Detecting land cover change at the Jornada Experimental Range, New Mexico with ASTER emissivities

French, Andrew N.; Schmugge, Thomas J.; Ritchie, Jerry C.; Hsu, A.Y.; Jacob, Frédéric; Ogawa, Kenta

doi:10.1016/j.rse.2007.08.020

Cited by 86 publications

(42 citation statements)

References 70 publications

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“…By comparing Figures 9b and 9d, we can see clearly that the variation of GLASS BBE is mainly determined by the seasonal variation of MODIS spectral albedos. In order to better characterize land surface [51,52], both the ASTER narrowband emissivity and MODIS narrowband emissivity products should be improved to consider the seasonal variation for vegetated areas. We are improving the GLASS BBE algorithm for vegetation to incorporate the seasonal variation of vegetation.…”

Section: Seasonal Patternmentioning

confidence: 99%

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

et al. 2013

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Abstract:This study compared three broadband emissivity (BBE) datasets from satellite observations. The first is a new global land surface BBE dataset known as the Global Land Surface Satellite (GLASS) BBE. The other two are the North American ASTER Land Surface Emissivity Database (NAALSED) BBE and University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) BBE, which were derived from two independent narrowband emissivity products. Firstly, NAALSED BBE was taken as the reference to evaluate the GLASS BBE and UWIREMIS BBE. The GLASS BBE was more close to NAALSED BBE with a bias and root mean square error (RMSE) of −0.001 and 0.007 for the summer season, −0.001 and 0.008 for the winter season, respectively. Then, the spatial distribution and seasonal pattern of global GLASS BBE and UWIREMIS BBE for six dominant land cover types were compared. The BBE difference between vegetated areas and non-vegetated areas can be easily seen from two BBEs. The seasonal variation of GLASS BBE was more reasonable than that of UWIREMIS BBE. Finally, the time series were calculated from GLASS BBE and UWIREMIS BBE using the data from 2003 through 2010. The periodic variations of GLASS BBE were stronger than those of UWIREMIS BBE. The long time series high quality GLASS BBE can be incorporated in land surface models for improving their simulation results.

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Section: Seasonal Patternmentioning

confidence: 99%

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

et al. 2013

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“…Numerous studies have proved the usefulness of Landsat imagery in agricultural land cover classification [7], forest dynamics monitoring [8], urban land use classification [9], other land cover dynamics or land use land cover (LULC) change detection [6,10,11]. Other satellite products such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery have also been widely used for regional scale land cover classification [12][13][14] or land cover change detection [15,16].…”

Section: Introductionmentioning

confidence: 99%

The Improvement of Land Cover Classification by Thermal Remote Sensing

Sun

Schulz

2015

Remote Sensing

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Land cover classification has been widely investigated in remote sensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remote sensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE) using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR) bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images.

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“…The setup of the instrument facility was supported by the European Fund for Economic and Regional Development (AIRSPEC Project, grant 2008-01- [15][16][17][18][19][20]. Financial support by the Fonds National de la Recherche Luxembourg (FNR) for the HYPERFOREST Project (Advanced airborne hyperspectral remote sensing to support forest management, research grant INTER/STEREO/09/01) is greatly acknowledged.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…A number of approaches based on different assumptions have been developed to estimate emissivity [16,17], for instance the temperature-emissivity separation algorithm (TES, [16]). However, for agricultural applications the TES procedure tends to underestimate emissivities and consequently overestimate land surface temperatures, with potential emissivity errors of up to 2.0%, resulting in errors of 2-3 °C [18]. Hyperspectral approaches can rely on a large number of wavelengths which allows a good fit to the Planck radiance to determine land surface temperature or canopy temperature at higher accuracies than multispectral procedures [15,19].…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications

Schlerf¹,

Rock

Lagueux³

et al. 2012

Remote Sensing

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Abstract:A new instrument has been setup at the Centre de Recherche Public-Gabriel Lippmann to measure spectral emissivity values of typical earth surface samples in the 8 to 12 μm range at a spectral resolution of up to 0.25 cm −1 . The instrument is based on a Hyper-Cam-LW built by Telops with a modified fore-optic for vertical measurements at ground level and a platform for airborne acquisitions. A processing chain has been developed to convert calibrated radiances into emissivity spectra. Repeat measurements taken on samples of sandstone show a high repeatability of the system with a wavelength dependent standard deviation of less than 0.01 (1.25% of the mean emissivity). Evaluation of retrieved emissivity spectra indicates good agreement with reference measurements. The new instrument facilitates the assessment of the spatial variability of emissivity spectra of material surfaces-at present still largely unknown-at various scales from ground and airborne platforms and thus will provide new opportunities in environmental remote sensing.

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Detecting land cover change at the Jornada Experimental Range, New Mexico with ASTER emissivities

Cited by 86 publications

References 70 publications

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

The Improvement of Land Cover Classification by Thermal Remote Sensing

A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications

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