BRITISH GEOLOGICAL SURVEY TECHNICAL REPORT WC/92/28

Remote sensing techniques for hydrogeological mapping in semi-arid

basement terrains

D Greenbaum

This report was prepared for the Overseas Development Administration

Bibliographic Reference:

Dr D Greenbaum. Remote sem'ng techniques for hydrogeologi cal mapping in semi-arid basement terrains. British Geological Survey Technical Report we192128

Cover:

Naike artist's impression of the village borehole in idealised rural Zimbabwe. Extract froin an original oils on hardboard painting by Malven Mangena

NERC copyright 1992

Report submitted June 1992 British Geological Survey, Keyworth, Nottingham

CONTENTS

1. INTRODUCTION 1

1.1 Aims and objectives 1.2 Methodology 1.3 Test area selection

1 2 2

2. REMOTELY SENSED DATA 3

2.1 Satellite imagery 2.2 Aerial photography 2.3 Preliminary site selection

3 7 8

3. SATELLITE IMAGE PROCESSING 8

3.1 Introduction 8 3.2 Spectral band combinations 9 3.3 Vegetation indices 9

9 3.3.1.1 Ratio-based indices 11 3.3.1.2 Perpendicular Vegetation Index 11 3.3.1.3 Green Vegetation Index (Tasselled Cap) 15 3.3.1.4 Effect of soil background on vegetation indices 15

3.3.2 Vegetation index images 23 3.4 Multi-date images 24

3.4.1 Principles 24 3.4.2 Image production 25

3.4.2.1 Transformation of multi-date data sets to radiance values 25 27

3.3.1 Relationships between vegetation indices

3.4.2.2 Display of multi-date images

4. IMAGE INTERPRETATION AND FIELD CORRELATIONS 29

4.1 Interpretation of vegetation patterns 4.2 False-colour composite images

4.2.1 Introduction 4.2.2 Land use and topographic effects

4.2.2.1 High relief terrain 4.2.2.2 Low relief terrain

4.3 Comparison of FCC and DVI images 4.4 Multi-date imagery

29 31 31 32 32 33 35 44

5. DISCUSSION AND CONCLUSIONS 49

ACKNOWLEDGEMENTS 51

REFERENCES 52

SUMMARY

The results are presented of a study to evaluate the use of commercially available satellite imagery for groundwater exploration in semi-arid regions underlain by crystalline basement rocks. Image processing techniques have been developed that optimally enhance surface features related to the presence of moisture and/or groundwater; these properties are primarily soil tone, and vegetation distribution and vigour. Soil tones are best represented on false-colour composites such as 4-5-1 or 4-5-7 but these images are not always reliable indicators of vegetation density. Digitally integrated multi-date images have proved difficult to understand but the study has shown that dry-season imagery is generally the most useful for groundwater exploration in this type of environment. Mathematical models have been developed to compare a variety of vegetation indices; it is shown that the Difference Vegetation Index @VI) is the most representative indicator of biomass over a range of vegetation densities for a variety of soil backgrounds. A new image product has been developed which presents the DVI as a colour-coded image superimposed on a relief background. This can be readily interpreted and understood by non-experts in terms of vegetation density, and can provide useful ancillary information at the sub-regional level in groundwater exploration programmes. As a consequence of man’s effect on the environment, severe constraints arise in interpreting imagery, which make the natural variations difficult to detect and isolate. In Zimbabwe, a major problem for remote sensing interpretation is the difference between the heavily overgrazed communal lands, where the natural patterns of vegetation have largely been destroyed, and the farm lands. The study has re-emphasised the valuable contribution that aerial photographs can make when used in conjuction with synoptic satellite imagery.

The overall aim of the project was to develop techniques that could be used in the production of reconnaissance hydrogeological maps. This has been partially achieved and the study has gone some way towards identifying factors which complicate the interpretation of imagery in the semi-arid, rural environment. It is concluded that remote sensing can provide valuable inputs to regional hydrogeological maps where good ground correlation is available and where the image interpretation data is integrated with other information on water resources.

The study was carried out under the ODA/BGS Programme of Research and Development in Developing Countries, forming part ofthe British Government’s overseas aid programme. The work was undertaken in collaboration with the Ministry of Energy, Water Resources and Development of the Republic of Zimbabwe whose support and generous help are gratefully acknowledged.

1: INTRODUCTION

1.1 Aims and objectives

This is the first of two final reports carried out under the ODA R & D project 'Hydrogeological mapping and borehole siting in areas of difficult hard-rock terrain'; this volume is concerned solely with remote sensing studies at the regional and sub-regional level. Site-specific studies describing the use of ground geophysical techniques are covered in a separate report. Work carried out in an earlier phase of the project is reported in Greenbaum and Amos (1991).

Many rural communities in arid and semi-arid areas are dependent on underground supplies for the provision of drinking water. In arid and semi-arid areas of crystalline basement, where the rocks lack primary intergranular porosity and permeability, underground water supplies are mostly restricted to the weathered overburden (regolith) and to structural traps (e.g. fractures, fissures and intrusive bodies) in the crystalline rocks. Groundwater exploration in these areas depends on a knowledge of both regional variations in hydrogeological conditions and local, site-specific features of the geology. Regional factors include rainfall, climate, relief and geology. The biosphere is the product of all these influences acting together; the aim of using remote sensing is to isolate regional and sub- regional factors of significance to groundwater in a cost-effective and rapid manner. Satellite images and aerial photographs provide information on the solid and superficial rocks, structures, geomorphology , patterns of vegetation, vegetation vigour and soil moisture, all of which have potential importance in groundwater exploration. This information, though contained within remotely sensed images, is poorly understood and is commonly masked by other effects (which are mostly man-made and often dominating) so that natural features of relevance are difficult to recognise. The objective of this study was to identify these influences and develop methods to isolate and identify factors of importance, especially in relation to vegetation cover. In this way remote sensing may be able to play a more central role in helping to guide, speed and reduce the overall costs of groundwater exploration.

It must be stressed that the production of a hydrogeological map of SE Zimbabweper se was not an objective of this research; rather the aim was to examine the potential of remote sensing to input to the compilation of such maps and to develop image processing approaches to realise this. Clearly, any form of hydrogeological map requires considerable inputs from other sources which were not within the remit of this study.

Digital satellite imagery and aerial photographs contain information that is to a large extent complementary; consequently, each have an important role to play in groundwater exploration. Most satellite images have a lower spatial resolution than aerial photographs but possess a greater potential for discriminating between different types of surface material (or condition) due to their multispectral properties. Their wide field of view makes them particularly useful in regional studies. Conversely, aerial photographs have the advantages of high spatial resolution, stereoscopy, cheapness and ease of availability; they are ideal for detailed interpretation at the local scale. Although air photos are extensively used for borehole siting their interpretation tends to be empirical and as a result their potential is not fully utilised. The combined use of both types of data (satellite imagery to provide

information at the regional level and aerial photography at the local level) promises substantial benefits.

Two areas in Zimbabwe have been studied, one in the east of the country and the other in the southeast. Various approaches have been taken to try to isolate and extract hydrogeologically meaningful information contained in the satellite data. New processing techniques have been developed to produce images from which this information can be extracted. These have included the digital merging of imagery of different dates, the analysis of dry-season false-colour composites, and the comparison of different types of vegetation- index image.

1.2 Methodology

The underlying approach taken has been to develop satellite image enhancement techniques to provide the most interpretabable image in terms of surface features and ground conditions, and to assess the relevance of interpreted features to groundwater through field checks. The image processing work has concentrated heavily on the spectral properties of Landsat Thematic Mapper (TM) imagery and in particular on those spectral bands that exhibit reflectance variations related to vegetation. Images acquired in different seasons and from different rainfall zones in east and southest Zimbabwe were compared. Studies included an evaluation of different spectral band combinations; the digital integration of multi-season imagery; comparative studies within particular land-use categories; and the use of vegetation indices to map the preservation of green vegetation during times of drought. Comparisons between aerial photographs and false-colour Landsat imagery were undertaken both to provide textural inputs to the interpretation of imagery and to better understand the significance of the monochrome tones of conventional black-and-white aerial photographs in regard to vegetation, soil type and presence of moisture. This is important since photographs are likely to remain the basis for detailed borehole siting for some time to come.

1.3 Test area selection

Much of Africa and Asia can be described as being arid to semi-arid. So far as groundwater is concerned these countries share a broadly similar geology (crystalline basement rocks with low primary intergranular porosity and low permeability) and experience similar problems of inadequate supplies, particularly in rural areas. It should be noted that many of these areas receive a moderate annual rainfall even though this is seasonal and tends to be erratic from year to year. The present (1991/92) drought in southern Africa - one of the most severe in this region for many years - indicates the unpredictability of the seasonal rains. The main ground problems are poor infiltration, surface run-off and high levels of evaporation, combined with the fundamental problem of limited aquifer storage. Zimbabwe was chosen for this study as it is typical of many moderate-to-low rainfall areas. It also has a good database of geological and hydrogeological information from previous work which is important in evaluating the use of remotely sensed data. Furthermore, it is a country in which logistical support is available from the Ministry of Energy, Water Resources and Development (MEWRD).

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Whereas the presence of groundwater is not directly observable on images or photographs to any great extent, remote sensing can offer a means of mapping features that are indirectly related to subsurface moisture or water. Such indications are most evident in areas of low rainfall. In the early part of the study the importance of Season of acquisition - in particular, periods following low or high rainfall conditions - was investigated to determine how far this affected the information content of an image. Substantial differences were found between wet- and dry-season images, largely resulting from vegetation changes, but the significance of season and year of acquisition of imagery had not previously been properly investigated from a groundwater viewpoint. An example of the variation in the mean monthly rainfall in Zimbabwe during the period 1980 to 1988 is shown in Figure 1. Similar plots for other regions of the country were used as a basis for deciding the suitable time windows for the imagery. The selection of a study area was constrained by the availability of cloud-free images for these time intervals. Finally, after several such comparisons of data listings and rainfall plots, an area in eastern central Zimbabwe, corresponding to Landsat path 169 row 073, was selected for which virtually cloud-free scenes were available covering the main periods of interest. This region is moderately dry with a mean annual rainfall varying from approximately 600 to 900 mm.

