Detection and Monitoring of Small-Scale Diamond and Gold Mining Dredges Using Synthetic Aperture Radar on the Kadéï (Sangha) River, Central African Republic

Alessi, Marissa A.; Chirico, Peter G.; Sunder, Sindhuja; O’Pry, Kelsey L.

doi:10.3390/rs15040913

Cited by 6 publications

(10 citation statements)

References 26 publications

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“…Among these, one site undergoes land leveling, while the remaining six sites are associated with the transportation of coal gangue. In addition, the interferometric decorrelation caused by excessive deformation is an important characteristic of mine activities [40][41][42][43][44][45]. It is necessary to utilize optical imagery for the identification of such patches.…”

Section: Mining Activities Analysis Combined Insar With Multi-tempora...mentioning

confidence: 99%

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An Approach for Monitoring Shallow Surface Outcrop Mining Activities Based on Multisource Satellite Remote Sensing Data

Li,

Wang,

Wang

et al. 2023

Remote Sensing

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Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing.

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Section: Mining Activities Analysis Combined Insar With Multi-tempora...mentioning

confidence: 99%

“…by excessive deformation is an important characteristic of mine activities [40][41][42][43][44][45]. It is necessary to utilize optical imagery for the identification of such patches.…”

Section: Mining Activities Analysis Combined Insar With Multi-tempora...mentioning

confidence: 99%

An Approach for Monitoring Shallow Surface Outcrop Mining Activities Based on Multisource Satellite Remote Sensing Data

Li,

Wang,

Wang

et al. 2023

Remote Sensing

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“…Ammirati, et al, (Ammirati et al, 2020) in his study applied Differential Interferometric Synthetic Aperture Radar DInSAR technique to monitor surface deformations caused by subsurface artisanal gold mining. Alessi, et al, 2023(Alessi et al, 2023) is one of the two studies that used SAR images in the study of impact of artisanal mining activities. However, in their study they only attempted to detect the river dredging machine used in the dredging of the river for alluvial gold deposit where they adopted a fairly straight-forward techniques of search for unidentified marine objects (SUMO).…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

Detecting and Monitoring Artisanal Mining Operations in Semi-Arid Terrain Using Multitemporal SAR Data for InSAR Coherence Estimation and Unsupervised Classification

Chike,

Balz

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The impact of artisanal mining in sub-Saharan Africa and other developing countries are often huge as they leave trails of environmental and socio-economic effects on the people and the environment. 123 Multi-temporal SAR data acquired between 2019 and 2023 was used. SARPROZ was used to co-register the images and other preprocessing steps like radiometric and geometric correction to transform the images from SAR coordinates to geographic coordinates after generating all the interferograms. A stack of the 122 coherence map was created. Unsupervised classification was implemented on the stack. Principal component analysis of dimensionality reductions method yielded a far better result than the other unsupervised cluster methods attempted, it showed a very high classification accuracy of the terrain. The principal component analysis worked by computing the covariance matrix of the stacked coherence map then performed eigen-decomposition on it to yield eigenvectors and eigenvalues, The eigenvectors corresponding to the largest eigenvalues represent the principal components. These principal components capture the directions of maximum variance, and the eigenvectors provides a reduced-dimensional representation of the image stack which is then used to reconstruct an approximation of the original image that captures the essential features of the original SAR data. Backscatter intensity of the SAR images processed for this study period for unsupervised change detection and land cover classification, delineated the different features and classes based on long term coherence values.

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“…The synthetic aperture radar (SAR) is an important remote sensing technology capable of all-weather and all-time imaging, widely utilized in fields including natural environment monitoring, agricultural mapping, and change detection [1][2][3][4][5]. High-resolution wideswath (HRWS) imaging has emerged as a critical developmental direction for spaceborne SAR systems [6].…”

Section: Introductionmentioning

confidence: 99%

A Sparse SAR Imaging Method for Low-Oversampled Staggered Mode via Compound Regularization

Liu,

Pan,

Zhu

et al. 2024

Remote Sensing

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High-resolution wide-swath (HRWS) imaging is the research focus of the modern spaceborne synthetic-aperture radar (SAR) imaging field, with significant relevance and vast application potential. Staggered SAR, as an innovative imaging system, mitigates blind areas across the entire swath by periodically altering the radar pulse repetition interval (PRI), thereby extending the swath width to multiples of that achievable by conventional systems. However, the staggered mode introduces inherent challenges, such as nonuniform azimuth sampling and echo data loss, leading to azimuth ambiguities and substantially impacting image quality. This paper proposes a sparse SAR imaging method for the low-oversampled staggered mode via compound regularization. The proposed method not only effectively suppresses azimuth ambiguities arising from nonuniform sampling without necessitating the restoration of missing echo data, but also incorporates total variation (TV) regularization into the sparse reconstruction model. This enhances the accurate reconstruction of distributed targets within the scene. The efficacy of the proposed method is substantiated through simulations and real data experiments from spaceborne missions.

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Detection and Monitoring of Small-Scale Diamond and Gold Mining Dredges Using Synthetic Aperture Radar on the Kadéï (Sangha) River, Central African Republic

Cited by 6 publications

References 26 publications

An Approach for Monitoring Shallow Surface Outcrop Mining Activities Based on Multisource Satellite Remote Sensing Data

An Approach for Monitoring Shallow Surface Outcrop Mining Activities Based on Multisource Satellite Remote Sensing Data

Detecting and Monitoring Artisanal Mining Operations in Semi-Arid Terrain Using Multitemporal SAR Data for InSAR Coherence Estimation and Unsupervised Classification

A Sparse SAR Imaging Method for Low-Oversampled Staggered Mode via Compound Regularization

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