A1 - Total Land Use by Mining and Milling topographical footprint

Transcription

1 A. LAND-USE A1 - Total Land Use by Mining and Milling topographical footprint Topographical Footprint: The total areal footprint (2-dimensional) used by the mine/mill is an overarching proxy for a variety of environmental and social impacts. It includes the technical infrastructure, places for spoil heaps & dumps, active mining/milling area & not closed/remediated parts. The assumption is that the larger the area the larger the impact with respect to defined administrative boundaries or a defined type of landscape. Requirements Mono-temporal mapping of the selected land use type at either small scale (for regions, provinces, etc.) or large scale (for few individual mines/mills). Variable(s) to be determined Data acquisition Source of information Total area used by mining and milling. Cadastral information in digital form (GIS), topographic maps or land use coverage available from international, national or provincial regulatory bodies. Land use classifications derived from remote sensing imagery. Methods & Standards Suggested sensor systems Thematic mapping approach: Manual or automated land-use extraction by visual interpretation of remote sensing imagery. Image classification routines (supervised & unsupervised) available within most modern software packages. Standardized sampling strategies for input data & validation procedures are well established. Geometric delineation approach: Automated or manual interpretation of SAR or high spatial resolution optical imagery. Thematic mapping approach: Small scale: multispectral optical sensors Large scale: high resolution optical sensors Geometric delineation approach: High spatial resolution SAR or optical sensors.

2 Pre-processing & auxiliary data For thematic classification approaches atmospheric pre-processing might be necessary. Using an automatic classification approach, information input for training the classifier and information for validation are needed. Caveats: Misclassification due to spectral similarities (e.g. bare soils, places with geo-technical activities). Additional level of detail when describing physical properties of the parcels would require hyperspectral remote sensing data. Examples Land cover classification from Landsat TM + SPOT imagery, West Rand, South Africa BRGM CGS Yellow: mining activities Green: agriculture Dark grey: veld (savannah) Light grey: residential areas Olive: urban and townships Light blue: surface waters Dark blue: wetlands Red: irrigation and trees

3 Assessment of land use classification (from Aster Imagery) with IKONOS very high resolution image and identification of classified surface features BRGM CGS (Cevrel et al. 2005) Introducing morphology into image classification minimises misclassification due to spectral convergence BRGM Blue : metal roofs, red : tailings dams and heaps

4 A2 - Mining Land Use Intensity Topographical footprint vs. amount of extracted products: A time series of this indicator gives an impression about the space occupied by the mining and milling/energy conversion operation vs. the amount of end product that leaves the operation as marketable product. Changes in space intensity can point to less efficient residues management, lower quality of ore/coal, or deeper mining required. Requirements Multi-temporal mapping of the selected land use (see A1). Multi-temporal statistics on amount of extracted products. Variable(s) to be determined Data acquisition Source of information Total area used by mining and milling. Amount of extracted product. Cadastral information in digital form (GIS), topographic maps or land use coverage available from international, national or provincial regulatory bodies. Land use classifications derived from remote sensing imagery. Amount of extracted products to be provided by the operator or authorities. Methods & Standards Same as indicator A1. Suggested sensor systems Pre-processing & auxiliary data Same as indicator A1; multi-temporal approaches require sensors imaging the area of interest at sufficiently high repetition rate. The availability of an image archive might be exploited for backward time series. For classification approaches atmospheric pre-processing might be necessary. Using an automatic classification approach, information for training the classifier and information for validation are needed taking temporal variations into account (seasonal variability, atmospheric variability, illumination variability, etc.). Caveats: Same as indicator A1. Availability of extracted products statistics. A 3D perspective might have to be developed, in particular for vertical mining.

