Master Thesis
Im Rahmen des
Universitätslehrganges „Geographical Information Science and Systems“
(UNI GIS MSc) Centre for GeoInformatics (Z_GIS) Paris Lodron University Salzburg
zum Thema
“ DEVELOPMENT OF A FOREST CLASSIFICATION METHOD FOR THE CHERANGANI HILLS – KENYA ”
using ASTER satellite imagery
vorgelegt von
forest engineer Michaela Ehmann
U1324; UniGIS MSc Jahrgang 2007
zur Erlangung des Grades
“Master of Science (Geographical Information Science & Systems) – MSc(GIS)”
Gutachter:
Ao. Univ. Prof. Dr. Josef Strobl Mitbetreuer
Dr. Sandra Eckert & Christian Hergarten, CDE Bern
Kakamega, 30.03.2010
in the Cherangani Hills South part that received little attention up to now. The adjacent urban areas highly depend on the preservation of the water catchment areas and the local communities rely on the forests for substantial use. How- ever some sites show severe signs of deforestation and it is therefore a great wish of the author that this study will help to initiate community reforestation activities that lead to a better understanding, sustainable utilisation and conver- sation of these forests.
Acknowledgements
Looking for a topic for my master thesis in Kenya in the field of forest monitoring I found many links to the Greenbelt Movement and Prof. Wangari Maathai.
Herewith I want to thank her for giving me the inspiration for this thesis.
"It is the people who must save the environment. It is the people who must make their leaders change. And we cannot be intimidated. So we must stand up for what we believe in." Wangari Maathai
Secondly I want to thank the UNIGIS TEAM, Prof. Dr. Strobl and Julia Moser for their support and feedback to all of my concerns and questions.
Special thanks go to Dr. Sandra Eckert and Christian Hergarten from the Centre of Development in Bern for their valuable tips and feedback on my work and Dr.
Hans-Peter Liniger for linking me up with them.
Thanks also go to Shazia and David Jenkins and Ncholastika, who assisted me during the field work, for sharing their enthusiasm for forests with me and to Pe- ter Ndunda for giving me an insight into the work of Greenbelt Movement.
Last but not least I thank my husband for his support and my son for his love, which helped me to complete this thesis.
Declaration
I confirm that this master thesis was written without help of others and without using any sources other than the ones cited and that this thesis has not been submitted in its present form to another examination board. All passages in this thesis that are quoted from other sources are marked accordingly.
Kakamega, Kenya, 30.03.2010 Michaela Ehmann
"A picture shows me at a glance what it takes dozens of pages of a book to expound."
Ivan Turgenev (Fathers and Sons, 1862)
The Cherangani Hills are one of the five mountainous areas of Kenya that be- long to the natural water catchment areas. The adjacent urban areas highly de- pend on the preservation of the forests for sustainable water supply and the local communities rely on them for subsistence use. Various factors like e.g.
population growth and poverty induce an increased pressure on the existing resources and endanger the reserves. Blackett (1994) states that the original gazetted area size for the Cherangani Hills East was 79,075 ha, which de- creased up to 1994 to 65,309 ha. This was equal to a loss of 17%. The calcu- lated loss for the complete area from gazettement to 2005 is with 30646ha (24%) even higher. A lot of research was done on the more famous forest re- serves, like Mount Kenya and the Aberdares and the Mau forest complex. The last mentioned obtained unfortunate popularity because of its recent resettle- ment activities. The Cherangani Hills, even though they are one of the five main water towers and therefore included in many concepts received little attention up to now.
Quite often there is not enough money available to perform extensive invento- ries and research studies on site. Evaluation of remote sensing data in line with utilisation of available ancillary data can be a powerful tool to overcome time and cost restrictions. This is especially valid for remote and difficult accessible areas.
In this study a parallelepiped classifier approach was chosen, based on the analysis of the ground truth data, to obtain the current spatial distribution and size of forests reserves, forest types and open areas. Four final level II output classes could be obtained (1) hardwood; (2) conifer (3); open areas and (4) cul- tivation sites. The first set up of the forest classes with a broadleaf - needleleaf approach was revised because of the spectral similarity of the Podocarpus sp.
with the other needleleaf (Juniperus procera, Pinus patula) tree species. The quality was assessed trough an error matrix. A high overall accuracy of 85.29%
and a Kappa coefficient of 0.79 were obtained. Based on this findings land
compared to the original gazetted areas was 21%, what is even more than the change for Cherangani Hills East found by Blackett in 1994. In combination with the potential natural vegetation data a recommendation map for reforestation projects was developed. The cost-effectiveness and the transferability of the results were discussed. The findings can be a helpful starting point for further reaching environmental studies, development of monitoring procedures as well as for highly needed reforestation projects.
Keywords: Cherangani Hills, reforestation, remote sensing, Aster
Zusammenfassung
Die Cherangani Hills sind eine der fünf Bergregionen Kenya’s, die zu den natürlichen Wasser Einzugsgebieten des Landes gehören. Die angrenzenden städtischen Gebiete sind bezüglich einer nachhaltigen Wasserversorgung von der Erhaltung der Wälder abhängig und für die lokalen Gemeinden ist Subsistenznutzung essentiell. Viele Faktoren, wie zum Beispiel das Bevöl- kerungswachstum und Armut verursachen einen erhöhten Druck auf die existierenden Resourcen und gefährden die Wald Reservate. Blackett be- schreibt in der 1994 durchgeführten Forstinventur eine Flächenabnahme für Cherangani Ost von ursprünglich 79075ha auf 65309ha, dies entspricht einem Verlust von 17%. Der Verlust fuer das Gesamtgebiet beläuft sich seit der offiziellen Registrierung bis 2005 auf 30646ha (24%). Viele Studien sind für die berühmteren Gebiete, wie Mount Kenya, die Aberdares und den Mau Forest Komplex durchgeführt worden. Der zuletzt genannte erlangte unglückliche Berümtheit aufgrund seiner erst kürzlich durchgeführten Umsiedlungs Aktionen.
Obwohl die Cherangani Hills zu den fünf sogennaten „main water towers“
(Haupt Wassertürmen) gehören und aus diesen Grund auch in vielen Konzepten enthalten sind, erhielten Sie bisher wenig Aufmerksamkeit.
Fostinventuren und Forschungsprojekte scheitern oft an unzureichenden Geldmitteln. Die kombinierte Auswertung von Fernerkundungsdaten und
Restriktionen zu überwinden. Dies gilt insbesondere für abgelegene und schwer zugängliche Gebiete.
