Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

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.

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Target detection algorithm for airborne thermal hyperspectral data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-827-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 827-832 ◽

Cited By ~ 1

Author(s):

R. Marwaha ◽

A. Kumar ◽

P. L. N. Raju ◽

Y. V. N. Krishna Murthy

Keyword(s):

Spectral Resolution ◽

Hyperspectral Image ◽

Imaging System ◽

Hyperspectral Data ◽

High Spectral Resolution ◽

Infrared Range ◽

Digital Photograph ◽

Minimum Noise Fraction ◽

Noise Fraction ◽

Minimum Noise

Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm−1 to 1280 cm−1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm−1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

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Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

Remote Sensing ◽

10.3390/rs12061009 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1009

Author(s):

Xiaoxiao Feng ◽

Luxiao He ◽

Qimin Cheng ◽

Xiaoyi Long ◽

Yuxin Yuan

Keyword(s):

Image Fusion ◽

Spatial Resolution ◽

Spectral Resolution ◽

Spatial Information ◽

Spectral Unmixing ◽

High Spectral Resolution ◽

Remote Sensing Image Fusion ◽

Fusion Methods ◽

Invariant Regions ◽

The Difference

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.

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A New Highly Stable Multi-Decade Satellite Climate Data Set Derived from Polar Hyperspectral Infrared Sensors

10.5194/egusphere-egu2020-3754 ◽

2020 ◽

Author(s):

Chris Hepplewhite ◽

Larrabee Strow ◽

Howard Motteler ◽

Sergio de Souza-Machad ◽

Steven Buczkowski

Keyword(s):

Spectral Resolution ◽

Virtual Instrument ◽

Spectral Response ◽

Short Wave ◽

High Spectral Resolution ◽

Climate Data ◽

Climate Trends ◽

Polar Orbit ◽

Data Set ◽

Long Wave

NASA's Atmospheric Infrared Sounder (AIRS) started the continuous measurement of the Earth's upwelling infrared radiation at high spectral resolution in Sept. 2002 in a 13:30 polar orbit. &#160;The AIRS record was supplemented by the CrIS sensor flying on the NASA SNPP platform, also in the 13:30 polar orbit, in 2012. &#160;In 2018 a second CrIS sensor on NOAA's JPSS-1 platform (NOAA-20) began operation, also in the 13:30 orbit. &#160;Two more CrIS sensors are presently being procured for the JPSS-2 and 3 satellites, which will extend this record from 2002 through ~2040. &#160;EUMETSAT's METOP-A/B/C provide very similar hyperspectral observations starting with the IASI sensors in the 09:30 orbit, starting in 2007, which will be continued with METOP-SG for years to come. &#160;Inter-calibration of all of the operating sensors shows agreement generally to 0.2K or better in brightness temperature. &#160;More importantly, we have shown that the radiometric stability of the AIRS sensors is in the 0.002 K/year range or 0.02K/decade, based on measurements of CO2 and SST trends. &#160; Similar stability is expected for CrIS and IASI. &#160;Community consensus suggests that direct radiance trending, followed by conversion of these trends to geophysical quantities will yield the most accurate climate trends. &#160;Here we introduce a new satellite hyperspectral infrared radiance product we call the "Climate Hyperspectral InfraRed Product (CHIRP)" that combines AIRS, CrIS, and IASI into a homogeneous Level 1 radiance product with a common spectral response and channel centers for all three satellites. &#160;This grid is equivalent to an interferometer with optical path differences of 0.8/0.6/0.4 cm for the long-wave/mid-wave/short-wave spectral bands. &#160;This corresponds to a virtual instrument with the same spectral resolution of the JPSS-1 CrIS sensor in the long-wave, with 25/50% degradation in spectral resolution in the mid-wave/short-wave. &#160;This choice allows accurate conversion of the long AIRS record to an equivalent interferometer record. &#160;Conversion of IASI to CHIRP is trivial. &#160;Conversion of all sensors to the CHIRP spectra grid permits simple adjustments of inter-satellite radiometric bias differences since all measurements are first converted to a common spectral grid. &#160;Multiple methods (SNOs, statistical inter-comparisons) indicate these adjustments can be made to the 0.03K level or better. &#160;&#160;A sample application of CHIRP to climate trending will be given by showing multi-decade anomalies of temperature, humidity, and ozone profiles retrieved from CHIRP radiance anomalies, a retrieval that requires almost no a-priori information. &#160;This data set should yield definitive measurements of water-vapor feedback and heavily contribute to our understanding of both tropospheric and stratospheric temperature trends. &#160; Initial production of CHIRP radiances that combine AIRS and CrIS are expected to begin in late 2020. &#160;

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Retrieval of Ice Cloud Properties from AIRS and MODIS Observations Based on a Fast High-Spectral-Resolution Radiative Transfer Model

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-020.1 ◽

2013 ◽

Vol 52 (3) ◽

pp. 710-726 ◽

Cited By ~ 23

Author(s):

Chenxi Wang ◽

Ping Yang ◽

Steven Platnick ◽

Andrew K. Heidinger ◽

Bryan A. Baum ◽

...

