Assessment of Dry Season Transpiration Using IKONOS Images: Serowe Case Study, Botswana

Assessment of dry season transpiration using IKONOS images:

Serowe case study, Botswana

Milton Keeletsang February, 2004 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Assessment of dry season transpiration using IKONOS images, Serowe case study, Botswana

by

Milton Keeletsang

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation (Groundwater Resources and Environmental Management)

Thesis Assessment Board

Chairman: Prof. Dr. A.M.J. Meijerink (ITC) External Examiner: Drs. J.W .A. Floppen (IHE) Supervisor 1: Dr. Maciek Lubczynski (ITC) Supervisor 2 : Dr. Y.A. Hussin (ITC) Memeber : Dr. R. Becht (ITC)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

International Institute for Geo-information science and Earth Observation ii Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

I certify that although I may have conferred with others in preparing for this assignment, and drawn upon a range of sources cited in this work, the content of this thesis report is my original work. Signed … … … … … … … … .

International Institute for Geo-information science and Earth Observation iii Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Disclaimer

This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

International Institute for Geo-information science and Earth Observation iv Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Abstract

There is lack of surface water in the semi arid area of Serowe, Botswana. Groundwater remains the main and permanent source of reliable drinking water in the area. W ater management is entirely focused on the available groundwater resources, which depends on recharge and tree transpiration. Some tree species in the area have developed root systems longer than 60m enabling them to tap large water quantities compared to recharge.

Transpiration of savanna vegetation species (Terminalia Sericea, Burkea Africana, Acacia eroloba, Ochna pulchra, Dischrostachys cineria, Acacia fleckii, Boscia albitrunca and Lonchocarpus nelsii was evaluated by sapflow measurements using Granier‘s Thermal Dissipitation Probes method. The normalized transpiration of these species varied from 0.52 l/day (Dischrostachys cineria) to 16.32 l/day (Boscia albitrunca). Individual tree transpiration was scaled up to plot level using IKONOS images. The mean plot transpiration derived from Maximum likelihood classification is 0.41 mm/day while that of Prior probability classification is 0.38 mm/day. These values are too high compared to the values obtained by Mapanda (2003). Possible source of errors are related to the classification of trees and the effect of the understory.

The spatial distribution of transpiration in Serowe area is associated with species type and distribution. Remote sensing has proved to be a valuable tool in upscaling transpiration and assessing its spatial distribution.

International Institute for Geo-information science and Earth Observation v Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Acknowledgements

I am grateful to the contribution of the following individuals and institutions that made this thesis a success. I am grateful to the Botswana Department of Geological Survey (DGS) and the International Institute for Geo-information Science and Earth Observation (ITC) for giving me the opportunity to do my Msc study.

I wish to express my thanks to my colleagues at the Botswana Department of Geological Survey, Pelotshweu Phofuetsile, Magowe Magowe, Ceasar Sebina, Tefo Rahube, Tshegofatso Zwikula, Kabelo Sekalaba, Thomas Kellner, Tebogo Segwabe, Dikitso Gaboalafhswe, Paul Makobo and Obokile Obakeng for being with me throughout the whole research. Mr Pelotshweu Phofuetsile, Deputy Director of the Botswana Geological Survey, thanks for giving me courage and teaching me how to work with other people. Thanks to Sebina and Tshego for the e-mails which kept me close to home.

I would like to express my deepest gratitude to my supervisors Dr Maciek Lubczynski and Dr Hussin for their guidance and critical comments that made this research a success. You taught me science and how to conduct a research. Thanks to Mr Obokile Obakeng for his critical comments. You made me believe that nothing is impossible as long as one puts more effort.

I also wish to thank Prof. Dr. A.M.J. Meijerink, always had time for me and his advice were ready. It was always a pleasure to discuss with you. I wish to thank Arno van Lieshout for having confidence in me, thanks man.

Thanks to the technical support, W an Bakx and colleagues for helping me with images whenever I needed them.

I wish to thank my cousin Martin Tshupeng and my fiancé Gomotsang Tshoso for taking care of my parents when I was not there. You stood by me and made sure made sure that I finish my work in the best possible manner.

At ITC, several people contributed to the success of this thesis. Thanks to my classmates especially Gloria Manuela, Lal Muthuwatta, Rafael Coetze, Namu Mangisi and Marcello by providing a good environment for me in the Netherlands.

Special thanks to Christoph Mujetenga. Man, you have been with me through out the thesis writing. You made sure that at least I eat something before I sleep. You will always be a brother to me and I am grateful of you man.

Last but not least, thanks to my friends Ottilie Angula, Melody Ngidi, Potjo Tsoene and Mclesia Mbaisa for your support and providing a good environment in the Netherlands.

International Institute for Geo-information science and Earth Observation vi Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

To the memory of my Uncle Mr Batole Keeletsang (may your soul rest in peace) and for Taboka Shwapane, Anasticia Chikadzana Tshupeng and Mr Hudson, Bapi Tshupeng

International Institute for Geo-information science and Earth Observation vii Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Table of contents

1. Introduction...... 1 1.1. Background...... 1 1.2. Objectives ...... 2 1.3. Problem statement...... 2 1.4. Research Questions...... 2 1.5. Hypotheses...... 2 1.6. Assumptions...... 2 1.7. Limitations ...... 3 1.8. Literature review...... 3 1.8.1. Transpiration...... 3 1.8.2. How to measure tree transpiration ...... 3 1.8.3. Biometric parameters and transpiration...... 4 1.8.4. Upscaling of transpiration...... 4 2. Description of the study area ...... 5 2.1. Location ...... 5 2.2. Climate...... 6 2.2.1. Temperature and Relative humidity...... 6 2.2.2. Rainfall...... 6 2.2.3. Evapotranspiration ...... 7 2.3. Vegetation...... 8 2.3.1. Vegetation density ...... 9 2.3.2. Species characteristics ...... 10 2.3.3. Interaction between vegetation and groundwater...... 14 2.4. Soils...... 14 2.5. Geology...... 15 2.5 Hydrogeology ...... 16 2.5.1. Depth to water table...... 17 3. Material and Methodology...... 19 3.1. Materials ...... 19 3.2. Methods...... 19 3.2.1. Sapflow measurements in Serowe ...... 21 3.3. Biometric variables measurement...... 24 3.4. Sapflow Analysis ...... 26 3.5. Vegetation mapping...... 26 3.5.1. Introduction...... 26 3.5.2. Ground data acquisition ...... 27 3.5.3. Digital image analysis...... 29 3.6. Remote sensing transpiration upscaling procedure...... 32 3.7. Transpiration verification...... 34 4. Tree Transpiration...... 35 4.1. Biometric characteristics estimates...... 35 4.2. Sap Velocity estimates...... 41 4.2.1. Relationship between sap velocity and biometric parameters ...... 41 4.3. Tree Transpiration estimates...... 44 4.3.1. Relationship between Discharge Qs and the crown area...... 45 5. Transpiration upscaling using RS ...... 48 5.1. Introduction...... 48

International Institute for Geo-information science and Earth Observation viii Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

5.2. Plot Transpiration of dry season ...... 48 5.3. Tranpsiration mapping using Remote sensing...... 49 5.3.1. 4-Channel light sensor results...... 49 5.3.2. Image classification results...... 52 5.3.3. Transpiration upscaling...... 58 5.3.4. Spatial distribution of transpiration ...... 59 5.4. Transpiration verification...... 61 6. Discussion...... 64 6.1. Biometric characteristics...... 64 6.2. Sapflow velocity ...... 64 6.3. Tree Transpiration...... 64 6.4. Vegetation mapping...... 65 6.5. Transpiration upscaling...... 66 6.6. Spatial variation of transpiration...... 67 7. Conclusion and recommendations ...... 68 7.1. Conclusion ...... 68 7.2. Recommendations...... 68 References...... 69

International Institute for Geo-information science and Earth Observation ix Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Table of figures

Figure 2.1 Location of the study area 5 Figure 2.2 Average temperature at Mokongweng station 6 Figure 2.3 Annual precipitation for Serowe area 6 Figure 2.4 Mean monthly precipitation from 1986 to 2003 7 Figure 2.5 Variation of ETo at Mokongweng station 8 Figure 2.6 Vegetation map of Serowe (Zziwa 2003) 9 Figure 2.7 NDVI derivedfrom Landsat 7 TM satellite images 10 Figure 2.8 Distribution of soil units in the study area 15 Figure 2.8 Thickness of the Ntane sandstone 16 Figure 2.10 Depth to groundwater (m) 18

Figure 3.1 Research methodology 20 Figure 3.2 Location of the IKONOS area 23 Figure 3.3 Image classification process 31 Figure 3.4 Layout of confusion matrix 32 Figure 3.5 Aggregate layout 33

Figure 4.1 Relationship between stem area and sapwood area 35 Figure 4.2 Relationship between sapwood area and crown area 37 Figure 4.3 Relationship between stem area and crown area 39 Figure 4.4 Relationship between sap velocity and crown area 41 Figure 4.5 Relationship between sap velocity and sapwood area 42 Figure 4.6 Mean daily transpiration for 11 species 43 Figure 4.7 Temporal variation of transpiration 44 Figure 4.8 Relationship between discharge Qs and crown area Ac 45 Figure 4.9 Relationship between flux Qn and crown area Ac 46

Figure 5.1 Plot transpiration of the study area (Mapanda 2003) 47 Figure 5.2 4-channel sensor feature space 48 Figure 5.3 Comprison between mean spectral reflectances of species in Serowe area 49 Figure 5.4 Error bars displaying how each band separate the tree species 50 Figure 5.5 Classification feature spaces 52 Figure 5.6 Serowe IKONOS area vegetation maps 53 Figure 5.7 Comparison between original and classified maps 54 Figure 5.8 Graphical presentation of the accuracies 57 Figure 5.9 Plot transpiration 59 Figure 5.10 Aggregated transpiration maps 60 Figure 5.11 Spatial distribution of transpiration 60 Figure 5.12 Transpiration verification 61 Figure 5.13 Vegetation map derived from the 1st November 2001 image 63 Figure 5.14 Spatial distribution of transpiration from Nov. image 63

International Institute for Geo-information science and Earth Observation x Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

List of Plates Plates 1 Vegetation along the escarpment 9 Plate 2 Terminalia sericea 11 Plate 3 Burkea Africana 11 Plate 4 Boscia albitrunca 12 Plate 5 Lonchocarpus nelsii 12 Plate 6 Ochna pulchra 13 Plate 7 Dischrostachys cineria 13 Plate 8 Longitudinal cross section of acacia erioloba with sap Velocity sensor probes inserted (Mapanda, 2003 23 Plate 9 Set up of sapflow and meteorological at site 4 (Zziwa, 2003) 24 Plate 10 Radiation shields protecting the probes from direct sunlight 24 Plate 11 A tree trunk inside a bucket with eosine B solution 25 Plate 12 Cracked pieces of tree stems 26

List of Tables

Table 2.1 Lithostratigraphy of the Serowe area (W CS, 1998) 16 Table 2.2 Transmissivity (T) and Storativity (S) results from pump testing (SGS, 1998) 17

Table 3.1 Species collected 28 Table 3.2 IKONOS Original, Pre and Post CalCoef 30 Table 3.3 IKONOS Bandwidth and ESUN values (W m-2I-1) 30

Table 4.1 Descriptive statistics of the biometric characteristics in Serowe study area 34 Table 4.2 Sapflow velocity of the tree species in Serowe 40

Table 4.3 Discharge Qs and normalised daily transpiration Qn 43

Table 5.1 Area covered by species 54 Table 5.2 Prior probability classification confusion matrix 55 Table 5.3 Table 5.3 Maximum likelihood classification confusion matrix 56 Table 5.4 Descriptive statistics for transpiration (mm/day) in the 57 Serowe IKONOS area

Table 5.5 Verification of plot 76 62 Table 5.6 Field plots check of transpiration 62

International Institute for Geo-information science and Earth Observation xi Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

SET UP F THE REPORT

Chapter 1: Gives a general introduction and outlines the Research problem, objectives and Questions

Chapter 2: Gives a brief description of the study area, that is, Location, Climate, Vegetation, Soils, Geology and Hydrogeology

Chapter 3: Describes the methodology and how it was adapted on the research area and the sampling techniques. Remote sensing techniques used to assess transpiration are presented in this chapter

Chapter 4: Present the results for individual tree transpiration with more emphasis of the seven species found in the IKONOS area.

Chapter 5: Present image classification results, upscaling and verification results.

Chapter 6: Discussion of the results

Chapter 7: Conclusion and recommendations

International Institute for Geo-information Science and Earth Observation xii Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

1. Introduction

1.1. Background There is a lack water resource in the semi arid area of Serowe. This problem is further increased by the growing population which is increasingly using the resources for agriculture, industrial, domestic and other uses. The area is characterized by very low and unpredictable rainfall pattern. The only source of water in the Kalahari is therefore groundwater which depends on recharge and transpiration (Lubczynski, 2000). Trees play a very important role in groundwater balance than any other vegetation because of their roots, which enables them to use more water. Trees and their understory vegetation extract water from the subsurface at a rate close to potential transpiration (Granier, 1987). Therefore information on tree transpiration is important for an optimal water management.

In recent years, there has been an increased awareness of the effect of tree transpiration on the decline of groundwater levels. Granier (1986) documented that application of agronomic methods (energy balance and aerodynamic methods) in measuring transpiration in forests fail due to canopy heterogeneity of tree species, topography and horizontal extension of forests. However, measuring transpiration in homogeneous forest such as plantations involves less uncertainty because they usual have trees with same size, regular spacing and canopies with gaps (Mapanda, 2003). Difficulties in transpiration measurements occur when dealing with woodlots and open savannah vegetation. These types of vegetation have varying tree sizes, open and irregularly spaced. Recent studies by Magombedze (2002), Fregoso (2002), Mapanda (2003) and Zziwa (2003) have shown that sapflow measurements are the most appropriate methods for estimating transpiration of woodlots and savannah vegetation

The sap-flow method for estimating transpiration provides an accurate solution for determining the vapour flux from the dry canopy in a forest stand (Granier, 1987; Diawara et al., 1991; Granier et al., 1990; Kelliher et al., 1992). It has been used for determining the transpiration of the overstory in multistory stand (Loustau et al., 1990) and allows of a single layer in a given canopy to be measured separately. Moreover, sapflow can be continuously monitored for a long periods with minimal maintenance, thereby providing a continuous data.

The focus of this study is to assess groundwater discharge through transpiration. The study is a follow up to transpiration studies done in Serowe (Botswana) by Fregoso (2002), Mapanda (2003) and Zziwa, (2003) to measure sapflow rates by use of in situ thermal dissipation probes (TDP) œ Granier method. Sapflow estimates of individual trees can be scaled up to stand level and to vegetation level using remote sensing images.

This research contributes to the understanding of groundwater discharge through transpiration of savannah trees in the Kalahari semi arid environments, for a better management of the available groundwater resources. It also closes the gap between water engineers and foresters regarding the hydrological role of trees. The research is part of the ongoing research project the —Kalahari Research Programme (KRP)“ of which aim is to have an understanding of

International Institute for Geo-information Science and Earth Observation 1 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana) hydrogeological dynamics of the semi arid Kalahari environments by employing the most recent initiatives in GIS and Remote Sensing as demonstrated by Timmermans and Meijerink (1999). The KPR has established a monitoring network for water balance studies in the Serowe area.

1.2. Objectives The objectives of this study are to,

Assess transpiration of individual trees species in Serowe area upscale individual tree transpiration to plots level assess the spatial variation of transpiration.

1.3. Problem statem ent Serowe is a surface water poor area and relies on groundwater. Due to population growth and industrialization, there is a great competition of water among stakeholders. The boreholes in the area are over pumped. Rainfall is the only source of recharge. Most of the rainwater is lost through transpiration and evaporation. In the Serowe area, the water transpired by trees is not quantified and the spatial distribution of transpiration is not established.

1.4. Research Questions W hat are the magnitudes of transpiration in savannah trees? How can transpiration be upscaled? How does transpiration vary spatially?