In a second phase of the work, an additional scene in a slightly drier region to the south of original area was studied. For this area, a cloud-free scene (169/074) dated 23 July 1986 (corresponding in date with one of the scenes to the north) was processed. Rainfall here is generally less (500 to 700 mm) with most areas studied being around 600 mm. The areas covered by the two Landsat scenes are shown on the Zimbabwe Mean Annual Rainfall map (Figure 2).

2: REMOTELY SENSED DATA

2.1. Satellite imagery

Four types of digital satellite data are presently widely available. These are:

Landsat MSS: 4 spectral bands; 79 m resolution Landsat TM: 7 spectral bands; 30 m resolution SPOT XS: 3 spectral bands; 20 m resolution (+ stereo) SPOT P: 1 spectral band; 10 m resolution (+ stereo)

Previous hydrogeological research by BGS in SE Zimbabwe (Greenbaum 1985, 1986, 1987) made use of Landsat MSS imagery; its moderate resolution, low cost and ease of processing make it useful for regional studies of faults and fractures at scales up to 1:25O,OOO or slightly greater. By comparison, Landsat TM data provides higher spatial resolution (30 m) combined with good spectral range at moderate cost. This data has been available since about 1983 and good quality, cloud-free cover now exists for many parts of southern Africa. SPOT imagery has much better spatial resolution, allowing enlargement up to larger scales, but its spectral range is no better than that of Landsat MSS. Unless the study requires very high spatial detail (at perhaps 1:25,000 - in which case it might anyway be better to use aerial photographs which are far cheaper and more readily available) there seems little advantage in choosing SPOT in this type of study. TM imagery can be enlarged to scales up to 1:5O,OOO. The

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broader spectral range of Landsat TM imagery, allowing the potential discrimination of different ground cover types and surface conditions, made it more appropriate to this type of semi-regional study. Another important consideration, particularly so far as developing countries are concerned, is that Landsat TM imagery is very much cheaper than SPOT on a unit area basis (a SPOT Scene covers one-ninth the area of a Landsat image at around one- third to one-half the cost of a TM image).

The Landsat satellites occupy sun-synchronous, near-polar orbits, and acquire imagery along a 185 km wide ground swath during each southward pass. The data are formatted as Scenes measuring 185 km across track by 170 km along track. The TM instrument is a line-scanning electro-optical sensor that records EM radiation in seven spectral bands; three in the visible (bands 1 to 3), one in the near infrared (band 4), two in the shortwave infrared (bands 5 and 7), and one in the thermal infrared (band 6). The positions of the visible and reflected infrared channels were carefully chosen after assessing the results of several years’ work mainly concerned with vegetation; these bands have obvious relevance to studies concerning groundwater. The exception is band 7, which was selected primarily for its uses in geology, in particular its ability to detect clays and other minerals containing hydroxyl ions. Spatial resolution is 30 m in all bands except the thermal channel 6 , which has a resolution of 120 m. Due to its orbit, the satellite passes over the equator at the roughly the same local time each morning; repeat coverage is possible every 16 days but, due to weather conditions, good quality, cloud-free imagery is seldom available with such frequency.

The bands used in the false-colour composites selected for this study have the following individual properties:

Band 1: visible blue

Band 4: strong reflectance from green vegetation

Band 5: responsive to soil tones (& moisture)

Band 7: moderate reflectance from soils but absorption by clay minerals

The band sequence in each image (e.g. TM4-5-1 and TM4-5-7) refers to the red-green-blue (RGB) additive primary colours. Each pixel in each band can be assigned one of 256 brightness values, so that a total of 2563 colour combinations are possible (although the number of visibZe colours is considerably reduced by the photographic reproduction processes used). Equal brightness values in each of the three bands produce a grey tone (varying from black to white) while different relative contributions of the three primary colours produce a range of hues and brightnesses. In the following sections on image interpretation, image colours are referred to qualitatively. Clearly, the observed colours on the false colour composites represent differences in reflectance between surface materials in the visible and infrared wavelengths; the actual colours have no significance in themselves and serve only as a shorthand means of describing the relative contributions from the three spectral bands Used.

Satellite imagery has the advantage in regional studies that large features and patterns can be more easily recognised. However, despite the moderate spatial resolution (30 m) of the

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Landsat TM, the lack of both fine spatial detail and stereoscopic viewing often makes it difficult to determine precisely what ground features are causing a particular effect, so thay it is often difficult from the imagery alone to assess the significance of ’anomalies’. In general, anomalies are recognised as areas of unusual patterns and colour tones suggestive of abnormal surface conditions.

2.2. Aerial photography

Aerial photography is the earliest and probably still the most widely used form of remotely- sensed data. Photographs have many practical advantages and are ideal for detailed geological and hydrogeological field investigations. Aerial photographs are available at a range of scales, commonly 15000 to 1:80,000; for detailed site work, scales of 1:2O,OOO to 1:40,000 are recommended. In Zimbabwe, the common general scales of photography are 1:8O,OOO and 1:25,000; re-survey of the whole of Zimbabwe at 1:25,000 takes place every five years. The availability of aerial photographs varies from country to country but is generally good. By comparison with satellite data, aerial photographs are very affordable. (In Zimbabwe, the office of the Surveyor-General is located centrally in Harare and provides an excellent, low- cost, over-the-counter service of supplying aerial photographs (and topographic maps) to the public).

One of the advantages of high spectral resolution and stereoscopy is that features and ground elements of potential interest can often be identified directly rather than being merely discriminated as ‘ground categories’ without any real understanding of their physical nature. Although satellite imagery is excellent for detecting features such as lineaments or tonal changes, the comparatively low resolution of the data often does not enable the interpreter to determine the nature of the feature being observed. For example, red tones on a satellite image often indicate green vegetation (red is commonly used for the near infrared band which is the band that responds most strongly to vegetation) but, given the available spatial resolution, the type of the vegetation cover (e.g. forest, grasslands, cultivation) can often only be guessed at; this can have important consequences in interpretation. By contrast, when low-level aerial photographs (at say 1:25,000) are used, there is usually no difficulty in identifying the nature of the vegetation cover from its textural characteristics (e.g. forest, scattered trees, grass etc). Because of their high spatial resolution, aerial photographs are very useful for checking features observed on satellite imagery; they often provide an immediate explanation, thus avoiding the need for a field visit.

On the other hand, aerial photographs have the limitation that features of very large size or characterised by spectral differences (colour tones) may go undetected. This is particularly true of major structures, such as faults or joints, manifested on images as lineaments. In current usage, a lineament is any linear or slightly curved feature, or combination of features, observable on a map, photograph or image and thought to be of geological origin (O’Leary et al. 1976). Important structures are often expressed as lineaments, combining elements of landscape, vegetation and soil tone; these can be easily missed at the scale and resolution of aerial photographs but are readily evident on smaller-scale satellite images.

One of the main benefits of aerial photographs is that the land surface can be seen stereoscopically with a vertical exaggeration that emphasises even small differences in relief. Although much attention is given to the recognition of lineaments in basement terrain (both

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from satellite images and aerial photographs), the geomorphology of the landscape can provide other information for the hydrogeologist of probably equal importance. Indeed, it is the clues provided by the geomorphology, and in particular the drainage system, that allow the hydrogeologist to begin to understand the pattern of local groundwater movement and to focus on favourable sites for further study.

2.3 Preliminary site selection

At its most fundamental level, the initial stage of borehole or dug well target selection simply involves choosing the most appropriate site within a catchment of moderate size. This will usually be somewhere near to the base of slope, possibly in a location showing dark, humic soils indicating the occurrence of seasonal wetlmoist conditions. In virtually all cases, the provisional site is then assessed using ground geophysical surveys. Thus, remote sensing is typically only the first stage of borehole siting, the purpose of which is to home in, as quickly and as accurately as possible, on the best site for more detailed survey work. The successful use of air photos in this way is commonly put down more to the experience of the hydrogeologist, rather than being viewed as a logical and conscious process. Nevertheless, it involves a step-by-step process making use of various clues provided at the surface. If a more rigorous analysis of remotely sensed data is to be carried out, some further 'classification' of surface features may be needed. This is where digital satellite data can be of benefit since, not only does the imagery provide information from several regions of the EM spectrum, but the data can be combined and enhanced in different ways to emphasise features of particular relevance and interest.

3: SATELLITE IMAGE PROCESSING

3.1 Introduction

The Landsat TM records radiance in seven spectral regions in the visible, reflected and thermal infrared parts of the spectrum. Many of these spectral bands were designed to monitor vegetation and these properties can be made use of to enhance features of potential relevance to groundwater. It must be stressed, however, that the most appropriate processing in regard to groundwater exploration is not a well-defined quantity. This is because comparatively little research into this application has been done together with the fact that the object of study - groundwater - is not directly observable but must be inferred through its influence on other factors that are visible at the surface. Since this indirect relationship will depend on a whole range of conditions within a particular area, only general guidelines can be used to determine the best approach. In practice, processing will need to be optimised for each particular region (although, once established, this could apply over quite large areas covering, possibly, several Landsat scenes).

Three types of imagery product have been investigated: (a) false-colour band combinations from a single date of acquisition; (b) digitally-merged, multi-date, change-detection images using data integrated from different seasons; and (c) vegetation index images from dry-season imagery. Each of these is described in more detail below.

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3.2 Spectral band combinations

Selecting three bands from the seven possible TM bands (six if the 120 m thermal band 6 is excluded) for a colour composite image, presents a common problem when processing satellite imagery. It is best approached using the known responses of the individual bands to different surface materials and ground conditions. Because in the visible and reflected infrared regions, the bands tend to be highly correlated (except in the presence of vegetation), much of the information is duplicated from band to band, resulting in significant data redundancy. Highly correlated multispectral false-colour composite imagery contains only a small (but significant) amount of additional information over a single band black-and- white image. A correlation matrix provides a good indication of the least correlated bands; typically, the data subdivides into the visible bands (1 to 3), the near infrared (4) and the shortwave infrared bands (5 and 7), so that a combination involving one band from each group often provides the most informative image. It is also important to consider the variance of the data in each band; in theory at least, of those bands which are least correlated, the ones with the highest individual variances provide the greatest discrimination of surface materials. Despite the fact that bands 5 and 7 are quite strongly correlated, important differences between these two bands under certain, geologically significant, circumstances means that they are often useful together in colour composites. A final decision on which bands to use thus depends on both a statistical assessment of the data and an inspection of various products to determine the most useful combination(s). Despite the value of the statistical approach, the use of 'automated' statistical techniques to determine the most- informative 3-band combination (e.g. Chavez et al. 1982, Sheffield 1985) are not really appropriate in this case since the specific features of interest to groundwater are difficult to define purely on such a basis.