Keyword(s):

Radiative Transfer ◽

Spectral Resolution ◽

Spectral Response ◽

Thermal Infrared ◽

High Spectral Resolution ◽

Radiative Transfer Model ◽

Transfer Model ◽

Retrieval Method ◽

Ice Cloud ◽

Cloud Properties

AbstractA computationally efficient high-spectral-resolution cloudy-sky radiative transfer model (HRTM) in the thermal infrared region (700–1300 cm−1, 0.1 cm−1 spectral resolution) is advanced for simulating the upwelling radiance at the top of atmosphere and for retrieving cloud properties. A precomputed transmittance database is generated for simulating the absorption contributed by up to seven major atmospheric absorptive gases (H2O, CO2, O3, O2, CH4, CO, and N2O) by using a rigorous line-by-line radiative transfer model (LBLRTM). Both the line absorption of individual gases and continuum absorption are included in the database. A high-spectral-resolution ice particle bulk scattering properties database is employed to simulate the radiation transfer within a vertically nonisothermal ice cloud layer. Inherent to HRTM are sensor spectral response functions that couple with high-spectral-resolution measurements in the thermal infrared regions from instruments such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer. When compared with the LBLRTM and the discrete ordinates radiative transfer model (DISORT), the root-mean-square error of HRTM-simulated single-layer cloud brightness temperatures in the thermal infrared window region is generally smaller than 0.2 K. An ice cloud optical property retrieval scheme is developed using collocated AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A retrieval method is proposed to take advantage of the high-spectral-resolution instrument. On the basis of the forward model and retrieval method, a case study is presented for the simultaneous retrieval of ice cloud optical thickness τ and effective particle size Deff that includes a cloud-top-altitude self-adjustment approach to improve consistency with simulations.

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Land cover classification based on object-oriented with airborne lidar and high spectral resolution remote sensing image

10.1117/12.2244142 ◽

2016 ◽

Author(s):

Fangfang Li ◽

Zhengjun Liu ◽

Qiangqiang Xu ◽

Haicheng Ren ◽

Xingyu Zhou ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Spectral Resolution ◽

Object Oriented ◽

Land Cover Classification ◽

Remote Sensing Image ◽

Airborne Lidar ◽

High Spectral Resolution

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A Novel Adversarial Based Hyperspectral and Multispectral Image Fusion

Remote Sensing ◽

10.3390/rs11050492 ◽

2019 ◽

Vol 11 (5) ◽

pp. 492 ◽

Cited By ~ 3

Author(s):

Xukun Luo ◽

Jihao Yin ◽

Xiaoyan Luo ◽

Xiuping Jia

Keyword(s):

Spatial Correlation ◽

Spectral Resolution ◽

Real World ◽

Hyperspectral Image ◽

Selection Process ◽

Multispectral Image ◽

High Spectral Resolution ◽

Spectral Correlation ◽

Spatial Quality ◽

Band Image

In order to reconstruct a high spatial and high spectral resolution image (H2SI), one of the most common methods is to fuse a hyperspectral image (HSI) with a corresponding multispectral image (MSI). To effectively obtain both the spectral correlation of bands in HSI and the spatial correlation of pixels in MSI, this paper proposes an adversarial selection fusion (ASF) method for the HSI–MSI fusion problem. Firstly, the unmixing based fusion (UF) method is adopted to dig out the spatial correlation in MSI. Then, to acquire the spectral correlation in HSI, a band reconstruction-based fusion (BRF) method is proposed, regarding H2SI as the product of the optimized band image dictionary and reconstruction coefficients. Finally, spectral spatial quality (SSQ) index is designed to guide the adversarial selection process of UF and BRF. Experimental results on four real-world images demonstrate that the proposed strategy achieves smaller errors and better reconstruction results than other comparison methods.