1.5. Hypotheses

Individual tree transpiration can be estimated from the biometric parameters and sap velocity of trees Individual tree transpiration can be upscaled using remote sensing high resolution images Transpiration in woody plants varies spatially and temporally between species

1.6. Assum ptions W e assumed the following:

Under story vegetation transpiration is small in semi arid and arid environments. The forest stand sapflow represents forest stand transpiration. Vapour pressure deficit is constant throughout the open savannah vegetation canopy. A relationship at one scale will be the same at another. Measured velocity is representative of the sap velocity of certain tree species. Eosine dye test provide accurate estimates of conductive sapwood area.

International Institute for Geo-information Science and Earth Observation 2 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Soil heterogeneity does not influence the measurements of sap velocity Sap velocity is not dependent on the biometric characteristics of a tree

1.7. Lim itations The sap velocity measurements were done in the months of September and October while the images used for scaling up individual tree transpirations were from February 2002 (IKONOS). Results are affected by variations in leaf cover. Generally plants have no leaves in September and most plants are in full leaf by February.

1.8. Literature review

1.8.1. Transpiration

Allen et al (1998) defined transpiration as the vaporization of liquid water contained in plant tissues and the removal to the atmosphere. Approximately 98% to 99% of the water absorbed by plants roots passes the plant and is evaporated into the atmosphere (Hinckey et al, 1978). For transpiration to take place, two conditions have to be met;

Sufficient energy must be available to convert the liquid water into water vapour The vapour pressure gradient across the leaf and its boundary layer must be sufficient to overcome the stomatal resistance diffusion (Hickley, et al, 1978).

Physical parameters which influence tree transpiration are air temperature, wind speed, radiation and soil moisture. W ith respect to radiation, tree transpiration helps to keep the radiation level of trees below lethal value. Transpiration acts as a plant regulating factor, it cools the leaf and reduces the effective radiation load by some amount. W ind speed has two- fold implications on transpiration. The boundary layer around a leaf is quiet thick in still air

and constitutes a major resistance to the flux of H2 and CO2. A slight increase in air turbulence or wind speed around tree leaf causes an increase in transpiration. However it should noted that a further increase in wind speed might reduce transpiration because wind speed will cool the leaves, reducing the vapour pressure gases. Readers needing more information regarding these factors are referred to Kramer and Kozlowski (1960) and Roseberg (1974).

1.8.2. How to m easure tree transpiration They are a vast historical methods used in estimating tree transpiration. The common ones are radioisotopes (Kline et al 1976), absolute weight (Fry, 1965, Kotar 1972), energy balance and heat pulse methods (Grainer, 1975, Morikawa 1974). From these methods, the Granier Thermal Dissipition Probes (TDP) method yield better results in estimation of individual tree transpiration and the are cheap to use.

International Institute for Geo-information Science and Earth Observation 3 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

1.8.3. Biom etric param eters and transpiration Vertessy et al., 1994 examined the relationships between stem diameter, sapwood area and transpiration of 15 year old mountain ash (Eucalyptus regnans F. Muell.) forest containing silver wattle (Acacia dealbata Link.). They found out that the stem diameter explained 96% of the variation in sapwood area and 88% of the daily variation in spring transpiration in 19 mountain ash trees. In silver wattle trees, stem diameter explained 87% of the variation in sapwood area but a poor predictor of other variables. They cautioned that accurate determination of sapwood area is essential when estimating tree transpiration using the TDP method

1.8.4. Upscaling of transpiration The scaling pocessess involves taking information at one scale and using it to derive the process at another scale. Upscaling consists of taking information at a smaller spatial and shorter temporal scales and using that information to derive processes at a larger spatial and longer temporal scales

The most general solution to scaling up transpiration is to measure the flux through every tree in a plot of known area (e.g Doley and Grieve 1966, Kelliher et al.., 1992). However, censusing water use by the population of stems in a plot of sufficient size to minimize edge effects is not logistically and is practically difficult. An alternative is to scale limited measurements of tree water use by some scalar of tree size, knowing the distribution of that scalar for the entire stand. In 1963, Ladefoged scaled tree flux on the bases of (a) a relationship between sap flow and crown size, and (b) the stem area occupied by each tree in the stand, but the correlation between the tree flux and size was poor. Cermark and Kucera (1987) estimated stand transpiration from sapflow measurements based on allometric relationshpips between tree flux and basal area, and W erk et al., (1988) extrapolated flux measurements to stand level by means of area estimates. All the above methods presented unconvincing results of sap flux. Mapanda (2003) upscaled transpiration from individual trees to plot level using the correlation between the vegetation indices and average species transpiration. Unfortunatetly the correlation between transpiration and the vegetation indices were very low. However, this has brought a new light in upscaling of transpiration using remote sensing.

In this report individual tree transpiration would be scaled up using RS images and crowm areas of individual trees.

International Institute for Geo-information Science and Earth Observation 4 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

2. Description of the study area

2.1. Location Serowe is located in the Central District of the republic of Botswana, about 275 km north of the capital city Gaborone (Figure 2.1). According to the Central Statistics Office, 1997, the population of this village is approximately 70 000. The Kalahari Research project area lies in UTM coordinates 400000E to 497000E and 7545000N to 7501000N and covers an area of 2444 km2. The project area is divided into two main units by an escarpment, which is running NNW -SSE. The Kalahari sandveld is located on the western part of the escarpment while the hardveld is on the Eastern fringe (W CS, 2000). The project area main camp is accessible by a gravel road from the Orapa œ Serowe tired road.

Figure 2-1 Location of study area

International Institute for Geo-information Science and Earth Observation 5 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

2.2. Clim ate 2.2.1. Tem perature and Relative hum idity The climate of the area is semi-arid with cold, dry winters and hot summers. W inter temperatures can go below 0oC while summer temperatures can exceed 40oC. Figure 2.2 shows the average temperature measured at Mokongweng ADAS in Serowe for the period January 1998 to October 2002.

Figure 2.2 Average temperature at Mokongweng station

2.2.2. Rainfall The mean annual precipitation of the area is 447 mm (SGC, 1988). This figure is based on the data from 1936 to 2003. The mean annual precipitation from 1986 to 2003 (Figure 2.3) is approximately 450 mm and ranges from 291.6 mm in 1994 to a maximum of 1038.1 mm in 2000. Monthly average records from 1986 to 2003 shows that the precipitation is characterized by local, high intensity and short duration events that occur mostly between April to September (Figure 2.4).

1200 ) y / 1000 m m

( 800

n o

i 600 t a t i

p 400 i c e

r 200 P 0 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 9 9 9 9 9 9 9 9 9 9 8 8 8 8 3 2 1 0 9 8 7 6 5 4 3 2 1 0 9 8 7 6

Figure 2.3 Annual precipitations for Serowe area

International Institute for Geo-information Science and Earth Observation 6 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana) )

h 120.00 t n o

m 100.00 / m

m 80.00 (

n o i

t 60.00 a t i p

i 40.00 c e r p

20.00 n a e 0.00 M Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 2.4 Mean monthly precipitation from 1986 to 2003

2.2.3. Evapotranspiration Two major mechanisms are involved in transporting groundwater close to the surface and into the atmosphere: Evaporation and transpiration. Evaporation is a physical process, caused by heat energy input, providing water molecules with kinetic energy that transfers them from an inter-pore liquid phase into vapour phase. Transpiration is the process by which plants lose water to the atmosphere, which their roots extracted from the soil (Mazor, 1991). Evapotranspiration is a combination of both evaportion and transpiration.

Two types of evapotranspiration can be described; Reference Evapotranspiration (ETo) œ The maximum possible evapotranspiration according to prevailing atmospheric conditions and vegetative properties. The land surface in question (can be any part of the landscape that contains a certain fraction of vegetation) should be supplied by water such as soil moisture forms no limitation to stomatal aperture. Actual Evapotranspiration (ETa) œ The actual rate of evapotranspiration caused by the prevailing atmosphere, the real vegetation development and the actual soil moisture and soil temperature regimes in the root zone of the vegetation. In this section, only ETo will be discussed.

900 0.408D(Rn - G) + g u z (es - ea ) ETo = T + 273 D + g (1+ 0.34u z ) where ETo is Potential evapotranspiration [mm/day] 2 Rn is net radiation at the crop surface [MJ/m /day] G is soil heat flux [MJ/m2/day] T is air temperature at 2 m height [oC] z is height [m]

uz is wind speed at 2 m height [m/s]

es is saturation vapour pressure [kPa]

ea is actual vapour pressure (kPa) D is slope of vapour pressure [kPa] g is psychometric constant

International Institute for Geo-information Science and Earth Observation 7 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Figure 2.5 shows the fluctuations of ETo from 1998. These fluctuations are due to changes in seasons, that summer and winter on large scale, and the —local fluctuations are due to changes in the incoming radiation. On a cloudy day, the ETo goes to minimum while on a clear sky day, the ETo reaches maximum. In general, the evapotranspiration rates for Serowe are relatively high.

9.00 8.00 7.00 6.00 ) d / 5.00 m m

( 4.00

o

T 3.00 E 2.00 1.00 0.00 6 1 9 0 2 2 0 / 8 / 1 5 6 8 5 1 / / / / / / 1 3 0 0 0 0 2 0 / 4 2 6 4 0 2 / / / / / 0 0 0 0 0 0 9 3 2 0 2 1 0 8 1

Figure 2.5 Variation of ETo at Mokongweng station

2.3. Vegetation The Serowe biomass resource depicts a typical semi arid savannah environment with average tree height ranging between 3 m to 5 m. (Namayanga, 2002). Although the dominant type of vegetation in Serowe is open savannah, this may be divided into bushland, grassy woodland, dense woodland, dense wooded bushland, Forest, wooded Bushland, dense bushland, grassy bushland and woodland (Hernandez, 2002) as shown in Figure 2.6. On the eastern part of the escarpment, the dominant species are Acacia fleckii, Burkea Africana, Boscia albitrunca, Acacia erioloba, Lonchocarpus nelsii, Terminalia sericea, and Ochna pulchra. Along the escarpment, the dominant tree species are Croton grtissimus, Acacia erioloba, Terminalia sericea and Peltoforum africanum. Acacia tortilis and nilotica are dominant on the western part of the escarpment. Plate 1 shows a typical vegetation cover along the escarpment.

International Institute for Geo-information Science and Earth Observation 8 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Figure 2.6 Vegetation map of Serowe (Zziwa 2003)

Plate 1 Vegetation along the escarpment

2.3.1. Vegetation density The Normalized Difference Vegetation index (NDVI) is known to be highly correlated with vegetation parameters such as green leaf biomass and green leaf area. The magnitude of NDVI is related to the level of photosynthetic activity in the observed vegetation. In general, higher values of NDVI indicate greater vigour and amounts of vegetation (Parodi, 2000). NDVI values range from œ1 to +1 with œ1 values representing dry vegetation or bare land cover and for a complete healthy green vegetation cover its value is equal to +1. It is defined as r - r NDVI = 4 3 (2.1) r4 + r3 where r4 is reflectance for the near infrared band

International Institute for Geo-information Science and Earth Observation 9 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

r3 is reflectance for the red band Landsat 7 TM image for 1st October 2001 was used to map out the vegetation density in the study area using the NDVI. The calculations where performed in ILW IS GIS programme using a script according to equation 2.1. Figure 2.7 shows NDVI values ranging from 0.12 to 0.32. High NDVI values indicate healthy vegetation and low NDVI values indicate stressed vegetation.

Figure 2.7 NDVI derived from Landsat 7 TM satellite images

2.3.2. Species characteristics Many Savannah trees are deep rooted with legumes such as Acacias reaching depths of 3-20 m and even >53 m (Canadell et al., 1996). Eucalyptuses also have deep roots reaching 60 m. Studies of rooting systems of some trees found in a typical Savannah at Nylsvley were done by Scott and Maitre (1998) and these can be compared with the same species found in the Serowe as described below. Acacia erioloba, is dependent on readily available groundwater. Terminalia sericea is a fairly common shrub or small tree and grows up 6 to 9m tall but individual trees may reach 23 m height. It thrives in deep sandy soil. Terminalia sericea occurs in the woodlands. The root system is primarily shallow but can spread out from 0.12 œ 0.35 m depth and 3.6 œ 6.6 m from the stem.

International Institute for Geo-information Science and Earth Observation 10 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Plate 2 Terminalia sericea

Burkea africana: A fairly common tree on deep sand, it grows up to 12 m tall. It is the last large tree to lose its leaves during the dry season. It flowers in October with unspectacular creamy-white to green spikes and can easily be identified by its dark-red velvety young shoot. It is a drought resistant a perennial shrub africana roots spread up to 20.5 m from the tree, most roots were found in the upper 0.4 m.

Plate 3 Burkea africana

Boscia albitrunca: According to Cannadell et al., (1996), this specie is reported to have had very deep roots of up to 68 m in the central Kalahari

International Institute for Geo-information Science and Earth Observation 11 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Plate 4 Boscia albitrunca

Acacia fleckii: A multi-stemmed shrub, which is mainly found in the omurambas. It flowers in white spikes from November to January. Lonchocarpus nelsii: A small, common tree up to 5 m tall, widely distributed on deep sands as well as loamy sands. It flowers from September onwards.

Plate 5 Lonchocarpus nelsii

Ochna pulchra: It is a grows up to above 4 m in height. It is widely distributed on deep sands as well as loamy sands. It flowers together with the new leaves from September onwards and is amongst the

International Institute for Geo-information Science and Earth Observation 12 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

first trees to flower after the dry season. Ochna pulchra had roots extending up to 4 m from the stem and —individuals“ are often connected to others by lateral roots to form clones. Most roots stop at the bedrock (2.2 m depth).

Plate 6 Ochna pulchra

Dichrostachys cinerea: This is a small, deciduous drought resistant tree.

Plate 7 Dischrostachys cinerea

Acacia leuderitzii It is a large, fast growing species and grows up to a height of 5m. According to Barnes et al as cited by (Scott and Maitre, 1998), Acacia leuderitzii is one of the most widespread

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southern African acacias that will only grow where it has access to permanent underground water as it is not drought tolerant.

2.3.3. Interaction between vegetation and groundwater. Up to now, information on the interaction between vegetation and groundwater is poorly quantified. Due to the unconsolidated nature of the Kalahari sand, which permit deep penetration by roots, some scientist believe that during dry seasons, savannah vegetation tap groundwater. This is in agreement with observations made by Jennings (1974) in Phuduhudu area (appr. 450 km NW of the study area) from the inside of a disused borehole. The borehole was cased with unperforated casing to a depth of 68 m and water was at 141 m below the surface. The roots must have been below the 68 m, and the only tree of any size in the vicinity was Boscia albitrunca.

A similar study in the Gantsi district of Botswana have shown that Combretum imberbe (hardekool) appears to indicate fresh shallow groundwater while Acacia erioloba indicates saline groundwater. The ongoing research by Obakeng will use stable isotopes, Deutirium and Oxygen-18 to find the water source for savannah vegetation in the study area.

2.4. Soils The study area can be divided into two parts with regards to soils, the sandveld in the west and hardveld in the east (W CS, 2000). The sandveld consists of fine Kalahari sands while the hardveld consists of calcic luvisols and luvic arenosols (Figure 2.8)

Arenosols are the most common soil type and are sandy with minor silt and clay. Such composition explains their high permeability and low water retention capacity. Arenosols are not subject to crusting like other soils in the study area, therefore facilitating recharge to the underlying aquifers. Regosols consist of sandy loams to clay loams with minor coarse sand. They are less drained than the arenosols although they have big enough pore spaces to allow easy movement of water through the sand matrix.

Lixisols are well drained with sand content of about 20% and clay content of greater that 20% (Obakeng, 2000). Luvisols on the other hand have nearly equal proportions of coarse fine sand but with similar sand and clay content as the Lixisols. Luvisols and Lixisols have good water holding capacity. Vertisols are the most poorly drained soils in the area with clay content in excess of 50% (Obakeng, 2000).

