Analysis of Relationship between Vegetation Indices and Land Surface Temperature using GIS and Python in Middle Ganga Plain, India

2019 ◽  
Vol 2 (2) ◽  
pp. 53-61
Author(s):  
Aman Arora ◽  
Masood Siddiqui ◽  
Manish Pandey
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Temporal Distribution ◽  
Ganga Plain ◽  
Middle Ganga Plain

Global vegetation dynamics is a significant phenomenon being monitored from space. This study attempts to establish relationship among vegetation changes and land surface temperature using the data derived from satellite products in the Middle Ganga Plain, India using python programming. Ten years of MODIS Land Surface Temperature (LST) on board Terra, Normalized Difference Vegetation Index/Enhanced Vegetation Index (NDVI/EVI) (1km spatial and 8-days composite temporal resolution for LST and 250m spatial and 16-days composite for NDVI/EVI) has been used in this study. The average LST for the month of January was 23.0°C which fell to 15.7°C for the same month in 2015; whereas in March it was recorded to be 35.3°C and reduced to 32.3°C in 2015. Mean NDVI value has been recorded to be 0.44 in January 2000 which has slightly increased to reach 0.50 for the same month in 2015. For the month of September, it was recorded at 0.49 in 2000 and 0.52 for the same month in 2015. This paper attempts to analyze the spatio-temporal distribution and empirical relationship of vegetation cover and LST using Python.

Investiganting Correlation LST and Vegetation Indices Using Landsat Images for the Warmest Month: A Case Study of Iasi County

2018 ◽  
Vol 18 (1) ◽  
pp. 33-40
Author(s):  
Paul Macarof ◽  
Stefan Groza ◽  
Florian Statescu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Structural Variations ◽  
Landsat Images ◽  
Change Models

Abstract In this paper is investigating correlation between land surface temperature and vegetation indices (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index 2 - EVI2 and Modified Soil Adjusted Vegetation Index - MSAVI) using Landsat images for august, the warmest month, for study area. Iaşi county is considered as study area in this research. Study Area is geographically situated on latitude 46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. Land surface temperature (LST) can be used to define the temperature distribution at local, regional and global scale. First use of LST was in climate change models. Also LST is use to define the problems associated with the environment. A Vegetation Indices (VI) is a spectral transformation what suppose spatial-temporal intercomparisons of terrestrial photosynthetic dynamics and canopy structural variations. Landsat5 TM, Landsat7 ETM+ and Landsat8 OLI, all data were used in this study for modeling. Landsat images was taken for august 1994, 2006 and 2016. Preprocessing of Landsat 5/7/8 data stage represent that process that prepare images for subsequent analysis that attempts to compensate/correct for systematic errors. It was observed that the “mean” parameter for LST increased from 1994 to 2016 at approximately 5°C. Analyzing the data from VI, it can be assumed that the built-up area increased for the Iasi county, while the area occupied by dense vegetation has decreased. Many researches indicated that between LST and VI is a linear relationship. It is noted that the R2 values for the LST-VI correlations decrease from 1994 (i.g.R2= 0.72 for LST-NDVI) in 2016 (i.g.R2= 0.23 for LST-NDVI). In conclusion, these correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.

scholarly journals Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

2021 ◽  
Vol 13 (2) ◽  
pp. 323
Author(s):  
Liang Chen ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Chao Yang ◽  
Xiaorong Lu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Maximum Temperature Difference ◽  
Rapid Urbanization ◽  
The Impact ◽  
The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

scholarly journals Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Tahmid Anam Chowdhury ◽  
◽  
Md. Saiful Islam ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Land Use Land Cover ◽  
Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

scholarly journals The Effects of Land Use Zoning and Densification on Changes in Land Surface Temperature in Seoul

2019 ◽  
Vol 11 (24) ◽  
pp. 7056 ◽  
Author(s):  
Jae-Ik Kim ◽  
Myung-Jin Jun ◽  
Chang-Hwan Yeo ◽  
Ki-Hyun Kwon ◽  
Jun Yong Hyun
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Landsat 8 ◽  
Land Use Type ◽  
Coverage Ratio

This study investigated how changes in land surface temperature (LST) during 2004 and 2014 were attributable to zoning-based land use type in Seoul in association with the building coverage ratio (BCR), floor area ratio (FAR), and a normalized difference vegetation index (NDVI). We retrieved LSTs and NDVI data from satellite images, Landsat TM 5 for 2004 and Landsat 8 TIRS for 2014 and combined them with parcel-based land use information, which contained data on BCR, FAR, and zoning-based land use type. The descriptive analysis results showed a rise in LST for the low- and medium-density residential land, whereas significant LST decreases were found in high-density residential, semi-residential, and commercial areas over the time period. Statistical results further supported these findings, yielding statistically significant negative coefficient values for all interaction variables between higher-density land use types and a year-based dummy variable. The findings appear to be related to residential densification involving the provision of more high-rise apartment complexes and government efforts to secure more parks and green spaces through urban redevelopment and renewal projects.

scholarly journals Evaluating the Temperature Difference Parameter in the SSEBop Model with Satellite-Observed Land Surface Temperature Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1947 ◽  
Author(s):  
Lei Ji ◽  
Gabriel B. Senay ◽  
Naga M. Velpuri ◽  
Stefanie Kagone
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Temperature Difference ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Data ◽  
Climate Data ◽  
Modis Data ◽  
South Central

The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference (dT) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because dT is derived from the average-sky net radiation based primarily on climate data, validation of the dT estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop dT estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed dT (dTo) was computed from the LST difference between hot (NDVI < 0.25) and cold (NDVI > 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create dTo timeseries in the West Coast and South-Central regions. The comparison of dTo and modeled dT (dTm) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the dTm estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.

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 Application of Geo-informatics Technology to Access the Surface Temperature Using LANDSAT 8 OLI/TIRS Satellite Data: A Case Study in Ampara District in Sri Lanka

2020 ◽  
Author(s):  
Ibra Lebbe Mohamed Zahir
Keyword(s):  
Sri Lanka ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Land Surface Emissivity

Land Surface Temperature is a one of the key variable of Global climate changes and model which estimate radiating budget in heat balance as control of climate model. It is a major influenced factor by the ability of the surface emissivity. In this study, were used Landsat 8 satellite image that have Operational Land Imager and Thermal Infrared Sensor to calculate Land Surface Temperature through geospatial technology over Ampara district, Sri Lanka. The Land Surface Temperature was estimated with respect to Land Surface Emissivity and Normalized Difference Vegetation Index values determined from the Red and Near Infrared channels. Land Surface Emissivity was processed directly by the thermal Infrared bands. Pixels based calculation were used to effort at LANDSAT 8 images that thermal Band 10 various dates in this study. The results were achievable to compute Normalized Difference Vegetation Index, Land Surface Emissivity, and Land Surface Temperature with applicable manner to compare with land use/ land cover data. It determines and predicts the changes of surface temperature to favorable to decision making process for the society. Study area faces seasonal drought in Sri Lanka, the prediction method that how land can be efficiently used with the present condition. Therefore, the Land Surface Temperature estimation can prove whether new irrigation systems for agricultural activities or can transformed source of energy into useful form that introducing solar hubs for energy production in future.

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