Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines
The rapid advent in geoinformation technologies, such as Earth Observation (EO) and Geographical Information Systems (GIS), has made it possible to observe and monitor the Earth’s environment on variable geographical scales and analyze those changes in both time and space. This study explores the sy...
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doaj-a8049d9162f7448a970146c7c3e2010a2020-11-25T03:33:04ZengMDPI AGForests1999-49072020-07-011175075010.3390/f11070750Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector MachinesSotiria Fragou0Kleomenis Kalogeropoulos1Nikolaos Stathopoulos2Panagiota Louka3Prashant K. Srivastava4Sotiris Karpouzas5Dionissios P. Kalivas6George P. Petropoulos7Forestry Division of Megara, Minoos 12, 19100 Megara, GreeceDepartment of Geography, Harokopio University of Athens, El. Venizelou St., 70, Kallithea, 17671 Athens, GreeceSchool of Mining and Metallurgical Engineering, Sector of Geological Sciences, Laboratory of Technical Geology and Hydrogeology, National Technical University of Athens, 15780 Athens, GreeceLab Mineralogy-Geology, Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, GreeceRemote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, IndiaForestry Division of Western Attica, Department of Forest Mapping, Palikaridi 19-21, 12243 Egaleo, GreeceDepartment of Natural Resources Management & Agricultural Engineering, Soil Science Laboratory, Agricultural University of Athens, 118 55 Athens, GreeceDepartment of Geography, Harokopio University of Athens, El. Venizelou St., 70, Kallithea, 17671 Athens, GreeceThe rapid advent in geoinformation technologies, such as Earth Observation (EO) and Geographical Information Systems (GIS), has made it possible to observe and monitor the Earth’s environment on variable geographical scales and analyze those changes in both time and space. This study explores the synergistic use of Landsat EO imagery and Support Vector Machines (SVMs) in obtaining Land Use/Land Cover (LULC) mapping and quantifying its spatio-temporal changes for the municipality of Mandra–Idyllia, Attica Region, Greece. The study area is representative of typical Mediterranean landscape in terms of physical structure and coverage of species composition. Landsat TM (Thematic Mapper) images from 1993, 2001 and 2010 were acquired, pre-processed and classified using the SVMs classifier. A total of nine basic classes were established. Eight spectral band ratios were created in order to incorporate them in the initial variables of the image. For validating the classification, in-situ data were collected for each LULC type during several field surveys that were conducted in the area. The overall classification accuracy for 1993, 2001 and 2010 Landsat images was reported as 89.85%, 91.01% and 90.24%, respectively, and with a statistical factor (K) of 0.96, 0.89 and 0.99, respectively. The classification results showed that the total extent of forests within the studied period represents the predominant LULC, despite the intense human presence and its impacts. A marginal change happened in the forest cover from 1993 to 2010, although mixed forest decreased significantly during the studied period. This information is very important for future management of the natural resources in the studied area and for understanding the pressures of the anthropogenic activities on the natural environment. All in all, the present study demonstrated the considerable promise towards the support of geoinformation technologies in sustainable environmental development and prudent resource management.https://www.mdpi.com/1999-4907/11/7/750geoinformationremote sensingLandsat TMLULCchange detectionsupport vector machines |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sotiria Fragou Kleomenis Kalogeropoulos Nikolaos Stathopoulos Panagiota Louka Prashant K. Srivastava Sotiris Karpouzas Dionissios P. Kalivas George P. Petropoulos |
spellingShingle |
Sotiria Fragou Kleomenis Kalogeropoulos Nikolaos Stathopoulos Panagiota Louka Prashant K. Srivastava Sotiris Karpouzas Dionissios P. Kalivas George P. Petropoulos Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines Forests geoinformation remote sensing Landsat TM LULC change detection support vector machines |
author_facet |
Sotiria Fragou Kleomenis Kalogeropoulos Nikolaos Stathopoulos Panagiota Louka Prashant K. Srivastava Sotiris Karpouzas Dionissios P. Kalivas George P. Petropoulos |
author_sort |
Sotiria Fragou |
title |
Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines |
title_short |
Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines |
title_full |
Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines |
title_fullStr |
Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines |
title_full_unstemmed |
Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines |
title_sort |
quantifying land cover changes in a mediterranean environment using landsat tm and support vector machines |
publisher |
MDPI AG |
series |
Forests |
issn |
1999-4907 |
publishDate |
2020-07-01 |
description |
The rapid advent in geoinformation technologies, such as Earth Observation (EO) and Geographical Information Systems (GIS), has made it possible to observe and monitor the Earth’s environment on variable geographical scales and analyze those changes in both time and space. This study explores the synergistic use of Landsat EO imagery and Support Vector Machines (SVMs) in obtaining Land Use/Land Cover (LULC) mapping and quantifying its spatio-temporal changes for the municipality of Mandra–Idyllia, Attica Region, Greece. The study area is representative of typical Mediterranean landscape in terms of physical structure and coverage of species composition. Landsat TM (Thematic Mapper) images from 1993, 2001 and 2010 were acquired, pre-processed and classified using the SVMs classifier. A total of nine basic classes were established. Eight spectral band ratios were created in order to incorporate them in the initial variables of the image. For validating the classification, in-situ data were collected for each LULC type during several field surveys that were conducted in the area. The overall classification accuracy for 1993, 2001 and 2010 Landsat images was reported as 89.85%, 91.01% and 90.24%, respectively, and with a statistical factor (K) of 0.96, 0.89 and 0.99, respectively. The classification results showed that the total extent of forests within the studied period represents the predominant LULC, despite the intense human presence and its impacts. A marginal change happened in the forest cover from 1993 to 2010, although mixed forest decreased significantly during the studied period. This information is very important for future management of the natural resources in the studied area and for understanding the pressures of the anthropogenic activities on the natural environment. All in all, the present study demonstrated the considerable promise towards the support of geoinformation technologies in sustainable environmental development and prudent resource management. |
topic |
geoinformation remote sensing Landsat TM LULC change detection support vector machines |
url |
https://www.mdpi.com/1999-4907/11/7/750 |
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