scholarly journals Monitoring Spatiotemporal Environmental Changes in Dakahlia Governorate, Egypt Using Landsat Imagery

2021 ◽  
Vol 11 (3) ◽  
pp. 3843-3853
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Environmental Changes ◽  
Landsat Imagery ◽  
High Rate ◽  
Wild Plant ◽  
Population Increase ◽  
Moisture Index

Dakahlia Governorate (Egypt) is considered an important area for plant diversity, facing three major problems, namely: high rate of population increase, limited renewable natural resources, limited production of food, forage, and raw materials for industrial purposes. Two multispectral Landsat imageries (TM and OLI 8) on 6th of March 1999 and 19th of March 2019 were calibrated and processed to produce LULC, different spectral indices (land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference Built-up Index (NDBI), normalized difference moisture index (NDMI), modified normalized difference of water index (MNDWI) and normalized difference salinity index (NDSI)). Furthermore, the surface temperature was obtained by processing the thermal bands of the Landsat image. Applying the indices on the raw digital numbers to produce a comparative study with the resulted values to detect the environmental change over the two last decades. According to the analysis, the results detected a loss in the vegetation areas and the bare land in favor of urban areas and water bodies to fulfill the requirements of the residents and the developers. That was confirmed with the analysis of the NDBI, NDSI, and LST that showed an increase as a result of urban sprawling. On the other hand, the NDVI explained that the uncontrolled urban sprawl caused the loss of agricultural lands. There was a slight expansion in the wild plant habitats, represented mainly by sparse class and partially by moderately dense class. Accordingly, this could be a result of the mutation in the habitats to favor the spreading of the moderate and sparse vegetation class, which MNDWI and NDMI confirmed.

scholarly journals Spatial pattern of Land Surface Temperature over Umuahia North and Bende LGA, Abia State, Nigeria

2019 ◽  
Vol 3 (2) ◽  
pp. 210-217
Author(s):  
U. I. Uchendu ◽  
C. Kanu ◽  
K. C. Kanu ◽  
C. I. Mpamah
Keyword(s):  
Relative Humidity ◽  
Surface Temperature ◽  
Spatial Pattern ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Heat Islands ◽  
Vegetal Cover

This study evaluated the Spatial pattern of Land Surface Temperature (LST) over Umuahia North (Urban Area) and Bende LGA (Rural Area), Abia State, Southeast Nigeria. LANDSAT Imagery spanning Row 056 and Path 188, with 30m spatial resolution was captured on the 17th of May, 2018. Temperature and relative humidity were measured using a thermometer and multi-purpose Hydro-20 - 100 % model. Eight measurements were taken for each parameter at an interval of 8 hours at an elevation of 1.5m above the ground. Coordinates and elevation of the points were captured using a Garmin Handheld GPS. Data obtained were imported in compatible formats with ArcGIS 10.5 and the values for the un-sampled locations within the study area was determined through the interpolation of the collected data. A subset covering the study area was extracted for bands 1,2,3,4 and 5. Bands 1, 2 and 3 which are visible bands were used in generating a true colour composite image of the study area; the bands 4 and 5 which are not visible bands were used for the NDVI (Normalized Differential Vegetation Index). Result showed that Bende LGA had a vegetal cover of 45,741.26hectares out of a total of 60,152.76 hectares while Umuahia North had 19,689.09 hectares of vegetal cover out of a total of 24,459.75 hectares. Umuahia North had an average daily temperature of 31.309̊ C while Bende had 27.405̊ C. The average relative humidity in Bende LGA was 82.37% while Umuahia North was 67.274%. In conclusion, the study showed the existence of heat islands in the urban areas in Umuahia North LGA which was characterized by higher temperature but lower relative humidity. The heat island could be attributed to the gradual loss of vegetation cover and the increase in built-up environments in Umuahia North LGA.