5 A3 - Artisanal and Small-Scale Mining Small scale artisanal and sometimes illegal mines do contribute to mining related nuisances but are often unmonitored. Requirements Detection & mapping of the small mining activities. Variable(s) to be determined Data acquisition Source of information Methods & Standards Suggested sensor systems Pre-processing & auxiliary data Topographical footprint of artisanal small-scale mining. Number of operating or abandoned sites of artisanal and small-scale mining. Small scale land use classification derived from high spatial resolution remote sensing imagery. Same as indicator A1 Geometrical and relational spatial information might be required. Need for high spatial optical or RADAR imagery. High resolution optical or RADAR imagery For classification approaches atmospheric pre-processing might be necessary. Using an automatic classification approach, information input for training the classifier and information for validation are needed. Caveats: Same as indicator A1 Difficulty to distinguish between active and abandoned mines, unless partly re-vegetated. Difficulty to map small scale mining which leaves only minimal footprint at surface.

6 Examples Potential Illegal mining site detections: Separation of dwellings (1) and rivers bed with potential mining activities (2) using multiscale object-based automated image analysis in Numbi, Democratic Republic of Congo GMOSAIC project (Schoepfer & Kraunz, 2010) Comparison between illegal artisanal panning gold exploitation, as detected using Landsat imagery, and distance to the closest legal mining permits, French Guyana, BRGM

7 Activité d orpaillage 9km Concession 1km Activité d orpaillage Concessions 10km 1km LANDSAT 26/09/2002

8 A4 - Residential Land Use The spatial relationship between residential and mining areas could be an indicator for potential use conflicts and impacts on health and safety. Requirements Mono-temporal or multi-temporal mapping of the selected land use types either small scale (for regions, provinces, etc.) or greater scale (for few individual mines). Variable(s) to be determined Data acquisition Source of information Topographic footprint (and its development) of residential areas. Topographic footprint (and its development) of mining areas. Distance between residential areas and mining activities. Cadastral information in digital form (GIS), topographic maps or land use coverage available from international, national or provincial regulatory bodies. Land use classifications derived from remote sensing imagery. Building extraction derived from remote sensing imagery [BenHadj10] Methods & Standards Suggested sensor systems Same as indicator A1. Discrimination of different types of urban areas (industrial and commercial, residential, formal and informal settlements) down to the identification of individual buildings requires imagery of high spatial resolution and specialized classification methods. Most GIS software can provide statistics about distance from habitations to mining sites. Settlement areas automated extraction from SAR data. LIDAR can be used to map buildings. Same as indicator A1. Pre-processing & auxiliary data For classification approaches atmospheric pre-processing might be necessary. Using an automatic classification approach, information input for training the classifier and information for validation are needed. Caveats: Same as indicator A1. LIDAR surveys restricted to mapping of small areas (time/cost constraints).

9 Examples Inhabited area within 300 m from a mining activity (tailings dams and heaps), East Rand, South Africa, BRGM CGS Pink: inhabited area, green: tailings dams and heaps, red: inhabited area within 300 m from tailings dams and heaps. Discrimination between differently inhabited areas, Ikonos image, East Rand, South Africa. BRGM CGS Top left: industrial area Center: well organized residential area with single housing and gardens Lower left: well organized governmental township Right: informal settlement (shelters)

10 A5 - Informal Settlements Mining areas attract a variety of people and can cause the development of informal settlements in areas where there is not sufficient enforcement of zoning regulations. These suffer from poor piped water quality and in-house air pollution caused by using the bottom quality coal they collect. Requirements Multi-temporal mapping of the selected land use type at small scale. Variable(s) to be determined Data acquisition Sprawl of squatters & slums. Source of information Small scale cadastral information in digital form (GIS), topographic maps or legal land use plan available from provincial regulatory bodies. Comparison to small scale land use classification derived from remote sensing imagery. Methods & Standards Suggested sensor systems Discrimination of informal from legal settlements requires imagery of high spatial resolution and specialized classification methods. Thematic mapping approach: Manual/automated analysis of remote sensing imagery. Geometric delineation approach: Automated interpretation of radar imagery Thematic mapping approach: high resolution optical sensors Geometric delineation approach: urban footprint extraction using SAR or single-band optical data. Pre-processing & auxiliary data For classification approaches atmospheric pre-processing might be necessary. Using an automatic classification approach, information input for training the classifier and information for validation are needed. Caveats: Same as indicator A1. Difficulty to distinguish between legal and informal settlements.

