scholarly journals UAS Remote Sensing Products for Supporting Extraction Management and Restoration Monitoring in Open-Pit Mines

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 4
Author(s):  
Carabassa ◽  
Montero ◽  
Crespo ◽  
Padró ◽  
Balagué ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Cost Effective ◽  
Open Pit Mine ◽  
Open Pit ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Waste Dumps ◽  
Efficient Resource ◽  
Restoration Monitoring

Accurate mapping of open-pit mine areas is a prerequisite for the efficient resource management of extractive companies, but also detailed mapping is a requirement for public administrations, especially regarding the monitoring of restored areas. In previous works, our team has contributed to a better knowledge of the use of Unmanned Aerial Systems (UAS) technologies for soil/vegetation restoration monitoring purposes, and in this work, we present a novel protocol to support combined interests of both private companies and governmental agencies. We introduce a case study in which we show the capability of multispectral sensors onboard of a low-weight multicopter to describe land cover typologies in restored areas (such as grass, scrubs, trees, topsoil and mine spoils) by applying remote sensing and GIS techniques. Moreover, we assess the capability of digital terrain models (Digital Elevation Model, Digital Surface Model, Digital Slope Model) derived from photogrammetric techniques, to provide useful and fast topographic information for the proper management of open-pit mine exploitation and restoration. By applying these techniques, we present a cost-effective workflow adequate to monitor land cover dynamics in restored areas, but also volumetric changes in stockpiles, waste dumps and extraction faces. This combined approach, supporting both environmental and industrial needs, aims to enhance the collaboration between sectors, establishing synergies, reducing costs by sharing knowledge, and adding transparency to their relation.

scholarly journals Leveraging machine learning to produce a cost-effective national building height map of Ireland

2021 ◽  
Author(s):  
Eoghan Keany ◽  
Geoffrey Bessardon ◽  
Emily Gleeson
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Cost Effective ◽  
Land Cover Classification ◽  
National Scale ◽  
Building Height ◽  
Height Data

<p>To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.</p><p>Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.</p><p> </p><p>(1) G. Bessardon and E. Gleeson, “Using the best available physiography to improve weather forecasts for Ireland,” in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.</p><p>(2) E. Walsh, et al., “Using machine learning to produce a very high-resolution land-cover map for Ireland, ” Advances in Science and Research,  (accepted for publication).</p><p>(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki</p><p>(4) D. Frantz, et al., “National-scale mapping of building height using sentinel-1 and sentinel-2 time series,” Remote Sensing of Environment, vol. 252, 2021.</p><p>(5) M. Fitrzyk, et al., “Esa Copernicus sentinel-1 exploitation activities,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.</p>

scholarly journals Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha forest reserve, Nigeria

2014 ◽  
Vol XL-8 ◽  
pp. 613-616 ◽  
Author(s):  
O. S. Olokeogun ◽  
K. Iyiola ◽  
O. F. Iyiola
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Sustainable Forest Management ◽  
Cost Effective ◽  
Remote Sensing And Gis ◽  
Clear Understanding ◽  
Forest Reserve ◽  
Landsat Satellite

Mapping of LULC and change detection using remote sensing and GIS techniques is a cost effective method of obtaining a clear understanding of the land cover alteration processes due to land use change and their consequences. This research focused on assessing landscape transformation in Shasha Forest Reserve, over an 18 year period. LANDSAT Satellite imageries (of 30 m resolution) covering the area at two epochs were characterized into five classes (Water Body, Forest Reserve, Built up Area, Vegetation, and Farmland) and classification performs with maximum likelihood algorithm, which resulted in the classes of each land use. <br><br> The result of the comparison of the two classified images showed that vegetation (degraded forest) has increased by 30.96 %, farmland cover increased by 22.82 % and built up area by 3.09 %. Forest reserve however, has decreased significantly by 46.12 % during the period. <br><br> This research highlights the increasing rate of modification of forest ecosystem by anthropogebic activities and the need to apprehend the situation to ensure sustainable forest management.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals Visualizing the Spatiotemporal Trends of Thermal Characteristics in a Peatland Plantation Forest in Indonesia: Pilot Test Using Unmanned Aerial Systems (UASs)

2018 ◽  
Vol 10 (9) ◽  
pp. 1345 ◽  
Author(s):  
Kotaro Iizuka ◽  
Kazuo Watanabe ◽  
Tsuyoshi Kato ◽  
Niken Putri ◽  
Sisva Silsigia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Characteristics ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Plantation Forest ◽  
Vegetation Height ◽  
Spatiotemporal Trends ◽  
New Findings ◽  
Thermal Data ◽  
Aerial Systems

The high demand for unmanned aerial systems (UASs) reflects the notable impact that these systems have had on the remote sensing field in recent years. Such systems can be used to discover new findings and develop strategic plans in related scientific fields. In this work, a case study is performed to describe a novel approach that uses a UAS with two different sensors and assesses the possibility of monitoring peatland in a small area of a plantation forest in West Kalimantan, Indonesia. First, a multicopter drone with an onboard camera was used to collect aerial images of the study area. The structure from motion (SfM) method was implemented to generate a mosaic image. A digital surface model (DSM) and digital terrain model (DTM) were used to compute a canopy height model (CHM) and explore the vegetation height. Second, a multicopter drone combined with a thermal infrared camera (Zenmuse-XT) was utilized to collect both spatial and temporal thermal data from the study area. The temperature is an important factor that controls the oxidation of tropical peats by microorganisms, root respiration, the soil water content, and so forth. In turn, these processes can alter the greenhouse gas (GHG) flux in the area. Using principal component analysis (PCA), the thermal data were processed to visualize the thermal characteristics of the study site, and the PCA successfully extracted different feature areas. The trends in the thermal information clearly show the differences among land cover types, and the heating and cooling of the peat varies throughout the study area. This study shows the potential for using UAS thermal remote sensing to interpret the characteristics of thermal trends in peatland environments, and the proposed method can be used to guide strategical approaches for monitoring the peatlands in Indonesia.

