Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data
Land cover area estimates can be derived via design-based approaches using a probability (random) reference sample. The collection of samples is usually costly and requires an effective sampling design. Earth-Observation-based mapping approaches do not have this requirement but can be biased in prov...
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Format: | Article |
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MDPI AG
2022-09-01
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Series: | Remote Sensing |
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Online Access: | https://www.mdpi.com/2072-4292/14/19/4903 |
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author | Luca Kleinewillinghöfer Pontus Olofsson Edzer Pebesma Hanna Meyer Oliver Buck Carsten Haub Beatrice Eiselt |
author_facet | Luca Kleinewillinghöfer Pontus Olofsson Edzer Pebesma Hanna Meyer Oliver Buck Carsten Haub Beatrice Eiselt |
author_sort | Luca Kleinewillinghöfer |
collection | DOAJ |
description | Land cover area estimates can be derived via design-based approaches using a probability (random) reference sample. The collection of samples is usually costly and requires an effective sampling design. Earth-Observation-based mapping approaches do not have this requirement but can be biased in providing area estimates. Combining reference samples with remote sensing products can reduce sampling efforts and provide a more effective method to estimate land cover. The Copernicus High-Resolution Layer (HRL) provides remote-sensing-based data across Europe to support area estimation. Different methods are tested to estimate areas of imperviousness in four selected countries in Europe to demonstrate the use and shortcomings of existing reference information from the LUCAS survey program and the HRL Imperviousness products from 2015 and 2018. |
first_indexed | 2024-03-09T21:13:15Z |
format | Article |
id | doaj.art-ac70c24855334adcb5f30104f4c741b6 |
institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-09T21:13:15Z |
publishDate | 2022-09-01 |
publisher | MDPI AG |
record_format | Article |
series | Remote Sensing |
spelling | doaj.art-ac70c24855334adcb5f30104f4c741b62023-11-23T21:40:36ZengMDPI AGRemote Sensing2072-42922022-09-011419490310.3390/rs14194903Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference DataLuca Kleinewillinghöfer0Pontus Olofsson1Edzer Pebesma2Hanna Meyer3Oliver Buck4Carsten Haub5Beatrice Eiselt6EFTAS Fernerkundung Technologietransfer GmbH, Oststraße 2, 48145 Münster, GermanyDepartment of Earth and Environment, Boston University Arts & Sciences, 685 Commonwealth Avenue, Boston, MA 02215, USAInstitute for Geoinformatics, Westfälische Wilhelms-Universität Münster, Heisenbergstrasse 2, 48149 Münster, GermanyInstitute of Landscape Ecology, Westfälische Wilhelms-Universität Münster, Heisenbergstrasse 2, 48149 Münster, GermanyEFTAS Fernerkundung Technologietransfer GmbH, Oststraße 2, 48145 Münster, GermanyEFTAS Fernerkundung Technologietransfer GmbH, Oststraße 2, 48145 Münster, GermanyEuropean Commission, Eurostat, Rue Alphonse Weicker 5 L, 2721 Kirchberg, LuxembourgLand cover area estimates can be derived via design-based approaches using a probability (random) reference sample. The collection of samples is usually costly and requires an effective sampling design. Earth-Observation-based mapping approaches do not have this requirement but can be biased in providing area estimates. Combining reference samples with remote sensing products can reduce sampling efforts and provide a more effective method to estimate land cover. The Copernicus High-Resolution Layer (HRL) provides remote-sensing-based data across Europe to support area estimation. Different methods are tested to estimate areas of imperviousness in four selected countries in Europe to demonstrate the use and shortcomings of existing reference information from the LUCAS survey program and the HRL Imperviousness products from 2015 and 2018.https://www.mdpi.com/2072-4292/14/19/4903area estimationCopernicusLUCASregression estimator |
spellingShingle | Luca Kleinewillinghöfer Pontus Olofsson Edzer Pebesma Hanna Meyer Oliver Buck Carsten Haub Beatrice Eiselt Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data Remote Sensing area estimation Copernicus LUCAS regression estimator |
title | Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data |
title_full | Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data |
title_fullStr | Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data |
title_full_unstemmed | Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data |
title_short | Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data |
title_sort | unbiased area estimation using copernicus high resolution layers and reference data |
topic | area estimation Copernicus LUCAS regression estimator |
url | https://www.mdpi.com/2072-4292/14/19/4903 |
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