scholarly journals Satellite based seasonal land use classification and change detection analysis of landsat-8 operational land imager

2017 ◽  
Vol 120 ◽  
pp. 09004
Author(s):  
Wasim Pervez ◽  
Shoab Ahmad Khan ◽  
Ejaz Hussain ◽  
Faisal Amir
Keyword(s):  
Land Use ◽  
Change Detection ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Change Detection Analysis ◽  
Detection Analysis

scholarly journals SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 31-35 ◽  
Author(s):  
A. E. Akay ◽  
B. Gencal ◽  
İ. Taş
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Short Paper ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Spatiotemporal Change ◽  
Change Detection Analysis ◽  
Flooded Forest ◽  
Detection Analysis

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

scholarly journals LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 607-612
Author(s):  
W. Pervez ◽  
S. A. Khan ◽  
E. Hussain ◽  
F. Amir ◽  
M. A. Maud
Keyword(s):  
Change Detection ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Landsat 8 ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Channel Water ◽  
Four Seasons ◽  
Bare Land ◽  
Water Mapping

This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI) for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water). OLI support vector machine (SVM) classified data for the four seasons (i.e., spring, autumn, winter, and summer) was used to change detection results of six cases: (1) winter to spring which resulted reduction in dam water mapping and increases of bushes; (2) winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3) winter to autumn which resulted increase in dam water mapping; (4) spring to summer which resulted reduction of vegetation and shallow water; (5) spring to autumn which resulted decrease of vegetation; and (6) summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis.

scholarly journals EVALUATE THE CAPABILITY OF LANDSAT8 OPERATIONAL LAND IMAGER FOR SHORELINE CHANGE DETECTION/INLAND WATER STUDIES

2017 ◽  
Vol XLII-5/W1 ◽  
pp. 145-152 ◽  
Author(s):  
W. Pervez ◽  
S. A. Khan ◽  
E. Hussain ◽  
F. Amir ◽  
M. A. Maud
Keyword(s):  
Change Detection ◽  
Seasonal Change ◽  
Support Vector ◽  
Landsat 8 ◽  
Reservoir Water ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Open Land ◽  
Water Mapping ◽  
Scrub Land

This paper explored the capability of Landsat-8 Operational Land Imager (OLI) for post classification change detection analysis and mapping application because of its enhanced features from previous Landsat series. The OLI support vector machine (SVM) classified data was successfully classified with regard to all six test classes (i.e., open land, residential land, forest, scrub land, reservoir water and waterway). The OLI SVM-classified data for the four seasons (i.e. winter, spring, summer and autumn seasons) were used for change detection analysis of six situations; situation1: winter to spring seasonal change detection resulted reduction in reservoir water mapping and increases of scrub land; situation 2: winter to summer seasonal change detection resulted increase in dam water mapping and increase of scrub land. winter to summer which resulted reduction in dam water mapping and increase of vegetation; situation 3: winter to summer seasonal change detection resulted increase in increase in open land mapping; situation 4: spring to summer seasonal change detection resulted reduction of vegetation and shallow water and increase of open land and reservoir water; situation; 5: spring to autumn seasonal change detection resulted increase of reservoir water and open land; and Situation 6: summer to autumn seasonal change detection resulted increase of open land. OLI SVM classified data found suitable for post classification change detection analysis due to its resulted higher overall accuracy and kappa coefficient.

scholarly journals Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Odile Close ◽  
Sophie Petit ◽  
Benjamin Beaumont ◽  
Eric Hallot
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Change Detection ◽  
Signal To Noise Ratio ◽  
Land Conversion ◽  
Sustainable Resources ◽  
Detection Techniques ◽  
Detection Analysis ◽  
Conversion Rates ◽  
Sentinel 2

Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the atmosphere. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study investigates the use of Sentinel-2 data for analysing lands conversion associated to Land Use, Land Use Change and Forestry sector in the Wallonia region (southern Belgium). This region is characterized by one of the lowest conversion rates across European countries, which constitutes a particular challenge in identifying land changes. The proposed research tests the most commonly used change detection techniques on a bi-temporal and multi-temporal set of mosaics of Sentinel-2 data from the years 2016 and 2018. Our results reveal that land conversion is a very rare phenomenon in Wallonia. All the change detection techniques tested have been found to substantially overestimate the changes. In spite of this moderate results our study has demonstrated the potential of Sentinel-2 regarding land conversion. However, in this specific context of very low magnitude of land conversion in Wallonia, change detection techniques appear to be not sufficient to exceed the signal to noise ratio.

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