Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 2 Microwave data. Chairman: N. Lannelongue, Liaison: L. Krul

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Identifying agricultural crops in radar images. P. Hoogeboom

Document type:: Multivolume work

Structure type:: Chapter

131 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Identifying agricultural crops in radar images P.Hoogeboom Physics and Electronics Laboratory TNO, The Hague, Netherlands ABSTRACT: In 1980 a large SLAR flight program was carried out over an agricultural area in The Netherlands. A classification study on this multitemporal dataset (Ref. 1) showed that high accuracies are obtained from a simultaneous classification of 3 flights. In this paper the results of a follow-on study will be discussed. The goal is to obtain the best possible classification result in the earliest possible stage of the growing season. Therefore the SLAR flights from April, May, June and July were analyzed and the hierarchic classifier is intro duced. Very satisfying results were obtained from a combination of 3 flights: 1 in May, 2 in July at different incidence angles. 1 INTRODUCTION In this paper the results will be discussed from a follow-on study on previous classification experiments (Refs. 1, 3). This study forms a part of a broader national remote sensing research program for agricul ture and forestry, carried out by the ROVE-team (Radar Observation on VEgetation), a collaboration of several institutes (Ref. 1). The testsite on which the study is performed is situated in the Flevopolder, a reclaimed land area. Figure 1 shows a part of this polder, including the testarea. The latter contains 195 agricultural fields, of which 164 were suitable for this experiment (crop type known, reasonable dimensions). Frequently used crop types in this area are winterwheat, potatoes and sugarbeets (80% of total area). Onions and peas are also important crop types, but grown on smaller fields and therefore make up only 8.5% of the area. These 5 most occuring crop types were used in designing the classifier. Figure 1. Map of the Flevopolder with the testarea indicated (near 'Biddinghuizen'). The map shows an area of 35 x 35 km. The testarea measures 3.7 x 6.2 km. The area was imaged with an X-band SLAR system, using digital recording, on 5 different dates throughout the growing season. At each flight date recordings were made from 3 different altitudes, resulting in 3 incidence angle ranges, and from 2 opposite sides of the testarea. This flight campaign resulted in a multitemporal and multiangular database of the area. A selection of these flights is shown in fig. 2. The development of the radar backscatter through time can be viewed from this selection. The sampling interval is appr. 1 month. For July two images are shown: one is flown at 660 m altitude, like the other images shown, which results in a grazing angle range from 7.5° to 16° (right to left in the images). The second July image is flown at 1600 m, resulting in grazing angles between 18° and 35°. For comparison a croptype map is shown in figure 3. This figure results from the radar images, after registration and field segmentation. The segmentation is done manual by drawing the field boundaries in the image on an image processing system. Only the 3 main croptypes could be indicated here, because of the limited separability of gray tones in a black and white image after reproduction. However 80% of the area is covered by these 3 croptypes. The advantage of field segmentation of the radar data is two fold: 1. The influence of speckle on the classification result is reduced to practically zero. This also holds for small inhomogeneities within the fields. 2. The amount of data is tremendously reduced, since we end up with one value per image for every field, thus 164 vàlues for one image. For the classification experiment the radar data of the 6 mentioned images were combined with the groundtruth into one datafile. For every field there are 7 features, i.e. the true field label (croptype) and 6 average backscatter coefficients. 2 CLASSIFICATION EXPERIMENT The purpose of the experiment was to design a classifier on basis of the radar data of the 5 most important croptypes. The classifier should be able to distinguish between these croptypes as early as possible in the growing season. This is different from the previous experiment (Ref. 1), where we used the flights of June, July and August for classification. For operational applications an early result would be much more useful. Certainly, an improvement over the older experiment should be possible, considering the high contrast in the early season flights (fig. 2,

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