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Figure 2.8 Distribution of soil units in the study area

2.5. Geology The project area is underlain by strata of the Karoo Supergroup represented by extrusive volcanic strata of the Stormberg Basalt Formation, the largely aeolian sandstones of the Ntane Sandstone Formation and along the fringes of the Karoo escarpment, by the mudstones of the Mosolotsane Formation. Beneath these units occur the thick mudstones of the Tlhabala Formation. Table 2.1 shows the stratigraphy of the Serowe area. The description of the formations follows:

Kalahari sands overlie the stormberg basalts and are found on the western part of the escarpment. The thickness of the sands vary from 0 to 75 m

The Stormberg Basalts is a black/brown or purple amygdaloidal or massive sequence of successive lava flows, generally increasing in thickness westwards away from the escarpment. Basalts outcrops occur along the escarpment. The Stormberg Basalts overlie the Ntane Sandstone.

The Ntane Sandstone Formation is the principal aquifer of the project area. It consits of an upper arenaceous —massive member“ and a lower more argillaceous ”transition member‘. The upper unit is a sequence of buff/white or pink fine to medium grained eaolian sandstone, often poorly cemented and with a baked uppermost horizon (up to several meters thick) where it is overlain by the basalts. The lower unit is also largely arenaaceous, but intercalations of mudstone are more frequent. The thickness of this sequence varies from 20 to 120 m (Figure 2.9).

The Mosolotsane Formation comprises of red fluviatile and terrestrial argillaceous sediments having poor aquifer potential. Information about this formation is derived from borehole data.

Tlhabala Formation œ these are mixed sandstone/mudstones/coal sequence of the Ecca Group, and the basal Karoo Dwyka Group glaciogeneic deposits.

International Institute for Geo-information Science and Earth Observation 15 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Table 2.1 Lithostratigraphy of the Serowe area (W CS, 1998) Age Group Formation Lithology Cainozioc Kalahari group Kalahari sands Sands, calcretes, sandstones and mudstones Mesozoic Stormberg Lava Stormberg Variably weathered, group basalts green or reddish purple, amygdaloidal lava flows Lebung Group Ntane Sandstone Fine to medium grained, clean, friable sandstone, brownish red/pink Mosolotsane Tlhabala Sandstone, mudstones

Figure 2.9 Thickness of the Ntane sandstone

2.5 Hydrogeology The Ntane Sandstone is the main aquifer in the project area. It displays both confined and unconfined conditions below the Stormberg, and unconfined conditions where the Stormberg lavas have been removed by erosion (W CS 1988). Estimates of aquifer transmissivity and storativity were obtained from analysis of pump test data conducted by SGS (1988) and W CS (1998) and are presented in Table 2.2. The results show that the transmissivity and storativity are highly variable. The transmissivity values range from 1.5 to 199 m2/d and the storativity values range from 0.000013 to 0.07.

According to Obakeng (2000), the broad variations in transmissivity values are partly explained by variations in fracture density and the Ntane thickness which vary across the project area. Variations in storage coefficient values can be explained partly by variations in primary porosity within the sandstone aquifer

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Table 2.2 Transmissivity (T) and Storativity (S) results from pump testing (SGS, 1998 and W CS, 1998). Borehole Number T (m2/d) Storage value After 5315 199 7E-05 SGS 5317 13.3 ? SGS 5335 27 ? SGS 5336 10 ? SGS 5337 20 ? SGS 5343 10.5 ? SGS 8449 3 2.8E-02 W CS 8452 1.6 ? W CS 8472 1.5 4.0E-03 W CS 8477 15 4.0E-04 W CS 8492 34 7.5E-03 W CS 8493 12.5 1.1E-02 W CS 8672 17 1.0E-03 W CS 8673 52 4.0E-03 W CS 8673 85 1.3E-04 W CS 8707 5 1.0E-03 W CS

2.5.1. Depth to water table The study area is characterised by relatively deep water tables ranging from 20 to 80m (Figure 2.10). Groundwater levels in the Ntane sandstone lie partly above and partly below the base of the basalt cover. The surface is thus a composite feature being a piezometric surface where it occurs above the basalt and a water table where it is below or where there is no basalt. Groundwater levels in the Ntane sandstone and further to the west in the Sanakoma are generally 30 œ 70 m. The minimum recorded value is 11.87 m at BH 7102 (W CS, 2000). Below the escarpment the water table is much shallower and water is between 10 and 40 m depth SGC, (1988). Figure 2.10 below show the depth to groundwater based on water level data of 1997.

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Figure 2.10 Depths to groundwater (m)

International Institute for Geo-information Science and Earth Observation 18 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

3. Material and Methodology

3.1. Materials

The following materials were used in this research

Pan-sharpened IKONOS images obtained in 2nd February 2002. Pan-sharpened IKONOS images obtained in 1st November 2001 Granier TDP sapflow sensors GPS 4-channel light sensor 3-band camera Software: ILW IS 3.1, SPSS, AW SET, Microsoft excel and Microsoft word

3.2. Methods In order to achieve the objectives the following methodology was adopted and included: Pre- fieldwork, Fieldwork and Post fieldwork. The methodology followed in this research is presented in Figure 3.1.

Preliminary preparation This was the initial step and included the following:

Literature review of reports, journals and publications was carried out in order to get acquainted to the research topic. For the study area, literature on topography, hydrogeology, climate, geology vegetation, water balance and soils in the study area was reviewed.

By using satellite images, a familiarization of the area was carried out. Preliminary interpretation of the images helped to map out the sampling areas for sapflow measurements during fieldwork.

A database with available data was organized, screened and pre-processed in excel spreadsheets.

International Institute for Geo-information Science and Earth Observation 19 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Problem Formulation Literature review Research objectives

Research proposal

Preparation for field work

Preparation of a data Gathering available material base Image analysis

Raw data Data collection Reflectance data Meteorological data Sapflow data Biometric data Sapwood area data

Tree reflectances Plot data Plot sizes Crown diameter Stem diameter

Data entry

Database

Plot transpiration RS Transpiration Individual Transpiration Data analysis

Discussion, conclusions and recommendations

Figure3.1Research methodology

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Granier TDP sap flow method The transpiration of whole plants is closely approximated by sap flow rate in the main stem or trunk. The TDP heated needle is an improved heat dissipation sensor, as proposed by Grainer, which measures the temperature of a line heat source implanted in the sapwood of a tree, referenced to the sapwood temperature at a location well below the heated needle. The probe measure the sapwood heat dissipation which increases with sap flow and the resultant cooling of the heat source, as the apparent thermal conductance of sapwood increases with sap velocity. W hen the sap flow is zero or minimal, the temperature difference between the two sensors is maximal. W hen flow increase, this temperature decreases.

An important feature of the TDP is that a constant heat method is used, that is, the heating element of the probe stays on and permits continuous and frequent measurements. The TDP method gives continuous record of sap velocity. It provides flow rate, as the product of sap velocity and the sapwood area. It should be noted that the sap velocity varies from within the sapwood, as shown by Granier (unpublished data). Therefore what is being measured with the TDP method is an integral of sap flow velocity over the radial thickness of the sapwood. The fact that the length of the needles and the thickness of the sapwood cannot be exactly the same, introduces some uncertainty in the calculation of the sap flow velocity, reflected between the temperature difference as evident from Granier work.

In spite of the two sources of error cited in the paragraphs above and possible effects of environmental parameters on the temperature difference, the method produces reproducible results and reasonable agreement with independent estimates of the sap flow rate.

3.2.1. Sapflow m easurem ents in Serowe Sapflow data have been collected from 198 trees representing 11 species found in the Serowe area. Fregoso (2002) measured sap flow velocities of Acacia fleckii, Boscia albitrunca and Loncorcapus nelsii. Mapanda (2003)) and Zziwa (2003) measured sap flow velocities of Acacia erioloba, Acacia luederitzii, Burkea Africana, Boscia albitrunca Dichrostachys cineria, Ochna pulchra and terminalia sericea. During the current filed work, sap flow data was collected from Burkea Africana, Ziziphus macronata and Acacia karoo. These choices of the tree species was based on their abundance in the Serowe research area and their coexistence in the IKONOS area (site of systematic plot and experimentation measuring 10 * 10 km2). Sampling was done outside the IKONOS area (Figure 3.2) to avoid tree cutting in the experimental zone.

The selection of the site where sap flow measurements has to be done was based on the following criteria;

1. There should be a minimum of 18 trees of the same species at the site 2. The maximum distance between six trees at each site should be 10 m. This is because the logger-sensor cables are 10 m in length. 3. For each group of six trees, the maximum distance from the power source, (voltage 12) should be 18 m. This is because the power source-logger cables length is 18 m; anything longer than 18 m will result in voltage drop and affect the measurements. 4. Trees at each site should have varying biometric parameters to give a good representation of size distribution. 5. The stem and/or branches of the tree should not be dead.

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Sap flow velocity measurements was carried out using mobile monitoring system consisting of 18 TDPs. The mobile system was moved from one site to the other after 3 full days of monitoring. The following steps was adapted to install sapflow sensors; 1. The bark of each tree was removed with a knife or chisel about 0.5 m above the surface on the southern hemisphere. The direction was chosen to avoid direct radiation from the sun. 2. Two holes 10 cm apart, about 2.3 cm deep and 2 mm were drilled into the sapwood. 3. The thermal dissipation probes (TDP) needles covered with silicon fat (to improve conductance) were then inserted into the aluminium tubes. The tubes were marked blue and yellow and the yellow was always put on top (Plate 8). 4. Theostat was put around the needles to protect them from ants and water. 5. The sensors cables were then connected to the logger cables which in turn were connected to the power source cables. 6. Radiation shields (Plate 10) were placed around the sensors to protect the probes from solar radiation and water. The system would then be left to record readings at 30 minutes interval for a period of 3 days. During 3 a day period of measurements, the upper TDP will be continually heated by the power from the battery. The lower probe is not heated but together with the upper probe, they provide the reference temperature difference (DT) between the probes, which is inversely correlated with sap velocity (Granier, 1985). Transpiration measurements were recorded on half-hourly time intervals by the data logger. Plate 9 shows the set up of the unit at site 3 (Zziwa, 2003).

At each site micrometeorolical measurements, including horizontal wind speed u (m/s) at 2 m, air temperature, T (oC), net radiation, Rn (W /m2) at 2m, soil temperature at 0.05 and 0.15 m below ground level and soil heat flux at 0.1 m below ground level were monitored. After 3 days, the data will be down loaded into a laptop for later processing.

International Institute for Geo-information Science and Earth Observation 22 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

IKONOS area

Figure 3.2 Location of the IKONOS area

Plate 8. Longitudinal cross section of Acacia erioloba with sap velocity sensor probes inserted (Mapanda, 2003)

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Plate 9 Set up of sapflow and meteorological at site 4 (Zziwa, 2003)

Plate 10 Radiation shields protecting the probes from direct sunlight

3.3. Biom etric variables m easurem ent Once the sapflow measurements were done, stem diameter for each tree was measured using a caliper, with the measurements carried out in the middle of the previous location of the probes

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in the north-south and east-west directions. The crown diameter was also measured in the north-south and east-west directions. The coordinates of each site were taken using a GPS.

Trees were then cut, put in bucket with the eosine B dye solution (Plate 11) for determination of the conductive xylem area. This experiment was done on a clear day between 0800 hours to 1400 hrs. The cutting was done in the middle of the previous location of the probes and immediately put in the buckets with the dye solution for 3 hours to make evident of the different between the conductive xylem areas and the heartwood. After the 3 hours, a thin piece (appr. 1.5 cm thick) was cut and put in the sun to dry up before determination of the conductive sapwood area. Some pieces of trees also cracked during dying (Plate 12) and this posed a problem in determination of the conductive sapwood. In some species the sapwood was clearly distinguishable on the basis of colour. The sapwood area measurement was regressed against the crown area and the stem area. The resulting regression equations would be used to estimate the sapwood area of individual trees in from the area of crowns found on the IKONOS images.

The conductive sapwood was calculated using equation

As = stem diameter œ ((bark area) œ (heartwood area) œ (non coloured area)) 2

Plate 11. A tree trunk inside a bucket with eosine B solution

International Institute for Geo-information Science and Earth Observation 25 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Plate 12 Cracked pieces of tree stems

3.4. Sapflow Analysis The Granier (1985, 1987) sapflow method is analogous to the hot wire anemometer technique for measuring wind. Each probe measures the sapwood heat dissipation which increase with sapflow and the resultant cooling of the heat source as the apparent thermal conductance of sapwood increases with sap velocity. Laboratory experiments have shown that a reliable relationship exists between the observed temperature different and the velocity of sapflow;

1.231 V = 0.0119 ((WTmax œ T)/T)) (3)

W here V is the average velocity along the length of the probes, ∆T is the temperature diffirence observed between the heated and the reference needles, ∆Tmax is the value of ∆T when the sapflow is zero or minimal (generally taken as the peak of the night time). Tree discharge Qs (volume per unit time) is computed as,

Qs = V. Ax (4)

W here Ax is the cross sectional area of the xylem area (sapwood). The sapflow-derived transpiration of individual trees can be estimated as,

Qn = Qs/Ac (5)

W here Ac is the projected ground area of the tree crown. Plot transpiration can be estimated T = .∑Qs/A (6) where A is the area of the plot. 3.5. Vegetation m apping 3.5.1. Introduction A vegetation map is a special application of a vegetation classification (Kuchler 1988). Vegetation classification defines units based on the similarity of structural, floristic, and

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ecological characteristics of the vegetation. The classification units are used to label homogeneous patches of vegetation to make a vegetation map. A vegetation classification is usually developed first, and then the spatial relationships of the vegetation units are described in a map.

Vegetation mapping requires a combination of knowledge and experience in several disciplines. The investigator(s) must have considerable ecological knowledge of the area to be mapped including the ability to identify individual plant species, vegetation types, and the relationships of these types to other factors, such as topography, soil types, and moisture gradients, within the mapping area. It also requires that the investigator to have experience with general cartographic and GIS and remote sensing techniques. This is particularly important for the ecological interpretation of remote sensing data and digital image processing and map preparation. Most importantly, the investigator must clearly understand the relationships between these disciplines during the mapping process.

3.5.2. Ground data acquisition A major component of the work was the collection of data from the tree species under investigation. This work involved identification of suitable places where plot data could be collecting and identification of suitable training sites where data on vegetation characteristics could be collected from points that could be identified in the satellite images. 3.5.2.1. Plot data collection Mapanda (2003) sampled 1287 trees from plots whose areas were 35 *35 m2 at a spacing of 1 km in the IKONOS area. During the current field work, 98 more trees were sampled. A Garmin 12XL Geographical Positioning System (GPS) receiver was used to locate systematic plots in the study area and the coordinates of the trees within the plots. The following information was recorded from all the trees in each plot:

a) Stem diameter at 0.5 m above ground level (a single measurement taken with the arm of the caliper facing the center of the plot); b) Individual tree crown diameter (measured with the tape stretched towards the center of the plot); c) Overall crown diameter for trees in clusters (measured with the tape stretched towards the center of the plot); d) Tree co-ordinates; e) Tree species types; f) Tree number in the plot, g) Distance and bearing of each tree from the center of the plot; h) Comments on the general condition of the plot.

Plot transpiration was calculated using equation 6 and compared with the output of remote sensing mapping.

