scholarly journals Cartografía de calizas con datos hiperespectrales AISA Eagle II en una zona montañosa con vegetación densa: cómo orientar geológicamente la corrección atmosférica

2018 ◽  
pp. 125
Author(s):  
J. Buzzi ◽  
E. Costa ◽  
A. Riaza ◽  
O. Fernández ◽  
D. García-Sellés ◽  
...  

<p>Carbonated rocks are crucial targets for oil exploration, outcropping often in large areas with minimum spectral differences among geological units. The typical carbonate spectral absorptions in 2200 nm and 2300 nm, are excluded from the wavelength range of AISA Eagle II. AISA Eagle II hyperspectral data are processed in flight lines of 1024 swath pixels in the visible to near-infrared wavelength range (400 to 970 nm). The flight has a spatial resolution of 1 m and records a total of 128 channels with a spectral resolution of 4,8 nm. The area of study is a carbonate rocky mountain densely vegetated, covered by variably dense trees and bushes. Masking vegetation cover and shade effects is prior to any geological analysis using hyperspectral image processing. Carbonate units occur in mountain slopes, with small areas of ridges of rock outcrops and wide fans of loose material. The background soil of different geological units differ spectrally only by overall reflectance. Instead, limestone rocky outcrops display spectral responses with smooth typical iron oxide absorptions that distinguish them apart from loose boulders of limestone. Trying to enhance spectral differences in the visible wavelength range among carbonate geological units, an atmospheric correction using field spectra from geologically selected targets in a limestone quarry was performed. This way, it was possible to map apart lithologically similar detrital units dominated by carbonate in a river plain. The limy river bottom displays spectra with a straight line in the visible wavelength range due to abundant organic matter and small grain size. The spectra of the upper terraces record spectral absorption features related to iron oxide contents similar to the rock outcrops in ridges of mountains. The use of field spectra from geologically selected targets improves the mapping capability of hyperspectral imagery in areas with geological units with a homogeneous spectral response.</p>

2012 ◽  
Vol 132 (2) ◽  
pp. 25-30 ◽  
Author(s):  
Nozomu Hirokubo ◽  
Hiroshi Komatsu ◽  
Nobuaki Hashimoto ◽  
Makoto Sonehara ◽  
Toshiro Sato

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.


2009 ◽  
Vol 48 (5) ◽  
pp. 052001
Author(s):  
Shigehiko Mori ◽  
Keisuke Hasegawa ◽  
Toshiya Segawa ◽  
Yuta Takahashi ◽  
Shuichiro Inoue

Nano Letters ◽  
2015 ◽  
Vol 15 (12) ◽  
pp. 7949-7955 ◽  
Author(s):  
Florian Sterl ◽  
Nikolai Strohfeldt ◽  
Ramon Walter ◽  
Ronald Griessen ◽  
Andreas Tittl ◽  
...  

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