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JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12122016 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2016 ◽

Cited By ~ 2

Author(s):

Tao Zhang ◽

Puzhao Zhang ◽

Weilin Zhong ◽

Zhen Yang ◽

Fan Yang

Keyword(s):

Local Binary Pattern ◽

Hyperspectral Image ◽

Gabor Filter ◽

Spectral Characteristics ◽

Three Dimensional ◽

Real Data ◽

Hyperspectral Data ◽

Data Cube ◽

Data Sets ◽

Deep Network

The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.

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Forest change as a proxy for landslide occurrence - a Sentinel 2 based spatio-temporal landslide detection approach for two test sites

10.5194/egusphere-egu2020-4743 ◽

2020 ◽

Author(s):

Peter Mayrhofer ◽

Stefan Steger ◽

Ruth Sonnenschein ◽

Giovanni Cuozzo ◽

Clement Atzberger ◽

...

Keyword(s):

Land Cover ◽

Change Detection ◽

Temporal Change ◽

Information Source ◽

Spectral Characteristics ◽

Landslide Occurrence ◽

South Tyrol ◽

Detection Approach ◽

Multi Temporal ◽

Sentinel 2

Landslides represent a major threat to humans and result in high costs for the society. Landslide inventory maps depict the areas of past slope instabilities and are a valuable information source for authorities, spatial planners and risk managers. However, existing inventories are rarely complete, especially in sparsely populated and/or areas difficult to access. Previous work based on change detection and using approaches that automatically map distinct landslide events exploiting remote sensing data has shown promising results. The aim of this study was to test the applicability of multi-temporal change indices derived from Sentinel-2 (S2) for landslide detection for two landslide-prone study sites in Italy and China: South Tyrol and Longnan, respectively.The methodical approach was built upon a change vector analysis applied to annual cloud-free S2-composites at 10m spatial resolution to extract land-cover disturbances. Landslide areas in the time period 2015-2019 were analyzed on the basis of already known landslide location points, downslope-oriented moving windows and supervised classifications using the Receiver Operating Characteristic (ROC) curve. &#160;Subsequently, time-series analysis was applied to the detected landslide-affected areas and to derive temporal breakpoints (i.e. the timing of the landslide occurrence). Finally, applying a multi-temporal revegetation analysis, we accounted for false positives originating from agricultural activities or artefacts on single images. Our findings highlight that out of the 67 already known landslide locations in South Tyrol, only 9 (13.4%) were detectable by means of S2 data. Major challenges resulted from similar spectral characteristics of landslides and other land cover disturbances (especially tree logging). However, larger landslides were detectable both spatially and temporally by means of the multi-temporal change detection approach. By applying a quantitative accuracy assessment for the independent test site in Longnan, China, we are currently assessing the transferability and suitability of the developed approach for efficient spatial-temporal landslide mapping over large areas.

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Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy–Leaving Sun–Induced Chlorophyll Fluorescence

Remote Sensing ◽

10.3390/rs10101551 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1551 ◽

Cited By ~ 18

Author(s):

Neus Sabater ◽

Jorge Vicent ◽

Luis Alonso ◽

Jochem Verrelst ◽

Elizabeth Middleton ◽

...

Keyword(s):

Chlorophyll Fluorescence ◽

Spectral Resolution ◽

Transfer Functions ◽

Spectral Response ◽

High Spectral Resolution ◽

Transfer Model ◽

Atmospheric Effects ◽

Proximal Sensing ◽

Field Station ◽

Absorption Features

Estimates of Sun–Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O 2 ) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal–sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument’s spectral resolution. We demonstrate that for proximal–sensing scenarios compensating for atmospheric effects by simply introducing the O 2 transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O 2 absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O 2 absorption on the evaluated proximal–sensing SIF retrieval strategies, its dependency on surface pressure (p) and air temperature (T) was also assessed. As an example, we combined simulated spectral data with p and T measurements obtained for a one–year period in the Hyytiälä Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of T and p, if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature–dependent physiological responses of vegetation.

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Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings ◽

10.3390/proceedings2201280 ◽

2018 ◽

Vol 2 (20) ◽

pp. 1280 ◽

Cited By ~ 3

Author(s):

Laura Fragoso-Campón ◽

Elia Quirós ◽

Julián Mora ◽

José Antonio Gutiérrez ◽

Pablo Durán-Barroso

Keyword(s):

Land Cover ◽

Vegetation Cover ◽

Spectral Response ◽

Vegetation Indices ◽

Land Cover Classification ◽

Lidar Data ◽

Accuracy Enhancement ◽

Multispectral Data ◽

Sentinel 2

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

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