General Remarks

At the end of the above-described working steps, the gross development area had been – by and large - narrowed down to those pixels, which represent irrigated lands. Potential exemptions are the following:

• small water bodies and small settlements which were too small to be excluded by the filter procedure described above,

• fallow land,

• small miscellaneous areas which are neither water, nor settlements, nor fallow land (e.g.

quarries, gravel and rubble areas along the riverbeds, little areas of bush and shrub) Hence, the last working step now aimed at the following tasks:

• to filter out the latter two classes (fallows and miscellaneous areas respectively) and

• to sub-divide the irrigated areas further into different land use / land cover types.

In contrast to the working steps described before, this task now requires a digital image processing approach which makes use of the different spectral characteristics of the various land cover types. Hence, a supervised digital image classification of the remaining image pixels was performed.

Supervised image classification is a well-known standard image processing method which needs not be explained in detail.¹⁹ The method uses the fact that different types of land cover,

19 More detailed information on digital image classification is given in any remote sensing textbook, such as Mother (1999).

such as forest, pasture, water, bare soil, as well as different crop types feature different spectral characteristics. Once the correlations between different land cover classes and their respective spectral characteristics are established and verified for selected areas (the so-called 'training areas'), these correlations can be used to classify the image pixels of a satellite image into the respective ‘most likely’ land cover classes.

These ‘training areas’ are then used as decision reference for various classification algorithms (‘classifiers’). During the classification procedure, the selected classifier compares the spectral characteristics of the (still unclassified) image pixel with the spectral characteristics of the previously selected training areas. Using statistical procedures and decision rules, the classifier then selects the most likely land cover class for each pixel. Standards classifiers are, for example, Minimum Distance, Parallelepiped (‘Box’) and Maximum Likelihood. Most digital image processing software packages offer several variants for each classifier.

The most critical limitation of standard digital image classification methods lies in the fact that they use only the spectral reflectance of a pixel. Other image characteristics, such as form, pattern, size or location of features, which are quite important decision criteria in visual interpretation, are not taken into account. The quality and reliability of the classification results depend on various factors, the most important being:

• data quality of the satellite images,

• quality, number and representativeness of the training areas,

• reasonably large coherent areas of uniform land cover,

• clear spectral differences between different land cover classes,

• reasonable spectral homogeneity within each land cover class,

• numerous and reliable ground truth data,

• selection of the most appropriate classifier.

In the case of this study, these conditions could be met to a limited degree only! Considerable constraints limited the degree of detail and the degree of accuracy of the information extracted from the satellite data. The most important constraints, partly interrelated with each other, were:

• poor data quality (Landsat 2 data),

• unfavourable recording date of the satellite scenes,

• very limited possibilities to conduct field checks.

In the following, some additional comments are given on these constraints.

Poor data quality

While the data quality of the selected Landsat 7 scenes is excellent, the data quality of the three Landsat 2 images was poor. All three satellite scenes were full of systematic and unsystematic data errors including faulty data lines etc. The errors were too numerous and too extensively distributed over the satellite scenes to be removed with error correction routines. While these Landsat images were still sufficient for the visual interpretation tasks described in Chapter 3.4 and 3.5, a supervised digital image classification would have been a futile exercise! Apart from the insufficient data quality, there would have been no reliable ground truth data of the land cover situation which existed in the area more than 25 years ago! Hence, the Landsat 2 images were used for visual interpretation only.

Unfavourable recording date of the satellite images

A second major constraint was that three of the four Landsat 7 images were recorded rather early, in fact at a much too early stage of the growing season. The selected satellite scenes (Landsat 7) were taken May 27 (scenes 147 / 030 and 147 / 029, June 5 (scene 146 / 030) and June 27 (scene 145 / 030) respectively. As outlined in Chapter 2.1, the growing season starts in April and ends in September. To discriminate different crops by digital image classification, the

spectral characteristics of the different crops should be as different as possible. This, however, requires that the crops have reached a sufficiently mature crop development stage. Hence, 'optimal' satellite images should have been recorded late in the growing season. Around end of July / mid-August would have been the best period.²⁰ Unluckily, such more suitable images were not available for the area for year 2000 or 2001.

A field check, conducted by Chinese colleagues in June 2003, showed that during the period end of May / middle of June the crop-specific spectral differences are not yet sufficiently developed to allow for a good discrimination of different crops. As illustrated by Photo 1 and 2, at that time most plants are still fairly small and a major part of the recorded pixel signal is still dominated by the reflectance characteristics of bare soil. Therefore, the spectral signatures of different crops are still very similar, which considerably limits the potential for detailed crop discrimination (cp. Fig. 18). This especially holds true for crops like maize and sunflower, which at that phase of the growing season hardly cover 10% of the soil surface. Fig. 18 shows the reflectance curve for different crops at this early growing stage. As can be seen from the small differences between the two lines, the spectral characteristics of the two crops - here sunflower and soybean - are more or less identical at this early stage.

Another problem of the early recording date is that crop development differences within a particular crop may be considerable, depending on the respective time of sowing. A comparison of the two sunflower fields shown by Photo 1 and Photo 3 illustrates this problem. On Photo 1 the sunflowers are hardly visible while on Photo 3 the crop leaves cover already most of the soil surface. While this problem is most prominent for sunflowers, to some lesser extent it also holds true for maize and soybeans.

Fig. 19 and 20 illustrate the consequences for the spectral signal. Fig. 19 shows the spectral variations between four individual pixels belonging to the same sunflower plot. As illustrated, the form of the curves for the four pixels is fairly similar. However, the absolute reflection values may vary up to 20 DN-values (cp. in particular bands 1, 2 and 3).

The variation becomes even worse if spectral signals from different fields (covered with the same crop) are compared. Fig. 20 shows average spectral values for three different soybean fields which are located within the same area and even fairly close together. The figure clearly shows that there are considerable variations of the spectral signal, even between fields within the same area. The spectral differences are caused by differences in the crop development stage which is mainly due to different sowing dates. If the satellite data were recorded at a later

20 For a comparable land cover study of the lower course and the delta of the Amu-Darja, Ressl used Landsat scenes recorded in August (Ressl 1999, p. 73).

stage of the crop development cycle (e.g. in mid-July / early August) these 'in-field variations' and 'between-field' variations would have levelled out to a large extent.

It is clear that under these circumstances, crop-specific spectral reflectance signatures are very limited and, therefore, the crop discrimination potential as well. As a consequence, several little-developed crops had to be lumped together into one big land cover class.

Limitations regarding field check possibilities / limited ground truth

A third major constraint for the study was due to the fact that no team member actually visited the study area, neither for the usual pre-study reconnaissance trip, nor for the selection of the training areas, nor for the verification of the study results. The study area belongs to a region of China which is strategically and politically quite sensitive. For a foreigner, moving around freely in this area – as would have been required – is almost impossible and risky as well, in particular with ‘suspicious’ materials and instruments, such as satellite images and GPS. Apart from this problem, the field check would have caused considerable travel costs.

The cooperation with Chinese colleagues from the University of Urumqi offered an acceptable (though not fully satisfactory) way out. The Chinese colleagues were asked to compile ground truth data according to detailed prior instructions supplied by the authors. These instructions specified exactly where to go and what to check on each site. However, this assistance had logistic limitations regarding time availability, staff resources and the accessibility of the designated areas. As a consequence, only one field work campaign could be conducted at one selected moment during the study. This limited the number of the field check sites as well as their spatial distribution. It also ruled out the chance to interface field work and image processing more closely in order to adjust and optimise the processing results in an iterative way. More detailed information regarding the field check is given in Chapter 3.6.3.