Estimating global land surface broadband thermal-infrared emissivity using advanced very high resolution radiometer optical data

2013 ◽  
Vol 6 (sup1) ◽  
pp. 34-49 ◽  
Author(s):  
Jie Cheng ◽  
Shunlin Liang
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Thermal Infrared ◽  
Optical Data ◽  
Infrared Emissivity ◽  
Global Land ◽  
Very High

scholarly journals Evidence of Low Land Surface Thermal Infrared Emissivity in the Presence of Dry Vegetation

2007 ◽  
Vol 4 (1) ◽  
pp. 112-116 ◽  
Author(s):  
Albert Olioso ◽  
Guillem Soria ◽  
Jos Sobrino ◽  
Benoit Duchemin
Keyword(s):  
Land Surface ◽  
Thermal Infrared ◽  
Infrared Emissivity

Prediction of water temperature heterogeneity of braided rivers using very high resolution thermal infrared (TIR) images

2013 ◽  
Vol 34 (13) ◽  
pp. 4812-4831 ◽  
Author(s):  
Vincent Wawrzyniak ◽  
Hervé Piégay ◽  
Pascal Allemand ◽  
Lise Vaudor ◽  
Philippe Grandjean
Keyword(s):  
High Resolution ◽  
Water Temperature ◽  
Thermal Infrared ◽  
Braided Rivers ◽  
Very High

scholarly journals Multisensor Fusion of Landsat Images for High-Resolution Thermal Infrared Images Using Sparse Representations

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Sung Jin ◽  
Dongyeob Han
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Spatial Resolution ◽  
Land Surface ◽  
Spectral Response ◽  
Thermal Infrared ◽  
Environmental Research ◽  
Infrared Images ◽  
Thermal Infrared Images ◽  
Thermal Band

Land surface temperature (LST) is an important parameter in the analysis of climate and human-environment interactions. Landsat Earth observation satellite data including a thermal band have been used for environmental research and applications; however, the spatial resolution of this thermal band is relatively low. This study investigates an efficient method of fusing Landsat panchromatic and thermal infrared images using a sparse representation (SR) technique. The application of SR is used for the estimation of missing details of the available thermal infrared (TIR) image to enhance its spatial features. First, we propose a method of building a proper dictionary considering the spatial resolution of the original thermal image. Second, a sparse representation relation between low- and high-resolution images is constructed in terms of the Landsat spectral response. We then compare the fused images created with different sampling factors and patch sizes. The results of both qualitative and quantitative evaluation show that the proposed method improves spatial resolution and preserves the thermal properties of basic LST data for use with environmental problems.

scholarly journals Automated Coastline Extraction Using the Very High Resolution WorldView (WV) Satellite Imagery and Developed Coastline Extraction Tool (CET)

2021 ◽  
Vol 11 (20) ◽  
pp. 9482
Author(s):  
Fran Domazetović ◽  
Ante Šiljeg ◽  
Ivan Marić ◽  
Josip Faričić ◽  
Emmanuel Vassilakis ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Land Surface ◽  
Coastal Areas ◽  
Ease Of Use ◽  
Future Research ◽  
Mapping Technique ◽  
Surface Model ◽  
Stereo Pair ◽  
Very High

The accurate extraction of a coastline is necessary for various studies of coastal processes, as well as for the management and protection of coastal areas. Very high-resolution satellite imagery has great potential for coastline extraction; however, noises in spectral data can cause significant errors. Here, we present a newly developed Coastal Extraction Tool (CET) that overcomes such errors and allows accurate and time-efficient automated coastline extraction based on a combination of WorldView-2 (WV-2) multispectral imagery and stereo-pair-derived digital surface model (DSM). Coastline extraction is performed and tested on the Iž-Rava island group, situated within the Northern Dalmatian archipelago (Croatia). Extracted coastlines were compared to (a) coastlines extracted from state topographic map (1:25,000), and (b) coastline extracted by another available tool. The accuracy of the extracted coastline was validated with centimeter accuracy reference data acquired using a UAV system (Matrice 600 Pro + MicaSense RedEdge-MX). Within the study area, two small islets were detected that have not been mapped during the earlier coastline mapping efforts. CET proved to be a highly accurate coastline mapping technique that successfully overcomes spectral-induced errors. In future research, we are planning to integrate data obtained by UAVs infrared thermography (IRT) and in situ sensors, measuring sea and land surface temperatures (SST and LST), into the CET, given that this has shown promising results. Considering its accuracy and ease of use, we suggest that CET can be applied for automated coastline extraction in other large and indented coastal areas. Additionally, we suggest that CET could be applied in longitudinal geomorphological coastal erosion studies for the automated detection of spatio-temporal coastline displacement.

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