Of the various band combinations investigated, two were finally selected for detailed study. These were composites of bands 4-5-7 (RGB) for the northern image area (169/73) and bands 4-5-1 for the southern image area, both of which were found to possess information related to both vegetation and soil/rock types. The two images differ only in the substitution of band 1 for band 7, and so are of overall similar appearance. Both show similar characteristics and either would provide a suitable general product for groundwater studies in this type of environment. Band 4 is displayed in red in both cases; this is the near infrared spectral channel which is strongly reflective for healthy (photosynthesising) green vegetation, so that areas coloured red on both images correspond to vegetated ground.

3.3 Vegetation indices

3.3.1 Relationships between vegetation indices

The estimation of percentage ground cover, or quantity of vegetation (green biomass) is most easily accomplished by ratioing the near infrared band (TM4) with the visible-red band (TM3), after suitable correction of the bands for atmospheric effects. Pixels containing significant vegetation have a high DN value in TM4 compared to TM3, and this distinguishes vegetation from most other categories of surface material. The TM4/TM3 ratio is easily calculated but is somewhat empirical and does not provide a reliable quantitative measure of the vegetation density or biomass. Various other methods of estimating these quantities have been suggested with the intention of improving the accuracy of the estimates. They all still

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rely on the relative reflectance in these two bands. These were mainly developed using MSS data and were originally formulated in terms of MSS bands 5 and 6, or 5 and 7. Two good accounts of the various indices and their equivalence are Richardson and Wiegand (1977) and Perry and Lautenschlager (1984).

Vegetation indices based on ratios between two spectral bands include:

MSS Title TM eqivalent Title

(6-5)/(6+5) ND6

(7-5)/(7 +5) ND7 (4-3)/(4 + 3) Normalised Vegetation Index (WI)

.\/(ND6+0.5) TV16

J(ND7+0.5) TV17 J((4-3)/(4 + 3)) + 0.5) Transformed Vegetation Index (TVQ

Alternative TVIs were proposed by Perry and Lautenschlager:

((ND6 + 0.5)/ABS(ND6 + 0.5)) x [ABS(ND6 + 0.5)]

((ND7 + 0.5)/ABS(ND7 + 0.5)) x J [ABS(ND7 + 0.5)]

Two different Perpendicular Vegetation Indices (PVIs) were proposed which measure the perpendicular distance of a pixel from the soil line in either MSS5-MSS7 or MSSS-MSS6 feature space. The slope of the soil line differs from scene to scene and must be determined independently. A computationally simpler version, the Difference Vegetation Index (DVI) , was also described. The TM equivalent is determined in TM4-TM3 space. The advantage of the PVI and DVI is that they reduce the influence of the background soil on the index values. These indices can be categorised as baseline-based indices, as they measure departure from a baseline.

Another baseline-based index, which makes use of all the available spectral bands, is the Green Vegetative Index (GVI; the Greenness component of the Taselled Cap transform). This was originally defined using MSS imagery, but was later extended to Thematic Mapper data by Crist and Cicone (1984). The coefficients determined for these transforms have been published and are as follows (where [4], for example, represents DN values in MSS4 or TM4):

GVI(MSS): 0.283[4]-0.66[5] +0.577[6] +0.388[3

GVI(TM) : 0.2848[ 11-0.2435 [2]-0.5436[3] + 0.7243 [4] + 0.084 [5]-0.18[7]

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3.3.1.1 Ratio-based indices

The ratio of TM4/TM3 can theoretically range from 0 to infinity, although in practice the values tend to lie within the range 0 to 15, with a few extreme values due to noise. The values of (TM4-TM3)/(TM4+TM3) are completely limited to the range -1 to + 1. The TVI was devised as a square root in order to stabilise the variance, and the addition of 0.5 was made to avoid negative values in the square root calculation. However, since (TM4- TM3)/(TM4+TM3) ranges down to -1, the addition of 0.5 is not enough to ensure the absence of negative values. It was for this reason that the alternative TVIs were proposed by Perry and Lautenschlager; these ensure that the square root calculation is always made from positive values, and that the correct sign is incorporated in the initial term.

The relationships between TM4/TM3 and the NVI and TVI are best seen by plotting the indices against the ratio values. Using the Normalised Vegetation Index (NVI) or one of its derivatives, instead of the 4/3 ratio, merely increases the sensitivity of the vegetation measure in areas where vegetation density is relatively low, and decreases it in areas where it is high. Mathematically, as shown by Perry and Lautenschlager, the ratio and the indices are equivalent, (that is, there is a direct one-to-one relation between TM4/TM3 values and TVI or NVI values). The relationship between the TM4/TM3 values and the NVI values is a hyperbolic curve lying between -1 and + 1, asymptotic to the axes NVI = 1 (TVI = 1.5) and TM4/TM3 = 0 (Figure 3). The TVI and MTVI curves are biased versions of the same curve (Figure 4). The plot of ,/(TM4/TM3) versus TM4/TM3 is also a simple curve (Figure 5).

3.3.1.2 Perpendicular Vegetation Index

Reflectance in the red and NIR for unvegetated rock and soil are highly correlated, and form a well-defined line on a bivariate plot in TM4-TM3 or MSS7-MSS5 space. If the data have been corrected for atmospheric effects, this line passes through the origin of the plot; it is known as the soil line. Points corresponding to areas of vegetation diverge from this line, moving further away from it as the vegetation density increases. The PVI and DVI measure this departure from the soil line, as shown in Figure 6. It can be seen that points corresponding to a constant vegetation cover define lines parallel to the soil line, but displaced from it.

In general, the soil line in a plot of TM band 4 against band 3 has a slope of 0.8 to 0.9. Water plots below this line, and vegetation above it.

The DVI does not have a simple relationship with the 4/3 ratio values or the NVI index (Figures 7, 8 and 9). The DVI minimises the influence of rock-soil background on the vegetation index. It appears to give more reliable results than either the TM4/TM3 or the NVI at the lower end of the Soil Line (areas of low albedo). The calculation and use of the DVI are explained more fully in Section 3.3.1.4 below, which includes some results of mathematical modelling of a vegetation canopy.

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FIGURE 3: A plot of NVI [(TM4-TM3)/(TM4/TM3)] against TM4/TM3.

FIGURE 4: Modified Transformed Vegetation Index ( d o + 1.05) against TM4/TM3 ratio values. The curve is essentially the Same as in Figure 3.

FIGURE 5: d(TM4/TM3) values plotted against TM4/TM3 values.

FIGURE 6: A plot of TM4 against TM3 for various proportions of plant cover, calculated from the model. It can be seen that points corresponding to a uniform density of vegetation (indicated by percentage values) define lines that are parallel to the soil line. Displacement relative to the soil line, defined as PVI and DVI, are indicated by the arrows. Points W, X, Y and 2 correspond to bright, medium, dark and very dark soil, without vegetation. Point V corresponds to complete vegetation cover. Starting with bare soil, the spectral changes with increasing vegetation cover are traced out by the lines of points running toward the total vegetation point V.

FIGURE 7: A plot of TM4 against TM3, similar to that of Figure 6, with lines of equal TM4/TM3 ratio values superimposed. It can be seen that a single ratio value can correspond to a variety of vegetation densities, depending on the albedo of the background soil.

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FIGURE 8: Relationship between the NVI and DVI for four differently shaded versions of the same soil. Solar elevation angle is 60 degrees.

FIGURE 9: Relationship between TM4/TM3 ratio values and DVI for the same soil as in Figure 8.

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3.3.1.3 Green Vegetative Index (Tasselled Cap)

This is a data transform based on coefficients derived from agricultural scenes in north America (Kauth and Thomas 1976). The transform is analogous to principal components analysis but the rotation is not simply dependent on the Scene statistics. Optimal rotation of the n-dimensional data set was acheived by sequential applications of the Gram-Schmidt orthogonalisation process. Unfortunately, the optimal rotation for this particular Scene is not optimal for other scenes with a different data structure. Application of the published coefficients does not give the desired results in scenes where the soils, vegetation types and illumination differ from the originals. The process of determining new coefficients for each scene is too time consuming to be practicable.

3.3.1.4 Effect of soil background on vegetation indices

It has been shown by earlier workers that ratio-based indices can be seriously affected by the contribution from the soil background. This results in an over-estimation of the vegetation density on dark soils compared to light soils. The effect is illustrated in Figure 7, where a uniform TM4/TM3 ratio value can be seen to correspond to different vegetation densities above different soils.

A mineralogically uniform soil will plot as a sharply defined soil line. Position along that line is related to the albedo of the soil and, given that the composition of the soil does not change, the albedo is determined by shadowing or soil moisture. Shadowing may be due to differences in illumination caused by topography, self-shading by surface irregularities or shading by plants. All these will be partly affected by the solar zenith angle, and are therefore to some extent influenced by the season of the year and the time of day. Moisture in the soil reduces the albedo significantly, but also alters the relative band responses, so that moist soils not only plot closer to the origin, but deviate to some degree from the soil line. Variations in soil mineralogy, or the presence of organic matter (humus), may also alter the spectral response of the soil, with the result that it will no longer plot exactly on the same soil line.

In order to determine the most suitable measure of vegetation density for use in mapping possible zones of increased moisture, an investigation of the influence of soil shadowing on vegetation indices was carried out. A mathematical model was constructed that permits the calculation of the relative proportions of sunlit soil, sunlit plants, soil shaded by plants, and shaded plants (both self-shaded and shaded by adjacent plants), for varying degrees of vegetation density and for different solar zenith angles. Two typical plots of these values are shown in Figures 10 and 11. It is also possible to derive values for self-shaded soil.

Spectral values for end-members (unshaded vegetation with no visible soil background; unvegetated soils of different albedos, but on the same soil line) were extracted from a TM image of Kenya (raw DN values). These values were combined with the proportional values derived from the model for each of the categories (sunlit soil etc). For areas shaded by plants, it was assumed that a proportion of the flux in each band was transmitted by the plants to illuminate the shaded area; the plant was considered to be effectively one leaf thick, and the proportion of transmitted radiation was determined from laboratory measurements

15

FIGURE 10: The relative proportions of sunlit soil, sunlit vegetation, shaded soil and shaded vegetation, for a range of vegetation cover from 0% to loo%, with a solar elevation angle of 60 degrees.

