[389] - Evaluation of random forest-based analysis for the gypsum distribution in the Atacama desert
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Due to the speed of the filesystem and depending on the size of the archive and the file to be extracted, it may take up to thirty (!) minutes until a download is ready! Beware of that when confirming since you may not close the tab because otherwise, you will not get your file!
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Citation | ||
Hoffmeister, D., Herbrecht, M., Kramm, T., Schulte, P., 2020. Evaluation of random forest-based analysis for the gypsum distribution in the Atacama desert.Proc. of IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), March 22 - 26, 2020, Santiago, Chile, 25 - 28. DOI: 10.5194/isprs-annals-IV-3-W2-2020-25-2020. | ||
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Identification | ||
Title(s): | Main Title: Evaluation of random forest-based analysis for the gypsum distribution in the Atacama desert | |
Description(s): | Abstract: Gypsum-rich material covers the hillslopes above ∼ 1000 m of the Atacama and forms the particular landscape. In this contribution, we evaluate random forest-based analysis in order to predict the gypsum distribution in a specific area of ∼ 3000 km2, located in the hyperarid core of the Atacama. Therefore, three different sets of input variables were chosen. These variables reflect the different factors forming soil properties, according to digital soil mapping. The variables are derived from indices based on imagery of the ASTER and Landsat-8 satellite, geomorphometric parameters based on the Tandem-X World DEM™, as well as selected climate variables and geologic units. These three different models were used to evaluate the Ca-content derived from soil surface samples, reflecting gypsum content. All three different models derived high values of explained variation (r2 > 0.886), the RMSE is ∼ 4500 mg∙kg−1 and the NRMSE is ∼ 6%. Overall, this approach shows promising results in order to derive a gypsum content prediction for the whole Atacama. However, further investigation on the independent variables need to be conducted. In this case, the ferric oxides index (representing magnetite content), slope and a temperature gradient are the most important factors for predicting gypsum content. | |
Identifier(s): | DOI: 10.5194/isprs-annals-IV-3-W2-2020-25-2020 | |
Responsible Party | ||
Creator(s): | Author: Dirk Hoffmeister Author: Marina Herbrecht Author: Tanja Kramm Author: Philipp Schulte | |
Funding Reference(s): | Deutsche Forschungsgemeinschaft (DFG): CRC 1211: Earth - Evolution at the Dry Limit | |
Publisher: | ISPRS | |
Topic | ||
CRC1211 Topic: | Remote Sensing | |
Related Sub-project(s): | Z2 | |
Subject(s): | CRC1211 Keywords: Geomorphology, GIS, Satellite remote sensing | |
Topic Category: | GeoScientificInformation | |
File Details | ||
File Name: | isprs-annals-IV-3-W2-2020-25-2020.pdf | |
Data Type: | Text | |
File Size: | 1032 kB (1.008 MB) | |
Date(s): | Available: 2020-10-29 | |
Mime Type: | application/pdf | |
Data Format: | ||
Language: | English | |
Status: | Completed | |
Constraints | ||
Download Permission: | Free | |
General Access and Use Conditions: | No conditions apply | |
Access Limitations: | No limitations | |
Licence: | Creative Commons Attribution 4.0 International (CC BY 4.0) | |
Geographic | ||
North: | - | ![]() |
East: | - | |
South: | - | |
West: | - | |
Measurement Region: | Central focus area | |
Measurement Location: | --Central focus area-- | |
Specific Informations - Publication | ||
Status: | Published | |
Review: | PeerReview | |
Year: | 2020 | |
Type: | Event Paper | |
Page Range: | 25 - 28 | |
Event Name: | IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020) | |
Event Type: | Conference | |
Event Location: | Santiago, Chile | |
Event Period: | 22nd of March, 2020 - 26th of March, 2020 | |
Metadata Details | ||
Metadata Creator: | Dirk Hoffmeister | |
Metadata Created: | 2020-12-16 | |
Metadata Last Updated: | 2020-12-16 | |
Subproject: | Z2 | |
Funding Phase: | 1 | |
Metadata Language: | English | |
Metadata Version: | V43 | |
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Page Visits: | 58 | |
Metadata Downloads: | 0 | |
Dataset Downloads: | 1 | |
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