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

133 L L km. L § low ;velopment of the 10ut the growing :ypes. Although Intensity scales, se measurements, efficient of the le images and neasurements, late and in the factor was applied :e 4 is for Lng angle. The ) the calibration lie. The radar is ilibration. The > preprocessing care of the irrection and calibration letter, as was :ors. i large contrast Figure 4. Development of radar backscatter throughout the growing season for some croptypes. X-band SLAR, horizontal polarization, 15° grazing angle. exists between winterwheat and the other croptypes in April and May. In June the contrast is very small, while all the crops are in their growing stage. In July a good contrast is present between all the crop- types, whereas in August the development of the backscatter coefficient of potatoes interferes with the one for winterwheat. The large contrast between winterwheat and the other croptypes in the early growing season only exists at low grazing angles. It can be explained as follows: the wintercrops, like winterwheat, are planted before winter and start growing in this area in April. The other croptypes are planted in April and May and show their biomass not before the end of May. Although the ground coverage by the new plants is small, the backscatter at low grazing angles is increased, because the smooth soil alone gives a very small amount of backscatter at these angles, so the small leafs sticking out of the ground contribute consider ably to the total backscatter. At larger grazing angles say around 40°, the backscatter from the fields is much increased and the previously described effect is smaller, resulting in very little to no contrast between these croptypes. Thus we should be able to distinguish between winter- and summer crops from one flight in April or May, and since our testarea contains mainly one wintercrop, namely winterwheat, we should be able to identify all winterwheat fields. Figure 5 shows the histogram of the field averaged radar backscatter coefficients of the SLAR image from May. From this figure it is clear that the winterwheat fields can be completely separated from the other fields, simply by applying a threshold level. Now that the winterwheat is identified, we must try to classify the remaining fields from other flights. This demonstrates the hierarchy in our classifier in contrast with the previous classification experiment (Ref. 1) where the time dependence of the radar back scatter throughout the growing season was used as discriminator. Sofar the design of the classifier was straight Figure 5. Histogram of the May flight: winterwheat (right) is separated from the other croptypes. forward and rather simple. However to derive an optimum result more elaborate methods should be used to investigate the data. Our main purpose is to make a selection from the available features per field. Eigenvalue or principal component analysis can be used to reduce the dataset into a set of uncorrelated features. This is done by a dataprojection on two or more Eigen vectors, which are determined from the covariance matrix of the dataset. An evaluation of the dataset using this method showed that the first two eigen vectors contained 91% of the total variance, which means that the other four eigen vectors may be deleted. The first eigen vector is mainly determined from the April- and May features, whereas the second eigen vector is in fact a combination of the two July features, so the two flights at different altitudes. Since the datasets from April and May are highly correlated (correlation coefficient 0.91), the dataset of May was chosen as before and furthermore we selected the two July features. Figure 6 shows feature space plots for May versus July and for the 2 July features. A cross reference of the labels used in this and other figures can be found in Table 1. In both plots clusters of croptypes can be distinguished. A projection on one of the axes makes the classes inseparable, except for the wheat in May of course and the sugarbeets in July. The combination of the two July features means that we deal here with angular dependences to obtain discrimination. The short time interval between these two measurements more or less guarantees that the differences are only caused by the change in incidence angle. Therefore the clusters croptype label label potatoes sugarbeet winterwheat peas onions oats winterbarley beans grass seed spinach A B T E U H GR BO GZ SP 1 2 3 4 5 6 7 8 9 10 Table 1. Legend to plotlabels

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