Um die aktuelle Verteilung und Grösse der Waldgebiete, Waldtypen und offenen Flächen zu erhalten wurde in dieser Studie, basierend auf der Analyse der Geländeaufnahmen, ein Parallelipiped Klassifikations Ansatz gewählt. Als Resultat wurden vier Klassen auf einer Ebene II Klassifikation ermittelt (1) hardwood –Hartholz; (2) conifer (Koniferen); (3) offene Flaechen und (4) kultivierte Flaechen. Der ursprüngliche Ansatz mit Laubwald/Nadelwald Klassen wurde aufgrund der spektralen Ähnlichkeit der Poducarpus Arten mit den weiteren Nadelbaum Arten (Pinus patula, Juniperus procera) revidiert. Die Qualitaetsüberprüfung der Resultate erfolgte anhand einer Fehlermatrix. Die erzielte Gesamtgenauigkeit betrug 85.29% und der ermittelte Kappa Koeffizient 0.79. Basierend auf den ermittelten Klassen wurden Landnutzungskarten für diejenigen Waldreservate erstellt, die innerhalb der Aster Szene liegen.
Ausserdem wurden die Flächenveränderungen im Vergleich zu den ursprünglich gazetteten Flächen berechnet. Der Gesamtverlust von 21% liegt noch 4% höher als der von Blackett 1994 ermittelte. In Kombination mit der Karte fuer Potentiell natuerliche Vegetation wurde eine Karte für das Gesamtgebiet Cherangani-Südteil erstellt, die potentielle Gebiete für Aufforstungsflächen darstellt. Abschliessend wurde die Wirtschaftlichkeit der ermittelten Methode und die Übertragbarkeit auf andere Gebiete diskutiert. Die Ergebnisse können als Ausgangsbasis fuer weiterreichende Forschungen, Erarbeiten von Monitoringkonzepten und wichtige Aufforstungsprojekte dienen.
Schlagwörter: Cherangani Hills, Aufforstung, Fernerkundung, Aster
Acknowledgements... I Declaration... I Motto... II Abstract ... III Zusammenfassung ...IV List of Figures ...VIII List of tables...X Abbreviations ...XI
1. Introduction ...1
1.1 General background ... 1
1.2 Greenbelt movement... 2
1.3 Why Cherangani Hills?... 2
1.4 Objectives and expected results of the thesis ... 5
1.5 Structure of this thesis... 6
2. Overview Literature ...7
2.1 Gazetted forests in Kenya... 7
2.2 Deforestation and Reforestation... 8
2.3 Study Area ... 8
2.4 Satellite Data ... 12
2.4.1 ASTER Satellite Images... 12
2.4.2 ASTER GDEM... 15
2.5 Field data... 16
2.6 Classification... 16
2.7 Vegetation indices... 20
2.8 Accuracy Assessment... 22
3. Project Design...24
3.1 Field data... 24
3.1.1 General set up ... 24
3.1.2 Description of expected output classes... 24
3.2.1 General set up ... 34
3.2.2 Preprocessing... 34
3.2.3 Accuracy Assessment ... 35
3.2.4 ASTER GDEM... 35
4. Project Description ...36
4.1 Field Data ... 36
4.1.1 General setup ... 36
4.1.2 Ground Truth data ... 36
4.1.3 Ground Control Points... 37
4.1.4 Data collection sheet ... 38
4.1.5 Download and Editing of Field Data... 38
4.2 Satellite Data ... 38
4.2.1 ASTER Satellite Image... 38
4.2.2 ASTER GDEM... 39
4.3 Image Interpretation and Analysis ... 43
4.3.1 ASTER Image preprocessing... 43
4.3.2 Vegetation Indices... 45
4.3.3 Supervised Classification – Class Separability ... 46
4.3.4 Classification procedure ... 51
4.3.5 Accuracy Assessment ... 52
5. Results ...57
5.1 Forest Type Classification maps ... 57
5.2 Recommendation map for reforestation sites ... 68
5.3 Relationship spectral measurements to forest stand parameters ... 70
6. Discussion...72
6.1 Cost effective classification procedure? ... 72
6.2 Summary ... 74
6.3 Recommendations ... 76
7. References...78
2007... 4 FIG.2.1: GAZETTED FOREST AREAS IN KENYA,SOURCE MODIFIED UNEP,2007 ... 7 FIG.2.2:CHERANGANI FORESTS IN RELATION TO DISTRICT BOUNDARIES... 9 FIG.2.3: SPECTRAL RESPONSE PROFILE OF ASTER AND LANDSAT BANDS ,SOURCE:
HTTP://ASTERWEB.JPL.NASA.GOV/IMAGES/SPECTRUM.JPG... 14 FIG.2.4:ASTER SCIENTIFIC PURPOSES,SOURCE:
HTTP://WWW.GDS.ASTER.ERSDAC.OR.JP/GDS_WWW2002/EXHIBITION_E/A_GDS_E/SET_A_GDS_E .HTML... 15 FIG.2.5.ASTERGDEM PURPOSES,SOURCE: HTTP://WWW.ERSDAC.OR.JP/GDEM/E/1.HTML... 15 FIG.2.6: GENERAL/VEGETATIVE LEVEL I TO LEVE III CLASSIFICATION,SOURCE:FRANKLIN,2001, P.215
... 17 FIG.2.7: SCATTER DIAGRAM, EQUIPROBABILITY CONTOURS DEFINED BY A MAXIMUM LIKELIHOOD
CLASSIFIER,SOURCE LILLESAND ET AL.,2007, P.556 ... 19 FIG.2.8: SPECTRAL RESPONSE PATTERN OF DIFFERENT FOREST TYPES,SOURCE:KLEINSCHMITT ET AL.
