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Assessment of cropping system diversity in the Fergana Valley through image fusion of LANDSAT 8 and SENTINEL-1

Dimov, Dimo und Kuhn, Johannes und Conrad, Christopher (2016) Assessment of cropping system diversity in the Fergana Valley through image fusion of LANDSAT 8 and SENTINEL-1. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7, Seiten 173-180. XXIII ISPRS Congress, 2016-07-12 - 2016-07-19, Prag, Tschechien.

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Offizielle URL: http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/173/2016/

Kurzfassung

In the transitioning agricultural societies of the world, food security is an essential element of livelihood and economic development with the agricultural sector very often being the major employment factor and income source. Rapid population growth, urbanization, pollution, desertification, soil degradation and climate change pose a variety of threats to a sustainable agricultural development and can be expressed as agricultural vulnerability components. Diverse cropping patterns may help to adapt the agricultural systems to those hazards in terms of increasing the potential yield and resilience to water scarcity. Thus, the quantification of crop diversity using indices like the Simpson Index of Diversity (SID) e.g. through freely available remote sensing data becomes a very important issue. This however requires accurate land use classifications. In this study, the focus is set on the cropping system diversity of garden plots, summer crop fields and orchard plots which are the prevalent agricultural systems in the test area of the Fergana Valley in Uzbekistan. In order to improve the accuracy of land use classification algorithms with low or medium resolution data, a novel processing chain through the hitherto unique fusion of optical and SAR data from the Landsat 8 and Sentinel-1 platforms is proposed. The combination of both sensors is intended to enhance the object´s textural and spectral signature rather than just to enhance the spatial context through pansharpening. It could be concluded that the Ehlers fusion algorithm gave the most suitable results. Based on the derived image fusion different object-based image classification algorithms such as SVM, Naïve Bayesian and Random Forest were evaluated whereby the latter one achieved the highest classification accuracy. Subsequently, the SID was applied to measure the diversification of the three main cropping systems.

elib-URL des Eintrags:https://elib.dlr.de/109140/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Assessment of cropping system diversity in the Fergana Valley through image fusion of LANDSAT 8 and SENTINEL-1
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Dimov, Dimodimo.dimov (at) uni-wuerzburg.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Kuhn, JohannesNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Conrad, Christopherchristopher.conrad (at) uni-wuerzburg.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2016
Erschienen in:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Band:III-7
Seitenbereich:Seiten 173-180
Status:veröffentlicht
Stichwörter:Image Fusion, Crop mapping, Synthetic Aperture Radar, Image classification, Sentinel-1
Veranstaltungstitel:XXIII ISPRS Congress
Veranstaltungsort:Prag, Tschechien
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:12 Juli 2016
Veranstaltungsende:19 Juli 2016
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum
Hinterlegt von: Wöhrl, Monika
Hinterlegt am:07 Dez 2016 15:40
Letzte Änderung:24 Apr 2024 20:14

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