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

Abstract

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.

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

“…In general, the developed model can contribute to FLCC and LCC in CMALs, and more deep learning algorithm-based models should be investigated in future for the application of FLCC and LCC in complex landscapes. 2 of 28 lands, and Chen et al [13] reviewed FLCC of open pit mining landscapes by using remote sensing data. However, the FLCC of CMALs has not been investigated.…”

“…However, the FLCC of CMALs has not been investigated. This is significant for the scientific management of mines and agricultural development.Three significant landscape-specific characteristics seriously limit the accuracy of FLCC of CMALs [12][13][14]: significant three-dimensional terrain [15,16]; strong spatio-temporal variability [17,18]; and spectral-spatial homogeneity. Methods to reduce these characteristics were identified.…”

“…Methods to reduce these characteristics were identified. These include the extraction of effective features [12,13,[19][20][21] and feature sets [13,14,22] with feature reduction (FR) [12] and feature selection (FS) [12][13][14]23] and the construction of powerful classification models [13].Many low-level handcrafted features were proven to be effective for the LCC of CMALs [12-14] (e.g., texture and filter features and topographic information). Moreover, fusion of these features is necessary and one of the most effective approaches for this purpose.…”

Land use/Land Cover Change Assessment of Ysr Kadapa District, Andhra Pradesh, India using Irs Resourcesat-1/2 Liss Iii Multi-Temporal Open Source Data

“…It forms pits, transit sites, solid waste and miscellaneous mining elements. When these elements are superimposed with geo-environment an alternations occur in the LULC, Topography, Land Surface Temperature (LST) and environment [5].…”

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

“…However, extensive literature on the subject reveals that a large proportion of the new sinkholes is human induced (e.g., Bui, Panahi, et al, ; Parise, ). Anthropogenic activities, such as groundwater withdrawal, irrigation, dewatering for mining, or diversion of river flow, are increasing the frequency of sinkholes in many regions worldwide (Chen, Li, He, & Wang, ; Filippi & Bosák, ; Gutiérrez, Parise, De Waele, & Jourde, ; Parise, ; Tien Bui et al, ; Vattano et al, ). The formation of sinkholes involves the reduction of agricultural land, may significantly compromise safety, causes severe damage to infrastructure, and, in extreme cases, may result in the abandonment of agricultural areas and irrigation plans (Gunn, ; Hyland, ).…”