FIGURE 11: The relative proportions of sunlit soil, sunlit vegetation, shaded soil and shaded vegetation, for a range of vegetation cover from 0% to loo%, with a solar elevation angle of 20 degrees.

100

80

w n 6C I

v) + z I3

z v)

e 13 40

s

2c

0

100

80

w n 4 I 60 v) + 2 3

z 3 v) 40

9

8

20

0

- SUNONSOIL

- - - - - - SUN ON VEGETATION

SHADED SOIL

\ . . . . . . . . SHADED V E G E T A T I ~

/

FIGURE 10

0 20 40 60 80 100

% PLANT COVER

FIGURE 11

0 20 40 60 80 100

O h PLANT COVER

16

made with an IRIS Mk IV spectroradiometer. An additional contribution was assumed to come from the hemispherical diffuse skylight. These calculations provided integrated pixel values in each band for varying vegetation cover and solar zenith angle. The resulting values were then used in the calculation of ratio-based indices and the DVI, and these were plotted against the proportion of vegetation cover.

Figures 12 to 16 show the effects of shadowing on the various indices at two illumination angles. It is clear that the presence of shadow in pixels has a marked effect on the accuracy of the ratio-based indices, and only a minor effect on the relation between DVI and vegetation cover. On this basis, the DVI is a more reliable measure of vegetation cover than the ratio-based indices. However, if the scene contains another soil of significantly different composition, it may not plot on the same soil line as the first soil. In this case the DVI values will not be directly comparable from one soil to the other as the baseline has shifted. Figures 17 to 19 show the effect of vegetation on two different soils A and B with different spectra (A being the same soil as in Figures 12 to 16). It can be seen that although the DVI is no longer unaffected by the difference in soil, the effect is much less than with ratio-based indices.

The disturbing effects of spectrally different background soils on the DVI can be overcome if separate DVI measures can be made for each soil. This requires the calculation of the soil line slope for each soil as an input to the DVI calculations, and the delineation of each area of different soil. Figure 20 shows how close the final DVI values can be if this approach is adopted.

It can further be shown that the DVI is less sensitive to noise than the other indices, though this is not illustrated here. Also, all the indices, including the DVI, are sensitive to variations in the spectral properties of vegetation related to vegetation type.

None of these methods for estimating the proportion of vegetation cover is without some drawback, but in general the DVI is the most reliable. It should be the logical choice for use in multi-temporal studies of vegetation density as it reduces to a minimum the disturbing influences of changes in the soil background.

Calculation of PVI and DVI from plots of TM4 vs TM3

Refer to Figure 2 1.

y = Ax + B where B = q/p

sin e JO

m e =

17

FIGURE 12: TM4/TM3 values plotted against percentage plant cover for compositionally uniform background soils of differing albedo. Solar elevation angle is 20 degrees. Note that in the mid-ranges of vegetation cover, a TM4/TM3 value of 2, for example, may represent anything from 25% to 65% vegetation cover.

18

FIGURE 13: NVI values plotted against percentage plant cover for compositionally uniform soils of differing albedo. Solar elevation angle is 20 degrees.

FIGURE 14: A plot of DVI values against percentage plant cover for compositionally uniform soils of differing albedo. Solar elevation angle is 20 degrees. Note that a single DVI value can represent only a small range of plant cover, less than 5%.

FIGURE 15: A similar plot to Figure 12, but with a solar elevation angle of 60 degrees. The change in illumination makes little difference to the basic inaccuracy of the TM4/TM3 measure.

FIGURE 16: A similar plot to Figure 14, but with a solar elevation angle of 60 degrees. The accuracy of the DVI as a measure of plant cover is not affected by the change in illumination.

0.72

0.62

- $ 0.32

0.12

-0.08

0 20 40 60 eo 100

O h PLANT COVER

FIGURE 13

. - . VERY DARK SO

0 20 40 60 eo 100

O h PLANT COVER

790C

690C

5 3900 n

1 9oc

-100

8900

7900

6900

5 n

3900

1900

-100

I I I I I

J

- BRIGHT SOIL

-- MEDIUM SOIL

---- DARKSOIL

VERY DARK SOIL . . . . . . -- MEDIUM SOIL

---- DARKSOIL

VERY DARK SOIL . . . . . .

v

1 I I I 1 0 20 40 60 eo 100

70 PLANT COVER

FIGURE 14

I I I I I

i t

/ r'

- BRIGHTSOIL

- - MEDIUM SOIL

DARK SOIL

* a a f a * VERY DARK Sol

----

J I I 1 1 0 2 0 40 80 80 100

% PLANT COVER

FIGURE 15 FIGURE 16

19

FIGURE 17: TM4/TM3 ratio values plotted against percentage plant cover for two spectrally different soils (soils A and B), each of different albedo.

FIGURE 18: A plot of NVI against percentage plant cover for two spectrally different soils A and B, each of different albedo. Soil A is the same soil that has featured in all the previous figures.

FIGURE 19: DVI plotted against percentage plant cover for the same soils as in Figures 17 and 18. The spread of DVI values has increased compared to Figure 16, with a maximum potential error at the lower vegetation densities. A DVI value of 8 spans vegetation densities ranging from 10% to 25%, but this is still substantially more accurate than the TM4/TM3 or NVI indices, as can be Seen in the previous two figures.

FIGURE 20: A plot of DVI against percentage plant cover for the same soils A and B, but with separate calculations of the soil lines for the two soils, and calculation of DVI values from the appropriate soil line data. The DVI values for the two background soils are now equivalent.

6

4

3

r"

E t d

2

1

0

680C

480(

5 2801 n

80C

-1 20c

/ 1

/ /

/

- BRIGHT SOIL A

- - - VERY DARK SOIL 1

OIL B

/

/

-- BRIGHTS'

* * * * DARK SOIL B

0.66

0 .46

- 2 0.26

0.06

-0.14

- BRIGHT SOIL A

- - - - - BRIGHT SOIL B

* - - * - * * DARK SOIL B

VERY DARK SOIL ,

0 20 40 60 80 100

Oh PLANT COVER

FIGURE 17

0 20 4 0 60 80 100

% PLANT COVER

FIGURE 18

- BRIGHT SOIL A

- - - VERY DARK SOIL 1

- - BRIGHT SOIL B

-. -. . . DARK SOIL B

/ J I I I I I 0 20 40 60 80 100

% PLANT COVER

FIGURE 19

- BRIGHT SOIL A

- - - VERY DARK SOIL I

- - BRIGHT SOIL B - - * . * DARK SOIL B

0 20 4 0 60 80 100

PLANT COVER

FIGURE 20

20

I

r = "PVI"

q s "DVI"

FIGURE 21: Diagram to illustrate the calculation of the DVI and PVI from a plot of TM4 asainst TM3. Refer to text for details.

21

Therefore: 4= P

Squaring t h k - Q2 - - P2 r 2 p 2 1 - (-) P

r2 42 r 42 42 * r2 Therefore: - = - * (l--(-) ) = --

P2 P2 P P2 P2 * P2

P" P"

2 Rearranging this: q2 = r2 * (1 + 4)

P2

From this: r2 = q2 - - q2 q2 (1 + B2)

(1 + (,)I P'

Therefore: r (= PVI) = 4

J G 3

22

4 therefore PW = - K Where: (1 + B2> is a constant:

Therefore, r can be expressed solely in terms of q, and in this form is known as the Difference Vegetation Index @VI). [DVI = (1 + B2)]

DVI = q where q = (A + Bx) - y

this gives negative values for points plotting above the soil line; in order that the values should be positive the DVI is computed as

DVI = y - (A + Bx)

If atmospheric correction has been correctly performed:

A = 0 and DVI = y - Bx; (this is simpler to compute than the PVI).

3.3.2 Vegetation index images

All of these vegetation indices result in a new single band image in which the vegetation density is represented as DN (brightness) values. Low vegetation density is represented by low DN values, and the complete absence of vegetation corresponds to a DN value of zero; high vegetation densities have correspondingly high DN values. Because such images lack the usual clues to topography provided by shadow (which is effectively eliminated by the ratioing process), they are visually difficult to interpret. On their own they lack the detail provided by the topography, so that locating oneself with reference to ground features is difficult. Combining the vegetation index with other bands as a means of re-introducing topographic information is generally not satisfactory since it produces misleading colour effects. Alternatively, the vegetation index image can be level-sliced and superimposed on an image of the topography, but a great deal of information is thereby lost. Consequently a new method was sought which would allow the DVI image to be presented in colour together with essential information on the topography.

The method involves transforming a three-band colour composite from red-green-blue (RGB) colour space to the corresponding intensity-hue-saturation (IHS) colour space. The intensity component of this transformed image (which contains most of the topographic information) is retained, but the hue and saturation components are discarded and replaced by the DVI image. The colour information (hue) of the original is replaced by the DN values of the DVI, scaled so that low DVI values are blue, intermediate values are cyan, green and yellow, and high DN values are red. The best visual result was obtained when the saturation of the final colours was graded with saturation increasing progressively from the blues through to the reds; this was achieved by replacing the saturation component with a scaled version of the DVI image. This new composite in IHS space was then re-transformed back to RGB colour space. This image is further described in Section 4.3.

23

3.4 Multi-date images

3.4.1 Principles

Because digital satellite images can be co-registered on the computer with relative ease, different date images may be quantitatively compared and changes that have occurred measured. Such techniques are usually used to monitor man-made changes to the environment or to map the effects of natural catastrophic events. For these purposes scenes acquired at the same time of year are used so that seasonal effects are minimised. Less use has been made of the natural differences that occur in an area between seasons. By creating images of the natural changes that occur in vegetation patterns (for example, between wet and dry periods) information may be obtained concerning the preservation of areas of near-surface moisture and the pattern of sub-surface groundwater movement.

Seasonal variations in green vegetation density can be examined in a number of ways, and several of these were tried. All are based on the spectral differences between green vegetation (low reflectance in the visible red region and high reflectance in the NIR) and unvegetated soils and rocks (low to moderate reflectance in both regions). Some approaches are as follows:

1) calculate differences in vegetation-sensitive ratios (413 or 5/7) between wet- and dry-season imagery and identify areas of least difference;

2) calculate differences in band DN values between dates; for example, between TM4 values.

3) calculate differences in vegetation indices (NVI, PVI, DVI etc.) instead of band ratios;

4) determine differences between Taselled Cap Wetness or Greenness components for dry- and wet-period images.