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 SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

2020 ◽  
Vol 11 (2) ◽  
pp. 94-110 ◽  
Author(s):  
Syed Riad Morshed Riad Morshed ◽  
Md. Abdul Fattah ◽  
Asma Amin Rimi ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Moisture Index ◽  
Type Transformation ◽  
Spatial Environment ◽  
Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

scholarly journals Quantification of Urban Heat Island-Induced Contribution to Advance in Spring Phenology: A Case Study in Hangzhou, China

2021 ◽  
Vol 13 (18) ◽  
pp. 3684
Author(s):  
Yingying Ji ◽  
Jiaxin Jin ◽  
Wenfeng Zhan ◽  
Fengsheng Guo ◽  
Tao Yan
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Heat Island ◽  
Spring Phenology ◽  
Urban Heat ◽  
The Difference

Plant phenology is one of the key regulators of ecosystem processes, which are sensitive to environmental change. The acceleration of urbanization in recent years has produced substantial impacts on vegetation phenology over urban areas, such as the local warming induced by the urban heat island effect. However, quantitative contributions of the difference of land surface temperature (LST) between urban and rural (ΔLST) and other factors to the difference of spring phenology (i.e., the start of growing season, SOS) between urban and rural (ΔSOS) were rarely reported. Therefore, the objective of this study is to explore impacts of urbanization on SOS and distinguish corresponding contributions. Using Hangzhou, a typical subtropical metropolis, as the study area, vegetation index-based phenology data (MCD12Q2 and MYD13Q1 EVI) and land surface temperature data (MYD11A2 LST) from 2006–2018 were adopted to analyze the urban–rural gradient in phenology characteristics through buffers. Furthermore, we exploratively quantified the contributions of the ΔLST to the ΔSOS based on a temperature contribution separation model. We found that there was a negative coupling between SOS and LST in over 90% of the vegetated areas in Hangzhou. At the sample-point scale, SOS was weakly, but significantly, negatively correlated with LST at the daytime (R2 = 0.2 and p < 0.01 in rural; R2 = 0.14 and p < 0.05 in urban) rather than that at nighttime. Besides, the ΔSOS dominated by the ΔLST contributed more than 70% of the total ΔSOS. We hope this study could help to deepen the understanding of responses of urban ecosystem to intensive human activities.

scholarly journals Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

2020 ◽  
Vol 12 (7) ◽  
pp. 1082 ◽  
Author(s):  
Jianhui Xu ◽  
Feifei Zhang ◽  
Hao Jiang ◽  
Hongda Hu ◽  
Kaiwen Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Independent Variables

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

scholarly journals THE EFFECTS OF BUILT-UP AND GREEN AREAS ON THE LAND SURFACE TEMPERATURE OF THE KUALA LUMPUR CITY

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 107-112 ◽  
Author(s):  
N. A. Isa ◽  
W. M. N. Wan Mohd ◽  
S. A. Salleh
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Urban Climate ◽  
Geographical Information ◽  
Landsat 8 ◽  
Kuala Lumpur ◽  
Green Areas

A common consequence of rapid and uncontrollable urbanization is Urban Heat Island (UHI). It occurs due to the negligence on climate behaviour which degrades the quality of urban climate condition. Recently, addressing urban climate in urban planning through mapping has received worldwide attention. Therefore, the need to identify the significant factors is a must. This study aims to analyse the relationships between Land Surface Temperature (LST) and two urban parameters namely built-up and green areas. Geographical Information System (GIS) and remote sensing techniques were used to prepare the necessary data layers required for this study. The built-up and the green areas were extracted from Landsat 8 satellite images either using the Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI) or Modified Normalize Difference Water Index (MNDWI) algorithms, while the mono-window algorithm was used to retrieve the Land Surface Temperature (LST). Correlation analysis and Multi-Linear Regression (MLR) model were applied to quantitatively analyse the effects of the urban parameters. From the study, it was found that the two urban parameters have significant effects on the LST of Kuala Lumpur City. The built-up areas have greater influence on the LST as compared to the green areas. The built-up areas tend to increase the LST while green areas especially the densely vegetated areas help to reduce the LST within an urban areas. Future studies should focus on improving existing urban climatic model by including other urban parameters.