2006, P.23 ... 20 FIG.3.1:MIND MAP FOR THE EXPECTED OUTPUT CLASSES DERIVED FROM LITERATURE RESEARCH AND
PRE-EVALUATION OF THE ASTER DATA AND ANCILLARY DATA... 26 FIG.3.2: PHOTOGRAPH FELLED JUNIPERUS PROCERA WITH LOCAL FELLING TECHNIQUES,KIPKUNURR
FOREST,JANUARY 2010 ... 27 FIG.3.3 PHOTOGRAPH MIXED BROADLEAF/NEEDLELEAF WITH DOMINANT JUNIPERUS PROCERA AND
OLEA CAPENSIS WITH PODOCARPUS FALCATUS ,IN THE UNDERSTORY,KIPKUNURR FOREST JANUARY,2010 ... 29 FIG.3.4:MAYTENUS UNDATA TREE (LEFT), ... 30 CAPSULE WITH YELLOW VALVES, ORANGE ARIL COVERING SEED (RIGHT),SOURCE:PLANTS OF AFRICA,
HTTP://WWW.WORLDBOTANICAL.COM/AFRICAN_PLANTS.HTM... 30 FIG.3.5:PHOTOGRAPH JUNIPERUS-MAYTENUS UNDATA-RAPANEA-HAGENIA FOREST,KIPKUNURR
FOREST,JANUARY 2010 ... 31 FIG.3.6: PHOTOGRAPH MOUNTAIN BAMBOO,KIPKUNURR FOREST,JANUARY 2010... 31 FIG.3.7: GRASSLAND (UPPER LEFT), ROAD IN CULTIVATION AREA WITH FOREST PATCHES (LOWER RIGHT)
& CULTIVATION SITES (UPPER RIGHT), ... 32 JANUARY 2010,WEST OF KIPKUNURR FOREST... 32 FIG.3.8MIND MAP FOR FIELD WORK PARAMETERS DERIVED FROM LITERATURE RESEARCH AND PRE-
EVALUATION OF THE ASTER DATA AND ANCILLARY DATA... 33 FIG.3.9:FLOW CHART FOR IMAGE PREPROCESSING AND PROCESSING DERIVED FROM THE ASTER
DATA, FIELD DATA AND ANCILLARY DATA... 34 FIG.4.1:DISTRIBUTION OF SAMPLE PLOTS FOR GROUND TRUTH DATA... 37
FIG.4.4: CALCULATED SLOPES FOR THE STUDY AREA... 41
FIG.4.5:SURFACE PROFILE OF CHERANGANI SOUTH... 42
FIG.4.6:ASTERIMAGE BEFORE GEOMETRIC CORRECTION (LEFT)ASTERIMAGE AFTER GEOMETRIC CORRECTION,CORRECTION METHOD:CONTROL POINTS (RIGHT) ... 43
FIG.4.7:GEOMETRIC CORRECTION WITH TOPOGRAPHIC MAP ASTERIMAGE SCALE 1:50000(RIGHT) ... 44
TOPOGRAPHIC MAP SCALE 1:50000,VILLAGE BUGAR IN THE SOUTH PART OF THE ASTERIMAGE (LEFT) ... 44
FIG.4.8:GEOMETRIC CORRECTION WITH GOOGLE EARTH IMAGES LOCATION ROAD CROSSING... 44
KAPSOWAR, LEFT PICTURE TAKEN BEFORE CORRECTION APPLIED, RIGHT PICTURE AFTER GEOPROCESING... 44
FIG.4.9:CALCULATION OF GNDVI WITH THE MODEL MAKER IN ERDAS SOFTWARE... 45
FIG.4.10:NORMAL DISTRIBUTED DATA IN BAND 1 OF PODOCARPUS CLASS... 48
FIG.4.11: NOT NORMAL DISTRIBUTED DATA IN BAND 2 FOR THE PINUS CLASS... 48
FIG.4.12: MEAN SPECTRAL RESPONSE VALUES FOR FOUR DIFFERENT CLASSES... 48
FIG.4.13: MEAN SPECTRAL RESPONSE VALUES FOR SELECTED VEGETATION INDICES... 49
FIG.4.14: ELLIPSES FOR 3 CLASSES, DISPLAYED IN A FEATURE SPACE IMAGE DERIVED FROM BAND 1 AND 3... 50
FIG.4.15:CLASSIFICATION OF PINUS CLASS... 52
FIG.5.1:AREA SIZES FOR KAPCHEMUTWA FOREST RESERVE PER CLASS IN HA AND PERCENT... 57
FIG.5.2: CLASSIFIED RASTER IMAGE OF KAPCHEMUTWA FOREST RESERVE... 58
FIG.5.3:AREA SIZES FOR KAPCHEMUTWA FOREST RESERVE PER CLASS IN HA AND PERCENT... 59
FIG.5.4: CLASSIFIED RASTER IMAGE FOR SOGOTIO FOREST RESERVER... 59
FIG.5.5: CLASSIFIED RASTER IMAGE FOR KIPKUNURR FOREST RESEVE... 60
FIG.5.6:AREA SIZES FOR KIPKUNURR FOREST RESERVE IN HA AND PERCENT... 61
FIG.5.7:AREA SIZES FOR CHEMUROKOI FOREST RESERVE IN HA AND PERCENT... 62
FIG.5.8: CLASSIFIED RASTER IMAGE FOR CHEMUROKOI FOREST RESEVE... 62
FIG.5.9:AREA SIZES FOR CHEBOIT FOREST RESERVE IN HA AND PERCENT... 63
FIG.5.10: CLASSIFIED RASTER IMAGE FOR CHEBOIT FOREST RESEVE... 63
FIG.5.11:AREA SIZES FOR KERRER FOREST RESERVE IN HA AND PERCENT... 64
FIG.5.12: CLASSIFIED RASTER IMAGE FOR KERRER FOREST RESEVE... 64
FIG.5.13: CLASSIFIED RASTER IMAGE FOR PARTS OF KIPTABERR FOREST RESEVE... 65
FIG.5.14:AREA SIZES FOR KIPTABERR FOREST RESERVE IN HA AND PERCENT... 66
FIG.5.15:AREA SIZES FOR EMBOBUT FOREST RESEVE IN HA AND PERCENT... 66
FIG.5.16: CLASSIFIED RASTER IMAGE FOR PARTS OF EMBOBUT FOREST RESERVE... 67
FIG.5.17: RECOMMENDATION MAP FOR REFORESTATION ACTIVITIES... 70
FIG.6.1:OVERVIEW AREA LOSSES FOR THE 7 FULLY COVERED FOREST RESERVES... 75
List of tables
TAB.2.1:FOREST AREA CHERANGANI HILLS,SOURCE MODIFIED:AKOTSI &GACHANJA,2006,
BLACKETT H.,1994,HODGSON,1992... 10
TAB.2.2:TEN MOST COMMON SPECIES OF THE CHERANGANI HILLS,SOURCE MODIFIED BLACKETT, 1994... 12
TAB.2.3:ASTER SPECIFICATIONS,SOURCE: HTTP://WWW.GDS.ASTER.ERSDAC.OR.JP/GDS_WWW2002/EXHIBITION_E/A_GDS_E/SET_A_GDS_E .HTML... 13
TAB.4.1: SELECTED GDEM IMAGES... 39
TAB.4.2: SPECTRAL RESPONSE VALUES FOR PINUS PLANTATIONS... 47
TAB.4.3 SPECTRAL RESPONSE VALUES FOR PODOCARPUS PLANTATION MEDIUM DENSITY... 47
TAB.4.4: SPECTRAL RESPONSE VALUES FOR JUNIPER STANDS... 47
TAB.4.5: SELECTED CLASSES FOR CLASSIFICATION... 52
TAB.4.6:ERROR MATRIX, PIXEL VALUES FOR EACH KNOWN CLASS VERSUS EACH CLASSIFIED CLASS 53 TAB.4.7PRODUCER’S &USER’S ACCURACY FOR THE CLASSIFIED CLASSES... 53
TAB.4.8:OVERALL ACCURACY AND KAPPA STATISTIC... 54
TAB.4.9:CLASSES AFTER RE-EVALUATION,1 HARDWOOD,2 CONIFER,3 OPEN AREAS,4 CULTIVATION ... 55
TAB.4.10: ERROR MATRIX FOR CLASSES HARDWOOD,CONIFER, OPEN AREAS AND CULTIVATION SITES ... 55
TAB.4.11:PRODUCER ACCURACY, USER ACCURACY AND CONDITIONAL KAPPA FOR THE RE- EVALUATED CLASSES... 55
TAB.4.12: OVERALL ACCURACY AND OVERALL KAPPA STATISTIC... 55
TAB.5.1:COMPILED OVERVIEW OF FOREST AREA CHANGES FROM GAZETTE YEAR TO 2010 ... 68
EOS Earth observation satellite
ERSDAC Earth Remote Sensing Data Analysis centre EVI2 enhanced vegetation index 2
FAO Food and Agriculture Organization of the United Nations GBM Greenbelt Movement
GDEM global digital elevation model GPS Global positioning system
GNDVI Green Normalized Difference Vegetation Index NDVI Normalized Difference Vegetation Index
NGO Non Governmental organisation KFWG Kenya Forest Working Group
KIFCON Kenya indigenous forest conservation programme RMSE root mean square error
SR Simple ratio
SRGREEN Simple ration green band SRRED Simple ratio red band SWIR Shortwave infrared TIR thermal infrared
UNEP United Nations Environment Program VNIR visible and near infrared
1. Introduction
1.1 General background
Tropical forests are the most ancient and biological diverse of the world’s eco- systems. Although covering only 7% of the terrestrial surface this forests har- bour more than half of the world’s animal and plant species (Myers 1984). East African rainforests suffer large over-exploitation by humans and belong to the most threatened and least explored ecosystems on Earth (Köhler 2004).