5 ) mask out confusing areas of shrub and dambo and, using dry period imagery, determine vegetation densities near to streams and lineaments;

6) use classification techniques based on favourable indicators;

7) use multiple regression on July image to determine best fit equation for Band 4, and apply this to the September image; then calculate difference between observed and calculated Band 4;

8) carry out statistical transformations of combined data sets from two or more dates using techniques such as principal components analysis.

2 4

3.4.2 Image production

Before images of different dates can be co-registered they must be corrected for atmospheric effects, and the spectral data must be normalised to reduce between-scene variability caused by different solar illumination angles etc.

Light backscattered to the sensor from atmospheric particles is a major source of spectral contamination that reduces the contrast in the image and renders many of the subsequent processing techniques invalid. The effect is strongest in the short wavelengths, where in the blue region of the EM spectrum up to 80% of the flux entering the sensor may be due to atmospheric backscatter (or path radiance). Since this is wholly an additive factor, it can be corrected by subtracting an amount equivalent to the path radiance; the problem lies in making an estimate of the amount to be subtracted.

Various methods can be used to correct an image for path radiance. In this study the simplest approach was used which involves finding the histogram minimum. This method assumes that somewhere in the image there is an area corresponding to zero reflectance (deep shadow or deep, still water). Any electromagnetic radiation recorded from this area must therefore be the path radiance contribution. The pixels themselves do not have to be located, but are instead identified as the lowest DN values recorded in each band on the frequency histograms. The data in each band is then corrected by subtracting the relevant DN value from every pixel in that spectral band. The correction is greatest at the shorter visible wavelengths (blue) but less in the reflected infrared.

Following this, the images were co-registered by warping them to a common base, which can be a map projection or another image. In this project, the April and July images were warped to the September image. Subsequently, the images were transformed from DN values to radiance values by correcting for the gain and offset applied when the original ground reflectance signals were quantised in the satellite. The radiance values were then adjusted to allow for the different solar illumination geometries by converting to effective at-satellite reflectance. This is described below.

3.4.2.1 Transformation of multi-date data sets to radiance values

Pixel DN values are integers which do not relate directly to the radiation detected by the satellite sensors, measured in microwatts/cm2/steradian/micron. The incident flux is transformed to integer values by applying gain and offset factors, which differ from sensor to sensor, and quantising to fall within the range 0 to 255 (in the case of TM data). The raw DN values do not therefore represent the true relationship of reflected radiance between bands, or between instruments.

Furthermore, images of a scene acquired at different times of the year are not directly comparable because of differences in solar geometry and variations in atmospheric conditions. In order to make comparisons between images of the same area, acquired at different times and possibly by different sensors, it is therefore necessary to normalise the data. This is achieved by converting the DN values back to a measure of radiance, but only after some initial corrections are made.

25

The worst of the atmospheric effects is due to path radiance (backscatter) and this can be corrected by employing one of several possible techniques, the simplest of which is the minimum histogram method. This must be applied before any other corrections are attempted.

Working with multi-temporal data sets requires that they be co-registered to a common base. To achieve this, either one image is taken as a reference and the other images are warped to fit it, or all of the images are warped to fit a map base. Whichever method is used it is important to apply the warping method which has the least degrading effect upon the pixel values, since these represent spectral data; in practice, this means using nearest-neighbour resampling rather than bilinear or cubic convolution. This processing should be performed after atmospheric correction, but before any other processing is carried out.

The method of converting from DN values to radiance values for MSS and TM data has been described by Markham and Barker (1986), who provide all the necessary gain and offset (L and values for both series of instruments throughout their functional lives. The algorithm is applied separately to each band as follows:

where DNoi = raw DN (calibrated and quantised scaled radiance), after atmospheric correction, band i

L-,i = spectral radiance at DN",=O, band i

LIMx.i = spectral radiance at DN'li=QCh, band i

Qch = range of DN" (255 for TM; 63 or 127 for MSS except Band 4)

To further reduce between-scene variability (after conversion to radiance values), correction should be made for the different solar zenith angles and different sun-Earth distances of multi-date images. This will provide a measure of effective at-satellite reflectance. This can be performed by applying the following conversion to each band:

ASR = R x (a x sd2)/(K, x COS 8 )

where: ASR = at-satellite reflectance

and sd = solar distance at time of acquisition, in astronomical units (from Almanack)

Ki = mean solar exoatmospheric irradiance for band i (from Markham and Barker)

Ri = radiance in band j

8 = solar zenith angle at time and date of acquisition

26

The solar zenith angle has to be calculated for each image, from the following:

cos 8 = (cos L x cos h x cos d) + (sin L x sin d)

where: cos 8 = cosine of the solar zenith angle

L = Latitude (+ve N, -ve S)

d = solar declination (from Almanack)

h = time difference between acquisition and local solar noon (in temporal minutes) divided by 4 (converting to degrees), negative in morning, positive in afternoon.

In practice, it is possible to combine all these conversions into one process, going from corrected DN values to at-satellite reflectance in one step.

The result of this transformation is an image whose pixel values are no longer integer values. To preserve accuracy it is essential to store the images in real number format, and to carry out all subsequent processing in real number arithmetic. An image in real number format occupies four times as much memory space, and if the images are large, there may be storage problems. An additional disadvantage is that images can no longer be viewed directly; they have to be re-scaled to a range 0-255 before display. (Note: this processing was applied to the multi-date images of eastern Zimbabwe. It was not employed on the single image of south-eastern Zimbabwe since no direct combination or comparison with other images was possible).

3.4.2.2 Display of multi-date images

A major problem exists with difference images in that the greater part of the data distribution is clustered around values close to zero (i.e. no-difference). This is illustrated by a plot of the 4/3 ratio for imagery of two dates; on this, the diagonal x = y line represents points of no difference. Lines parallel to this on either side represent points of equal difference, positive on one side, negative on the other, with the degree of difference increasing away from the no-difference line. The no-difference line passes through the central cluster of points and includes both points with very low 4/3 values (no vegetation), and very high 4/3 values. Consequently, this is not a useful pointer to areas of extra moisture (see Figures 22, 23).

It is possible to combine the difference-image with an image showing degrees of vegetation cover, thus eliminating the low-vegetation, no-difference areas. A 4/3 ratio image, divided by the no-difference image, highlights area of high vegetation cover and least-difference values. However, in the areas tested the resulting image was found merely to highlight river vegetation and natural scrubland. The presentation of change detection data is discussed further in Section 4.4.

2 7

FIGURE 22: Scatterplot of TM4/TM3 ratio values for the September image against the April image. The line of no-change passes through the centre of the cloud, and includes points of very low to very high ratio values. No discrimination is achieved.

FIGURE 23: Scatterplot ;f TM4/TM5 ratio valu?; for the September image against the April image. As in Figure 22, the linc of no-change pases through the centre of the cloud.

28

4: IMAGE INTERPRETATION AND FIELD CORRELATIONS

4.1 Interpretation of vegetation patterns

An underlying assumption of the research was that the presence of shallow groundwater could be inferred from the distribution and density of green vegetation. In dry regions, where rainfall is seasonal and irregular and where the availability of water is therefore erratic, patterns of vegetation can give important clues to moisture variations in the near-surface zone. Among other things, vegetation patterns relate to the ability of plants to obtain moisture via their root systems, plants with tap roots providing the best indication of deeper, preserved moisture. Green vegetation is easily mapped using visiblehear-infrared satellite imagery (or even false colour aerial photographs). The only remote sensing techniques that can directly detect the effects of moisture are radar and thermal infrared imaging; less reliably, the reduced albedo of soil in the visible and reflected infrared spectral regions can provide indirect evidence of seasonal wet conditions. Dry season imagery was chosen for this study because of its potential value in providing information on the persistence of ground moisture and also because it provides the greatest contrast between bare soil and green plants.

Possible surface indications of moisture include:

1) dark soil tones (dampness) with increased absorption in all spectral bands;

2) greater organic content in soil, due to increased vegetation cover, with increased absorption in all bands;

3) increased vegetation cover;

4) increased plant litter on ground surface, and increased proportion of standing senescent vegetation (dry grass etc) with consequent changes in spectral properties;

5) differences in vegetation assemblages, and resulting differences in spectral pattern.

Moist soil in the semi-arid environment will almost always be colonised by vegetation and there is consequently little likelihood of directly observing soil moisture except in areas of seasonal flooding (dambos) where vegetation (other than grasses) is prevented from establishing itself owing to waterlogged conditions. Therefore, the main approach to moisture detection in environments of this kind has to be through the detectable attributes of the vegetation cover (density, greenness, stress, etc.). If there were no external factors, if the soil and geology was uniform, and if the vegetation was entirely natural, interpretation would be relatively straightforward. Unfortunately, the density and distribution of green vegetation and differences in the composition of plant communities are affected by many factors apart from the availability of water, and these can be overriding in areas of human habitation. Among the factors which affect the spectral properties of the land surface are:

(1) Cultivation and grazing. These have drastically altered natural patterns of vegetation over wide areas, although the extent of these man-made changes varies. In Zimbabwe, communal areas are over-cultivated and over-grazed, and the

29

vegetation cover appears very different to that of the surrounding farm lands underlain by similar rocks. This is evident on the Masvingo Landsat TM 4-5-1 image (Figure 24) (particularly in the north of the area) where the communal lands appear as ‘bleached’ areas as a result of the virtual lack of ground vegetation cover. Little of the original bush vegetation remains within any of the cultivated lands, and large areas of forest have been cleared.

(2) Influence of topography. Bouldery, hilly terrain usually retains a cover of natural vegetation (trees with a grass understorey; variable density), but in lower lying ground it is unlikely that many areas have escaped some modification. Other vegetational effects, though natural, are more related to geology than ground conditions. Thus, due to their weathering, granitic bornhardts often develop bare crowns caused by lack of topsoil development, while their lower flanks are heavily vegetated and there is a narrow skirt of thick forest around their base due to surface rainwater run-off. On the other hand, greenstone belts forming high ground tend to be very heavily vegetated throughout.

(3) Plant communities in Zimbabwe and other similar areas are highly altitude- dependent; i.e. differences in populations are strongly affected by temperature and rainfall. As a result, generalised conclusions cannot necessarily be applied from one area to another.