3.5.2.2. Ground truth and reflectance data collection Ground truth data for this study was collected during the field visit in September 2003. Original Pan sharpened IKONOS images obtained in 2nd February 2002 were used to identify individual trees in the IKONOS area. The projection of these images is PCS W GS 1984 UTM, zone-35. Assigning Near infrared band to red colour, Red band to green and Green

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band to blue colour, it was possible to differentiate most of the tree species in the IKONOS area. Individual trees species were sampled through out the IKONOS area so that there is a statistical representative of each species. The distribution of sites was limited to within reasonable access to tracks. Data collected in situ included; ñ Status of the individual trees ñ GPS co-ordinates for each tree ñ Crown area of each tree ñ Other species occurrence ñ Colour of leaves and status of the tree Table 3.1 presents the number of samples used for classification and accuracy assessment. Table 3.1 Species collected Species Sample set Test Set Acacia fleckii 25 15 Burkea Africana 21 10 Boscia albitrunca 17 9 Dischrostachys cineria 42 14 Lonchocarpus nelsii 32 12 Ochna pulchra 33 15 Terminalia serecia 19 8

Tree reflectance data was collected in the IKONOS area using the 4-channel light sensor. Spectral samples were taken in the tree crowns at an oblique angle ranging between 20o and 45o (depending on the crown height) only in the sunlit portions of the crows to avoid the effects of shadows. The data was collected between 0900 and 1430 hours. A total of 97 trees representing 7 species were sampled during the field visit. The sampling was limited by accessibility and whether conditions.

The 4-channel light sensor has the ability to simultaneously detect and measure four separate bands of light. Skye Instruments Ltd in the United Kingdom manufactured this equipment. The sensor was used both as cosine-corrected head and narrow angle sensor, by the use of removable diffuser. W ith the diffuser head fitted the sensors are fully cosine corrected (will accept incoming light from a above them according to Lambert,s Cosine Law), as required for the measurement of incident solar radiation. W hen the diffuser head is removed, the light acceptance of the sensor becomes a narrow angle (25o) cone shape. This makes it suitable for measuring radiation reflected from tree crowns, and the geometry of the cone shape acceptance defines the exact area of the crown monitored. Reflectance readings were recorded by a data logger, which was connected to the sensor.

The output from each channel is in the form of a current that is directly proportional to the amount of light falling on the sensor within the passband of the filter for that channel. The output is linear over many decades of light level, extending well beyond natural ranges. In the dark the current will always be zero.

Appendix 2 shows the calibration certificate of the sensor. The calibration certificate gives figures for both modes of use of the sensor, with and without the diffuser disc. The currents coming from the channels being measured without the diffuser disc were multiplied by the factors shown on the calibration certificate so that they will then be proportional to he levels of light detected by the channels.

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The bandwidth shown on the calibration certificate is the range (around the center wavelength) over which the sensitivity of the channels is greater that 50% of its peak. Data from the 4-channel light sensor was analyzed using SPSS software. The focus is to see how best the tree species can be discriminated. Spectral data from each band were compared with each other using the feature spaces. Reflectance curves were generated to compare the separability using the mean value and to see whether the shape resemble that of healthy vegetation. Descriptive statistics error bars were generated to assess the deviation from the mean value.

3.5.3. Digital im age analysis The focus of vegetation detection by remote sensing has shifted to the interpretation of satellite imagery. Images in digital format allow for numerical processing and analysis and the application of multivariate classification methods to the data. Approaches to classification are generally referred to as supervised or unsupervised depending on whether or not ground sampling has been used as as input to the classification. In this research, supervised classification was used. The steps in digital image analysis are;

ñ Radiometric correction ñ Image enhancement œimprove the quality of the image to improve interpretation, that is, to increase contrast between the points. ñ Image classification œ each pixel of the scene is classified

The objective of classification is to group together a set of observational units on the basis of their common attributes. The end product of a classification is a set of groups derived from the units of observation where, typically, units within a group share more attributes with one another than with units in other groups. For vegetation classification, the unit of observation is typically the "individual tree crown". The process of classifying a particular type of vegetation on the landscape requires a clearly defined objective for the classification and a familiarity with the variability across its range. The objective this classification is to create an independent vegetation classification map, attribute data on species.

3.5.3.1. Radiom etric corrections To provide the basis for analysing IKONOS imagery products in a physically meaningful frame of reference the digital image values must be converted to in-band radiance and then reflectance values at the sensor aperture. The IKONOS digital image values were converted to physical units of in-band radiance and then to reflectance values using equation 8.

2 p * DN /((CalCoef /10) / Bandwidth)l * d r p = *1000 (8) ESUN l * cos(qs)

W here: r p = planetary reflectance

ESUN l = band dependent mean solar exoatmospheric irradiance qs = solar zenith angle d = earth-sun distance, in astronomical units Bandwidth = IKONOS bandwidth (nm) DN = digital image values (digital numbers) (Fleming, 2003)

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Table 3.2 provides the IKONOS Original, Pre and Post CalCoef and Table 3.3 provide the IKONOS bandwidth and ESUN.

Table 3.2 IKONOS Original , Pre and Post CalCoef Spectral Band Original Pree 2/22/012 Post 2/22/012 Full scale CalCoef CalcCoef CalCoef Dynamic (DN*[m/W /cm2- DN*[mW /cm2- DN*[mW /cm2- Range sr]-1 sr]-1 sr]-1 (mW /cm2-sr) MS-1 (Blue) 637 633 728 2.98 MS-2 (Green) 573 649 727 3.32 MS-3 (Red) 663 840 949 2.87 MS-4 (VNIR) 503 746 843 3.75

Table 3.3 IKONOS Bandwidth and ESUN values (W m-2 um-1) Band Lower Upper 50% Bandwith Center ESUN 50% Pan 525.8 928.5 403 727.1 MS-1 (Blue) 444.7 516.0 71.3 480.3 1939.429 MS-2 (Green) 506.4 595.0 88.6 550.7 1847.400 MS-3 (Red) 631.9 697.7 65.8 664.8 1536.408 MS-4 (VNIR) 757.3 852.7 95.4 805.0 1147.856

3.5.3.2. Supervised classification Supervised classification incorporates knowledge from field sampling to relate measured spectral reflectance properties to know properties of the ground cover. Training areas for supervised classification were selected using ground truth data from Table 3.1 which was collected during the field campaign in September 2003. The overall objective of the training process was to define the spectral response patterns for each tree species. Apart from the tree species, backgrounds such as roads, soil, wood debris, grass and shrubs were classified as BRS.

Classifying the image with 4 * 4 m pixel was unsuccessful because most of the tree crowns are less than 16 m2, therefore 4 *4 m pixels were found to be too coarse making it impossible to delineate the crown areas of individual trees. Because this is the crucial step in the transpiration estimate using remote sensing techniques, the 4 * 4 m multi spectral bands were fused with the one meter Pan band through pan-sharpenning. The algorithm used for resampling is Brovey;

The Brovey transform is a method to fuse different data together using one image and the other image for spatial sharpness. The technique gives very good results for IKONOS merges, resulting in a better image than a conventional HSI transformation. It gives a good for vegetation crown delineation. The Brovey transform applied to IKONOS 2nd February 2002 data set used the following formula; NIR=[NIR/(BLUE + GREEN +RED + NIR) ]* PAN RED=[RED/ (BLUE + GREEN +RED + NIR)] * PAN GREEN=[GREEN /(BLUE + GREEN +RED + NIR)] * PAN BLUE=[BlUE / (BLUE + GREEN +RED + NIR)] * PAN

International Institute for Geo-information Science and Earth Observation 30 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

The normal steps to create a Brovey Transform image are: 1. Create a algorithm, or a virtual dataset, containing the bands from the Multispectral and Pan image 2. Save this algorithm (or virtual dataset). 3. Load the Brovey Transform algorithm 4. Change the input image to your new image.

The pan-sharpened image was enhanced to improve the visual interpretability of the image by increasing the apparent distinction between the features in the scene (Kief and Lillesand 1994). Contrast stretching was performed to expand the range of grey values over a wider range and thus making the image more suitable to the capabilities of a human vision. False colour composite images using near infrared, red and green bands were generated for visual interpretation and as an input for supervised classification.

Classification was then carried out using Prior probability and Maximum Likelihood method. Prior probability classification is based on probability of occurrence. To achieve this, all trees were countered and their occurrences were calculated.

The Maximum Likelihood classification is based on Bayesian probability theory. Each pixel‘s posterior probability of belonging to each class is estimated by using the mean and variance/covariance data of the signatures derived from the training sites. The outcome is conceptualised as an elliptical zone of characterisation of the signature. This is because maximum likelihood classifier assumes Gaussian distribution. The discriminant functions are given by:

1 -1 g (x) = - ln( 1 (x - m )T (x - m ) (10) i ƒi)- ƒi i 2 2 ii

W here µ is the vector of statistical averages of class i and ƒi the covariance matrix of class i. The final decision as to whether belongs to class i is given if:

gi (x) í g j (x), i ò j and

gi (x) í Ti

W here Ti is the threshold for class i, whose purpose is to reject pixels that are too far from the statistical averages in the feature space (Meijerink et al, 1994).

The classification procedureprocedure is summarised in Figure 3.3. 3.5.3.3. Accuracy assessm ent Confusion matrices were used to assess the accuracy of the classified images. The classified images were crossed with the test set in order to find out the intersection of the pixels belonging to the same vegetation class. The layout is presented in Figure 3.4

International Institute for Geo-information Science and Earth Observation 31 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Pan sharpened IKONOS Ground images Truth

Training samples

Supervised classification

Prior probability Maximum Likelihood vegetation map vegetation map

Accuracy Assessment

Figure 3.3 Image classification process

CLASSIFIED VEGETATION MAP

CONFUSION MATRIX Intersection of the pixels belonging to the same vegetation class

TEST SET

Figure 3.4 Layout of the confusion matrix

3.6. Rem ote sensing transpiration upscaling procedure Upscaling is moving from a small spatial and short temporal to a large spatial and long temporal scale. The principle of the proposed remote sensing transpiration mapping focuses on individual tree assessment with multispectral high-resolution image of IKONOS.

International Institute for Geo-information Science and Earth Observation 32 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

The following procedure was used to upscale individual tree transpiration to plot of 35 * 35 m2 using remote sensing;

1. Area numbering was performed on the classified map vegetation maps. Area numbering assigns unique pixel values in an output map for connected areas (areas consisting of pixels with the same value, class name, or ID) in an input map. The output of Area numbering is a map in which these connected areas are codified as Area 1, Area 2, etc. Furthermore, an attribute table is created for the output map. The table contains the new IDs; the class names, IDs, or values of the original mapping units; and the size (npix and area) of the unique output units. 2. A table of tree species with mean normalized saflow Qn values as attributes was created. 3. An attribute map was generated from the table in step 2. 4. Map aggregate sum, group factor 35 and offset 2 rows from top boundary and 2 rows from left margin of the IKONOS image was performed on the transpiration map derived in step 2. Figure 3.5 illustrate how the offset was set. The Aggregate Map operation in ILW IS software aggregates blocks of input pixels by applying an aggregation function: Average, Count, Maximum, Median, Minimum, Predominant, Standard Deviation or Sum. A new georeference was formed. 5. The aggregated map was divided by 1225 m2 and the result was a transpiration flux map Tflux. This map shows the spatial distribution of transpiration in the Serowe IKONOS area based on 35 * 35 m2 plots. 6. To compare the values of flux obtained by Mapanda (2003) and Remote sensing, the following statement was put on a Table containing the coordinates of the plots; Tplot=Mapvalue(Tflux,coord(X,Y))

Figure 3.5 Offset layout

International Institute for Geo-information Science and Earth Observation 33 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Column offset determination The central coordinates of the top left plot number 76 is (427000, 7535000)

The distance between the plot edge and the map left boundary was calculated as follows;

427000-426001-17 = 982 m;

Number of pixels = 982 m / 35 m = 28 pixels and the remainder is 2 m which is the offset.

Rows offset calculations The distance between the plot upper edge and the map upper boundary was calculated as follows;

7535999.8-7535000-17.5 = 982m Number of pixels = 982 m / 35 m = 28 pixels and the remainder is 2 m which is the offset

3.7. Transpiration verification To verify the results, plot 76 was selected. The initial step was to check whether the transpiration values corresponds to the right species. The sum of the values within the plot was computed and divided by the plot area to get the flux. The resulted flux was compared to that one of Mapanda (2003) and the one obtained from automatic aggregation (Figure 5.12).

To verify the spatial distribution of transpiration, another IKONOS image obtained in 1st November 2001 was classified and the steps above were followed to derive the transpiration. Unfortunately, the November image was obtained on a cloudy day. Only half was suitable for verification.

International Institute for Geo-information Science and Earth Observation 34 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

4. Tree Transpiration

The purpose of this chapter is to present the relationships between the biometric variables of the monitored trees, to assess the individual tree transpiration of all the species measured in Serowe. The biometric parameters, sap velocity and normalised transpiration of the seven species found in the IKONOS area are presented in Appendix 1.

4.1. Biom etric characteristics estim ates Initial analysis focused on the biometric parameters of the 11 species in the Serowe area. Descriptive statistics of the 11 species are presented in Table 4.1. Mean sapwood areas of the tree species ranged between 10.0 cm2 (Dichrostachys cinerea) to 159.5 cm2 Lonchocarpus nelsii. Acacia fleckii presents the smallest mean crown diameter of 2.45 m while Lonchocarpus nelsii show the highest mean crown diameter of 3.93 m. Boscia albitrunca has got the largest mean stem diameter of 19.6 cm while Acacia fleckii has the smallest mean stem diameter of 3.38 cm.

Table 4.1 Descriptive statistics of the biometric characteristics in Serowe study area Tree species Mean tree variable N Minimum Maximum Mean Std. Dev Ziziphus Stem diameter (cm) 18 6.65 16.7 9.91 3.13 Macronata Crown diameter (m) 18 1.4 5.95 2.9 1.20 Sapwood area (cm2) 18 25.98 185.65 68.91 47.37 Acacia Karoo Stem diameter (cm) 18 5.40 11.80 8.37 2.22 Crown diameter (m) 18 2.4 5.35 4.18 0.72 Sapwood area (cm2) 18 16.99 79.98 40.30 20.25 Acacia Stem diameter (cm) 23 5.8 16.2 10.5 3.3 erioloba Crown diameter (m) 23 1.5 3.5 2.7 0.6 Sapwood area (cm2) 23 10.7 89.0 42.0 24.5 Acacia Stem diameter (cm) 15 8.6 23.6 16.1 4.6 luederitzii Crown diameter (m) 15 2.6 5.5 3.8 0.9 Sapwood area (cm2) 15 27.1 191.2 107.4 56.7 Burkea Stem diameter (cm) 33 5.7 19.3 11.1 4.6 africana Crown diameter (m) 33 1.2 5.5 2.5 1.2 Sapwood area (cm2) 33 11.5 161.7 59.8 47.3 Dichrostachys Stem diameter (cm) 23 5.0 10.6 6.6 1.4 cinerea Crown diameter (m) 23 2.2 4.5 3.4 0.6 Sapwood area (cm2) 23 4.7 19.1 10.0 4.4 Ochna Stem diameter (cm) 17 5.7 20.1 12.0 4.9 pulchra Crown diameter (m) 17 1.2 4.6 2.6 0.8 Sapwood area (cm2) 17 5.7 112.2 45.0 32.8 Terminalia Stem diameter (cm) 32 5.6 15.7 8.7 2.5

International Institute for Geo-information Science and Earth Observation 35 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Tree species Mean tree variable N Minimum Maximum Mean Std. Dev sericea Crown diameter (m) 32 2.0 5.9 3.4 1.1 Sapwood area (cm2) 32 7.6 79.0 27.1 18.2 Boscia Stem diameter (cm) 11 8 29.0 19.6 7.8 albitrunca Crown diameter (m) 11 2.1 6 3.3 4.36 Sapwood area (cm2) 11 34.8 499.5 252 164.2 Loncorcapus Stem diameter (cm) 11 6.6 23 15.3 6 nelsii Crown diameter (m) 11 0.88 3.93 3.02 1.69 Sapwood area (cm2) 11 24.4 360.2 159.5 114.7 Acacia Stem diameter (cm) 11 11.8 18.0 13.8 7.0 Fleckii Crown diameter (m) 11 1.43 3.55 2.45 1.95 Sapwood area (cm2) 11 23.3 163.6 80.7 46.1

Regression analyses were used to establish the relationships between the biometric parameters of the tree species. A strong relationship exists between the stem area and sapwood area. The R2 values for all the 11 species are above 0.75. In other words, the sapwood area can be estimated from the stem area with more than 75% confidence (Figure 4.1). Regression equations shown in Figure 4.2 show that the crown area is a good estimator of the sapwood area, yielding R2 values of 0.56 (A.erioloba) to 0.95 (Lonchocarpus nelsii ). However, Dichrostachys cinerea show a very low R2 value of 0.39. Analysis of the crown area and the stem area of the seven species found in the IKONOS area show that they is a good correlation with R2 varying between 0.64 and 0.96 apart from Dichrostachys cinerea whose R2=0.39.

