Universität zu Köln

Huett, Christoph, Waldhoff, Guido and Bareth, Georg (2020). Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data. ISPRS Int. Geo-Inf., 9 (2). BASEL: MDPI. ISSN 2220-9964

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Abstract

Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during image acquisition. However, processing of SAR is more complicated and the sensors have development potential. Dealing with such a complexity, current studies should aim to be reproducible, open, and built upon free and open-source software (FOSS). Thereby, the data can be reused to develop and validate new algorithms or improve the ones already in use. This paper presents a case study of crop classification from microwave remote sensing, relying on open data and open software only. We used 70 multitemporal microwave remote sensing images from the Sentinel-1 satellite. A high-resolution, high-precision digital elevation model (DEM) assisted the preprocessing. The multi-data approach (MDA) was used as a framework enabling to demonstrate the benefits of including external cadastral data. It was used to identify the agricultural area prior to the classification and to create land use/land cover (LULC) maps which also include the annually changing crop types that are usually missing in official geodata. All the software used in this study is open-source, such as the Sentinel Application Toolbox (SNAP), Orfeo Toolbox, R, and QGIS. The produced geodata, all input data, and several intermediate data are openly shared in a research database. Validation using an independent validation dataset showed a high overall accuracy of 96.7% with differentiation into 11 different crop-classes.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Huett, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Waldhoff, Guido UNSPECIFIED UNSPECIFIED UNSPECIFIED Bareth, Georg UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-346085
DOI:	10.3390/ijgi9020120
Journal or Publication Title:	ISPRS Int. Geo-Inf.
Volume:	9
Number:	2
Date:	2020
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2220-9964
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Geosciences > Geographisches Institut
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language MULTI-DATA APPROACH; CLASSIFICATION; IMAGERY; COVER; GIS Multiple languages Geography, Physical; Remote Sensing Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/34608