(4) It is a feature of plant communities, particularly those of semi-arid environments, that different species respond to lack of water in different ways. Grasses, with shallow roots, respond quickly to moisture stress whilst trees, with deeper roots, respond more slowly. Natural areas of scrub have relatively high values of green biomass at all times of the year, even during drought. Whereas some plants lose their green leaves and remain in a ’dormant’ state for long periods until the new rains arrive others, perhaps because they can tap water at a greater depth, preserve a canopy of green leaves throughout long periods of drought. Other trees that remain green possibly obtain moisture from the atmosphere or from dew fall. Because of these differences, it is difficult to draw conclusions relating to ground moisture from a plant community as a whole without a fuller knowledge and understanding of its composition. Another interesting observation is that at the end of the normal dry season, and before any rain has fallen, many trees start to grow new green leaves apparently in anticipation of the rains (even when this fails to arrive). This is even true of grass which has been noted to start growing at the end of the dry season in seemingly very dry soil. Thus, inferences drawn from imagery acquired late in the dry season (at a time when one might logically expect plant stress through lack of water to be at a maximum) may provide misleading information.

(5) Dead vegetation, senescent grass, plant litter etc, have a very different spectral signature from green vegetation, and this can complicate the interpretation.

(6) Living and dead plants are cropped extensively by animals, particularly herds of goats, so that again simple interpretations can be unreliable.

30

(7) In general, moisture lingers longest along stream courses, and vegetation is best established along the banks of streams and gullies, without necessarily implying significant extra water supplies.

(8) Harvested fields may carry less vegetation than nearby uncultivated ground, or may carry more vegetation if irrigated.

(9) Different rock lithologies give rise to different soils with different spectral properties.

As well as these, there are other factors that influence the density of green vegetation as mapped from remotely sensed imagery, and these will clearly affect the reliability of the interpretation. In many cases only an incomplete explanation is available. Why, for example, within otherwise intensely grazed or cultivated areas is vegetation commonly found along even minor drainage channels? It seems unlikely that this is wholly the result of the presence of tall trees, out of the reach of goats and cattle. Perhaps instead, this indicates the preservation of moisture at shallow depths even along very minor channelways? Presumably, as the groundwater levels fall at the end of the rainy Season, the water table remains shallowest along drainages, so that vegetated drainage lines do not necessarily indicate the existence of underlying fracture-controlled groundwater conduits. This raises the question of how one distinguishes drainage channels that are related to deep circulating water in fractures from those that are vegetated only because they form topographic lows. One positive indication of fracture-related groundwater is the existence of a vegetated lineament traceable from one drainage basin to another over an interfluve, perhaps via a relatively minor gully.

Despite these uncertainties and doubtless other complications, the existence of areas of relatively unstressed (green) vegetation under dry-season conditions must relate, at least in part, to the availability of soil moisture, and thus possibly shallow groundwater. As the water table drops at the end of each rainy season, water availability falls below the reach of shallow rooting plants, but may initially remain accessible to deeper rooting plants including certain trees. Under conditions of prolonged drought, the groundwater level may fall beyond the reach of even deep rooted vegetation. The lowering of the water table is likely to become apparent first on the interfluves.

The creation of vegetation index images, particularly the DVI image described earlier, is an important step in mapping the distribution of green vegetation. The generation of this image is a considerable advance over conventional false-colour composite images in which the distribution and relative density of healthy vegetation is often masked or difficult to observe, particularly at lower densities. Nevertheless, the information contained in the two images is often complementary.

4.2 False colour composite images

4.2.1 Introduction

The groundwater significance of patterns and texthes in the Landsat images has been assessed in several ways: first from a knowledge of the reflectance properties of different

31

ground materials; second from direct ground correlations; and third by comparison with aerial photographs (1:80,000 and 1:25,000) as well as 150,000 topographic maps.

4.2.2 Land use and topographic effects

As mentioned earlier, rural land in Zimbabwe is broadly sub-divided into communal (tribal) and other (farm) land. These two land categories have a markedly different appearance on satellite imagery. The communal lands, which are heavily overgrazed and almost devoid of substantial vegetation, appear ‘bleached’ on imagery, whereas the farmlands (largely white- owned), which are less intensively grazed and have a much lower population density, exhibit a more luxuriant growth. Although the land sub-division is largely artificial, there are examples where the boundaries appear to correspond to differences in land quality which in turn possibly relate to geology and/or groundwater conditions. In any event, the main effect of the land use patterns is that it is usually impossible to make direct comparisons between the two types of land, and this is a major problem for regional interpretation of images. Although the more vegetated farmland areas might be thought to have more potential for hydrogeological mapping, this does not always appear to be the case; subtle variations in the more stressed vegetation in the denuded communal areas seems to provide more variability, and thus information, on relative moisture availability. What we may be seeing in such areas is the ability of sparse vegetation to survive or perhaps recover, which would again be a function of moisture availability.

Due to the land sub-division and other regional differences in geology, elevation, climate and rainfall, it is often difficult to make regional interpretations from imagery with great confidence. At best, one can work at the sub-regional scale to produce an interpretation for an area of either communal or farm land. Imagery at a scale of 1:1OO,OOO and areas up to about the size of a 1:5O,OOO map sheet are appropriate to this approach.

The situation with regard to upland areas is less variable in terms of land utilisation. Most such areas have limited agricultural potential and have been left in a more-or-less natural state. All upland areas may therefore be considered together.

The following general observations and references to colour tones, refer mainly to the 4-5-1 image centred on Masvingo (169/74), and to a lesser extent the 4-5-7 image to the north (169/73). It should be noted that references to colours in the following paragraphs are for convenience only and have meaning only in so far as they appear on the images in question. As false colours they have no absolute significance in themselves and could be changed by re-assignment of the band sequence or combination.

4.2.2.1 High relief terrain

Rugged, elevated areas often appear in tones of white, deep red or deep green. Comparison with aerial photographs indicates that:

deep red = heavy forest canopy deep green = dense scrub bush without many large trees white = bare rock or rock and scree

32

It would appear that trees in these areas retain good foliage throughout the dry season whilst bush does not.

4.2.2.2 Low relief terrain

The lowland areas are more important in terms of groundwater since it is here that rural water supplies are mostly needed.

Although the communal areas have been extensively grazed and largely cleared of natural vegetation, patterns still exist that appear to have a fundamental relationship to surface run- off and groundwater. On a needs basis, the communal lands are the primary targets for groundwater exploration since the farm lands usually have to support a much lower density of population and anyway tend to have a better supply of boreholes.

The communal areas are recognisable either as areas of bright tones with a spotted tinge of red (‘pink’ zones) or in various shades of green. In general, these can be interpreted as follows:

whitelred - green - seasonally cultivated ground

thin bush and grass with scattered trees on sandy, granitic soil

(i) Band 4 reflectance: On the FCCs, many areas show colour tones varying from almost white to pink and red. The degree of brightness indicates the content of light-coloured, probably sandy, granitic soils lacking significant green vegetation cover. Bare rock surfaces also appear nearly white. The high reflectance further indicates that the soils are dry since seasonally damp areas more usually appear dark due to the presence of moisture or the increased content of humus in the soil. Over much of the communal lands the actual colour pattern is white-spotted-with-red, resulting from the presence of scattered green-leaved trees or bush. This effect is common even though at the 30 m resolution of TM imagery individual plants cannot be distinguished. In most cases, the nature of this vegetation cover is easily confirmed by inspection of the aerial photographs. The danger of attempting to infer groundwater conditions from TM imagery without supporting ground information, or at least examining the aerial photographs, is demonstrated by the following example.

The communal lands are usually easily recognised by the regular form of their fenced boundaries and their generally bleached appearance. Within these large regions, discrete patches of ground showing pinWreddish tones are sometimes apparent which contrast sharply with surrounding land areas. Several such examples occur in the region to the south and east of Masvingo. Each is more or less equidimensional, roughly circular, and has a diameter of about 2 to 3 km. Examination of the aerial photographs indicates that these areas are presently uncultivated although remnants of older field boundaries can often be discerned within them, indicating that they were formally farmed. They contrast sharply with the surrounding areas which are often actively cultivated, and appear to be abandoned farmland which is now slowly returning to bush. At their present stage of growth, they are covered by thicker grass and scrub bush containing a few small trees.

One such apparently more-vegetated area was recognised in a remote sensing pilot study by Finch (1990), and interpreted (due to the apparent increase in vegetation cover) as an area

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of possibly increased availability of moisture. However, an examination of the aerial photographs (which were not available to Finch) and ground inspection by the present writer showed the area to be one of abandoned fields. The reason for the abandonment of such areas is usually unclear, and indeed there could be many possible reasons for it both natural and socio-economic. Such occurrences often encompass both interfluves and drainages although they Seem generally to occupy the local high ground. Either such areas were either never seriously farmed or they were abandoned because of dry, over-drained ground conditions. Thus one explanation for these areas of apparent increased vegetation might be exactly the opposite to that originally inferred! i.e. that they are areas where crops could not be successfully grown (perhaps during periods of prolonged drought) because the groundwater was deeper than that of surrounding areas. This is no criticism of the purely desk study undertaken by Finch but serves to demonstrate the problem of producing generalised conclusions over a large region without a detailed knowledge of the areas in question.

(ii) Band 5 reflectance: Areas appearing in shades of green are quite extensive and show considerable variation in actual colour tone, corresponding to different levels of band 5 reflectance together with low reflectance in other bands. Such areas broadly correspond to cultivated ground, marked by field boundaries, which were probably harvested and lying fallow at the time the imagery was acquired (middle dry season). More particularly, prominent band 5 reflectance correlates with visually darker red/brown soils. These areas are also discernable on the aerial photographs, although they are usually less noticeable. Indeed, many areas which are clearly distinguishable on the imagery and include subtle variations in tone can be only broadly discriminated on aerial photographs (Le the response to these features is much better in the SWIR than in the visible).

Soil reflectance appears to depend on both the local drainage conditions and the underlying geology. Studies of aerial photos in stereoscopic view indicate that variations in soil tone are often closely correlated to topography. For example, main ridge crests often show lighter- coloured soils due to rapid drainage and drying out, and the consequent lack of clay development. A spur coming off a ridge also represents a freely-draining area but, because it receives some groundwater drained from the main ridge, it often appears slightly darker. Spur flanks and main hill slopes on the other hand commonly appear dark, as do small saddles and actual drainage lines, due to such areas being subject to seasonal flooding. In general, soil features caused by subterranean and surface drainage seem to be superimposed upon patterns related to the bedrock geology; the latter patterns are often recognised by their lack of correspondence with the relief and their conformity of trend with local structural directions. Ground examination of these soils reveals a higher content of more mafic fragments suggesting derivation from mafic layers in the granites. Thus, when interpreting soil tones and colours, care must be taken to avoid false inferences regarding ground conditions which are the result of wholly geological factors.

