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Remote Sens. 2018, 10(1), 15; doi:10.3390/rs10010015

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

1,2
,
1,2
,
3
and
1,2,*
1
Faculty of Computer Science and Geological Survey of CUG, China University of Geosciences, Wuhan 430074, China
2
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
3
National Disaster Reduction Center of China, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Received: 2 November 2017 / Revised: 14 December 2017 / Accepted: 21 December 2017 / Published: 22 December 2017
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
View Full-Text   |   Download PDF [1943 KB, uploaded 22 December 2017]   |  

Abstract

Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines. View Full-Text
Keywords: land cover classification; open-pit mining area; remote sensing; review; fine-scale land cover classification; open-pit mining area; remote sensing; review; fine-scale
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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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Chen, W.; Li, X.; He, H.; Wang, L. A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques. Remote Sens. 2018, 10, 15.

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