Laurance & Bierregaard (1997) predict a loss of much of the world’s tropical rainforests in our lifetimes, with deforestation driven by an increasing human population and a rush towards industrialisation in developing nations as main causes. Only about 0.1% (10,000 km²) of the estimated 10 million km² of tropi- cal rainforest in the world occurs in Eastern Africa. Unlike the vast West and Central African forests, the forests of Eastern Africa are highly fragmented - dis- crete islands surrounded by comparatively arid woodland (Lovett & Waser 1993). Like most African countries Kenya has large areas of arid and semi arid land, with low annual rainfall and long dry seasons. Because of the difficult liv- ing conditions in these areas the main part of the population has settled in the more productive highlands, which causes a high pressure on this limited area (FAO, 2003). Developing countries face many challenges in setting up man- agement and monitoring programmes for forest areas that are exploited and over utilised. The Greenbelt Movement is one of the leading NGOs (Non Gov- ernmental Organisations) in Kenya enrolled in forest reforestation and conser- vation activities. With ongoing reduction of costs for remote sensing data and advanced techniques for image processing the use of remote sensing provides capabilities for mapping and monitoring of forests like never before. The devel- opment of a forest classification system is believed to assist the set up of moni- toring procedures and to give a starting point for further reaching environmental studies as well as to facilitate reforestation activities. A supervised classification approach based on an ASTER (Advanced Spaceborn Thermal Emission and Reflection Radiometer) satellite image, the Aster 30mx30m GDEM (global digi-
tal elevation model) and an on site field survey was chosen to address the ob- jectives of this thesis.
1.2 Greenbelt movement
With Prof. Wangari Maathai, the Nobel Peace Price Winer 2004, the Greenbelt Movement has one of the most famous leaders for environmental programs in Africa. The focus of the organisation is on tree planting activities in farms and public lands as well as on reforestation of degraded forest areas in the five main forested natural water catchments areas, that supply most Kenya’s urban areas.
In 2006 a tree planting and Bio carbon project was started in the Aberdare and Mount Kenya forests as a part of the clean Development mechanism. To date GBM (Greenbelt movement) has facilitated tree planting activities in Mount Kenya, Aberdare and Mau Complex. Reforestation activities are planed in Mount Elgon and Cherangani Hills. The reforestation projects are based on a 10 step approach which focuses on the consciousness and self determination of the communities on site. The ten steps are (1) raising awareness about the im- portance of the forests reserve for the local communities, (2) group information, (3) establishment of tree nurseries, (4) reporting and stakeholder consultation, (5) land preparation, (6) identification of critical intervention sites and participa- tory community mapping, (8) tree planting, (9) monitoring of tree planting sites and (10) validation and compensation. Up to now the greenbelt movement has planted over 45 Mio trees and has supported over 4000 community groups from which 70 % are women (Ndunda, 2009).
1.3 Why Cherangani Hills?
Forests and forestry play an important role in Kenya (FAO, 2003). The forests fulfil various functions, such as soil protection, water reservoir and supply for agriculture and wildlife habitats. Timber is utilized and the use of non wood products, like plants, fruits, herbs and animal products is widespread among the communities living in or nearby forest areas (Waas, 1995). However forest cover is being depleted at a rapid rate and sustainable forest management
practices are not in place (Akotsi & Gachanja , 2006). Major threats to forests are
• Population growth, with increasing land demand for cultivation, which goes hand in hand with fire, overgrazing and shifting cultivation practices (UNEP, 1997). Nearly one third of the gazetted forests are estimated to be deforested for cultivation purposes (Routsalainen, 2004).
• Poverty, as an obstacle for effective land utilization and as reason for il- legal settlements inside forest areas. Forests often offer more fertile soils and hence better harvests on one hand and income opportunities through forest product sale on the other hand. (Makanji & Mochida, 1999)
• Illegal logging activities. Even though since 1985, logging has been banned in the indigenous forests of Kenya an increasing demand for tim- ber made illegal logging a lucrative business (FAO; 2003).
• Unfair trading and business practices of developed countries, that have a negative impact on land based produce from less developed countries (Makanji & Mochida, 1999)
• Government policies in the field of agriculture and forestry, that are in- adequate or not implemented (Makanji & Mochida, 1999)
All these results in severe loss of woody canopy cover and herbaceous cover, loss of topsoil, loss of soil fertility, decreased rainfall and frequent prolonged droughts (UNEP, 1997). Kenya’s forest cover has been depleted over the years and now stands at a critical 1.7% of the total land area (Akotsi & Gachanja, 2006). The FAO (2007) gives for the time from year 2000-2005 a 1-1.5% forest loss rate per year for Kenya, which is also in the African context above the av- erage (fig. 1.1).
fig. 1.1: calculated annual forest change rate for all African countries; Source FAO, 2007
Even though the Ministry of Water and Irrigation (2007, p.9) states, that “the right to water entitles every person to have access to sufficient, affordable water and sanitation of acceptable quality for personal and domestic use”, access to clean and fresh water is not a granted situation for all people living in Kenya.