(iii) Ambiguities: The sub-division into areas of relatively strong band 4 (pink) and band 5 (green) reflectance and the inferences attached to them, are not always reliable. Complications sometimes arise due to the superimposed effects of ground conditions, geology and land development as referred to above.

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4.3 Comparison of FCC and DVI images

The TM 4-5-7 and 4-5-1 FCCs are good general images for geological interpretation and provide some useful hydrogeological information. However, because band 4 reflectance is related to factors other than green vegetation, they contain much information that can be uniquely interpreted. Moreover, the FCC is a very ‘busy’ image and many of the variations are difficult to interpret without detailed local knowledge. By comparison, the DVI-IHS image is a convenient way of classifying spectral responses due solely to the presence of healthy vegetation because (1) the DVI is proportional to biomass over a range of vegetation densities (2) it is less affected by variations in soil background type and (3) the substitution of the DVI in an IHS transform allows variations in vegetation density to be clearly differentiated as colour differences against a topographic background.

The TM 4-5-1 and the DVI images for the full area are shown in Figures 24 and 25 and for several sub-areas at a scale of 1:100,0oO in Figures 26 to 29. The DVI image has a much simpler construction and the colours can be readily interpreted in terms of relative vegetation density. Areas of dense tree cover (bright red) correspond predominantly to forested uplands. Overall, these have a strong geological control (e.g. greenstone belts, mafic granulites) and a lesser dependence on land category since such areas are little used for agriculture regardless of whether they fall within farm or communal land. Smaller areas of thick vegetation also occur in deeply incised river valleys and the lowermost flanks of bornhardts.

Lower-relief areas, which occupy the greater part of the region and are the principal areas of population, are mostly represented in yellow, green, cyan and blue (in order of decreasing vegetation density). As before, a distinction must be made between farm and communal lands, and interpretations confined to one category or another (refer to Figures 24 and 25). Throughout the region, zones of either high DVI (red, yellow and green) or low DVI (green, cyan and blue) can be observed indicating broad differences in vegetation vigour. An example is seen in Figure 26 where the northern and western parts (A) are predominantly yellow and red while the south-east (B) is mainly green. The entire sub-scene occurs in an area of Limpopo Belt granulites within farm lands so that the explanation is unlikely to relate to land use and probably therefore indicates a fundamental difference in groundwater availability.

Comparing the FFC and DVI images for Figures 27 and 28 reveals that the DVI image does not support the interpretation of the several quasi-circular (false) anomalies observed on the FCC and discussed in section 4.2.2.2 (Figure 27 locations A, B, & C and Figure 28 location A) which are known to correspond to abandoned fields. Moreover, the complex variations in colour tones seen on FCCs (in particular involving green (band 5) which correlates strongly with visible soil colour) are replaced by a simpler colour pattern relating only to vegetation. Thus, the DVI image provides a more easily interpreted and reliable indicator of vegetation. This is particularly well demonstrated by Figure 27 where areas of stronger vegetation, uncomplicated by soil tone variations, are much more evident on the DVI image.

Nevertheless, the information on soil tones contained in the 4-5-1 FCC is of importance and needs to be integrated with the vegetation information in any overall interpretation. As discussed earlier, dark soil tones can indicate moist and/or humic-rich soils but similar reflectance characteristics can also result from bedrock variations. Geological soil patterns

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FIGURE 24: Part of Landsat TM scene 169/074 of SE Zimbabwe acquired 23 July 1986. The image is a false-colour composite of bands 4-5-1 (R-G-B). The image has been edge enhanced and contrast stretched to provide maximum information on ground soil colours and vegetation. TM band 4 in the near IR (0.76- 0.90 pm) is strongly reflective in pixels containing healthy, green vegetation; such areas appear in shades of red. Compare with Figure 25. Scale 1:712,500.

FIGURE 25: Part of Landsat TM scene 169/074 of SE Zimbabwe, acquired 23 July 1986. The image has been processed so that the colours represent gradations in vegetation density, ranging from blue (zero vegetation) through cyan, green and yellow to red (maximum vegetation cover). Vegetation density was calculated as the Difference Vegetation Index (DVI). The image was constructed by transforming the three-band false colour composite (bands 3, 4, 5) into intensity-hue-saturation colour space. The intensity component was retained to provide the topographic detail, but the hue and saturation components were replaced by the DVI, suitably scaled to fit the numerical limits of the components. This composite image was then re-transformed back into red-green-blue colour space. The colour-coded vegetation levels can now be perceived superimposed on the relief. Scale 1:712,500.

FIGURE 26: 1: 100,OOO extracts from Figure 24 (lower image - DVI) and Figure 25 (upper image - TM 4-5-1). For discussion, see text.

FIGURE 27: 1 : 100,OOO extracts from Figure 24 (lower image - DVI) and Figure 25 (upper image - TM 4-5-1). For discussion, see text.

FIGURE 28: 1: 100,000 extracts from Figure 24 (lower image - DVI) and Figure 25 (upper image - TM 4-5-1). For discussion, see text.

FIGURE 29: 1: 100,000 extracts from Figure 24 (lower image - DVI) and Figure 25 (upper image - TM 4-5-1). For discussion, see text.

can to some extent be distinguished by their continuity and parallelism with geological features; in Figure 27 (FCC), for example, dark green concentric rings around the elliptical bornhardt mass in the northwestern part of the image (location D) almost certainly relate to the weathering of this granite mass and are probably caused by mafic layering. By contrast, in Figure 29 D, E & F the dark soil tones at these locations also correspond to higher than average green vegetation on the DVI image and thus suggest more favourable ground conditions.

Typically in southern Africa, target sites for boreholes and dug wells are initially chosen using either a drainage feature or a lineament, following which detailed ground geophysical surveys are carried out to identify precisely the best drilling location. However, experience in SE Zimbabwe has shown that a high proportion of boreholes sited in this way turn out to be either dry or inadequate for a handpump yield. An analysis of borehole data for Masvingo Province (Greenbaum 1988; in press) found little positive correlation regionally between lineament length or direction and drilling success rate; thus, it is not possible in SE Zimbabwe to choose between lineaments on this basis even though a similar approach has been shown to work elsewhere in Africa (Odeyemi et al. 1985, Gelnett & Gardner, 1979, Malomo 1989). However, by taking account of vegetation vigour, the DVI image can provide a direct indication of the relative openness of an fracture and so help increase the chance of selecting a productive site.

As might be expected, higher DVI values occur along many drainage lines compared to interfluves but, interestingly, the distribution and extent of higher DVI values in relation to the topography is not constant across an image; in particular, vegetation density is not consistent between otherwise similar drainages. Zones of higher DVI occur both as narrow, sinuous lines of colour (yellow and red) confined to stream banks indicating the presence of tree-lined stream courses (e.g. Figure 27 location E ) and as broader zones extending over larger areas and indicating more widespread healthy vegetation (e.g. Figure 28 location B) . Commonly these areas of higher DVI also show a general correlation with the drainage network. Clearly, the implication is that groundwater is more available in these areas, but the DVI information must be interpreted in the context of other local information regarding land use. Only when variations resulting from man’s activity have been eliminated can the remaining areas of high DVI be used as an indicator of favourable catchments. Once again, it is important that interpretation takes account of the fundamental reflectance differences between farm and communal land.

In a similar way, the DVI image is a valuable complement to conventional lineament plots (interpreted from either satellite imagery or aerial photography) and provides a useful first- pass approach towards distinguishing potentially water-bearing, from dry, structures. Figures 28 and 30 illustrate how lineaments can be classified according to whether they are vegetated or unvegetated. Vegetated lineaments suggest the availability of groundwater and provide evidence for open, water-bearing fractures, whereas unvegetated lineaments are more likely to correspond to closed structures. Figure 30 shows that of the many lineaments seen on the FCC image (solid lines) only some are strongly vegetated (coincident with dotted lines interpreted from the DVI image). Moreover, the DVI image reveals the existence of other lineaments marked by higher vegetation density that are not conspicuous on the FCC (dotted lines alone). Figure 29 similarly shows part of a long NW-trending lineament (marked A - A’) which is less obvious in the FCC due to its poor relief expression; while not strongly

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FIGURE 30: Comparison between lineaments interpreted from the 4-5- 1 FCC (lines) and the DVI image (dots) in Figure 28. Coincident lineanients suggest open fractures, whereas those only present on the FCC are more likely to be closed structiires. Lineaments recognised only on the DVI image are probably subtle tonal features without major topographic expression which would repay further attention.

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anomalous, the DVI image shows the structure to be consistently more vegetated than surrounding areas. This would be regarded as a favourable site for further investigation. Several other drainage systems showing anomalous DVI values also appear quasi-linear on this image and may correspond to water-bearing structures (I3 - B’ and C - C’). In Figure 28 vegetated lineaments mostly trend ENE suggesting that this might be the most favourable direction in this area for borehole siting. This direction corresponds to the foliation in the granite gneisses which may have been opened during uplift and erosional unloading (Greenbaum 1986).

4.4 Multi-date imagery

This was a new and previously untried approach based on the assumption that natural seasonal variations in water availability will vary from place to place, and that these differences (largely manifested in changes to the vegetation pattern) may provide clues to groundwater availability. The idea of combining satellite images of different dates is not a new one (e.g. Gordon, 1980; Eyton 1983; Frank 1984; Pilon et al. 1988; and review by Singh 1989). Digital data are well suited to such a task because the computer can be used (a) to bring the images into exact geometric registration and (b) to combine them using appropriate mathematical enhancement techniques. The general aim of combining data is to detect and measure changes that have taken place at the Earth’s surface. Most investigations of this type concern anthropogenic changes (i.e those caused by man). In such studies, the investigator tries to eliminate as far as possible natural changes to the environment and therefore chooses images from the same season and near-anniversary dates.

