Universität zu Köln

Huett, Christoph, Koppe, Wolfgang, Miao, Yuxin and Bareth, Georg (2016). Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images. Remote Sens., 8 (8). BASEL: MDPI. ISSN 2072-4292

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Abstract

When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. Our study site, the Qixing farm in Heilongjiang province, China, is especially suitable to demonstrate this. As in most rice growing regions, there is a high cloud cover during the growing season, making LULC from optical images unreliable. From the study year 2009, we obtained nine TerraSAR-X, two Radarsat-2, one Envisat-ASAR, and an optical FORMOSAT-2 image, which is mainly used for comparison, but also for a combination. To evaluate the potential of the input images and derive LULC with the highest possible precision, two classifiers were used: the well-established Maximum Likelihood classifier, which was optimized to find those input bands, yielding the highest precision, and the random forest classifier. The resulting highly accurate LULC-maps for the whole farm with a spatial resolution as high as 8m demonstrate the beneficial use of a combination of x-and c-band microwave data, the potential of multitemporal very high resolution multi-polarization TerraSAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images for LULC classifications.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Huett, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Koppe, Wolfgang UNSPECIFIED UNSPECIFIED UNSPECIFIED Miao, Yuxin UNSPECIFIED UNSPECIFIED UNSPECIFIED Bareth, Georg UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-269186
DOI:	10.3390/rs8080684
Journal or Publication Title:	Remote Sens.
Volume:	8
Number:	8
Date:	2016
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2072-4292
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 RANDOM FOREST CLASSIFIER; TERRASAR-X; RICE; RADAR; IDENTIFICATION; CONTEXT; CHINA; AREA Multiple languages Remote Sensing Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/26918