scholarly journals Spatio-temporal analysis of land use dynamics and its potential indications on land surface temperature in Sokoto Metropolis, Nigeria

2018 ◽  
Vol 5 (12) ◽  
pp. 180661 ◽  
Author(s):  
K. O. Ogunjobi ◽  
Y. Adamu ◽  
A. A. Akinsanola ◽  
I. R. Orimoloye
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Urban Growth ◽  
Land Surface ◽  
Anthropogenic Activities ◽  
Global Environmental Change ◽  
The United States ◽  
United States Geological Survey ◽  
Landsat Images

Land use change is the main driving force of global environmental change and is considered as most central to various debates on sustainable development. Even though a large volume of literature materials is available on land use/land cover change for many areas, very little work has been done on land use and its implications on land surface thermal characteristics over the Sokoto area of Nigeria, despite the strategic importance of the zone, including urbanization, increased population as well as the climate in the area, which is dominated by warm harmattan wind blowing Sahara dust inland. Thus, this study aimed at investigating the implications of urban growth on temporal variations of land surface temperature (LST) using remote sensing and geographic information system (GIS) techniques over Sokoto Metropolis, Nigeria between 1986 and 2016. The change detection of each land use class was carried out for each period using Landsat images obtained from the archives of the United States Geological Survey (USGS). The results revealed that the area has undergone a drastic transformation where built-up area witnessed changes at 10.77%, farmland and vegetation increased at the rate of 0.72% and 2.15%, respectively, for the period of study (1986–2016). While bare soil and water body decreased at the rate of 0.56% and 1.11%, respectively, during the study period. This shows that there exists a transformation from bare surface (desert) to vegetated surface especially between years 2009 and 2016. The LST of Sokoto Metropolis was calculated from the satellite data, and the land surface temperature of each land use class was assessed for the study period. The maximum LST of Sokoto was 30.6°C, 32.8°C and 34.6°C for 1986, 1999 and 2016, respectively. This study has revealed the existence of a positive relationship between built-up area and LST over the area. This development might be as a result of anthropogenic activities through urban growth coupled with its potential impacts on urban climate. These are intensified by constant changes of the space, causing imbalance in the interactions between surface and atmosphere which may be extensively influenced or modified by various forms of land use.

scholarly journals Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature

2021 ◽  
Vol 13 (1) ◽  
pp. 1561-1577
Author(s):  
Sajjad Hussain ◽  
Muhammad Mubeen ◽  
Ashfaq Ahmad ◽  
Nasir Masood ◽  
Hafiz Mohkum Hammad ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Changes ◽  
Anthropogenic Activities ◽  
Cropping Pattern ◽  
Landsat Images ◽  
Urban Climatology

Abstract The rapid increase in urbanization has an important effect on cropping pattern and land use/land cover (LULC) through replacing areas of vegetation with commercial and residential coverage, thereby increasing the land surface temperature (LST). The LST information is significant to understand the environmental changes, urban climatology, anthropogenic activities, and ecological interactions, etc. Using remote sensing (RS) data, the present research provides a comprehensive study of LULC and LST changes in water scarce and climate prone Southern Punjab (Multan region), Pakistan, for 30 years (from 1990 to 2020). For this research, Landsat images were processed through supervised classification with maps of the Multan region. The LULC changes showed that sugarcane and rice (decreased by 2.9 and 1.6%, respectively) had less volatility of variation in comparison with both wheat and cotton (decreased by 5.3 and 6.6%, respectively). The analysis of normalized difference vegetation index (NDVI) showed that the vegetation decreased in the region both in minimum value (−0.05 [1990] to −0.15 [2020]) and maximum value (0.6 [1990] to 0.54 [2020]). The results showed that the built-up area was increased 3.5% during 1990–2020, and these were some of the major changes which increased the LST (from 27.6 to 28.5°C) in the study area. The significant regression in our study clearly shows that NDVI and LST are negatively correlated with each other. The results suggested that increasing temperature in growing period had a greatest effect on all types of vegetation. Crop-based classification aids water policy managers and analysts to make a better policy with enhanced information based on the extent of the natural resources. So, the study of dynamics in major crops and surface temperature through satellite RS can play an important role in the rural development and planning for food security in the study area.

scholarly journals The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan

2019 ◽  
Vol 10 (3) ◽  
pp. 40-49
Author(s):  
Aftab Ahmed Khan ◽  
Syed Najam ul Hassan ◽  
Saranjam Baig ◽  
Muhammad Zafar Khan ◽  
Amin Muhammad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Northern Region ◽  
Google Earth ◽  
Landsat Images

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.

scholarly journals The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan

2019 ◽  
Vol 10 (3) ◽  
pp. 40-49
Author(s):  
Aftab Ahmed Khan ◽  
Syed Najam ul Hassan ◽  
Saranjam Baig ◽  
Muhammad Zafar Khan ◽  
Amin Muhammad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Northern Region ◽  
Google Earth ◽  
Landsat Images

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.

scholarly journals Predicting changes in land use land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

Heliyon ◽  
2021 ◽  
pp. e07623
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Abdullah Al Rakib ◽  
Sumita Roy ◽  
Jannatul Ferdousi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Multi Temporal ◽  
Northwest Region

scholarly journals Monitoring and Prediction of Land Use Land Cover Changes and its Impact on Land Surface Temperature in the Central Part of Hisar District, Haryana Under Semi-Arid Zone of India

2019 ◽  
Vol 12 (3) ◽  
pp. 117-140
Author(s):  
Sunil Kumar ◽  
Swagata Ghosh ◽  
Ramesh Singh Hooda ◽  
Sultan Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Urban Growth ◽  
Land Surface ◽  
Agricultural Land ◽  
Land Use Land Cover ◽  
The City ◽  
Semi Arid

