mukherjee and Singh 2020). In this research, the LST values estimated from satellite images were validated by comparing them with the actual air temperature values measured simultaneously and derived from the Aswan Station 624140 (HESN).…”

Section: Lst Validationmentioning

confidence: 99%

Dynamics of human-induced lakes and their impact on land surface temperature in Toshka Depression, Western Desert, Egypt

Samra

2021

Preprint

View full text Add to dashboard Cite

Land surface temperature (LST) is a significant environmental variable that is appreciably influenced by land use /land cover changes. The main goal of this research was to quantify the impacts of land use/land cover change (LULC) from the drying of Toshka Lakes on LST by remote sensing and GIS techniques. Landsat series TM and OLI satellite images were used to estimate LST from 2001 to 2019. Automated Water Extraction Index (AWEI) was applied to extract water bodies from the research area. Optimized Soil-Adjusted Vegetation Index (OSAVI) was utilized to predict the reclaimed land in the Toshka region until 2019. The results indicated a decrease in the lakes by about 1517.79 km2 with an average increase in LST by about 25.02 °C between 2001 and 2019. It was observed that the dried areas of the lakes were converted to bare soil and are covered by salt crusts. The results indicated that the land use change was a significant driver for the increased LST. The mean annual LST increased considerably by 0.6 °C/y between 2001 and 2019. A strong negative correlation between LST and Toshka Lakes area (R-square = 0.98) estimated from regression analysis implied that Toshka Lakes drying considerably affected the microclimate of the study area. Severe drought conditions, soil degradation, and many environmental issues were predicted due to the rise of LST in the research area. There is an urgent need to develop favorable strategies for sustainable environmental management in the Toshka region.

show abstract

“…The emergence of hyperspectral remote sensing technology is undoubtedly a breakthrough in the field of remote sensing [1][2][3]. Hyperspectral sensors with dozens or even hundreds of spectral bands may effectively capture abundant spectral and spatial information on the Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

et al. 2021

View full text Add to dashboard Cite

Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to integrate spatial structure information into these frameworks to classify the HSI with limited training samples. In this study, an effective spectral-spatial HSI classification scheme is proposed based on superpixel pooling convolutional neural network with transfer learning (SP-CNN). The suggested method includes three stages. The first part consists of convolution and pooling operation, which is a down-sampling process to extract the main spectral features of an HSI. The second part is composed of up-sampling and superpixel (homogeneous regions with adaptive shape and size) pooling to explore the spatial structure information of an HSI. Finally, the hyperspectral data with each superpixel as a basic input rather than a pixel are fed to fully connected neural network. In this method, the spectral and spatial information is effectively fused by using superpixel pooling technique. The use of popular transfer learning technology in the proposed classification framework significantly improves the training efficiency of SP-CNN. To evaluate the effectiveness of the SP-CNN, extensive experiments were conducted on three common real HSI datasets acquired from different sensors. With 30 labeled pixels per class, the overall classification accuracy provided by this method on three benchmarks all exceeded 93%, which was at least 4.55% higher than that of several state-of-the-art approaches. Experimental and comparative results prove that the proposed algorithm can effectively classify the HSI with limited training labels.

show abstract

Assessing Land Use–Land Cover Change and Its Impact on Land Surface Temperature Using LANDSAT Data: A Comparison of Two Urban Areas in India

Cited by 113 publications

References 73 publications

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

Dynamics of human-induced lakes and their impact on land surface temperature in Toshka Depression, Western Desert, Egypt

Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

Contact Info

Product

Resources

About