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.
Highlights
Fine-scale land cover classification in open-pit mine areas (LCCMA) is important for a comprehensive understanding of the influence of mining activities on the regional geo-environment and for environmental impact assessment procedure at local scales around the world
This paper reviews the latest developments in fine-scale LCCMA using remote sensing techniques, focusing mainly on the proposed land cover thematic resolution, significant characteristics of MA, and the employed remote sensing data sources, features, feature selection methods, and classification algorithms
The conclusions are as follows: (1) the existing classification representation model of remote sensing information cannot accurately describe LCCMA characteristics; (2) there is a lack of sensitive feature combinations for LCCMA; and (3) there is a lack of efficient classification algorithms for the LCCMA
Summary
During the process of exploration, development, and beneficiation, open-pit mining areas (hereafter MA) may experience denudation, handling, and accumulation. The method (eventually integrated with results by object-oriented classification) of remote sensing data supported by field checks provide land cover/use datasets with high accuracy This integrated approach is straightforward when multitemporal information have to be collected, managed and analyzed. Land cover interpretation of surface mine areas has been performed using remote sensing techniques for almost 40 years, existing research often merges mining system elements into a single class, or simple subclasses such as developed land or construction land. Fine-scale land cover classification of mining areas is vital
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