Figure 4.1 Relationship between stem area and sapwood area

International Institute for Geo-information Science and Earth Observation 36 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Ochna pulchra Burkea africana ) 0.030 )

2 0.020 2 ^ 0.025 y = 5E-05x - 0.0013 ^ y = 5E-05x + 0.0003 m m

( 2 (

R = 0.661 0.015 2 a 0.020 a R = 0.8999 e e r r a a

0.015 0.010 d d o 0.010 o o o 0.005 w 0.005 w p p a 0.000 a 0.000 S S 0.0 100.0 200.0 300.0 400.0 0.0 100.0 200.0 300.0 400.0 Stem area (cm ^2) Stem area (cm ^2)

Terminalia sericea Dichrostachys cinerea ) ) 0.003 2 2 0.010 ^ ^ y = 3E-05x + 2E-05 0.003 m y = 4E-05x - 1E-04 m 2 ( ( 0.008 R = 0.9464

a R2 = 0.86 a 0.002 e 0.006 e r r a a 0.002

d 0.004 d 0.001 o o o o 0.002 0.001 w w p p a 0.000 a 0.000 S S 0.0 50.0 100.0 150.0 200.0 250.0 0.0 20.0 40.0 60.0 80.0 100.0 Stem area (cm ^2) Stem area (cm ^2)

Acacia fleckii Boscia albitrunca ) ) 0.060 2 0.020 2 ^ ^ 0.050 y = 8E-05x - 0.0002 m y = 6E-05x + 0.0009 m ( ( 2

0.015 2 R = 0.9968 a R = 0.9351 a 0.040 e e r r a 0.010 a 0.030

d d

o 0.020 o o 0.005 o w w 0.010 p p a 0.000 a 0.000 S S 0.0 50.0 100.0 150.0 200.0 250.0 300.0 0.0 200.0 400.0 600.0 800.0 Stem area (cm ^2) Stem area (cm ^2)

Lonchocarpus nelsii

) 0.030 2

^ y = 7E-05x - 0.0004 0.025 m 2 (

R = 0.9927

a 0.020 e r

a 0.015

d

o 0.010 o

w 0.005 p

a 0.000 S 0.0 100.0 200.0 300.0 400.0

Stem area (cm ^2)

Figure 4.1 cont. Relationship between stem area and sapwood area

International Institute for Geo-information Science and Earth Observation 37 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Figure 4.1 cont. Relationship between stem area and sapwood area

Ziziphus m acronata Acacia karoo

0.008 0.02 ) ) y = 0.0002x + 0.0014 2 2 y = 0.0005x + 0.0028 2 m 0.016 m 0.006

( R = 0.566 2 (

R = 0.6174 a a e 0.012 e r r a a

0.004

d d 0.008 o o o o w 0.004 w 0.002 p p a a S S 0 0 5 10 15 20 25 30 0 0 5 10 15 20 25 Crown area (m 2) Crowm area (m 2)

A. erioloba

Figure 4.2 Relationship between sapwood area and crown area

International Institute for Geo-information Science and Earth Observation 38 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Ochna pulchra Burkea africana ) )

2 0.03

2 0.020 ^

y = 0.0017x - 0.0037 ^ y = 0.0007x + 0.0015

m 0.03 m 2 (

2 ( R = 0.8475 R = 0.9152 0.015 a

0.02 a e e r r a a 0.02 0.010

d d

o 0.01 o o o 0.005

w 0.01 w p p a 0.00 a 0.000 S S 0.0 5.0 10.0 15.0 20.0 0.0 5.0 10.0 15.0 20.0 25.0 Crow n area (m ^2) Crow n area (m ^2)

Terminalia sericia Dischrostachys cinerea )

) 0.010 0.003 2 2

^ y = 0.0001x + 0.0003 ^ 0.003 0.008 y = 0.0003x + 0.0002 m 2 m ( R = 0.3927 (

2 a 0.002

a R = 0.8522

0.006 e e r r

a 0.002 a

0.004 d d

o 0.001 o o o 0.002 0.001 w w p p

a 0.000

a 0.000 s s 0.0 5.0 10.0 15.0 20.0 25.0 30.0 0.0 5.0 10.0 15.0 20.0 Crow n area (m ^2) Crow n area (m ^2)

Acacia fleckii Boscia albitrunca ) ) 2 0.020 2 0.060 ^ ^ y = 0.0019x - 0.0028 m m 0.050 ( y = 0.0004x + 0.0011 ( 2

0.015 R = 0.9485 a 2 a 0.040 e

R = 0.9102 e r r a a

0.010 0.030 d d o

o 0.020 o 0.005 o

w 0.010 w p p a 0.000 a 0.000 S S 0.0 10.0 20.0 30.0 40.0 50.0 0.0 10.0 20.0 30.0 Crow n area (m ^2) Crow n area (m ^2)

Lonchocarpus nelsii

) 0.030 2

^ y = 0.0009x + 0.0003 0.025 m 2 ( R = 0.9528

a 0.020 e r

a 0.015

d

o 0.010 o

w 0.005 p

a 0.000 S 0.0 10.0 20.0 30.0 40.0 Crow n area (m ^2)

Figure 4.2 (cont) Relationship between sapwood area and crown area

International Institute for Geo-information Science and Earth Observation 39 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Ochna pulchra Terminalia sericea

400.0 250.0 )

y = 21.215x + 11.436 ) 2 2 y = 5.2474x + 11.369 ^ 2 ^ 200.0 300.0 R = 0.6461 2 m m R = 0.8088 c c ( (

150.0 a

200.0 a e e r r 100.0 a a

100.0 m m

e 50.0 e t t S 0.0 S 0.0 0.0 5.0 10.0 15.0 20.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Crow n area (m ^2) Crow n area (m ^2)

Burkea Africana Dichrostachys sericea

400.0 100.0 ) )

2 y = 13.722x + 27.887 y = 3.6685x + 9.5426 2 ^ 300.0 2 ^ 80.0 R2 = 0.3925

m R = 0.8274 m c c ( ( 60.0

a 200.0 a e e r r 40.0 a a

100.0 m m 20.0 e e t t S 0.0 S 0.0 0.0 5.0 10.0 15.0 20.0 25.0 0.0 5.0 10.0 15.0 20.0 Crow n area (m ^2) Crow n area (m ^2)

Acacia fleckii Boscia albitrunca

300.0 700.0 ) ) 2 y = 6.0905x + 6.131 y = 23.888x - 32.533 2 ^ 250.0 2 600.0 R = 0.9238 ^ 2 m R = 0.948 m c 200.0 500.0 ( c

(

a 150.0 400.0 a e r e r a 300.0

100.0 a

m 200.0 m e

t 50.0 e

t 100.0 S 0.0 S 0.0 0.0 10.0 20.0 30.0 40.0 50.0 0.0 10.0 20.0 30.0 Crow n area (m ^2) Crow n area (m ^2)

Lonchocarpus nelsii

400.0 )

2 y = 12.404x + 9.8994 ^ 300.0 R2 = 0.9644 m c (

a

e 200.0 r a

m 100.0 e t S 0.0 0.0 10.0 20.0 30.0 40.0 Crow n area (m ^2)

Figure 4.3 Relationship between Stem area and crown stem area

International Institute for Geo-information Science and Earth Observation 40 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

4.2. Sap Velocity estim ates Table 4.2 presents the descriptive statistics of the sapflow velocities of the monitored species in Serowe. This results were computed using equation 3. Boscia albitrunca has the highest mean sap velocity of 3.84 cm/hr while Acacia luederitzii has the smallest rate of 0.608 cm/hr. Although Acacia fleckii has got the mean stem diameter its sapflow velocity is much greater that of Burkea Africana, Acacia erioloba, Ochna puichra, Dischrostachys cineria and Acacia luederitzii indicating that sap velocity various tree characteristics.

Table 4.2 Sapflow velocity of the trees species in Serowe Sap velocity (cm/hr) Tree species Minimum Maximum Mean Standard deviation Acacia Karoo 0.03 3.42 1.05 0.251 Ziziphus macronata 0 2.76 1.144 0.632 Burkea Africana 0.290 3.974 1.326 0.879 Acacia erioloba 0.473 1.767 1.023 0.385 Acacia fleckii 0.90 6.76 2.50 1.95 Acacia luederitzii 0.225 1.559 0.608 0.428 Boscia albitrunca 1.26 9.97 3.84 2.29 Dichrostachys cineria 0.091 3.044 0.857 0.743 Ochna pulchra 0.372 3.940 1.408 1.178 Lonchocarpus nelsii 1.08 9.99 3.35 3.15 Terminalia sericea 0.190 4.455 1.683 1.079

4.2.1. Relationship between sap velocity and biom etric param eters Sap velocity V is independent of the crown area and the sapwood area. Plots of sap velocity against the crown area Ac of the seven dominant species found in the IKONOS area is presented in Figure 4.4. Burkea African shows a more localised distribution of points compared to other species. Up to now, this localisation is not yet understood. Velocity V against sapwood area Ax is shown in Figure 4.5. For Burkea African and Lonchocarpus nelsii, the plot an almost similar velocity for different sapwood area.

International Institute for Geo-information Science and Earth Observation 41 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Ochna pulchra Burkea Africana

5.000 5.000

) 4.000 4.000 ) r h h / 3.000 / 3.000 m m c 2.000 c (

( 2.000

V 1.000 V 1.000 0.000 0.000 0.000 5.000 10.000 15.000 20.000 0.000 10.000 20.000 30.000 Ac (m 2) Ac (m 2)

Term inalia sericea Dichrostachys cineria

5.00 1.000 4.00 0.800 ) ) r r h h / 0.600

/ 3.00 m m

c 0.400 c

2.00 ( (

V V 1.00 0.200 0.00 0.000 0.00 10.00 20.00 30.00 0.000 5.000 10.000 15.000 20.000 25.000 Ac (m 2) Ac (m 2)

Acacia fleckii Boscia albitrunca

8.00 5.00

) 4.00 )

6.00 r r h h / 3.00 /

4.00 m m

c 2.00 c ( (

2.00 V

V 1.00 0.00 0.00 0.00 10.00 20.00 30.00 40.00 50.00 0.00 10.00 20.00 30.00 Ac (m 2) Ac (m 2)

Lonchocarpus nelsii

12.00 10.00 ) r 8.00 h / 6.00 m c (

4.00 V 2.00 0.00 0.00 10.00 20.00 30.00 40.00 Ac (m 2)

Figure 4.4 Relationships between sap velocity and crown area

International Institute for Geo-information Science and Earth Observation 42 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Ochna pulchra Burkea Africana

5.000 5.000 4.000 4.000 ) r ) r h

/ 3.000 h

/ 3.000 m m c 2.000 ( c 2.000

(

V 1.000 V 1.000 0.000 0.000 0.000 100.000 200.000 300.000 0.000 50.000 100.000 150.000 200.000 Ax (cm 2) Ax (cm 2)

Terminalia sericea Dichrostachys cineria

1.000 5.00 0.800 )

4.00 r ) h r

/ 0.600 h

/ 3.00 m c m 0.400 ( c 2.00 (

V 0.200 V 1.00 0.000 0.00 0.000 10.000 20.000 30.000 0.00 20.00 40.00 60.00 80.00 100.00 Ax (cm 2) Ax (cm 2)

Acacia fleckii Boscia albitrunca

8.00 5.00 4.00 ) 6.00 ) r r h h / / 3.00 4.00 m m c c 2.00 ( (

V 2.00 V 1.00 0.00 0.00 0.00 50.00 100.00 150.00 200.00 0.00 200.00 400.00 600.00 Ax (cm 2) Ax (cm 2)

Lonchocarpus nelsii

15.00 ) r

h 10.00 / m c

( 5.00

V 0.00 0.00 100.00 200.00 300.00 400.00 Ax (cm 20

Figure 4.5 Relationship between sap velocity and sapwood area.

International Institute for Geo-information Science and Earth Observation 43 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

4.3. Tree Transpiration estim ates The 3 day discharge rates Qs and normalised transpiration Qn of trees in Serowe is presented in Table 4.4. The values were computed using equations 5. The mean Qn rates range from 0.059 mm/day (Dischrostachys cineria) to 1.55 mm/day (Boscia albitrunca). Figure 4.6 shows a graphical presentation of transpiration between the broad-leaved and the spiky species. In general, broad-leaved species transpire much more compared to spike species. Lonchocarpus nelsii is the only spike species which show a considerably amount daily transpiration.

Table 4.3 Discharge Qs and Normalised daily transpiration Qnof the species in Serowe area Discharge Qs (l/day) Transpiration Qn (mm/day) Tree species Minimum Maximum Mean Minimum Maximum Mean Acacia Karoo 0.133 5.458 1.016 0.021 0.262 0.102 Ziziphus 0.87 7.264 1.892 0.041 1.103 0.288 macronata Burkea Africana 1.039 10.586 1.982 0.00001 0.891 0.364 Acacia erioloba 0.3049 2.893 0.953 0.058 0.360 0.165 Acacia fleckii 0.58 13.7 5.071 0.08 0.64 0.26 Acacia luederitzii 0.0001 4.972 0.9534 0.00002 0.4042 0.073 Boscia albitrunca 2.43 80.03 26.32 0.29 4.87 1.55 Dichrostachys 0.061 3.212 0.516 0.0051 0.276 0.059 cineria Ochna pulchra 0.149 10.878 3.326 0.059 2.17 0.660 Lonchocarpus 0.69 30.95 11.31 0.21 2.04 0.88 nelsii Terminalia sericea 0.123 8.481 1.367 0.011 0.310 0.133

Broad-leaved Species with spikes

Figure 4.6 Mean daily transpiration for 11 tree species in Serowe

Figure 4.7 show the variation of transpiration of 2 Ziziphus macronata at site 1. Transpiration varied among species of the species. On diurnal bases, transpiration begins in the morning as the stomata

International Institute for Geo-information Science and Earth Observation 44 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

open, the evaporative gradient increases, and dew evaporates (Fritschen and Doraiswamy 1973), reaches maximum in the afternoon and decreases as the evaporative demand and/or stomatal apearture decreases.

Figure 4.7 Temporal Transpiration of 2 Ziziphus macronata trees.

4.3.1. Relationship between Discharge Qs and the crown area

Apart from Acacia fleckii and Lonchocarpus nelsii, all species show a good relationship between the discharge Qs and crown area Ac (Figure 4.8). Boscia albitrunca and Terminalia sericea show R2 values of 0.91 and 0.72 respectively indicating that tree discharge depends on the crown area. Qs can be estimated from the crown area using the regression equations developed. May be the low correlation seen between the crown area and Qs of Acacia fleckii and Lonchocarpus nelsii may be due to errors in field measurements especially in estimating the crown diameters

There is no relationship between the normalised sapflow and crown area (Figure 4.9). The points are scattered throughout the plotting area making it difficult to put a trend line. Burkea Africana, Ochna pulchra and Terminalia sericea show regions of clustering implying that the normalised sapflow of these species do not vary much with changes in crow area. Lonchocarpus nelsii and Boscia albitrunca points are widely scattered.