Management of forests in Kenya is therefore at the same time management of water resources. Hence the main focus of most Kenyan forest institutions and organisations is on the five water catchment areas Mt. Kenya, the Aberdare Range, the Mau complex, Mt. Elgon and Cherangani Hills (Akotsi & Gachanja, 2006). Deforestation in these areas affects already the nation’s water resources and the drought in 1999-2000 made it quite obvious that water is a limited re- source. The important role of the forests was underlined when in 2000 the re- duced levels in power generating dams caused power cuts, which affected the whole economy and led to raising inflation (UNEP, 2001). About the more fa- mous forests Mt. Kenya, Aberdare Range and Mau more studies have been carried out up to now. Little knowledge is available about the Cherangani Hills Therefore this study has partly pilot character and will hopefully provide a start- ing point for improved monitoring in this specific region (Ndunda, 2009).
1.4 Objectives and expected results of the thesis
The main target of this study is the development of a cost effective method for forest type classification and production of a forest type map that fits the pur- pose to support the monitoring of forests in the water catchment area Cheran- gani Hills in common and that specifically supports reforestation activities or- ganised by greenbelt movement and conducted by local communities. This im- plies to find the answers to the following questions:
• What are the spatial distribution and size of forest types in the Cheran- gani hills that can be determined using Aster satellite images?
• Where are deforested areas located, that can be recommended for re- forestation?
• How cost effective is the developed method?
The expected results of the study are:
• A forest type classification map with attached detailed explanation
• A map outlining sites recommended for reforestation of degraded areas
• Evaluation of the developed method in terms of suitability and cost effec- tiveness
1.5 Structure of this thesis
The thesis consists of 6 chapters.
Chapter 2 gives an overview over the available literature. Hence the quality of environmental studies highly depends on the knowledge of the research area, great effort was undertaken to cover the current situation of gazetted forest ar- eas in Kenya in common and specifically of the research area. Beside that the specifications of the Aster Satellite Data, important factors of field data collec- tion and classification procedures are described.
Chapter 3 deals with the project design. The preparation set-up for obtaining ground truth data and possible classification output classes are described. Be- side that a flowchart visualizes the workflow of Image data preprocessing and processing and a detailed description of the steps is given.
Chapter 4 consists of the project description. It is reported how the field data was obtained and made available for computing. Furthermore the utilized Aster Data is presented and the image interpretation and analysis methods are pre- sented. The chosen parallelepiped classification approach is evaluated and the results of the accuracy assessment are described and discussed.
Chapter 5 focuses on the results, namely the forest type’s classification maps for each forest reserve and the recommendation map for reforestation projects.
The spatial distribution and the calculated sizes of the output are visualized in maps and pie charts. An evaluation of the relationship between spectral meas- urements and forest stand parameters completes the chapter.
Chapter six summarizes the done work, discusses the cost-effectiveness of the developed method and gives recommendations for further utilisation
2. Overview Literature 2.1 Gazetted forests in Kenya
Kenya can be subdivided in three phytogeographical zones. There are three main regions of forest zones: Coastal forests, afromontane forest and western floristic zone. The main part of Kenyan forests belongs to the afromontane zone. This area can be found from the highlands of Guinea and Liberia in the West to Ethiopia in the east and the Drakensburg Mountains in the south (White 1983). There are seven sub-divisions in the afromontane system. However in Kenya only one of them prevails, which is called the Imatong Usambara Afromontane system (White 1978). The total forest cover of Kenya’s gazetted forests reaches 1.4 million ha. This is equal to 1.7% of total land area, which is far below the internationally recommend minimum of 10% (UNEP, 2010). The study area is located in the western part of Kenya (fig. 2.1).
fig. 2.1: gazetted forest areas in Kenya, Source modified UNEP, 2007 Cherangani Hills
The study area belongs to the Montane Forest Region, where the estimated area of closed canopy forest without bamboo is 387087ha, with an average standing volume of 253.3 m3/ha and an average timber volume of 61.2m3/ha.
The main tree species are Syzgium guineense, Macaranga capensis, Neobou- tonia macrocalyx, Xymalos monospora and Tabernaemontana stapfiana. The main commercial species are Juniperus procera, Podocarpus sp., Octea usam- barensis, Olea capensis and Vitex kiniensis (Wass, 1995).
2.2 Deforestation and Reforestation
One of the main objectives of this thesis is to locate reforestation sites. A short literature overview about the terms deforestation and reforestation shall provide the basis for further activities.
Definitions Deforestation:
UNFCCC (2001): “The direct human-induced conversion of forested land to non-forested land”.
FAO (2000): “The conversion of forest to another land use or the long-term re- duction of the tree canopy cover below the minimum 10 percent threshold”.
Definitions Reforestation:
FAO (2000): “Establishment of forest plantations on temporarily unstocked lands that are considered as forest.”
UNFCCC (2001): “The direct human-induced conversion of non-forested land to forested land through planting, seedling and/or the human-induced promotion of natural seed sources, on land that was forested but that has been converted to non-forested land. “
2.3 Study Area
The Cherangani Hills consist of a number of forest reserves covering the Cher- angani Hills on the western ridge of the Great Rift Valley in western region.
They are one of the five main water catchment areas in the country and play an
important role to ensure sustainable water supply for the future (Akotsi &
Gachanja, 2006).The value of the water catchment protection was estimated for 8291 ha of Cherangani to reach a value of 21 million Kenyan Shilling (Waas, 1995).
Administration
The forest belongs to the West Pokot and Marakwet districts (fig. 2.2) and to the constituencies Marakwet East and West, Keiyo North, Sigor, Cherangani and Kapenguria (Akotsi and Gachanja, 2006).
fig. 2.2: Cherangani forests in relation to district boundaries
From the 15 protected forest blocks with a total size of 97,397.44ha (2005) of the total Cherangani area nine can be found in this area. They are the Cheboit, Chemurokoi, Kaisungor, Kapchemutwa, Kerrer, Kipkunurr, Sogotio and parts of Embobut and Kiptaberr forest (see table 2.1). For convenience they will be re- ferred to throughout this thesis as Cherangani South Part. The most of them have been gazetted in the year 1941, only Embobut and Kerrer in the year 1954 (Blackett H., 1994). The total original gazetted area was 128,043ha compared with 97,397.44ha in 2005. This means a loss of 30,646ha (24%), whereby in 1988 13,786ha have been officially degazetted in Lelan Forest block for settle-
ments (Blackett H., 1994). Table 2.1 shows the figures for the original gazetted areas (Hodgson, 1992), compared to the area sizes found in the KIFCON (Kenya Indigenous Forest Conservation Programme) inventory 1994 for the Cherangani Hills east part and the figures obtained from the change detection analysis for the years 2003-2005 from the KFWG (Kenya Forest Working Group).
