scholarly journals Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face

2018 ◽  
Vol 10 (10) ◽  
pp. 1518 ◽  
Author(s):  
Stephane Boubanga-Tombet ◽  
Alexandrine Huot ◽  
Iwan Vitins ◽  
Stefan Heuberger ◽  
Christophe Veuve ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Resolution ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Thermal Infrared ◽  
Field Studies ◽  
Hyperspectral Data ◽  
Spectral Bands ◽  
Remote Sensing Systems ◽  
Viewing Perspective

Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology.

LAI Retrieval Based on PROSPECT-SAILH Model from Multi-Angular Data of WiDAS Imaging System

2012 ◽  
Vol 518-523 ◽  
pp. 5697-5703
Author(s):  
Zhao Yan Liu ◽  
Ling Ling Ma ◽  
Ling Li Tang ◽  
Yong Gang Qian
Keyword(s):  
Remote Sensing ◽  
Imaging System ◽  
Remote Sensing Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Sensing Data ◽  
Spectral Bands ◽  
Sensing Technology ◽  
Leaf Optical Properties ◽  
Angular Data

The aim of this study is to assess the capability of estimating Leaf Area Index (LAI) from high spatial resolution multi-angular Vis-NIR remote sensing data of WiDAS (Wide-Angle Infrared Dual-mode Line/Area Array Scanner) imaging system by inverting the coupled radiative transfer models PROSPECT-SAILH. Based on simulations from SAILH canopy reflectance model and PROSPECT leaf optical properties model, a Look-up Table (LUT) which describes the relationship between multi-angular canopy reflectance and LAI has been produced. Then the LAI can be retrieved from LUT by directly matching canopy reflectance of six view directions and four spectral bands with LAI. The inversion results are validated by field data, and by comparing the retrieval results of single-angular remote sensing data with multi-angular remote sensing data, we can found that the view angle takes the obvious impact on the LAI retrieval of single-angular data and that high accurate LAI can be obtained from the high resolution multi-angular remote sensing technology.

scholarly journals Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

2014 ◽  
Vol 11 (23) ◽  
pp. 6827-6840 ◽  
Author(s):  
M. Réjou-Méchain ◽  
H. C. Muller-Landau ◽  
M. Detto ◽  
S. C. Thomas ◽  
T. Le Toan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Mitigation Strategies ◽  
Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

scholarly journals Using Remote Sensing Based Metrics to Quantify the Hydrological Response in a City

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1763 ◽  
Author(s):  
Charlotte Wirion ◽  
Willy Bauwens ◽  
Boud Verbeiren
Keyword(s):  
Remote Sensing ◽  
Potential Evapotranspiration ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Hydrological Response ◽  
Area Index ◽  
Impervious Area ◽  
Urban Catchments ◽  
The City ◽  
Total Impervious Area

We propose a remote-sensing based metric approach to evaluate the hydrological response of highly urbanized areas and apply it to the city of Brussels. The model is set-up using 2 m resolution hyperspectral data. Next, it is upscaled to the city level, using multi-spectral Sentinel-2 data with 20 m resolution. We identify the total impervious area, the vegetation cover and the leaf area index as important metrics to derive a timeseries of spatially distributed net rainfall, runoff and infiltration from rainfall data. For the estimation of the actual evapotranspiration we use the potential evapotranspiration and the available water storage based on the interception, the depression storage and the infiltration. Additionally, we route the runoff to the outlet of selected sub-catchments. An important metric for the routing is the timing to the outlet which is approximated using the total impervious area and the hydrological distance to the outlet. We compare our approach to WetSpa model simulations and reach R 2 values of 98% for net rainfall, 95% for surface runoff, 99% for infiltration and 97% for cumulative evapotranspiration. The routing in the Watermaelbeek catchment is evaluated with discharge observations and reaches NSE values of 0.89 at a 2 m resolution and 0.88 at a 20 m resolution using an hourly timestep. At the timestep of 10 min and a 20 m resolution the NSE is reduced to 0.76. For the Roodebeek catchment we reach an NSE of 0.73 at a spatial resolution of 20 m and an hourly timestep. The results presented in this paper are optimistic for using spatial and temporal metrics retrieved from remote sensing data to quantify the water balance of urban catchments.

scholarly journals Methods of calculating landslide volume using remote sensing data

2020 ◽  
Vol 149 ◽  
pp. 02009
Author(s):  
Maira Razakova ◽  
Alexandr Kuzmin ◽  
Igor Fedorov ◽  
Rustam Yergaliev ◽  
Zharas Ainakulov
Keyword(s):  
Remote Sensing ◽  
Sea Level ◽  
Remote Sensing Data ◽  
Field Studies ◽  
Aerial Photographs ◽  
Field Observations ◽  
Sensing Data ◽  
Quantitative Estimates ◽  
Landslide Volume ◽  
Engineering Protection

The paper considers the issues of calculating the volume of the landslide from remote sensing data. The main methods of obtaining information during research are field observations. The most important results of field studies are quantitative estimates, such as the volume of the embankment resulting from a landslide, morphometric indicators, etc. The study of a remote and remote object was carried out by remote methods using aerial photographs in the Ile Alatau foothills at 1,600 meters above sea level. The obtained materials from the mudflow survey will be useful in developing solutions to mitigate the effects of disasters and in the design of measures for engineering protection from landslides.

scholarly journals GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

2020 ◽  
Vol 12 (4) ◽  
pp. 656 ◽  
Author(s):  
Luoma Wan ◽  
Yinyi Lin ◽  
Hongsheng Zhang ◽  
Feng Wang ◽  
Mingfeng Liu ◽  
...  
Keyword(s):  
Hong Kong ◽  
Spectral Resolution ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Species Discrimination ◽  
Infrared Range ◽  
Landsat 8 ◽  
Mangrove Species ◽  
Spectral Bands ◽  
Species Mapping

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.

scholarly journals Fast reconstruction of hyperspectral radiative transfer simulations by using small spectral subsets: application to the oxygen A band

2013 ◽  
Vol 6 (5) ◽  
pp. 8339-8370 ◽  
Author(s):  
A. Hollstein ◽  
R. Lindstrot
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spectral Resolution ◽  
Principal Components ◽  
Hyperspectral Data ◽  
Simple Method ◽  
Computational Burden ◽  
Versatile Tool ◽  
Fast Reconstruction ◽  
Simulation Results

Abstract. Hyperspectral radiative transfer simulations are a versatile tool in remote sensing but can pose a major computational burden. We describe a simple method to construct hyperspectral simulation results by using only a small spectral subsample of the simulated wavelength range, thus leading to major speedups in such simulations. This is achieved by computing principal components for a small number of representative hyperspectral spectra and then deriving a reconstruction matrix for a specific spectral subset of channels to compute the hyperspectral data. The method is applied and discussed in detail using the example of top of atmosphere radiances in the oxygen A band, leading to speedups in the range of one to two orders of magnitude when compared to radiative transfer simulations at full spectral resolution.

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