International Institute for Geo-information Science and Earth Observation 45 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Burkea Africana Lonchocarpus nelsii y = 0.2501x + 0.4481 R2 = 0.4769 12.000 12.000 10.000 y = 0.5786x + 0.0835

) 10.000 )

y 2 R = 0.6462 y

a 8.000

a 8.000 d d / / l

6.000 l 6.000 ( (

s 4.000 s 4.000 Q 2.000 Q 2.000 0.000 0.000 0.000 5.000 10.000 15.000 20.000 0.000 5.000 10.000 15.000 20.000 25.000 Ac (m 2) Ac (m 2)

Terminalia sericea Dischrostachys cineria 0.400 10.000 y = 0.0208x + 0.0246

) 0.300 2 8.000 y = 0.2409x - 1.0395 y R = 0.6583 a )

2 d / y 6.000 R = 0.7259 l 0.200 ( a

d s /

l 4.000 ( Q

0.100 s 2.000 Q 0.000 0.000 -2.0000.00 10.00 20.00 30.00 0.0 5.0 10.0 15.0 20.0 Ac (m 2) Ac (m 2)

Acacia fleckii Boscia albitrunca

15.00 60.00 50.00 y = 1.7666x - 5.1654 ) )

y y = 0.2307x + 0.2515 2 y R = 0.9102 a 10.00 2 a 40.00 d R = 0.3724 d / / l l 30.00 ( (

s 5.00 s 20.00 Q Q 10.00 0.00 0.00 0.00 10.00 20.00 30.00 40.00 50.00 0.00 10.00 20.00 30.00 Ac (m 2) Ac (m 2)

Lonchocarpus nelsii

40.00

) y = 0.4964x + 4.7191

y 30.00 2 a R = 0.2129 d /

l 20.00 (

s 10.00 Q 0.00 0.00 10.00 20.00 30.00 40.00 Ac (m 2)

Figure 4.8 Relationship between Discharge Qs and crown area Ac.

International Institute for Geo-information Science and Earth Observation 46 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Ochna pulchra Burkea Africana

2.500 1.000 ) )

y 0.800

y 2.000 a a d d / / 1.500 0.600 m m m

m 0.400 1.000 ( (

n n 0.200

0.500 Q Q 0.000 0.000 0.000 5.000 10.000 15.000 20.000 0.000 5.000 10.000 15.000 20.000 25.000 Ac (m 2) Ac (m 2)

Dichrostachys cineria Terminalia sericea

0.140 0.350 0.120 0.300 ) ) y y

a 0.100 a 0.250 d d / 0.080 / 0.200 m m

m 0.060 m 0.150 ( (

n 0.040 n 0.100

Q 0.020 Q 0.050 0.000 0.000 0.000 5.000 10.000 15.000 20.000 25.000 0.00 10.00 20.00 30.00 Ac (m 2) Ac (m 2)

Boscia albitrunca Acacia fleckii

2.00 0.70

) 0.60 ) y 1.50 y a

a 0.50 d / d / 0.40 m 1.00 m m ( m 0.30

(

n

0.50 n 0.20 Q Q 0.10 0.00 0.00 0.00 10.00 20.00 30.00 0.00 10.00 20.00 30.00 40.00 50.00 Ac (m 2) Ac (m 2)

Lonchocarpus nelsii

2.50 )

y 2.00 a d / 1.50 m

m 1.00 (

n

Q 0.50 0.00 0.00 10.00 20.00 30.00 40.00 Ac (m 2)

Figure 4.9 Relationship between Flux Qn and crown area Ac

International Institute for Geo-information Science and Earth Observation 47 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

5. Transpiration upscaling using RS

5.1. Introduction From the 11 species where transpiration was measured, 7 dominant species were used to estimates plot transpiration in the IKONOS area. These species are Burkea Africana, Dischrostachys cineria, Lonchocarpus nelsii, Acacia fleckii, Boscia albitrunca, Terminalia sericea and Ochna pulchra.

5.2. Plot Transpiration of dry season Mapanda (2003) dry season plot transpiration is shown Figure 5.1 and it ranges from 0.001 to 0.15 mm/d. The average plot transpiration of the area is 0.023 mm/d. This value is well below the potential evaporation and reflect the impact of a severe drought. The sampled plots are dominated by Dischrostachys cineria (42.3%)and Acacia fleckii (36.6%).

Figure 5.1 Plot Transpiration of the study area (Mapanda 2003)

International Institute for Geo-information Science and Earth Observation 48 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

5.3. Tranpsiration m apping using Rem ote sensing In this sub-chapter, results of the 4-channel light sensor will first be presented. These results aided in classifying the image by identifying the bands used which best discriminate the different tree species. This would be followed by image analyses results.

5.3.1. 4-Channel light sensor results The objective of these analyses is to evaluate the ability of the 4-channel Light sensor to separate the vegetation species, which aid in classification of IKONOS image. Figure 5.2 present the feature spaces for the different band combinations of the 4-channel light sensor. The results show that Blue and Green bands are highly correlated. However, some minor discrimination can be made, for, example, Lonchocarpus nelsii can be separated from Ochna pulchra. The Blue-Red bands can separate Ochna pulchra but can not differentiate between Lonchocarpus nelsii and Acacia fleckii and Boscia albitrunca, Burkea Africana and Dischrostachys cineria. Using the Blue-NIR bands and/or Green-NIR and/or red-NIR, Boscia albitrunca can be separated from Dischrostachys cineria but not from Burkea Africana.

Feature space for Green and NIR Feature space for Red and NIR

AF 250 250 AF 200 BA 200 BA 150 DC 150 DC 100 LN 100 LN 50 TS 50 TS 0 BB 0 BB 0 20 40 60 80 0 20 40 60 80 OP OP Green (W /m ^2) Red (W /m ^2)

Feature space for Blue and Green Feature Space for Green and Red AF

) BA 2 80 80 ^ AF DC

m 60 / 60 BA LN

W 40 ( DC 40

TS

n 20 LN 20 e BB e r 0 TS 0 OP G 0 50 100 BB 0 20 40 60 80 OP Blue (W /m ^2) Green (W /m ^2)

Feature Space for Blue and NIR Feature space for Blue and Red AF 250 A 80 BA F 200 60 DC B 150 40 LN A D 20 TS 100 50 C 0 BB LN 0 50 100 OP 0 0 20 40 60 80 100 TS Blue (W /m ^2) B lue (W /m^2)

Figure 5.2 4-channel sensor Feature space

International Institute for Geo-information Science and Earth Observation 49 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

The spectral reflectance curves of tree species in Serowe is graphed in Figure 5.3. The general shapes of the graphs resemble that of healthy vegetation with low reflectances in the visible portion of the spectrum and high reflectances in the near infrared region. Although the general shapes of the curves between the species are somewhat similar, a number of spectral differences allow the separation of this species from each other.

Tree reflectances without diffuser

200

180

160 )

2 140

^ BB m / 120 DC W

( AF

e 100 OP c

n LN a t 80 c BA e l

f TS

e 60 R

40

20

0 400 450 500 550 600 650 700 750 800 850 900 wavelength (nm )

Figure 5.3 Comparison between mean spectral reflectances of species in Serowe area

Reflectance of the species in channel 1 from 452nm to 522 nm is grouped into 3 categories, that is, low, medium and high. Lonchocarpus nelsii and Acacia fleckii show very low reflectances, Ochna pulchra and Terminalia sericea show medium blue reflectance while the reflectances of Burkea Africana and Dischrostachys cineria is high. Burkea Africana , Dischrostachys cineria ,Ochna pulchra and Terminalia sericea show almost similar reflectance in channel 2 from 522 to 604 nm while Lonchocarpus nelsii and Acacia fleckii stands alone. Individual species can be separated from each other in the red band although some show similar reflectance. In the red band, broad-leaved species show higher reflectance compared to spike or thorn species. Distinct separation of each species is from 755 to 948 nm in the near infrared band. Like in the red band, broad leaved species show much higher reflectances compared to spike species. In the near infrared band Boscia albitrunca show the highest reflectance while Burkea Africana is least in reflecting. Figure 5.4 shows the error bars of the mean reflectance on each band of the different species. As described above, the red and near infrared bands better separate the species compared to the blue and green.

International Institute for Geo-information Science and Earth Observation 50 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Figure 5.4 Error bars displaying how each band separate the tree species

International Institute for Geo-information Science and Earth Observation 51 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

5.3.2. Im age classification results 5.3.2.1. Classification feature spaces Figure 5.5 shows the feature spaces of seven main vegetation species found in the Serowe IKONOS area. Like in the 4-channel light sensor, the NIR band plays a very important role in discriminating the seven tree species in the Serowe IKONOS area. Feature space of NIR and Red shows that the species possess different spectral reflectances. However, there is no clear distinction between Boscia albitrunca and Acacia flecki. These two species are separable on the NIR-blue and NIR-green feature spaces. The Blue-Green, Blue-Red and Green-Red feature spaces can not separate the tree seven species from each other.

International Institute for Geo-information Science and Earth Observation 52 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Figure 5.5 Classification Features space

5.3.2.2. Vegetation m aps Prior probability classified vegetation map is presented in Figure 5.6a and Maximum Likelihood classified map is shown in Figure 5.6b. Both maps show almost the same spatial distribution of the species. The main difference in the distribution is the density. Acacia fleckii is concentrated in the eastern part of the study area. Only a few trees occur in the south western part of the image. They are two dense areas for this species; one is located on northern part of the image towards the edge and the other located in the central region of the image.

Burkea Africana mostly occurs in the south western part of the study area. This species rarely exist in the north. Maximum Likelihood classification show more abundance of this species compared to prior probability classification.

Boscia albitrunca occur on the northern part of the area and is denser in the middle of the area. This specie is associated with Acacia fleckii and Ochna pulchra. Prior probability classification show a denser of this species compared to prior probability classification. A combination of visual inspection of Appendix 3 and field knowledge conclude that this species is over estimated.

Dischrostachys cineria the most abundant of the seven species dominate the southern part of the area where it is interacted with Lonchocarpus nelsii, Burkea Africana and Boscia albitrunca. It is also found on the north eastern corner of the image where it co-occur with Acacia fleckii. A visual

International Institute for Geo-information Science and Earth Observation 53 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

inspection of these species shows that both classifications show the same density and distribution. It is obvious from Appendix 3 that Dischrostachys cineria is overestimated.

Lonchocarpus nelsii appear to be uniformly distributed through out the IKONOS area although it appears to be more concentrated on the western part of the area when using maximum Likelihood classification. Ochna pulchra is denser in the middle of the area and stretches northward. This species is less abundant in the western part of the area. Maximum Likelihood classification gives a denser of this species in the middle compared to prior probability classification.

Terminalia serecia is the least abundant of the seven species and is found on the south western part of the area.

Table 5.1 show the area covered by each species. According to Prior probability classification, Dischrostachys cineria covers 31.51% and Burkea Africana 2.42%. According to Maximum Likelihood classification, Burkea Africana covers 5.37% of the IKONOS area which is almost twice that Prior Probability classification. Bare soil, grasses and other non vegetative material designated BRS covers 27.13% using Prior Probability classification while the value go down to 20.32% when using Maximum Likelihood classification. They is a difference of 6006130 m2 for the area covered by Ochna pulchra between Maximum Likelihood and Prior Probability classified maps. The spatial distribution of each species is presented in Appendix 3.

A B

Figure 5.6 Serowe IKONOS area vegetation maps

To verify the classification, the original and classified maps where compared with each other. For example, Figure 5.7 shows how Boscia albitrunca, Dischrostachys cineri and Burkea African how they appear at one of the sampled points. The figure demonstrates that the classifier was able to

International Institute for Geo-information Science and Earth Observation 54 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

recognise these species quite easily. However, it is clear from the classified map that it was difficult for the classifier to make a clear distinction between Dischrostachys cineria and the understory.

Figure 5.7 Comparison between the original and classified maps

Table 5.1 Area covered by species Prior Probability classification Maxim um Likelihood classification Area covered % Area covered % Diff AF 6238427 6.24 6983794 6.98 745367 BA 2420780 2.42 5374632 5.37 2953852 BB 11240459 11.24 10041738 10.04 1198721 DC 31519024 31.51 31017766 31.01 501258 LN 7185588 7.18 6084071 6.08 1101517 OP 13750497 13.75 19756627 19.75 6006130 TS 526086 0.53 448372 0.45 77714 BRS 27139140 27.13 20313001 20.32 6826139 Total 100020001 100 100020001 100

5.3.2.3. Accuracy assessm ent The two classified maps accuracies were assessed using confusion matrices. The correctly classified pixels in each association are outlined along the green diagonal. For Prior probability classification (Table 5.2), the least correctly classified tree species is Lonchocarpus nelsii (65%) which is confused with Terminalia sericea and Dischrostachys cineria. The second poorly classified tree species is Burkea Africana which is confused with Lonchocarpus nelsii and Terminalia sericea. The poor differentiation of Lonchocarpus nelsii from Terminalia sericea and Dischrostachys cineria, and of Burkea Africana from Lonchocarpus nelsii and Terminalia sericea bring the overall accuracy 79.21%.

International Institute for Geo-information Science and Earth Observation 55 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Table 5.2 Prior probability classification confusion matrix

AF BA BB BRS DC LN OP TS ACCURACY

AF 121 0 6 7 0 0 4 0 0.88

BA 0 109 0 0 36 46 0 17 0.52

BB 6 0 221 10 0 0 4 0 0.92

BRS 6 0 0 392 3 0 0 0 0.98

DC 0 3 3 7 234 41 0 0 0.81

LN 0 13 4 2 16 176 0 0 0.83

OP 6 0 42 51 17 0 85 0 0.42

TS 0 36 0 1 0 6 0 159 0.79

Reliability 0.87 0.68 0.8 0.83 0.76 0.65 0.91 0.9

Average accuracy = 76.90% Average reliability = 80.23% Overall accuracy = 79.21%

Table 5.3 present the confusion matrix for Maximum Likelihood classification. Contrary to the Prior probability classification, Ochna pulchra is the least classified tree species with 59% reliability. Ochna pulchra is confused with Boscia albitrunca,BRS and Dischrostachys cineria. However, the Ochna pulchra row give 88% average accuracy compared 42% in Prior probability classification. The second less classified species is Terminalia sericea which is mostly confused with Burkea Africana. The overall accuracy of Maximum Likelihood classification is 81.22%.

The average accuracy is calculated as the sum of the accuracy figures in column Accuracy divided by the number of classes in the test set. The average reliability is calculated as the sum of the reliability figures in row Reliability divided by the number of classes in the test set. The overall accuracy is calculated as the sum of all correctly classified pixels (diagonal elements) divided by the total number of test pixels

International Institute for Geo-information Science and Earth Observation 56 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Table 5.3 Maximum Likelihood classification confusion matrix AF BA BB BRS DC LN OP TS ACCURACY

AF 125 0 11 13 0 0 8 0 0.8

BA 0 176 0 0 15 28 0 72 0.6

BB 3 0 216 0 3 5 35 0 0.82

BRS 5 0 4 385 6 1 23 1 0.91

DC 0 7 0 3 225 13 16 0 0.85

LN 0 13 0 0 37 164 0 4 0.75

OP 5 0 10 0 2 0 119 0 0.88

TS 0 12 0 0 0 0 0 125 0.91

Reliability 0.91 0.85 0.9 0.96 0.78 0.78 0.59 0.62

Average accuracy = 81.54% Average reliability = 79.72% Overall accuracy = 81.22%

A graphical presentation of the two classifications average accuracies is presented in Figure 5.7. The accuracies for the two classifiers do not vary much from each other apart from the classification of Ochna pulchra which give a value of 0.42 for Prior probability and 0.88 for Maximum likelihood classification. Both classifiers give a low value for Burkea Africana.

120

100 )

% 80 (

y PP c

a 60 r

u MM c

c 40 A

20

0 AF BA BB BRS DC LN OP TS

Figure 5.8 Graphical presentation of the accuracies

International Institute for Geo-information Science and Earth Observation 57 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

5.3.3. Transpiration upscaling Remote sensing upscaled plot transpiration is shown in Appendix 4. The values differ with respect to the method used for classifying the image. The descriptive statistics of the plot transpiration is shown in table 5.4. For maximum Likelihood classification, the values range from 0.03 to 0.98 mm/day with a mean value of 0.41 mm/day. Using, prior probability classification, the transpiration varies from 0.05 to 0.96 mm/day and the mean value is 0.38 mm/day. Mapanda (2003) values range between 0 and 0.15 mm/day. Point maps (Figure 5.9) show the transpiration of each plot in the IKONOS area. The values derived from prior probability and maximum likelihood do not differ much. However, in almost all the plots, maximum likelihood values are higher than that of prior probability.

