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Mapping of grassland using seasonal statistics derived from multi-temporal satellite images

Zillmann, Erik and Weichelt, Horst and Montero Herero, Enrique and Esch , Thomas and Keil, Manfred and Wolvelaer, van, Joeri (2013) Mapping of grassland using seasonal statistics derived from multi-temporal satellite images. MultiTemp 2013 - 7th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 25.-27. Jun. 2013, Banff, Alberta, Canada.

Full text not available from this repository.

Abstract

Grasslands cover about 40 % of the earth’s surface. Due to its great expanse and diversity, low-cost tools for inventory, management and monitoring are needed. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and to support large scale grassland management. In the context of “GIO land” (Copernicus initial operations land), which is currently implemented by the European Environment Agency (EEA), the permanent grasslands of 39 countries in Europe has to be mapped with an overall classification accuracy of more than 80 %. Since grassland canopy density, chlorophyll status and ground cover is highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use time series to characterize the phenological dynamics of grasslands throughout the year to be able to discriminate among them and other vegetation which shows similar spectral response such as crops. The article outlines the adopted classification method using multi-temporal, multi-scale and multi-source remotely sensed data. The approach is based on the supervised decision Tree (DT) classifier C5 in combination with previous image segmentation and seasonal statistics of bio-physical parameters. In this paper the results of entire Hungary are presented. The accuracy assessment of the grassland classification was carried out using 340 sample points mainly derived from a ground-based European field survey program. The multi-temporal grassland classification of Hungary reached an overall accuracy of 92.2 %.

Item URL in elib:https://elib.dlr.de/83352/
Document Type:Conference or Workshop Item (Paper)
Title:Mapping of grassland using seasonal statistics derived from multi-temporal satellite images
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Zillmann, Erikzillmann (at) rapideye.comUNSPECIFIED
Weichelt, HorstRapidEye AGUNSPECIFIED
Montero Herero, EnriqueIndraUNSPECIFIED
Esch , ThomasDLRUNSPECIFIED
Keil, ManfredDLRUNSPECIFIED
Wolvelaer, van, JoeriEurosenseUNSPECIFIED
Date:2013
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-3
Status:Published
Keywords:grasslands; remote sensing; multi-seasonal; bio-physical parameters
Event Title:MultiTemp 2013 - 7th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images
Event Location:Banff, Alberta, Canada
Event Type:international Conference
Event Dates:25.-27. Jun. 2013
Organizer:University of Calgary, Alberta, Canada
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Fernerkundung der Landoberfläche (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Keil, Manfred
Deposited On:01 Aug 2013 08:38
Last Modified:21 Jan 2015 07:55

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