167 research outputs found
APPLICATION OF REMOTE SENSING TO ESTIMATE ABOVE GROUND BIOMASS IN TROPICAL FORESTS OF INDONESIA
This work aims to estimate Above Ground biomass (AGB) of a tropical
rainforest in East Kalimantan, Indonesia using equation derived from the stand
volume prediction and to study the spatial distribution of AGB over aforest area.
The potential of remote sensing and field measurement data to predict stand
volume and AGB were studied Landsat ElM data were atmospherically corrected
using Dark Object Subtraction (DOS) technique, and topographic corrections were
conducted using C-correction method Stand volume was estimated using field data
and remote sensing data using Levenberg-Marquardt neural networks. Stand
volume data was converted into the above ground biomass using available volume
- AGB equations. Spatial distribution of the AGB and the error estimate were then
interpolated using kriging. Validated with observation data, the stand volume
estimate showed integration of field measurement and remote sensing data has
better prediction than the solitary uses of those data. The AGB estimate showed
good correlations with stand volume, number of stems, and basal area
Introducing flotation for accurate quartz versus feldspar separation in cosmogenic nuclides methods applied to fluvial sediments in Pamir
Abstract HKT-ISTP 2013
A
Resolution enhancement for drill-core hyperspectral mineral mapping
Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling
Tectonic and climatic forcing on the Panj river system during the Quaternary
Abstract HKT-ISTP 2013
B
Seismotectonics of the Pamir
Based on a 2 year seismic record from a local network, we characterize the deformation of the seismogenic crust of the Pamir in the northwestern part of the India-Asia collision zone. We located more than 6000 upper crustal earthquakes in a regional 3-D velocity model. For 132 of these events, we determined source mechanisms, mostly through full waveform moment tensor inversion of locally and regionally recorded seismograms. We also produced a new and comprehensive neotectonic map of the Pamir, which we relate to the seismic deformation. Along Pamir's northern margin, where GPS measurements show significant shortening, we find thrust and dextral strike-slip faulting along west to northwest trending planes, indicating slip partitioning between northward thrusting and westward extrusion. An active, north-northeast trending, sinistral transtensional fault system dissects the Pamir's interior, connecting the lakes Karakul and Sarez, and extends by distributed faulting into the Hindu Kush of Afghanistan. East of this lineament, the Pamir moves northward en bloc, showing little seismicity and internal deformation. The western Pamir exhibits a higher amount of seismic deformation; sinistral strike-slip faulting on northeast trending or conjugate planes and normal faulting indicate east-west extension and north-south shortening. We explain this deformation pattern by the gravitational collapse of the western Pamir Plateau margin and the lateral extrusion of Pamir rocks into the Tajik-Afghan depression, where it causes thin-skinned shortening of basin sediments above an evaporitic décollement. Superposition of Pamir's bulk northward movement and collapse and westward extrusion of its western flank causes the gradual change of surface velocity orientations from north-northwest to due west observed by GPS geodesy. The distributed shear deformation of the western Pamir and the activation of the Sarez-Karakul fault system may ultimately be caused by the northeastward propagation of India's western transform margin into Asia, thereby linking deformation in the Pamir all the way to the Chaman fault in the south in Afghanistan
Hyde: The First Open-Source, Python-Based, Gpu-Accelerated Hyperspectral Denoising Package
As with any physical instrument, hyperspectral cameras induce different kinds
of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial
step for analyzing hyperspectral images (HSIs). Conventional computational
methods rarely use GPUs to improve efficiency and are not fully open-source.
Alternatively, deep learning-based methods are often open-source and use GPUs,
but their training and utilization for real-world applications remain
non-trivial for many researchers. Consequently, we propose HyDe: the first
open-source, GPU-accelerated Python-based, hyperspectral image denoising
toolbox, which aims to provide a large set of methods with an easy-to-use
environment. HyDe includes a variety of methods ranging from low-rank
wavelet-based methods to deep neural network (DNN) models. HyDe's interface
dramatically improves the interoperability of these methods and the performance
of the underlying functions. In fact, these methods maintain similar HSI
denoising performance to their original implementations while consuming nearly
ten times less energy. Furthermore, we present a method for training DNNs for
denoising HSIs which are not spatially related to the training dataset, i.e.,
training on ground-level HSIs for denoising HSIs with other perspectives
including airborne, drone-borne, and space-borne. To utilize the trained DNNs,
we show a sliding window method to effectively denoise HSIs which would
otherwise require more than 40 GB. The package can be found at:
\url{https://github.com/Helmholtz-AI-Energy/HyDe}.Comment: 5 page
Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops
Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called “hyperclouds”, i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements
Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic
Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel; (2) lithological mapping based on spectral information; and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and Søndre Strømfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for Søndre Strømfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches
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