Figure 5.9A Plot transpiration derived from Maximum likelihood method

International Institute for Geo-information Science and Earth Observation 58 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Figure 5.9B. Plot transpiration derived from prior probability method

Table 5.4 Descriptive statistics for transpiration (mm/day) in the serowe IKONOS area Std. N Minim um Maxim um Mean Deviation Mapanda (2003) 78 0 0.15 0.02 0.03 Prior probability 78 0.05 0.96 0.38 0.18 Maxim um Likelihood 78 0.03 0.98 0.41 0.21

5.3.4. Spatial distribution of transpiration Figure 5.10A is an aggregated (35 * 35 m2) transpiration map in l/day derived from maximum likelihood classification and Figure 5.10B is derived from prior probability. The aggregated values range from 0 to 1500 l/day/plot. High values are found in the North-eastern part of IKONOS area where Boscia albitrunca, Burkea Africana, Ochna pulchra and Acacia fleckii dominate.

International Institute for Geo-information Science and Earth Observation 59 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

A B

Figure 5.10 Aggregated transpiration maps

The spatial distribution of transpiration in the Serowe IKONOS area is presented in Figure 5.11. Figure 5.11A is the result derived from maximum likelihood while Figure 5.11B is derived from prior probability classification. The spatial distribution of transpiration is associated with species distribution and type. Both maps show that high transpiration values are found on the North Eastern part of the IKONOS area. They are minor differences between the two maps, for example, in the southern part of the area, the maximum likelihood method show wider area of low values compared to prior probability.

B A

Figure 5.11 Serowe IKONOS area transpiration maps

International Institute for Geo-information Science and Earth Observation 60 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

5.4. Transpiration verification

35 m

Tree species found in plot 76. AF œ Acacia fleckii BA œ Burkea Africana BB œ Boscia albitrunca DC œ Dischrostachys cinerea LN œ Lonchocarpus nelsii OP œ Ochna pulchra

Upscaling process

2 Colour Tree species Qn (l/day/m ) Green Acaci fleckii 0.26 Pink Ochna pulchra 0.66 Blue Dischrostachys cinerea 0.059 Purple Lonchocarpus nelsii 0.364 Dark blue Boscia albitrunca 1.55 Sandy brown Bare soil, grasses and shrubs 0

35 m T(prior probability) = 0.21 mm/day

Aggregation T(Mapanda, 2003) = 0.10 mm/day Process 35 m

35 m

Figure 5.12 Transpiration verification at plot 76

International Institute for Geo-information Science and Earth Observation 61 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Table 5.5 Verification at plot 76. Tree species Crown coverage Qs (l/day) Prior Mapanda Prior Mapanda Mean Qn Prior Mapanda probability (2003) probabil (2003) (//day/m2 probabilit (2003) ity ) y Acacia Acacia 128 366.21 0.26 33.28 95.21 fleckii fleckii Dischrostac Dischrosta 110 134.82 0.059 6.49 7.95 hys cinerea chys cinerea Boscia Boscia 122 7.54 1.55 189.1 11.69 Albitrunca Albitrunca Lochocarpus 4 0.88 3.52 ? nelsii Ochna 42 0.66 27.72 ? pulchra Ziziphus 14.13 0.288 4.07 macronata Terminalia 29.83 0.113 3.37 serecia Burkea 1 0.364 0.364 africana Bare soil 889 672.45 0 0 0 Sum 260.47 112.30 Flux 0.21 0.10 (mm/day)

Table 5.5 shows that the classification allocate 122 m2 as the area of Boscia albitrunca compared to 7.54 m2 of field measurements. Boscia albitrunca have got a normalized transpiration of 1.55 l/day/m2 therefore over estimating the area will result in high transpiration fluxes. The species specific maps (Appendix 3) shows that remote sensing has over estimated the distribution of Boscia albitrunca.

On the other hand field measurements at plot 76 allocate 366.21 m2 to Dischrostachys cinerea compared to 128 m2 derived from remote sensing. However, Dischrostachys cinerea, does not alter the transpiration very much because its mean normalized transpiration is very low, 0.059 l/day/m2.

Table 5.6 presents a filed check of transpiration for plots 12 ,44, 78 and 109. The computed values are close to that of Mapanda (2003) but slightly higher apart from plot 109. Remote sensing derived values are too high compared to these values.

Table 5.6 Field check of plots of transpiration in mm/day Plot X Y Mapanda Prior Max. Field check (2003) probability Likelihood (mm/day) 12 434000 7527000 0.117 0.56 0.6 0.168 44 427000 7530000 0.074 0.27 0.21 0.098 78 427000 7534000 0.115 0.33 0.4 0.215 109 435000 7535000 0.134 0.4 0.44 0.106

International Institute for Geo-information Science and Earth Observation 62 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Figure 5.13 shows the prior probability classified vegetation map of the 1st November 2001 IKONOS image. The dominant species according to this figure is Dischrostachys cineria. Contrary to Figure 5.6, figure 5.13 shows that there is a high density of Ochna pulchra and Boscia albitrunca towards the south western corner of the area. The north-west south-east direction is dominated by Ochna pulchra, Boscia albitrunca and Burkea Africana. Dischrostachys cineria dominate the southern, northern and middle parts of the image.

Figure 5.13 Vegetation map derived from the 1st November 2001 image

The spatial distribution of transpiration derived from the 1st November 2001 IKONOS image is shown in Figure 5.14. The values range from 0.07 to 0.83 mm/day with a mean value of 0.39 mm/day and a standard deviation of 0.22. The spatial variation of transpiration is controlled by species distribution and density. High transpiration values are along the north-west south-east direction where Boscia albitrunca, Ochna pulchra and Burkea Africana dominate. In the southern, part of the middle and northern parts of the image where Dischrostachys cineria dominate, the flux is low. The white patches show where heavy clouds were located.

Figure 5.14 Spatial distribution of transpiration from 1st November 2001.

International Institute for Geo-information Science and Earth Observation 63 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

6. DISCUSSION

6.1. Biometric characteristics

Crown diameter, sapwood area and stem area are all important parameters in upscaling of transpiration. In the Serowe area, there is a strong statistical correlation between the sapwood area and the other tree parameters analysed. The stem area explains 79 to 99 % of the variations in sapwood area. This relationship is expected because the stem diameter mainly depends on the thickness of the sapwood and for some trees, which have not yet developed the heartwood, the sapwood contributes more than 96% of the total stem thickness (Allen and Grime, 1995). Because the stem diameter is easy to measure, it is possible to make reliable assessments of related stand parameters in a plot. However, the invasive cut and dye tracing method (Tyree and Zimmermann., 2002) for the determination of sapwood area sometimes result in errors due to cracking and poor colouration of the stem (Plate 10). Therefore it is necessary to try the non-invasive methods in determination of the sapwood area.

The crown area explains 50 to 94% of the variations in sapwood area. This relationship is vital because the sapwood area is estimated from crown area using regression equations which in turn is used with. The crown diameter responds to seasonal changes by losing leaves making its estimation more difficult. A poor estimation crown diameter can lead to poor transpiration estimates.

6.2. Sapflow velocity The average sap velocity of the tree species in the Serowe area range from 0.06 cm/hr (Acacia Luederitzii) to 3.84 cm/hr (Boscia albitrunca) (Table 4.2). Observation by Zziwa (2003), Mapanda (2003) and Fregoso (2002) found that there is a very poor relationship between sapflow velocity and the biometric parameters. According to Mapanda (2003), the poor relationship between the sap velocity and the biometric parameters could be a result of water conservation by savannah vegetation.

6.3. Tree Transpiration The mean daily normalised transpiration of main species in dry season varies from 0.034 mm/day (Dichrostachys cinerea) to 1.55 mm/day (Boscia albitrunca) (Table 4.5). Although Acacia Luederitzii has the lowest mean sap velocity, its normalized transpiration is higher than that of Dichrostachys cinerea. This clearly shows that transpiration depends not only on sap velocity but also on the biometric parameters. The transpiration varies within trees of the same species and among species. The variations of transpiration could be attributed to the depth of the water table. W hen the water table is deep, savanna vegetation tend to develop roots long enough to tap water deep and usual the stem and branches tend to be smaller. In some species, loss of water is further reduced by development of thorns and very small leaves.

The other factor regarding the variations in transpiration is related to the measurements of sapflow. These range from errors dealing with the biometric parameters (as earlier discussed) to the equipment used. For example, Burkea Africana produces a lot of gum and this might have greatly affected the needles sensitivity. The low transpiration of Dichrostachys cinerea and Terminalia sericea may be due to their thin sapwood bands. Kostner et al., (1998) pointed out that the Thermal Dissipation probes method underestimates the actual sapflow of species

International Institute for Geo-information Science and Earth Observation 64 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

with narrow sapwood such as Fraximus excelsior. Dichrostachys cinerea and Terminalia sericea low transpiration rates could be due to their thin sapwood area.

6.4. Vegetation mapping Ground remote sensing using the 4-channel light sensor has proved to be a useful tool in determining the spectral separability of the vegetation species in serowe IKONOS area. As expected the near-infrared band is useful in separating the tree species compared to the blue, green and red. The near-infrared reflectance (Figure 5.3) of Boscia albitrunca, Ochna pulchra, Dichrostachys cinerea, Acacia fleckii and Burkea Africana is some magnitudes greater than that of the visible part of the spectrum. In the visible spectral region high absorption of radiation energy is due to leaf pigments, primarily chlorophylls, although the carotenoids, xanthophylls, and anthocyans also have an effect (Gates et al., 1965; Rabideua et al., 1946). The reflectivity of tree canopies in the near-infrared is controlled by the internal cellular structure of leaves (Mestre, 1935). Perhaps the most striking situation in Figure (5.3) is the reflectance of Terminalia sericea and Loncocarpus nelsii, which show the same level of reflectance in the visible and near infrared part of the spectrum. This is an indication that by the time of reflectance measurements the leaves of the two tree species leaves lacked chlorophyll (Knipling, 1970). Since both parts have the same level of near-infrared reflectance, it can be concluded that neither the absence nor presence of chlorophyll is responsible.

Supervised pixel based vegetation classification method using Maximum Likelihood (Figure 5.6A) gave an overall accuracy of 81.54% but gives more trees than expected The average accuracy for bare soil, grasses, roads and shrubs is 91%. This value is lower than that of prior probability method, which gives an accuracy of 98% for bare soil, grasses, roads and shrubs. The problem with maximum likelihood classification was setting up the correct threshold; high and intermediate threshold values gave too much interconnected pixels of some tree classes than expected. Low threshold values over exaggerate the distribution of individual trees.

Vegetation classification based on prior probability classification (Figure 5.6B) gave an overall accuracy of 79.21% designating 31.51% as the area covered by bare soil, grasses and shrubs (Table 5.1). Compared to the maximum likelihood classification, the distribution of trees derived prior probability is more realistic. This is because the classification is based on the probability of occurrence. For each feature vector, the distances towards tree species class means are calculated. This includes the calculation of the variance-covariance matrix for each tree class i. Furthermore, for each class, the prior probabilities are taken into account (Gorte, B, 1998).

The two classification methods can be improved by collection more ground truth and applying object-based classification. Ground survey techniques should include photographing and image analysis of ground cover to identify how ground and different types of grasses and shrubs might influence the spectral signatures of the in the IKONOS image. It is obvious from the classification result that the pixel-based classification could not distinguish some species from each other resulting in over estimation of species like Boscia albitrunca, Ochna pulchra and Dichrostachys cinerea. This can be overcome, to a certain degree, by advanced information according to shape and texture characteristics. W here IKONOS imagery actually provide valuable information is in its recognition of single units of a class within the whole area of another class. It is often the case that this property, in combination with texture

International Institute for Geo-information Science and Earth Observation 65 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

information, can also distinquish objects of different classes with the same or almost the same spectral signature (property of object oriented classification). A research carried by W idayati et al (2002) in Sumberjaya, Indonesia has concluded that object based classification (segmentation) is superior to pixel based multi spectral classification when classifying mixed vegetation using IKONOS images. Unfortunately, the softwares used for sementation were unavailable during the processing of the data. Spectral and textural information can be tested simultaneously during segmentation and subsequent classification.

6.5. Transpiration upscaling The crown area is used to upscale transpiration from individual tree to plot level because the transpiration is crown dependent. Mapanda (2003) Vertessy et al., (1995), Kostner et al., (1998) and Hatton and W u (1995) argue that it is better to use stem diameter for scaling up than leaf area because it is a stable parameter. However, it should be noted that transpiration vary seasonal and the canopy size regulates how much water to be transpired, therefore it is proper to use a parameter that has a direct impact on the water usage than one that one that show minute response to the changes. In addition, the stem diameter is used to estimate the sapwood in which in some trees the relationship is not very good. Errors in estimating plot transpiration depend on how representative the selected sample trees for measuring sapflow are. Thornburn et al., (1993) cited in Hatton and W u (1995) found a poor linear relationship between Eucalyptus largiflorens water use and leaf area at the end of a dry summer. Results from the current research found that there is a good relationship between the crown area and water usage by trees which means the relationship between water usage and crown area depends on the location and tree species type.

The remote sensing method applied in estimating plot transpiration of the Serowe IKONOS area has given promising results. The mean plot transpiration calculated from Prior probability map is 0.38 mm/day and from maximum likelihood map is 0.85 mm/day. The difference between the two values lies on the vegetation classification results. Maximum likelihood classification gave more trees compared to prior probability thereby the value of transpiration will be high. However, it should be noted that at this stage of the research, it is difficult to say which value is more reliable, more data still need to be collected for validation. However, remote sensing derived transpiration rates are higher compared to Mapanda (2003), who‘s plot transpiration mean is 0.05 mm/day.

They are a number of factors leading to these differences: ñ Transpiration verification at plot 76 (Figure 5.14 and table 5.5) have shown that Boscia albitrunca is over estimated. This species have got a high normalized transpiration and over estimation of its area of coverage will result in high plot fluxes. ñ It is possible that Mapanda (2003) sampling plots and plots calculated using map aggregate do not match; they may be some major offset.. Map verification (Figure 5.12) of plot 76 show a huge difference between Mapanda (2003) value and the value derived through remote sensing. ñ They are species like A. caffra, A. nigrescenns and P.africanum and many others which are located in the IKONOS area, but their sapflow has not been measured. During image classification, these species would be assigned to one of the tree classes and their sapflow would be estimated thereby raising the discharge Qs of each plot, but during field sampling, they were neglected. ñ It is possible that some of the understory could have been classified as tree species thereby raising the remote sensing transpiration value.

International Institute for Geo-information Science and Earth Observation 66 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

ñ Errors could have occurred during the filed estimation of the biometric parameters of the tree species but with high-resolution images, it was possible to delineate the crowns giving a better estimate.

.

6.6. Spatial variation of transpiration Figure 5.9 shows the spatial distribution of transpiration in the Serowe IKONOS area. The distribution of transpiration is associated with species type. The distribution of transpiration derived from Maximum likelihood classifier (Figure 5.11A) show a spatial trend that extends in the south-west north-east direction and the uppermost portion of the IKONOS area. The spatial variation derived from prior probability classification (figure 5.11B) is almost similar to that of maximum likelihood but it also extends more to the northwest from the middle of the image. Although the spatial distribution of transpiration is associated with the type of species, there is need to look at other factors like geomorphology, soil types, depth to groundwater table and landuse in order to come up with a better explanation regarding the distribution.

Assessment of the distribution of transpiration using the 1st November 2001 and 2nd February 2002 images indicated that estimating transpiration using images obtained in different periods while upscaling using the same data set from dry season measurements result in minor differences in the spatial distribution. Such differences could be attributed to the reflectance properties of the vegetation, which changes seasonal.

