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ISPRS Int. J. Geo-Inf. 2016, 5(5), 57; doi:10.3390/ijgi5050057

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

1
Department of Geography, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
2
Department of Geography and Regional Research, University of Vienna, A-1010 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Qiming Zhou, Zhilin Li and George P. Petropoulos
Received: 12 March 2016 / Revised: 13 April 2016 / Accepted: 22 April 2016 / Published: 28 April 2016
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
View Full-Text   |   Download PDF [2991 KB, uploaded 28 April 2016]   |  

Abstract

The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future. View Full-Text
Keywords: GEOBIA; data mining; machine learning; Landsat GEOBIA; data mining; machine learning; Landsat
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Minaei, M.; Kainz, W. Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran. ISPRS Int. J. Geo-Inf. 2016, 5, 57.

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