11 Examples Fully automated urban settlements detection using space-born high resolution SAR, South Africa DLR: settlements are displayed in red

12 A6 - Sites Set Aside, Protected Areas On the basis of the current land use planning/zoning regulations at the site, this indicates constraints on mine development and also indicates zones sensitive to environmental impact arising from the mine. Requirements Mono-temporal mapping of the selected land use types: nature reservations, site of spiritual value, etc. Variable(s) to be determined Data acquisition Source of information Footprint of nature reserves, sites of spiritual value and similar. Footprint of mining operations Cadastral information in digital form (GIS), topographic maps or land use coverage available from international, national or provincial regulatory bodies of protected and cultural areas and mining operations. Land use / land cover analysis derived from remote sensing imagery. Methods & Standards Suggested sensor systems see indicator A1 see indicator A1 Pre-processing & auxiliary data For classification approaches atmospheric pre-processing might be necessary. Using an automatic classification approach, information input for training the classifier and information for validation are needed. Caveats: Same as indicator A1.

13 Examples land-use classification of East Rand, South Africa, from ASTER Imagery. BRGM CGS Dark green: agricultural lands Brown: grass land (Veld) Red: contaminated waters Yellow orange: tailings dams and heaps Blue: surface waters Light green: wetlands.

14 A7 - Surface Water Courses Percentage area covered by surface waters: Changes in surface areas of open water bodies can point to mining-induced changes in the water balance. Requirements Corrected DEM Springs and other water bodies locations. Variable(s) to be determined Data acquisition Source of information Extension of surface waters Drainage pattern Drainage hierarchy, drainage density, watersheds. Cadastral information in digital form (GIS), topographic maps available from international, national or provincial regulatory bodies about water bodies. DEM can be derived from topographical maps or various remote sensing technologies: o (automated) stereo-pair matching or photogrammetric analysis of optical imagery o radar interferometry for SAR images o LIDAR surveys Microwave (active or passive) high resolution sensors can provide information about humidity and therefore map springs. multispectral or hyperspectral high resolution remote sensing can be used to map water bodies. Methods & Standards DEM might need to be registered and corrected. Surface water courses can be computed using standards GIS software. Suggested sensor systems Microwave high resolution sensors for springs or water bodies mapping. Any DEM computation suitable sensors (LiDAR, SAR, Stereo) depending on the site. Hyperspectral optical or thermal sensors can provide complementary information. Pre-processing & auxiliary data DEM derived from remote sensing usually need a correction. LiDAR digital surface model needs to be transformed into digital terrain model. Satellite DEMs usually need an absolute registration and validation with ground control points. Artefacts due to stereo matching insufficiencies need to be corrected. Caveats: Availability of recent enough topographic map or DEM time series Accuracy of DEM.

15 Examples Strahler drainage network computed from SRTM overlaid on SRTM colour coded elevation. green = lower areas, tan = higher areas, region of Kazarman, Kyrgyzstan.

16 A8 Recultivation Success on Mined-Out Areas and Waste/Spoil Heaps The remediation of mining and milling sites, including waste management areas (waste/spoil heaps) may include the remediation of residues management sites with predetermined plant communities. Ground-covering vegetation is the best provision against wind erosion and an easy to monitor measure of remediation efforts. Designated closed and remediated mining areas covered by specific pioneer vegetation might be returned to agricultural use. Requirements Mono-temporal or multi-temporal mapping of vegetation species. Determination or monitoring of vegetation health and growth. Variable(s) to be determined Data acquisition Footprint of mined-out areas & waste/spoil heaps. Plant species, vegetation health status and coverage/density. Source of information Methods & Standards Suggested sensor systems Footprint of mined-out areas & waste/spoil heaps from cadastral information in digital form (GIS), topographic maps or land use coverage provided by regulatory bodies or delineated by image classification (see indicator A1) A model linking spectral features to vegetation health must be established. Numerous approaches are available in the literature. Typically a chemical analysis of plant constituents will be linked to spectral absorptions. For vegetation spectral analysis the VIS+NIR region are of high importance. Inclusion of SWIR region facilitates information retrieval for wetlands and dry vegetation. Pre-processing & auxiliary data Retrieval of vegetation parameters requires careful atmospheric correction & illumination correction. Used classifiers and models need training/calibration and validation data. Caveats: Seasonal growth characteristics of vegetation need to be taken into account. Vegetation might expose strong BRDF effects. There are limitations in the ability to separate plant species, depending on the sensor used.

