DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Determining suitable image resolutions for accurate supervised crop classification using remote sensing data

Löw, Fabian and Duveiller, Gregory (2013) Determining suitable image resolutions for accurate supervised crop classification using remote sensing data. In: Proceedings of SPIE (8893), pp. 1-15. SPIE. SPIE Remote Sensing, 23. - 26. Sept. 2013, Dresden. ISBN doi: 10.1117/12.2028634.

Full text not available from this repository.


Mapping the spatial distribution of crops has become a fundamental input for agricultural production monitoring using remote sensing. However, the multi-temporality that is often necessary to accurately identify crops and to monitor crop growth generally comes at the expense of coarser observation supports, and can lead to increasingly erroneous class allocations caused by mixed pixels. For a given application like crop classification, the spatial resolution requirement (e.g. in terms of a maximum tolerable pixel size) differs considerably over different landscapes. To analyse the spatial resolution requirements for accurate crop identification via image classification, this study builds upon and extends a conceptual framework established in a previous work1. This framework allows defining quantitatively the spatial resolution requirements for crop monitoring based on simulating how agricultural landscapes, and more specifically the fields covered by a crop of interest, are seen by instruments with increasingly coarser resolving power. The concept of crop specific pixel purity, defined as the degree of homogeneity of the signal encoded in a pixel with respect to the target crop type, is used to analyse how mixed the pixels can be (as they become coarser), without undermining their capacity to describe the desired surface properties. In this case, this framework has been steered towards answering the question: “What is the spatial resolution requirement for crop identification via supervised image classification, in particular minimum and coarsest acceptable pixel sizes, and how do these requirements change over different landscapes?” The framework is applied over four contrasting agro-ecological landscapes in Middle Asia. Inputs to the experiment were eight multi-temporal images from the RapidEye sensor, the simulated pixel sizes range from 6.5 m to 396.5 m. Constraining parameters for crop identification were defined by setting thresholds for classification accuracy and uncertainty. Different types of crops display marked individuality regarding the pixel size requirements, depending on the spatial structures and cropping pattern in the sites. The coarsest acceptable pixel sizes and corresponding purities for the same type of crop were found to vary from site to site, and some crops could not be identified using pixels coarser than 200 m.

Item URL in elib:https://elib.dlr.de/87377/
Document Type:Conference or Workshop Item (Speech)
Title:Determining suitable image resolutions for accurate supervised crop classification using remote sensing data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Löw, Fabianfabian.loew (at) uni-wuerzburg.deUNSPECIFIED
Journal or Publication Title:Proceedings of SPIE
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-15
Series Name:Earth Resources and Environmental Remote Sensing/GIS Applications IV
ISBN:doi: 10.1117/12.2028634
Keywords:Crop identification, Crop monitoring, Middle Asia, Pixel purity, Random forest, Spatial scale, Supervised classification
Event Title:SPIE Remote Sensing
Event Location:Dresden
Event Type:international Conference
Event Dates:23. - 26. Sept. 2013
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:02 Feb 2014 21:09
Last Modified:08 May 2014 23:31

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.