scholarly journals Impacts of Vegetation and Topography on Land Surface Temperature Variability over the Semi-Arid Mountain Cities of Saudi Arabia

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 762 ◽  
Author(s):  
Ahmed Ali Bindajam ◽  
Javed Mallick ◽  
Saeed AlQadhi ◽  
Chander Kumar Singh ◽  
Hoang Thi Hang
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Environmental Conservation ◽  
Mountain Areas ◽  
Extreme High Temperature ◽  
Semi Arid

Land surface temperature (LST) can fully reflect the water–heat exchange cycle of the earth surface that is important for the study of environmental change. There is little research on LST in the semi-arid region of Abha-Khamis-Mushyet, which has a complex topography. The study used LST data, retrieved from ASTER data in semi-arid mountain areas and discussed its relationship with land use/land cover (LULC), topography and the normalized difference vegetation index (NDVI). The results showed that the LST was significantly influenced by altitude and corresponding LULC type. In the study area, during the summer season, extreme high-temperature zones were observed, possibly due to dense concrete surfaces. LST among different types of land use differed significantly, being the highest in exposed rocky areas and built-up land, and the lowest in dense vegetation. NDVI and LST spatial distributions showed opposite trends. The LST–NDVI feature space showed a unique ABC obtuse-angled triangle shape and showed an overall negative linear correlation. In brief, the LST could be retrieved well by the emissivity derived NDVI TES method, which relied on upwelling, downwelling, and transmittance. In addition, the LST of the semi-arid mountain areas was influenced by elevation, slope zenith angle, aspect and LULC, among which vegetation and elevation played a key role in the overall LST. This research provides a roadmap for land-use planning and environmental conservation in mountainous urban areas.

scholarly journals A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 389-394 ◽  
Author(s):  
R. Bala ◽  
R. Prasad ◽  
V. P. Yadav ◽  
J. Sharma
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sensor Data ◽  
Landsat 8 ◽  
Temperature Relation

<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>

scholarly journals SPATIOTEMPORAL MULTI-SATELLITE BIOPHYSICAL DATA ANALYSIS OF THE EFFECT OF URBANIZATION ON LAND SURFACE AND AIR TEMPERATURE IN BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 47-54
Author(s):  
A. Baloloy ◽  
R. R. Sta. Ana ◽  
J. A. Cruz ◽  
A. C. Blanco ◽  
N. V. Lubrica ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
High Rate ◽  
Landsat 8 ◽  
Landsat Images

Abstract. Urbanization can be observed through the occurrence of land-use changes as more land is being transformed and developed for urban use. One of the Philippine cities with high rate of urbanization is Baguio City, known for having a subtropical highland climate. To understand the spatiotemporal relationship between urbanization and temperature, this study aims to analyze the correlation of urban extent with land surface and air temperature in Baguio City using satellite-based built-up extents, land surface temperature (LST) maps, and weather station-recorded air temperature data. Built-up extent layers were derived from three satellite images: Landsat, RapidEye and PlanetScope. Land-use land cover (LULC) maps were generated from Landsat images using biophysical indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI); while RapidEye and PlanetScope built-up extent maps were generated by applying the visible green-based built-up index (VgNIR-BI). Mean LST values from 1988 to 2018 during the dry and wet seasons were calculated from the Landsat-retrieved surface temperature layers. The result of the study shows that the increase in the built-up extent significantly intensified the LST during the dry season which was observed in all satellite data-derived built-up maps: RapidEye+PlanetScope (2012–2018; r = 0.88), Landsat 8 (2012–2018; r = 0.63) and Landsat 5,7,8 (1988–2018; r = 0.61). The main LST hotspots were detected inside the Central Business District where it expanded gradually from year 1998 (43 ha) to 2011 (83 ha), but have increased extensively within the years 2014 to 2019 (305 ha). On average, 98.5% of the hotspots detected from 1995 to 2019 are within the equivalent built-up area.

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