Area (ha) 2005 Area (ha) 1993 Cherangani EAST
Original gazetted Area (ha)
Kamitira 1943.53 n/a 1910
Kapolet 1624.01 n/a 1552
Kiptaberr 12788.79 n/a 12886
Kapkanyar 6670.71 n/a 23270
Kaisungor 1087.22 1086 1068
Chemurokoi 3973.61 3966 3966
Kipkunurr 15868.77 15176 15176
Cheboit 2523.60 2489 2489
Sogotio 3549.70 3561 3561
Kapchemutwa 8860.41 n/a 9349
Embobut 21655.65 21934 21934
Lelan 14495.14 14820 28605
Kerrer 2237.82 2160 2160
Toropket 119.48 117 117
TOTAL 97397.44 65309 128043
table 2.1: Forest Area Cherangani Hills, Source modified: Akotsi & Gachanja, 2006, Blackett H., 1994, Hodgson, 1992
Topography, soils and climate
The forests are located on an undulating plateau at around 2800 m.a.s.l., framed by the Elgeyo escarpment to the east and the Cherangani Highway to the west. Some ridges rise above 3000 m with the highest peak at 3365 m, which is called the Cheptoket Peak, located in Lelan Forest (Blackett H., 1994).
They form the upper catchment of the Nzoia, Kerio and Turkwel river (Akotsi &
Gachanja, 2006).
Most of the forests in Kenya grow on volcanic soils. The soils in the Cherangani Hills are an exception, as they are comprised of metamorphic rocks, with quartzite ridges and occasional marble veins. The soils have been developed on biotite gneisses or on undifferentiated basement system rocks. The moder- ately fertile friable clay loams and sandy clay loams are generally well drained
and moderately deep to very deep with a topsoil rich in organic matter. They are classified as humic Acrisols, humic Cambisols and humic Nitrosols in the upper and upper middle level uplands. On the highest sites and on major scarps only humic Cambisols can be found (Blackett, 1994).
Three agro climatic zones can be distinguished in the study area: the Lower Highland zone at an altitude between 1,850m and 2,450m with a mean annual temperature 14°C to 18°C, the Upper Highland zone between 2,450 and 3,050m with a range from 10°C to 14°C and the Afro-alpine zone above 3,050m with less than 10°C (Muchena et al., 1988). The mean annual rainfall lies in the range 1200 to 1400mm, with two peaks in April/May and in October/November (Blackett, 1994).
Utilisation and Threats
The shifting cultivation without any soil conservation techniques on the lower steep slopes with shallow soil is described as a main cause for soil deterioration (Kerr, 1995). Grazing pressure is high and open grazing country can be found within and around the forest blocks. The Embobut forest block has had prob- lems with illegal settlements since the early 1980s and burning and encroach- ment activities have been reported on the eastern side of Kipkunurr forest in 1984 (Blackett H., 1994). Another reason for forest loss is the land assignment for tea plantations. In Cherangani–Elgeyo-Escarpment 2320ha were planned and up to 1990/1991 551ha were planted. The suitability of the areas was not taken into consideration and a follow up study stated only a minor potential for tea growing areas (Waas, 1995). A change detection analysis carried out by the Kenya Forest Working Group (2004) found out, that an area of 153ha has been deforested in the time from 2000 to 2003, 51ha of it in the Marakwet east and 102ha in the Sigor constituency. Compared to the other water catchment towers this change seems moderate, however it is important to bear in mind that large areas have been deforested before 2000 (Akotsi & Gachanja, 2004).
Vegetation
Blackett (1994) describes three forest types for the area of the Cherangani Hills.
• Juniperus-Nuxia-Podocarpus falcatus forest on the southern slopes
• Podocarpus falcatus forest with Olea and Euphorbia on the eastern slopes
• Juniperus-Maytenus undata-Rapanea-Hagenia forest in the upper peaks area in valleys
The last mentioned is the only area in Kenya to contain this type of forest.
In moist highlands at 2400m – 3000m Arundinaria alpina forms the bamboo zone, with an upper limit at 3360m. At lower elevations the bamboo forms an undergrowth in tree forests or grows in patches from 2150m often in association with Podocarpus latifolius (Maundu & Tengäs, 2005).
Blackett (1994) noted in the KIFCON inventory the ten most common trees:
Species Family
Podocarpus latifolius Podocarpaceae
Maytenus undata Celastraceae
Dracaenaena afromontana Dracaenaeceae Allophylus abyssinicus Sapindaceae
Olea capensis Oleaceae
Rapanea melanophloes Myrsinaceae Neoboutonia macrocalyx Euphorbiaceae Euphorbia obovalifolia Euphorbiaceae
Prunus africanum Rosaceae
Dombeya torrida Sterculiaceae
Table 2.2: Ten most common species of the Cherangani Hills, Source modified Blackett, 1994
2.4 Satellite Data
2.4.1 ASTER Satellite Images
The choice of appropriate satellite data depends on various factors, such as spatial resolution, spectral resolution and purchase price. The spatial resolution of the satellite image has a direct impact on the output map. Regional planning will require a map in a scale from 1:25000 to 1:50000. Possible sensors to achieve this target are LANDSAT, ASTER and SPOT. SPOT and LANDSAT
have both a long history in delivering multispectral data for vegetation analysis (Wulder et al, 2003). ASTER has become recently a alternative, as it combines cost effectiveness, adequate spatial resolution and a broad spectral resolution (Marçal et al., 2005).
The Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) is a scientific sensor operating on Terra satellite, which is part of the EOS (Earth Observation Satellite) project. It was launched in September 1999.
The Terra spacecraft has a circular, near polar orbit with an equatorial crossing at local time of 10:30 a.m. and a recurrent cycle of 16 days. The specifications are listed in table 2.3.
table 2.3 :Aster specifications, Source:
http://www.gds.aster.ersdac.or.jp/gds_www2002/exhibition_e/a_gds_e/set_a_gds_e.html
The sensor has 14 spectral bands and covers a wide spectral region from the visible to the thermal infrared. The ASTER instrument is composed of three subsystems: The Visible and Near Infrared (VNIR), the Shortwave Infrared (SWIR), and the Thermal Infrared (TIR). The ASTER Swath width is 60km. The Pixel size in the Visible and Near Infrared (VNIR) is 15m, in the Shortwave In- frared (SWIR)30 m and in the Thermal Infrared (TIR)90 m (Abrams et al, 2002).