Abstract Land use Land cover have significance in relation to Land, the most vital and fundamental resource pertaining to the urban development. Unprecedented urban growth has a noteworthy impact on natural landscape by converting natural land-cover in Haryana. Hisar, an area recognized for rapid urban growth is less explored in terms of research. The present research has shown a significant change in land use in terms of expansion of built-up area from 3.7 % (1991) to 5.0 % (2001) and 6.2 % (2011) by encroaching into agricultural land. Despite the clear difference between average land surface temperature for built up and non-built up area, grazing land and sandy waste, bare land in the rural surrounding possess higher temperature compared to the city core which contradicts the reported impact of urbanization earlier. Such contrary pertains to sparse vegetation cover leading to reduced evaporative cooling during dry pre-monsoon summer in the rural surrounding. On the other side, green parks and plantation in the city contribute to lower mean temperature because of high rates of evapotranspiration and produce ‘oasis effect’ in the present study area located in semi-arid climatic zone. Regression analysis between temperature and Normalized Difference Vegetation Index, Normalized Difference Built-up Index exhibited a strong negative and positive correlation respectively (Pearson’s r: between -0.79 to -0.87 and between 0.79 to 0.84 respectively). Future land use prediction project an increase (1.3 %) in built-up area from 2011 to 2021. This study recommends urban plantation and prohibition to overgrazing to check the heat effect.

scholarly journals SPACE BASED SPATIO-TEMPORAL ASSESSMENT OF LAND SURFACE TEMPERATURE IN KARUNAGAPPALLY MUNICIPALITY, A FAST GROWING CITY IN THE WESTERN COAST OF INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 773-779
Author(s):  
S. Satheendran S. ◽  
S. Chandran S. ◽  
A. Varghese
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Population ◽  
The United States ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Rapid Urbanization

<p><strong>Abstract.</strong> Urbanization is the process by which towns and cities are formed and become larger as more and more people begin living and working in central areas. According to 2001 census, the urban population of the country was 286.11 million, living in 5161 towns, which constitutes 27.81% of the total country’s population. However, the same as per 2011 census has risen to 377.16 million viz. 32.16% of the total country’s population and the number of towns has gone up to 7935. The rate of urban growth in the country is very high as compared to developed countries, and the large cities are becoming larger mostly due to continuous migration of population to these cities. India’s current urban population exceeds the whole population of the United States, the world’s third largest country. By 2050, over half of India’s population is expected to be urban dwellers. This creates enormous pressure on existing urban infrastructure.</p><p>Urbanization trend in the State of Kerala shows marked peculiarities. The main reason for urban population growth is the increase in the number of urban areas and urbanization of the peripheral areas of the existing major urban centers. However, unlike the other parts of the country the Urbanization in Kerala is not limited to the designated cities and towns. The difference between rural and urban agglomerations is very negligible as far as Kerala is concerned. The Kerala society by and large can be termed as urbanized. Kerala has been witnessing rapid urbanization since 1980.</p><p>The present study, is an attempt to analyses the extent of land use/ land cover changes in the Municipality over the years from 2012 to 2017 and land surface variation over the years from 2000 to 2017.The land use/ land cover pattern of 2012 to 2017 was extracted from High resolution images of the study area were downloaded from Google Earth API and the Land Surface Temperature changes were analyzed from the thermal bands of the Landsat Imageries.</p>

scholarly journals Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1450 ◽  
Author(s):  
Vicente García-Santos ◽  
Joan Cuxart ◽  
Daniel Martínez-Villagrasa ◽  
Maria Jiménez ◽  
Gemma Simó
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Validation Studies ◽  
Land Surface ◽  
Thermal Infrared ◽  
The United States ◽  
Stray Light ◽  
Sensor Data ◽  
United States Geological Survey ◽  
Landsat 8

After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 µm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.

scholarly journals UNDERSTANDING CURRENT AND FUTURE LANDUSE DYNAMICS WITH LAND SURFACE TEMPERATURE ALTERATIONS: A CASE STUDY OF CHANDIGARH

2018 ◽  
Vol IV-5 ◽  
pp. 79-86 ◽  
Author(s):  
G. Nimish ◽  
M. C. Chandan ◽  
H. A. Bharath
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Urban Growth ◽  
Land Surface ◽  
City Planning ◽  
Economic Value ◽  
Changing Patterns ◽  
Business As Usual ◽  
The City

<p><strong>Abstract.</strong> Conversion of pervious layer to impervious layer through unplanned urbanization has been a major cause of natural disturbances across the world. However, urbanization is considered a metric that defines the socio-economic value of the city planning and management, if unplanned leads to many serious implications on the environment such as ecological imbalance, increased concentration of pollutants, loss of bio-diversity, etc. A steep increase in population growth, migration to cities and conversion of vegetated lands into other land use classes have been prime factors in expanding urban regions at faster urban growth rates causing sprawl in the periphery and the regions adjacent to the city resulting in increased temperatures. This paper addresses two major facades of urban growth: modelling urban growth pattern with business as usual scenario and understanding the land surface temperature (LST) dynamics with changing patterns of land use. Chandigarh administrative boundary with 10 km buffer has been considered for this analysis to understand the dynamics and sprawl. The outcome indicates that built-up has increased from 3.7% in 1991 to 16.16% in 2017 and the mean LST of the study area has increased by almost 12<span class="thinspace"></span>&amp;deg;C (months of March–May). Future prediction performed shows that there would be an extensive outgrowth due to saturated infilling in the core of the city. This analysis would be helpful to planners to understand region specific growth and transitions.</p>

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