Satellite sensor data analysis of urban surface temperatures and land use / land cover

2010 ◽  
Author(s):  
Wang Shanshan ◽  
Chen Xi ◽  
Bao An-ming ◽  
Alishir Kurban
Keyword(s):  
Land Use ◽  
Data Analysis ◽  
Land Cover ◽  
Sensor Data ◽  
Urban Surface ◽  
Land Use Land Cover ◽  
Surface Temperatures ◽  
Satellite Sensor Data ◽  
Satellite Sensor

Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh

2020 ◽  
Vol 18 ◽  
pp. 100314 ◽  
Author(s):  
Abdulla - Al Kafy ◽  
Md. Shahinoor Rahman ◽  
Abdullah-Al- Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Muhaiminul Islam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Surface Temperatures ◽  
Land Surface Temperatures

scholarly journals A COMPARATIVE STUDY OF ADVANCED LAND USE/LAND COVER CLASSIFICATION ALGORITHMS USING SENTINEL-2 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 665-670 ◽  
Author(s):  
K. Nivedita Priyadarshini ◽  
M. Kumar ◽  
S. A. Rahaman ◽  
S. Nitheshnirmal
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Unsupervised Classification ◽  
Sensor Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Land Use Land Cover ◽  
Sentinel 2A

<p><strong>Abstract.</strong> Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10<span class="thinspace"></span>m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.</p>

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