Detection of Opencast Mining Sites from Multispectral Satellite Images

2021 ◽  
pp. 161-171
Author(s):  
Jorge Mario Londoño-Peláez
Author(s):  
I. Statham ◽  
C. Golightly ◽  
G. Treharne

AbstractThe Department of the Environment and the Welsh Office jointly sponsored a South Wales Desk Study into the feasibility of producing thematic maps of the Mining subsidence risk for planners. A method was to be developed and tested for a 25 km2 Pilot Area centred on Ebbw Vale.The study was started by collecting geological and mining data for the Pilot Area and compiling this information on a map. It was then intended to prepare the mining subsidence map from this map by back analysing subsidence incidents to determine the hazardous areas. This simple approach could not be followed for two reasons. First, the record of mining was incomplete and it was therefore necessary to define ‘worked’ seams and areas from the total historical record and not only from the surviving mine plans. Secondly, insufficient subsidence incidents were discovered in the Pilot Area to carry out meaningful back analysis.The study was extended to collect all readily available records of subsidence incidents throughout the Coalfield. A total of 388 were traced, mainly dating from 1960. About threequarters were collapses through superficial materials and were either close to the outcrop or above mine entries, the remaining 25% propagated to the ground surface through rock. The upper limit of migration was typically 8 times the extracted height where the dip was shallow, incrasing to as much as 18 times for steep dips. Selected grout contracts and opencast mining sites were also examined to gain information on the present condition of old workings and to study the ‘reputations’ of the various seams for voids.The results of the coalfield study were applied to the Pilot Area to produce a 1:10 000 scale Development Advice Map, ‘Mining Subsidence for use by developers and planners’. The map is zoned to show areas where specialist advice is necessary to support a planning application and it also shows all mine entries traced by the desk study. The method of production is general for the South Wales Coalfield and further maps could be prepared, using the Coalfield Study, quickly and cheaply.


2021 ◽  
Vol 1 (1) ◽  
pp. 8-15
Author(s):  
Pier Matteo Barone ◽  
Rosa Maria Di Maggio ◽  
Silvia Mesturini

Despite widespread concern over missing persons, there has always been little clarity on what the word “missing” means. Although the category of young runaways is, indeed, an important cluster, other popular concepts related to disappearances describe a portion of missing persons. Thus, the following question persists: What exactly does “missing” mean? In this brief communication, we would like to open a discussion about the social phenomenon of missing persons and the consequent deployment of people and techniques to find those persons. In particular, the benefits of some forensic geoarchaeological approaches that are not yet fully standardized will be highlighted, such as geographic profiling and the use of multispectral satellite images, in order to provide materials for future searching protocols.


1986 ◽  
Vol 123 (4) ◽  
pp. 393-403 ◽  
Author(s):  
S. M. Berhe ◽  
D. A. Rothery

AbstractInteractive digital processing of multispectral satellite images (Landsat MSS) using principal components transformations and spatial filtering has clarified the position of continuous sutures linking apparently isolated Pan African (late Proterozoic) ophiolites. These have been field-checked and an arrangement of Pan African suture zones is proposed. Spatial filtering has also highlighted faults with various trends which can be related to the late Precambrian tectonics of the Horn of Africa region.


2018 ◽  
Vol 10 (10) ◽  
pp. 1555 ◽  
Author(s):  
Caio Fongaro ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Lucas Safanelli ◽  
Wanderson Mendes ◽  
...  

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.


2020 ◽  
Vol 12 (15) ◽  
pp. 2353
Author(s):  
Henning Heiselberg

Classification of ships and icebergs in the Arctic in satellite images is an important problem. We study how to train deep neural networks for improving the discrimination of ships and icebergs in multispectral satellite images. We also analyze synthetic-aperture radar (SAR) images for comparison. The annotated datasets of ships and icebergs are collected from multispectral Sentinel-2 data and taken from the C-CORE dataset of Sentinel-1 SAR images. Convolutional Neural Networks with a range of hyperparameters are tested and optimized. Classification accuracies are considerably better for deep neural networks than for support vector machines. Deeper neural nets improve the accuracy per epoch but at the cost of longer processing time. Extending the datasets with semi-supervised data from Greenland improves the accuracy considerably whereas data augmentation by rotating and flipping the images has little effect. The resulting classification accuracies for ships and icebergs are 86% for the SAR data and 96% for the MSI data due to the better resolution and more multispectral bands. The size and quality of the datasets are essential for training the deep neural networks, and methods to improve them are discussed. The reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.


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