International Institute for Geo-information Science and Earth Observation 67 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

7. Conclusion and recommendations

7.1. Conclusion The Granier sapflow method has proved to be successful in estimating sap velocity of individual trees in the Serowe area as long as researchers are aware of the errors that might occur during the measurements.

There is a good relationship between the biometric parameters, crown area, stem area and sapwood area in the Serowe tree species. The stem area is a good estimator of the sapwood area.

Supervised pixel based classification using prior probability classifier has proved to be better than Maximum likelihood. However, both classifications over estimate the distribution of Boscia albitrunca resulting high transpiration rates.

Transpiraion upscaling using remote sensing has proved to be a useful tool although this is the first step. The average transpiration in Serowe IKONOS area is 0.38 mm/day based on prior probability method. The spatial variation of transpiration is species controlled. Higher values are on the northeastern part of the study area.

7.2. Recom m endations Proposed research in Serowe:

More sampling of plot data is needed for validation of the results. Determine the water patterns of savannah vegetation at locations varying in soil and groundwater depth Assess the relationship between tree size and partition of water sources (i.e groundwater Vs soil moisture Determine the reflectance of the understory and bare soil to see how much does it affect the classification Use object based classification to improve the current maps

International Institute for Geo-information Science and Earth Observation 68 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

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Appendix 1

Biometric variables and water fluxes for the monitored trees Terminalia sericea Average Average sapflow Stem area Crown area Sap velocity Average sapflow normalised TREE ID (cm2) (m2) (cm/hr) (l/day) (mm/day) 25 67.93 11.34 0.190 0.123 0.011 26 52.81 8.55 0.704 0.389 0.045 27 62.91 7.79 0.631 0.424 0.054 28 80.12 14.19 0.332 0.326 0.023 32 51.53 4.34 0.552 0.238 0.055 33 37.39 3.46 0.863 0.290 0.084 34 24.63 2.99 1.608 0.386 0.129 36 30.19 6.83 1.011 0.291 0.043 43 35.26 8.81 1.384 0.726 0.082 44 26.42 3.63 3.128 0.560 0.154 45 32.67 6.61 0.412 0.157 0.024 49 27.81 3.80 0.791 0.190 0.050 50 29.22 4.91 1.966 0.472 0.096 51 25.07 4.52 2.305 0.553 0.122 52 31.67 5.11 2.006 0.482 0.094 53 37.39 6.61 2.383 0.572 0.087 54 32.67 3.63 1.950 0.468 0.129 67 63.62 7.79 1.524 1.149 0.147 106 102.07 13.20 1.136 1.164 0.088 109 86.59 12.88 1.183 0.542 0.042 110 47.78 9.35 0.780 0.174 0.019 111 193.59 27.34 4.455 8.481 0.310 113 107.51 14.52 4.331 4.013 0.276 116 33.70 3.46 0.796 0.263 0.076 119 91.61 20.43 3.023 3.801 0.186 120 58.77 15.90 1.861 1.353 0.085 121 102.97 14.52 2.020 2.162 0.149 123 65.04 6.83 2.235 1.212 0.177 124 60.82 4.34 2.292 0.830 0.191 125 112.16 19.63 2.814 3.161 0.161 126 117.86 18.10 2.314 3.717 0.205 132 111.22 24.19 0.865 5.068 0.209

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Burkea africana

Stem area Average Sap Average sapflow Average sapflow TREE ID (cm2) Crown area (m2) velocity (cm/hr) (l/day) normalised 31 65.04 2.41 1.195 0.832 0.34591 35 28.75 1.04 2.409 0.925 0.89074 38 77.76 2.84 1.403 1.441 0.50834 39 50.27 3.14 1.259 2.055 0.65403 40 291.04 15.90 3.974 10.585 0.66557 41 40.72 5.52 3.974 4.578 0.82994 42 67.93 2.54 0.917 0.614 0.24120 55 142.08 8.81 1.372 2.931 0.33253 56 132.73 4.52 1.293 1.881 0.41573 57 91.61 1.83 0.816 0.803 0.43973 58 29.22 1.77 1.110 0.511 0.28944 59 35.26 1.43 1.485 0.667 0.46569 60 129.69 4.43 2.060 3.631 0.81967 61 39.59 1.28 0.884 0.458 0.35876 62 43.59 1.77 1.458 0.577 0.32670 63 207.39 7.79 0.734 1.622 0.20812 64 263.02 13.85 0.482 1.518 0.10960 65 84.95 2.99 0.827 0.843 0.28240 66 27.81 1.43 0.879 0.245 0.17106 68 76.20 3.30 1.817 1.483 0.44922 69 52.81 3.46 0.950 0.577 0.16659 73 289.53 12.57 1.156 4.153 0.33050 74 191.13 12.88 1.115 3.211 0.24924 75 41.85 2.27 1.414 0.577 0.25423 76 25.07 2.01 1.023 0.322 0.16000 77 37.94 1.43 0.776 0.279 0.19516 93 126.68 6.61 1.179 2.266 0.34308 114 41.85 3.46 0.800 0.366 0.10576 118 253.06 23.76 1.111 3.595 0.15132 122 29.71 1.54 2.920 0.806 0.52348 134 187.48 10.46 0.291 2.455 0.23463 135 246.06 17.72 0.398 6.440 0.36342 137 258.73 15.55 0.304 2.164 0.13914

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Ochna pulchra

Average sapflow Stem area Average Sap Average sapflow normalised TREE ID (cm2) Crown area (m2) velocity (cm/hr) (l/day) (mm/day) 70 25.07 2.27 1.921 4.056 1.78694 71 27.34 1.13 2.203 0.428 0.37852 72 25.07 2.01 3.190 4.364 2.17028 105 42.43 3.80 3.034 1.580 0.41571 108 101.18 5.94 3.940 4.747 0.79926 112 69.40 4.71 2.717 1.225 0.25974 115 32.67 2.99 0.705 0.149 0.04986 128 175.54 5.31 0.595 2.738 0.51564 129 92.46 5.52 0.372 1.153 0.20899 130 279.07 16.62 1.175 10.878 0.65456 131 163.99 4.15 0.600 3.892 0.93683 133 317.31 9.90 0.475 5.326 0.53812 136 260.16 6.61 0.415 2.731 0.41352 138 177.89 8.81 1.079 6.420 0.72832 144 229.66 6.61 0.388 2.095 0.31724 149 119.79 5.11 0.608 3.097 0.60638 150 76.98 3.80 0.519 1.670 0.43922

Lonchocarpus nelsii Average Sap TREE Stem area Crown area velocity Average Average sapflow ID (cm2) (m2) (cm/hr) sapflow (l/day) normalised 1 379.94 28.60 1.380 9.250 0.32343 2 67.89 5.90 1.080 1.240 0.20875 3 200.96 11.70 2.450 8.880 0.75897 7 34.19 2.50 1.170 0.690 0.28049 36 314.00 27.30 3.290 18.050 0.66020 44 206.02 15.20 9.990 30.950 2.03618 45 38.47 3.30 7.210 4.250 1.28788

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Dichrostachys cineria

Stem area Average Sap Average sapflow Average sapflow TREE ID (cm2) Crown area (m2) velocity (cm/hr) (l/day) normalised 19 19.63 5.52 0.899 0.101 0.01838 20 20.03 6.38 2.044 0.240 0.03769 21 20.43 9.90 1.132 0.166 0.01674 22 29.71 6.83 2.100 0.312 0.04571 23 21.24 8.81 3.047 0.380 0.04314 24 22.06 6.16 1.413 0.183 0.02974 29 69.40 15.90 0.828 0.378 0.02375 30 27.81 6.38 0.541 0.082 0.01282 46 87.42 12.88 0.505 0.219 0.01704 47 42.43 11.95 0.315 0.061 0.00512 48 41.85 13.85 0.091 0.131 0.00943 103 40.15 11.64 1.736 3.212 0.27586 104 32.67 9.62 0.772 0.219 0.02272 127 36.32 10.46 0.519 2.385 0.22795 139 25.52 4.71 0.735 0.544 0.11530 140 39.59 7.07 0.524 0.615 0.08705 141 40.72 8.81 0.598 0.628 0.07127 142 31.67 6.16 0.463 0.468 0.07601 143 23.33 3.63 0.498 0.413 0.11389 145 43.59 15.90 0.176 0.298 0.01875 146 48.40 11.34 0.174 0.262 0.02310 147 27.81 11.34 0.356 0.285 0.02512 148 35.78 8.55 0.243 0.296 0.03460

International Institute for Geo-information Science and Earth Observation 76 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Acacia Fleckii Average Sap Average TREE Stem area Crown area velocity sapflow Average sapflow ID (cm2) (m2) (cm/hr) (l/day) normalised 13 203.48 39.60 0.900 3.030 0.07653 14 50.24 10.20 3.300 3.100 0.30452 16 254.34 37.40 3.500 13.700 0.36641 20 50.24 8.00 2.810 2.360 0.29353 21 94.99 12.40 1.200 2.100 0.16922 22 113.04 17.20 1.000 2.100 0.12181 23 153.86 18.90 1.400 3.400 0.18028 27 34.19 6.40 1.030 0.580 0.09091 32 60.79 7.80 1.470 1.660 0.21309 37 38.47 7.10 1.210 0.760 0.10750 38 211.13 30.20 2.800 6.950 0.23021 47 120.70 20.20 6.760 12.980 0.64162

International Institute for Geo-information Science and Earth Observation 77 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Boscia albitrunca

Average Sap Average TREE Stem area Crown area velocity sapflow Average sapflow ID (cm2) (m2) (cm/hr) (l/day) normalised 4 415.27 16.4 9.970 80.030 4.87097 15 415.27 21.0 4.430 33.530 1.59439 24 130.63 9.1 1.260 2.640 0.29075 29 547.11 24.0 2.760 27.890 1.16354 30 522.53 19.1 2.890 27.400 1.43832 31 44.16 3.5 2.910 2.430 0.70231 33 314.00 15.0 4.110 25.740 1.71257 34 637.62 28.3 4.130 49.560 1.75310 35 78.50 4.4 3.380 4.970 1.12190 42 369.65 19.2 4.590 32.430 1.68555 43 89.87 4.2 1.870 2.860 0.68916

International Institute for Geo-information Science and Earth Observation 78 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Appendix 2

4-channel Light sensor calibration certificate

SKYE INSTRUMENTS LIMITED, LLANDDRINDOD W ELLS, POW YS, W ALES

CALLIBRATION CERTIFICATE

UNIT TYPE SKR1850/I SERIAL NUMBER 0203 25813

Sensitivities with cosine-corrected diffuser

Channel No. Center of ^ Bandwidth W atts/mV Zero Offset (nm) (nm) (mV) 1 487 70 5.41 0 2 561 87 5.35 0 3 687 133 5.56 0 4 852 193 12.17 0

Relative Sensitivities as narrow angle sensor without diffuser

Channel 1 1.00 Channel 2 1.29 Channel 3 1.12 Channel 4 2.71

To render all channels equally sensitive for ratio measurements, the voltage from each channel should be multiplied by these factors. N.B. Above figures refer to sensor voltage produced by light falling on the sensor in the passband of that channel.

International Institute for Geo-information Science and Earth Observation 79 Assessment of dry season transpiration using IKONOS images, Serowe case study (Botswana)

Appendix 3

Maps show the distribution of each tree species in the IKONOS image

Distribution of Acacia fleckii

Distribution of Burkea Africana

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Distribution of Boscia abitrunca

Distribution of Dischrostachys cinerea

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Distribution of Loncorcapus nelsii

Distribution of Ochna pulchra

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Distribution of Terminalia sericea

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Appendix 4

Plot Transpiration derived from Remote sensing

Coordinate Coordinate T(prior T(m axim um Plot No X Y Mapanda_T probability) likelihood) 2 432000 7528000 0.013 0.29 0.32 3 433000 7528000 0.001 0.49 0.56 5 435000 7528000 0.01 0.48 0.47 7 435000 7527000 0.065 0.67 0.73 10 432000 7527000 0.002 0.15 0.16 11 433000 7527000 0.008 0.26 0.22 12 434000 7527000 0.117 0.56 0.6 13 432000 7529000 0.027 0.53 0.57 14 432000 7530000 0.014 0.61 0.66 15 433000 7530000 0.067 0.78 0.79 16 433000 7529000 0.018 0.59 0.63 17 434000 7529000 0.054 0.96 0.98 19 434000 7530000 0.07 0.63 0.66 25 429000 7530000 0.002 0.38 0.41 27 430000 7530000 0.018 0.3 0.25 28 431000 7530000 0.012 0.56 0.66 29 429000 7529000 0.006 0.38 0.37 30 430000 7529000 0.008 0.38 0.44 31 431000 7529000 0.011 0.21 0.23 35 430000 7528000 0.015 0.28 0.31 36 431000 7528000 0.004 0.32 0.39 39 429000 7527000 0.002 0.31 0.23 40 430000 7527000 0.021 0.14 0.11 41 431000 7527000 0.012 0.2 0.2 44 427000 7530000 0.074 0.27 0.21 45 428000 7530000 0.007 0.25 0.19 48 427000 7529000 0.008 0.21 0.18 49 428000 7529000 0.004 0.17 0.16 53 427000 7528000 0.107 0.24 0.23 55 428000 7528000 0.21 0.36 0.31 57 427000 7527000 0.018 0.39 0.39 58 428000 7527000 0.101 0.31 0.26 61 427000 7533000 0.03 0.4 0.44 62 427000 7532000 0.012 0.2 0.23 64 428000 7533000 0.014 0.35 0.38 65 428000 7532000 0.055 0.16 0.08 67 427000 7531000 0.137 0.35 0.25 68 428000 7531000 0.023 0.31 0.22 69 429000 7531000 0.019 0.11 0.11 70 429000 7532000 0.004 0.18 0.21 71 429000 7533000 0.054 0.27 0.36 72 430000 7533000 0.003 0.4 0.5

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Coordinate Coordinate T(prior T(m axim um Plot No X Y Mapanda_T probability) likelihood) 73 430000 7532000 0.002 0.18 0.23 74 430000 7531000 0.063 0.35 0.43 76 427000 7535000 0.097 0.2 0.23 78 427000 7534000 0.115 0.33 0.4 81 428000 7535000 0.067 0.05 0.03 83 428000 7534000 0.065 0.13 0.11 84 429000 7534000 0.248 0.26 0.28 85 429000 7535000 0.102 0.21 0.24 86 430000 7535000 0.018 0.48 0.54 87 430000 7534000 0.004 0.31 0.37 90 431000 7535000 0.015 0.22 0.3 91 431000 7534000 0.202 0.25 0.31 92 432000 7535000 0.071 0.58 0.65 93 433000 7535000 0.052 0.5 0.57 94 432000 7534000 0.002 0.52 0.59 95 433000 7534000 0.03 0.31 0.37 97 431000 7533000 0.015 0.62 0.67 98 432000 7533000 0.055 0.39 0.53 99 433000 7533000 0.021 0.2 0.27 100 431000 7532000 0 0.61 0.69 101 432000 7532000 0.003 0.44 0.55 102 433000 7532000 0.046 0.54 0.65 104 431000 7531000 0.007 0.59 0.66 105 432000 7531000 0.019 0.56 0.66 106 433000 7531000 0.062 0.51 0.56 108 434000 7535000 0.186 0.41 0.46 109 435000 7535000 0.134 0.4 0.44 110 434000 7534000 0.001 0.35 0.43 111 435000 7534000 0.005 0.85 0.9 112 434000 7533000 0.095 0.35 0.27 113 435000 7533000 0.054 0.47 0.55 117 435000 7532000 0 0.12 0.15 118 434000 7532000 0.001 0.18 0.18 120 435000 7531000 0.12 0.6 0.68 121 434000 7531000 0.037 0.55 0.58 130 435000 7529000 0.048 0.6 0.62

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