Figure 2.3 shows the spectral response profiles of all bands as a function of the wavelength separated in VNIR, SWIR and TIR bands in a comparison to the LANDSAT bands. ASTER band 1 reflects in the visible green, band 2 in the
visible red and band 3 in the near infrared. Band 5 to 9 give spectral response in the shortwave infrared and band 10-14 in the thermal infrared. Compared to LANDSAT, ASTER lacks the channel 1 (blue spectral response), but is more differentiated and has a higher spectral resolution in the Short Wave infrared and Thermal Infrared (Abrams et al., 2002).
fig 2.3: spectral response profile of ASTER and Land sat bands ,Source:
http://asterweb.jpl.nasa.gov/images/spectrum.jpg
Jensen (2000), Franklin (2001) as well as Wulder et al. (2003) mention the suit- ability of the ASTER sensor for land cover and forest classification purposes. In several studies ASTER has been successfully utilised for vegetation and forest classification. Gebreslasie et al. (2009) used it for the estimation of forest struc- tural attributes for Eucalyptus sp. in South Africa, Marcal et al. (2005) for classi- fication of land use in Portugal, Heiskanen (2005) for prediction of LAI and bio- mass of birch in Finland, Kato e al. (2001) for forest and wetland mapping in Southeast Hokkaido and Eckert (2006) for forest classification in Patagonia, just to mention some of them.
According to the Earth Remote Sensing Data Analysis Centre (ERSDAC, 2009) the ASTER sensor is well suitable among other purposes to elaborate vegeta- tion distribution and dynamics. Following illustration 2.4 gives an overview about the indicated purposes. For vegetation studies the VNIR and SWIR bands are most interesting.
figure 2.4: ASTER scientific purposes, Source:
http://www.gds.aster.ersdac.or.jp/gds_www2002/exhibition_e/a_gds_e/set_a_gds_e.html
2.4.2 ASTER GDEM
The ASTER GDEM (Global digital Elevation Model) data, acquired by the ASTER Sensor, has a 30m (1 arc-second) grid of elevation postings. It is avail- able free of charge since June 2009. It is specially supposed to support the process of development of digital elevation models in the fields of disaster moni- toring, hydrology, energy and environmental monitoring. The following figure 2.5 illustrates the indicated purposes of the ASTER GDEM.
fig. 2.5. ASTER GDEM purposes, Source: http://www.ersdac.or.jp/GDEM/E/1.html
The ASTER GDEM is available in Geotiff format with geographic lat/long coor- dinates. It is referenced to the WGS84/EGM96 geoid. Accuracy is, estimated by
the producer, at 20 meter for the vertical dimension and 30 meters horizontally, both with a 95% confidence.
2.5 Field data
Field data that can be taken in line with the project objectives is more beneficial than ancillary data. Hence some points have to be taken into consideration do- ing the initial planning. The field team to be selected should be interdisciplinary and should be well informed about the capabilities and limitations of remote sensing techniques. A preliminary visual analysis of the area will help to realize areas that might be difficult to classify. All land cover types, geographic features (e.g. lakes, rivers) and anthropogenic influences (e.g. settlements, cultivation areas) should be determined in spatial distribution and size (Sanches -Azofeifa et al., 2003).
For the purpose of classification of remotely sensed data the attributes most typically mapped are those described by nominal variables. Those are for in- stance forest cover type and species dominance (Sanches-Azofeifa et al., 2003). Furthermore important parameters are land cover (physiognomic), vege- tation type (e.g. hardwood versus conifer), ecological land type (land system), forest types, that are categorized according to species composition and struc- ture (e.g. ponderosa pine versus mixed conifer) and forest types, that are cate- gorized according to detailed species composition and canopy structure (e.g.
ponderosa pine with less than 60 % canopy closure) (Sanches -Azofeifa et al., 2003).
2.6 Classification
Output classes
The results will usually depend on the purpose of classification, the environ- mental context, the skills of the image analyst and the spectral separability of the classes. The starting points of the classification are the image and a list of the desired output classes. The hierarchical vegetative classification system is one often used for forest classification. It is an open end approach, where by
level I starts with general land use classes like e.g. forest, grassland and vil- lages and then proceeds with more detailed classes. For the purpose of forest management and planning normally a Level II and Level III classification system is used (Franklin, 2001). The features are classified into more specific land- scape units such as dominant species or canopy cover. Figure 2.6 shows an example for a Level I to Level III general/vegetative classification.
fig. 2.6: general/vegetative level I to level III classification, Source: Franklin, 2001, p. 215
Furthermore it can be distinguished between vegetative and ecosystematic ap- proach, or more general the parametric or landscape approach. The ecosys- tematic approach includes soil and topographic factors (Franklin, 2001).
Classification method
After the selection of the different information classes it has to be decided which classification method will be taken. There are various possibilities, like super- vised versus unsupervised and hard versus soft classification. Wulder (2003) recommends the supervised classification in cases of sufficient training data and mentions that up to now the hard classification methods are mainly used to produce thematic maps in subsequent analysis (Wulder, 2003). In case of hard supervised classification parametric like the maximum-likelihood-classifier, minimum-distance and parallelepiped (mean and standard deviation) can be distinguished from the non-parametric (minimum, maximum) and artificial neural networks. The maximum-likelihood-classifier is one of the most often used in land-use classifications. Even though this method needs normally distributed and uncorrelated data, which is not always necessarily the case, it is often used as a starting point in comparison to other classifiers. Wulder et al. (2003) rec- ommend the use if few training sites are available and the classes can be well separated. The subclasses should be set up and iterative “cluster-busting” may
improve the results. For classifying an unknown pixel this method uses the mean vector and the covariance matrix. However very often remotely sensed measurements do not meet the requirements of parametric classifiers. In that case the parallelepiped or box classifier can be used as an alternative. The pixel value is calculated based on the minimum and maximum values of the training data, which are assigned in a k-dimensional box, whereby k is the num- ber of bands used. In case of overlapping boxes it is advised to use the mini- mum distance to class mean rule (Wulder et al., 2003).
