Forest-Cover Change Detection Using Support Vector Machines

2016 ◽  
pp. 212-227
Keyword(s):  
Support Vector Machines ◽  
Change Detection ◽  
Forest Cover ◽  
Support Vector ◽  
Forest Cover Change ◽  
Vector Machines

Use of a dark object concept and support vector machines to automate forest cover change analysis

2008 ◽  
Vol 112 (3) ◽  
pp. 970-985 ◽  
Author(s):  
Chengquan Huang ◽  
Kuan Song ◽  
Sunghee Kim ◽  
John R.G. Townshend ◽  
Paul Davis ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Forest Cover ◽  
Support Vector ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Object Concept ◽  
Dark Object ◽  
Vector Machines

scholarly journals Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 750 ◽  
Author(s):  
Sotiria Fragou ◽  
Kleomenis Kalogeropoulos ◽  
Nikolaos Stathopoulos ◽  
Panagiota Louka ◽  
Prashant K. Srivastava ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Forest Cover ◽  
Spectral Band ◽  
Mixed Forest ◽  
Landsat Tm ◽  
Geographical Information ◽  
Support Vector ◽  
Geoinformation Technologies ◽  
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 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.

A comparison of support vector machines and manual change detection for land-cover map updating in Massachusetts, USA

2013 ◽  
Vol 4 (9) ◽  
pp. 882-890 ◽  
Author(s):  
B. Schwert ◽  
J. Rogan ◽  
N. M. Giner ◽  
Y. Ogneva-Himmelberger ◽  
S. D. Blanchard ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Change Detection ◽  
Support Vector ◽  
Map Updating ◽  
Vector Machines ◽  
Land Cover Map

scholarly journals Spectral-Spatial Aware Unsupervised Change Detection with Stochastic Distances and Support Vector Machines

2020 ◽  
Author(s):  
Rogério Negri ◽  
Alejandro C. Frery ◽  
Wallace Casaca ◽  
Samara Azevedo ◽  
Maurício Araújo ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Change Detection ◽  
Support Vector ◽  
Vector Machines

Submitted paper to IEEE-TGRS at January 10th, 2020.

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