Mapping Soil Degradation using Remote Sensing Data and Ancillary Data - South-East Moravia, Czech Republic

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027049%3A_____%2F18%3AN0000067" target="_blank" >RIV/00027049:_____/18:N0000067 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41210/19:79454 RIV/00216208:11310/19:10378889

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/22797254.2018.1482524" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/22797254.2018.1482524</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/22797254.2018.1482524" target="_blank" >10.1080/22797254.2018.1482524</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mapping Soil Degradation using Remote Sensing Data and Ancillary Data - South-East Moravia, Czech Republic

Popis výsledku v původním jazyce

Data on the real extent of soil that is degraded by erosion represent important information for the purposes of conservation policy. However, this type of data is rarely available for large areas. A remote-sensing-based method for identifying of eroded areas at the regional scale has been tested using a combination of time series of free access Sentinel-2 image data, airborne orthoimages and ground-truth data. The unsupervised classification ISODATA of the Sentinel-2A images has been performed. The minimum distance method has been applied for the assignment of unsupervised classes to four erosion classes using the ground-truth data. The automatic classification of eroded soils achieved an overall accuracy of 55.2 % for three distinguished classes. An accumulated class has been eliminated as no unsupervised classes were assigned to this erosion class. A simplified classification of two classes (strongly eroded and other soils) reached an accuracy of 80.9%. The overall accuracy of the simplified classification increased to 86.9% after the visual refinement using orthoimages. This study shows the potential of the tested approach to produce valuable data on actual soil degradation by erosion. The limitations of the method are related to the soil cover variability, masking effect of clouds, vegetation or litter and the spectral separability of individual classes.

Název v anglickém jazyce

Mapping Soil Degradation using Remote Sensing Data and Ancillary Data - South-East Moravia, Czech Republic

Popis výsledku anglicky

Data on the real extent of soil that is degraded by erosion represent important information for the purposes of conservation policy. However, this type of data is rarely available for large areas. A remote-sensing-based method for identifying of eroded areas at the regional scale has been tested using a combination of time series of free access Sentinel-2 image data, airborne orthoimages and ground-truth data. The unsupervised classification ISODATA of the Sentinel-2A images has been performed. The minimum distance method has been applied for the assignment of unsupervised classes to four erosion classes using the ground-truth data. The automatic classification of eroded soils achieved an overall accuracy of 55.2 % for three distinguished classes. An accumulated class has been eliminated as no unsupervised classes were assigned to this erosion class. A simplified classification of two classes (strongly eroded and other soils) reached an accuracy of 80.9%. The overall accuracy of the simplified classification increased to 86.9% after the visual refinement using orthoimages. This study shows the potential of the tested approach to produce valuable data on actual soil degradation by erosion. The limitations of the method are related to the soil cover variability, masking effect of clouds, vegetation or litter and the spectral separability of individual classes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

European Journal of Remote Sensing

ISSN

2279-7254

e-ISSN

2279-7254

Svazek periodika

sup1

Číslo periodika v rámci svazku

52

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

16

Strana od-do

108-122

Kód UT WoS článku

000475928900009

EID výsledku v databázi Scopus

2-s2.0-85049792270