Training stage
While the classification process itself is a strictly automated procedure, the compilation of the training data is more individual and depends on several fac- tors. It is that stage when the skills of the classifier are asked most. Sufficient reference data as well as a complete and representative training data set are considered indispensable. The output of the training stage is the compilation of a set of statistics describing the spectral response pattern of each land cover class. For the maximum likelihood classifier the minimum of pixels for each training class is n+1, whereby n is the number of bands used for the classifica- tions. If e.g. 4 band are used a minimum of 5 pixels are necessary. However a more practical value are 10n to 100n pixel, since the estimation of the mean vector and covariance matrices will significantly improve with a higher number of training pixels. During the training stage usually a refinement of the training areas is needed. Since normally not all pixels of the training areas are spectral pure (e.g. bare ground in a crop field) some will have to be deleted and eventu- ally further training sets have to be set up (Lillesand et al., 2007). Methods suit- able for the refinement process are:
• Histograms, for evaluation of the normal distribution of single bands (Lillesand et al., 2007)
• Statistics, which describe e.g. the minimum, maximum and mean values of a signature to evaluate and compare signature (ERDAS, 2008)
• Contingency matrix to give a quick overview of the percentage of the sample pixels, that are classified as desired (ERDAS, 2008)
• Two dimensional scatter diagrams for evaluation of separability of two or more bands (see fig. 2.7)
In the multispectral scatter diagram ellipsoidal contours of same equiprobability are calculated (fig 2.7). The appropriation is made according to the principal of the highest possible probability (Lillesand et al., 2007). Figure 2.7 shows the equiprobability contours for the classes Urban (U), Sand (S), Corn (C), Hay (H), Forest (F) and Water (W).
fig. 2.7: scatter diagram, equiprobability contours defined by a maximum likelihood clas- sifier, Source Lillesand et al., 2007, p. 556
To choose the best bands for classification beside the spectral response pattern of the ASTER sensor also knowledge of the class reflectance patterns is neces- sary. All kind of healthy green vegetation will absorb in the red and blue band very effectively and reflect in the green and near infrared band. This gives the unique reflectance pattern with two peaks, one in the green and one in the VNIR band, with which vegetation can easily be distinguished from other land use classes (Jensen, 2000). To distinguish different vegetation classes is more challenging. However there are some factors to assist in the process. For in- stance will an increase of relative vegetation amount lead to an increase in near infrared spectral response and a decrease in mean red spectral response. As an example closed canopy cover would appear darker in the red band and
brighter in the near infrared (Franklin, 2001). Although different tree type classes will usually give a different spectral response. Figure 2.7 shows the spectral response pattern of different tree type classes broadleaf forest (Laub- wald), mixed forest (Mischwald) and needleleaf forest (Nadelwald) forest, in the different channels 1-4, evaluated in a forest habitat type study in south Ger- many (Kleinschmit et al., 2006).
fig. 2.8: spectral response pattern of different forest types, Source: Kleinschmitt et al.
2006, p. 23
2.7 Vegetation indices
Over 20 years vegetation indices are commonly used in remote sensing studies to support the classification process. The overall goal is to reduce the number of channels and to gather the important information into the remaining channels (Franklin, 2001). Although external effects like sun angle and internal effects like canopy cover background, slope and aspect are normalized (Jensen, 2000). There are many different vegetation indices and hence only some of the most widely used and main interesting for this study are described.
Simple Ratio Index
Ratio Images are often used to compensate for variations in illumination condi- tions caused from different topographic conditions. For instance will a shaded forest on a north exposition give another value than a similar forest on a sun exposed south position. Image rationing will countervail this effect (Lillesand et
al., 2007). The simple ratio uses the near Infrared and the VIS bands (Wulder et al., 2003) and is calculated as follows:
SR= NIR/VIS
NDVI (Normalized Difference Vegetation Index)
The NDVI is the most widely used Index in vegetation studies. It is based on the high absorption of sunlight in the visible wavelength and high reflectance in the near infrared wavelength (Jensen, 2000 & Wulder et al, 2003).It is calculated as follows:
NDVI = (NIR — VIS)/(NIR + VIS)
For ASTER bands following combination can be adapted:
NDVIA=Channel 3-Channel 2/Channel 3+Channel 2(Miura et al. 2008)
The calculation results can range from -1 to +1. Whereby 0 means no vegeta- tion and close to one (0.8-0.9) is an indication for high density and healthy vegetation.
GREEN NDVI (Green Normalized Difference Vegetation Index)
A possible variation of the NDVI is the GREEN NDVI, whereby instead of the red channel the green channel is taken. It is more sensitive to Chlorophyll con- centration and can therefore provide better estimation of pigment concentration (Gitelson et al, 1996). It is calculated as follows:
GNDVI = (NIR — GREEN)/(NIR + GREEN)
Even though the NDVI is one of the most common used, it has been shown, that there are some restrictions in tropical forests. Lucas et al. (2004) mention the problem of relatively invariance of the NDVI in high dense forest areas as well as problems with atmospheric and surface properties, like e.g. water va- pour and surface material. For this reasons further indices have been devel- oped like e.g. GEMI (global environment monitoring index), with improved sen-
sitivity of atmospheric and soil brightness effects (Lucas et al.,2004), SAVI (soil adjusted vegetation index) with improved sensitivity to soil brightness (Wulder et al., 2003) and EVI (enhanced vegetation index), generated for the MODIS sen- sor, with improved sensitivity in high biomass regions and reduction of atmos- pheric influences (Jensen, 2000). Miura et al. (2008) designed the EVI 2 Index for ASTER, which had to be modified because of the missing blue channel of the ASTER Sensor. It is calculated as follows:
EVI2=2.4x [(Channel 3-Channel2)/(Channel 3+Channel 2+1)]
2.8 Accuracy Assessment
The Accuracy Assessment is an important step to finalize the classification.
Hence it will give an idea about the quality of the correctness of the output data.
The accuracy assessment should be well planed ahead in the project set up. To ascertain an independent reference data set the training data should be split in data used for the classification and data kept aside for the post classification evaluation. This data set should be preferable representative and have a suffi- cient size to guarantee statistically valid figures. As a standard value 50 sam- ples of each class should be included in the accuracy assessment (Lillesand et al., 2007). There are various methods and the most common ones are shortly described.
Classification Error Matrix
In the error matrix the results of the classification are compared with the ground truth data for each category in a square matrix. The rows and columns have the same number as the categories that are assessed. From the error matrix mis- matches such as omission (exclusion) and commission (inclusion) can be seen.
Moreover descriptive measures that can be calculated from the error matrix are overall accuracy, user’s accuracy and producer’s accuracy (Lillesand et al., 2007).
Overall Accuracy
The overall accuracy compares the sum of the proper classified pixels versus the sum of all pixels. It is a generalized average measure assessing the overall classification result disregarding the class related accuracy levels.
Producers Accuracy
The producer’s accuracy evaluates the omission error for each category. It is calculated by comparing the number of proper classified pixels for each cate- gory versus the number of training data pixels that have been used for that category.
Users Accuracy
The user’s accuracy measures the error of commission. It is calculated by divid- ing the correct classified pixels of a certain class by the total number of refer- ence pixels of that class.
Kappa Coefficient
The kappa coefficient considers the error of commission as well as the error of omission and gives therefore a more comprehensive value. The overall kappa statistic is calculated from the total sum of the diagonal multiplied by the total sum of each row multiplied by the total sum of each column divided by the summation of each row multiplied by the summation of each column.