In the case of natural change detection, as here, it is important to choose images representing seasonal extremes of conditions so that the maximum effects of change can be seen. The changes that interest the hydrogeologist are likely to relate to variations in soil moisture, vegetation biomass and plant vigour. In semi-arid areas the greatest difference in vegetation cover can be expected between the middlelend of the dry season and the middle/end of the rainy season. In Zimbabwe and similar areas the distribution of rainfall is often highly erratic, not only on a year-to-year basis but also from one part of the country to another within the same season. It is therefore important that images are selected carefully after examination of the local rainfall records over several years. In the present study, rainfall charts for Zimbabwe were examined for a number of areas in the less than 900 mm per annum region, and the results compared against a list of available cloud-free imagery. By plotting the dates of available scenes against local rainfall an area was selected in east-central Zimbabwe corresponding to Landsat path 169, row 073 (see Figures 1 and 2).

Although the mean annual rainfall over this region was slightly higher than would have been wished, the choice of area was severely constrained by the availability of cloud-free imagery for three appropriate dates. Despite this reservation, it was felt that the area contained sufficient land/climatic categories for useful analysis. Three dates of imagery were selected representing a range of likely groundwater conditions:

3 September 1984 end of dry season, following a long drought (little vegetation)

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15 April 1985 end of good rains (good vegetation)

23 July 1986 middle dry season (moderate vegetation)

Before the images could be co-registered a number of pre-processing operations needed to be carried out to ensure that the data were corrected for seasonal differences in illumination, as described earlier. Scene co-registration was performed on a scene to scene basis, which is relatively quick and efficient since control points between scenes (corresponding pixels) are easy to choose. (In cases where the final product is to be a corrected base map it is suggested that the best approach is first to warp one of the images to the topographic map using ground control points and then to warp the other images to the first, corrected one. In this way, each image is re-sampled only once. This is preferred to the method suggested by Townshend et al. 1988).

The analysis of change as an indication of different groundwater and near-surface moisture conditions mainly concerns the detection of variations in vegetation pattern and, to a lesser extent, soil tone. Several approaches were tried. Essentially, these involved:

the subtraction (or division) of corresponding bands for different dates;

the combination of band ratios from different dates;

the combination of NVI images for different dates;

multi-date principal components analysis (PCA).

Figures 31a to c show examples of NVI images over a small sub-area (15 km x 15 km) in the SE part of the Landsat scene (Bepe Hills) for each of the three image dates. Bright tones represent high vegetation density and dark tones low density. As would be expected, the relative overall brightness is greatest for the April image and lowest for the September image. Figures 31d to f show the effects of subtracting NVI values between pairs of image dates. These “VI difference images’ show where changes in vegetation density occur and provide an indication of the degree of change. April-minus-September provides the greatest contrast, depicted in bright tones; black area indicate no change. However, such images do not discriminate between areas where the vegetation is high in the two images from others where it is low, both of which situations are depicted in similar dark tones. Since it is the areas of preserved high vegetation that are of interest such difference images only make sense if compared (or combined) with original NVI data.

One attempt to produce such a combination is shown in Figures 32a to c. For each of the three NVI-difference images (Figures 31d to f ) the the mean value of the distribution (representing ‘no change’) was found. Two separate piecewise linear contrast stretches were then carried out, the first to preserve only the positive changes in NVI (i.e. increases in vegetation) and scale them over the full 255 DN range, and the second to preserve and scale only the negative changes. A third ‘band’ of information was then calculated as the sum of the two separate NVIs to provide an estimate of the ‘average’ vegetation density at each pixel location. These three new ‘bands’ were finally displayed in red (positive NVI change), green (total NVI) and blue (negative NVI change) to produce a new false-colour composite. These

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FIGURE 31: (a), (b), and (c) NVI images for September 1984, April 1985 and July 1986 respectively for a small area in the SE part of scene 169-073 (Bepe Hills). (d), (e) and ( f ) NVI-difference images for July 1986-September 1984, April 1985- September 1984 and April 1985-July 1986 repectively. For details and discussion, see text.

images are an improvement but are still difficult to interpret due to a lack of colour separation. In theory, areas of medium to strong NVI showing little seasonal change (i.e. areas appearing in green hues) are of most interest. These correspond to vegetation along some drainages and over farm lands in the northern part of the sub-scene.

Another approach to natural change mapping is the use of statistical transformations such as principal components analysis. Here, a separate PCA was carried out on each of the three images in turn using TM bands 3 and 4 only as inputs. In theory, PC1 provides an average of the two input bands whilst PC2 provides a contrast between them, and should yield a result with properties somewhat similar to a vegetation index. Two different colour images are shown in Figures 32d and e, 32d combining PCls for the three images dates and 32e combining the PC2s. The PC2 image should contain the most useful information but shows fewer contrasts than had been expected. The final color image (Figure 320 is a simple FCC of bands 4-5-7 for April 1985 which is provided for comparison.

In general, it has been found difficult to interpret these multi-date images and they have provided less information than had been anticipated. This is in part due to the fact that the multi-date combinations depict changes related to many causes, only some of which are relevant to groundwater. Many of the more striking changes probably relate to differences in land-use between seasons, although only some of these are understood. For example, a striking feature in one of the September 1984 images (not shown here) is the presence of anomalously low reflectance within some fields in all spectral bands except band 7, contrasting sharply with the images of other dates. Local enquiries with farmers indicated that the cause was probably the seasonal burning of harvested fields. However, without such local information on a detailed scale this explanation could only have been guessed at. There are many other unexplained features of a similar type for which no definite answer is available.

Problems were also experienced in creating an image product that effectively highlighted features of interest. One category of interest is areas of vegetation that persist throughout both the wet- and dry-seasons, as these could indicate the preservation of near-surface moisture. However, areas of little change are not easily visible on images produced using standard change-detection models. The use of PCA is a possible solution but because the calculated components are complex combinations of the original spectral bands, their interpretation in terms of physical surface conditions is difficult. Nevertheless, it is still felt that detectable seasonal changes in ground conditions exist related to water infiltration and groundwater flow, even though the limited time spent on this part of the investigation could not fully evaluate them.

The main conclusion from the comparison of images of different seasons is that imagery acquired during the middle to end of the dry season, when plants begin to show stress due to lack of moisture, are often best for hydrogeolgical purposes. The careful study of enhanced FCCs of bands such as 4-7-1, 4-5-1 or 4-5-7 can provide information on likely targets where soil moisture persists. Further detail can be obtained from an examination of aerial photographs. Using this approach at least one spring was ‘discovered’ through the use of remote sensing.

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FIGURE 32: (a), (b) and (c) combinations of positive NVI changes (red), total NVI (green) and negative NVI changes (blue) for the April-July, April-September and July- September image pairs respectively. (d) Principal components 1 for the April, September and July images (red, green, blue respectively) derived from input bands 3 and 4 of each image. (e) Principal components 2 for the April, September and July images (red, green, blue respectively) derived from input bands 3 and 4 of each image. (f) April 1985 bands 4-5-7, for comparison. Same area as Figure 31. For discussion, see text.

5: DISCUSSION AND CONCLUSIONS

This overall aim of this part of the research programme was to assess the contribution that satellite imagery can make to regional groundwater exploration in semi-arid terrain. The basis of the approach was that the ground surface provides clues to the hydrogeology in terms of the geomorphology , relative permeability, structure, soil moisture, vegetation pattern and so forth. Most remote sensing studies pay attention to the former aspects, in particular lithological and structural controls. This study has largely focussed on vegetation patterns and on evidence of soil moisture.

A detailed comparison and modelling of vegetation indices has shown that a simple infrared/red ratio does not provide a reliable, quantitative measure of vegetation density or biomass and that other, more sophisticated (but easily calculated) indices are necessary. No single index is without drawbacks. Of the several tested, the Difference Vegetation Index (DVI) provides the most reliable estimate of vegetation cover under a range of densities and vegetation types. Of particular importance is the fact that the DVI is much less sensitive to the albedo of the background soil, a factor that is of great significance in semi-arid areas where vegetation density is low. It is also much less sensitive to noise in the image data.

A new way of presenting vegetation index data in a more interpretable image form has been developed. This combines the intensity information (topography) from an intensity-hue- saturation transform of a normal three-band colour Landsat image with the vegetation index data scaled as both hue and saturation. The result is an image of colour-coded vegetation density superimposed on the relief. This provides an easily-interpreted way to map subtle variations in vegetation pattern and density. It is of particular value in assessing the likely water-bearing potential of lineaments.

One of the principal difficulties encountered throughout this study was the difficulty of separating natural effects from those resulting from man’s activities. Major differences in the intensity of grazing exist between the communal and farm lands in Zimbabwe with the result that different interpretation criteria must be used to assess these two land categories. Unfortunately, in those areas where groundwater supplies are most needed (the heavily overgrazed communal lands) so little remains of the natural vegetation that remote sensing can provide only very incomplete information. Because of the extent of clearing and denudation, it is seldom obvious whether or not patterns of bush vegetation have real significance in terms of soil moisture and groundwater.

The study has developed new approaches to image processing that can quickly provide improved false-colour spectral band composites as well as vegetation index images. When used in conjunction with aerial photograph and ground data, these provide a valuable guide to successful target selection. It is, however, clear from work done that the remotely sensed data cannot be interpreted over large regions without knowledge of, and reference to, local ground conditions, particularly in relation to man’s activity. Aerial photographs remain an important tool in site selection; even the 30 m resolution of Landsat TM imagery is not enough to fully understand observed patterns and tones without reference to stereo photographs. Whereas panchromatic aerial photographs are generally poor for interpreting vegetation vigour, they do contain valuable, though difficult to interpret, information on soil

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tones, some of which relates directly to moisture and surface water infiltration patterns. The complementary information provided by TM imagery can help identify the presence of green vegetation and thus the existence of moist soil conditions.

The processing of multi-date images has provided some new insights into data integration but in general the products of this phase of the work have been difficult to interpret. The main conclusion to come out of this work is that for most hydrogeological purposes, dry-season imagery is likely to provide most information on stressed vegetation.

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ACKNOWLEDGEMENTS

Many people have provided direct and indirect help to the work reported on here. I should like to express my sincere thanks to the staff of the Ministry of Energy, Water Resources and Development (MEWRD), Harare for the valuable logistical support and encouragement provided. In particular I wish to thank Mr G, Nhunhama (Chief Hydrogeologist), Mr S . Muchini and Mr M. Mtetwa. Help was also provided by Mr P. Sinnett-Jones. Dr B.J. Amos carried out much of the original work on the vegetation indices. Finally, the support of the British High Commission in Harare is gratefully acknowledged.

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