17 Examples Sokolov site: Statistical classification of the Norway spruce health status by integrating the C ab, REP and expsipi (HyMap 2009 data). Color scale 1-5 health status classes for the trees without visual damage symptoms 1 - the worst and 5 - the best result. Czech GS and Faculty of Science, the Charle s University in Prague.

18 A9 - Areas Affected and their Potential Use The type and economic value of potential alternative uses of affected areas and their surrounding are location specific. The relevant characteristics need to be identified. Once described and validated by ground work, they can potentially be monitored by EO techniques. Requirements Mono-temporal mapping of land cover & land use types around mine sites either small scale (for regions, provinces, etc.) or large scale (for few individual mines). Variable(s) to be determined Mining affected land cover / land use types. Land value (opportunity cost) per land use/cover type. Data acquisition Source of information Methods & Standards Suggested sensor systems Land value per type of land use / land cover (urban, agriculture, pasture, leisure, etc.) provided by local authorities. Land use around mine sites from cadastral information in digital form (GIS), topographic maps or land use coverage provided by regulatory bodies or delineated by image classification (see indicators A1 and A6). Morphology and topology derived from DEM. see indicator A1 see indicator A1 Pre-processing & auxiliary data Caveats: See indicator A1. For classification approaches atmospheric preprocessing might be necessary. Using an automatic classification approach, information input for training the classifier and information for validation are needed.

19 Examples Analysis of multi-temporal Landsat time series data: land-cover changes as a result of large scale mining in the period: artificial surfaces (AS), agricultural areas (AA), and forest-seminatural areas (FSA). Matějíček and Kopačková 2010

20 A10 - Soil Fertility of Remediated Areas Related to A3 and A4, but assessing the re-cultivation (re-vegetation) potential of decommissioned soils, rather than the actual vegetation. Also focusing on agricultural plants, rather than perennial plants. Requirements Variable(s) to be determined Data acquisition Mono-temporal mapping of soil constituents at small scale soil mineralogy & presence of contaminants biophysical data layers Source of information Methods & Standards Suggested sensor systems Natural vegetation maps can give basic information on which vegetation type to be expected. Usually such information is only available at large scales. Local small-scale biophysical parameters (e.g. sunlight intensity, length of growing season, water availability) can be derived from high resolution digital elevation models, climatic models and hydrologic models. Small scale soil mineralogy and contaminants can be derived from remote sensing images. Elevation models (automated) stereo-pair matching or photogrammetric analysis of optical imagery radar interferometry for SAR images LIDAR surveys. Soil mineralogy mapping (automated) mapping methods based on spectral end members quantitative abundance estimation using mixture models or spectral feature regression models Elevation models high spatial resolution optical and SAR sensors with stereo capability LIDAR Soil mineralogy mapping high spatial resolution hyperspectral optical sensors Pre-processing & auxiliary data For mapping an atmospheric pre-processing is necessary together with in-situ reference measurements for calibration and validation. For quantitative analysis a model linking spectral features to soil constituent s concentration must be established, typically by chemical analysis of collected soil samples. Caveats: Data availability and costs. Accuracy of DEM and climate information.

21 Examples re-vegetation potential of the Erzberg siderite mine, Austria MINEO GBA, : Map of Fe-carbonate weathering intensity Parameters taken into account Lithology Fe-Carbonate weathering intensity Vegetation status Vegetation buffer zones Slope Sunlight irradiation intensity Map of re-vegetation potential A11 - Existence and legal status of environmental impact assessments

22 For the operation and the remediation phase. Related to A7, but exploring the legal basis and indicating whether a lack of regulation or a lack of enforcement is the main course in case of negative impacts. Not accessible to EO-techniques