scholarly journals Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China

2010 ◽  
Vol 7 (3) ◽  
pp. 3397-3421 ◽  
Author(s):  
J. Colin ◽  
R. Faivre ◽  
M. Menenti
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
River Basin ◽  
Surface Density ◽  
Roughness Length ◽  
Heihe River ◽  
Surface Model ◽  
Aerodynamic Roughness Length ◽  
Passive Remote Sensing ◽  
Aerodynamic Roughness

Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest about the estimation of the surface turbulent flux parameterisation from passive remote sensing lead to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. LIDAR points were used to extract a Digital Surface Model (DSM) and a Digital Elevation Model (DEM) from a single flight pass over an irrigated area covered by field crops, small trees arrays and tree hedges, with a ground resolution of 1 m and a total surface of 7.2 km2. As a first step, the DSM is used to estimate the plan surface density and frontal surface density of obstacles to wind flow and compute a displacement height and roughness length following strictly geometrical approaches. In a second step, both the DSM and DEM are introduced in a Computational Fluid Dynamics model (CFD) to calculate wind fields from the surface to the top of the Planetary Boundary Layer (PBL), and invert wind profiles for each calculation grid and compute a roughness length. Examples of the use of these three approaches are presented for various wind direction together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.

Land Use Planning and Sustainable Development

2021 ◽  
pp. 330-350
Author(s):  
Mohd Nazish Khan ◽  
Samreen Fatima
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Sustainable Development ◽  
Land Cover ◽  
Land Use Planning ◽  
Cost Effective ◽  
Land Resources ◽  
Gis Technology ◽  
Future Demands ◽  
Processing Techniques

This chapter explores the ability of remote sensing and GIS technology for the preparation of land use land cover plan in an effective manner. It is an established fact that remote sensing products are being widely used for land use and land cover products to facilitate their stakeholders to provide them cost effective and reproducible mechanism to extract meaningful information for the development of infrastructure in particular region at any scale. Remote sensing has produced different high resolution datasets that may be convert into fruitful products by using recently developed image processing techniques. India is vast country, having natural resources as land resources which needs reallocation as per future demands. Present legislation and regulation are hardly enough for sustainable development of land resources.

scholarly journals The application of remote sensing data for the assessment of man-made environments in the coal regions of the south of the Far East

2018 ◽  
Vol 56 ◽  
pp. 04016
Author(s):  
Marina Bubnova
Keyword(s):  
Remote Sensing ◽  
Far East ◽  
Remote Sensing Data ◽  
Qualitative Description ◽  
Open Pit ◽  
Economic Damage ◽  
Waste Dumps ◽  
Coal Deposits ◽  
Ndvi Index ◽  
Biomass Determination

The paper is concerned with analysis state of surfaces of waste dumps coal deposits according to Earth remote sensing. It is noted, that formed because of open-pit operations at Bikinsky and Raichikhinsky coal mining enterprise areas denudation accumulative violations were determinate by using programs for satellite images interpretation, as well as certain large forms of man-made relief (such as quarries, dumps, sedimentation tanks) were identified, followed by the calculation of their areas and the assessment of the value of environmental and economic damage to the environment, i.e. it was possible to give not only a qualitative description of the pollution process, but also its quantitative assessment. The approach to biomass determination based on vegetation NDVI index is presented. The conclusion is based on possibility of selfrestoration of the plant ecosystem on the surface of mining landscapes without special work on the reclamation of disturbed lands.

scholarly journals Coupling Satellite Images and Unmanned Aerial Vehicle Data to Monitor the Exploitation of Open-Pit Mine

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Tong Si Son ◽  
Quang Toan LE ◽  
Thi-Huyen-Ai TONG ◽  
Vu Giang NGUYEN ◽  
Phan Long VU ◽  
...  
Keyword(s):  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Satellite Images ◽  
Mining Industry ◽  
Open Pit Mine ◽  
Open Pit ◽  
Remote Sensing Technique ◽  
Vertical Range ◽  
High Resolution Satellite Images ◽  
Aerial Vehicle

Discovering the variation of an open-pit mine in vertical, horizontal, and temporal dimensions as well to characterize the stages and the trends of the exploitation are indispensable tasks which provide information supporting decision making and planning for sustainable development of the mining industry. Remote sensing technique with the advantages of multi-spatial, multi-spectral, multi-temporal resolution is a promising solution to meet the  information requirement. This study proposes an approach of coupling the high-resolution satellite images and Unmanned Aerial Vehicle (UAV) data to observe the variation of Tan An open rocky mine during its lifetime. Five satellite images with the resolution of 0.5 m acquired in 2006, 2012, 2014, 2016, 2018, and two ortho-images with 0.034 m resolution constructed from UAV photos captured in 2019, 2020 are used to make land cover maps. The analysis of land cover changes discovers 3 stages of open-pit mine exploitation consisting of unprompted exploitation, exploiting outbreak and stable exploitation corresponding to the changes in the mine. Besides, two Digital Surface Models (DSM) constructed by UAV photos are compared to calculate the elevation and volume changes. The assessment of the correlation between elevation change and land cover change indicates that the mineral exploitation is in the vertical range from 645 m to 660 m, and the exploitation trend is following the horizontal expansion rather than the deep excavation. Additionally, this experiment results in 79,422 m3 of mineral taken from the mine, and 34,022 m3 of soil used for the restoration within a year from June 2019 to June 2020.

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