Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

Band 11 der Reihe Fernerkundung und angewandte Geoinformatik

Wafa Mohamed Tahir Nori

Kurzübersicht

This research evaluates the potential of remote sensing for monitoring forest cover change in El Rawashda forest, Sudan, using Landsat ETM and Terra ASTER imagery. This was accomplished by performing eight change detection algorithms.
ISBN: 978-3-944101-20-0
Veröffentlicht: Dezember 2014, 1. Auflage, Einband: Broschur, Abbildung und Tabellen: zahlr., 34 davon farbig, Seiten 134, Format 176x250, Gewicht 0.3 kg
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39,80 €

Detection of land cover changes in El Rawashda forest

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Wafa Mohamed Tahir Nori

Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

Band 11 der Reihe „Fernerkundung und angewandte Geoinformatik“
Herausgegeben von Univ. Prof. Dr. habil. Elmar Csaplovics, Lehrstuhl Remote Sensing, FR Geowissenschaften, TU Dresden

134 Seiten, Format DIN B5, Zahlreiche Abbildungen, davon 34 farbig. Sprache: Englisch. Preis: 39,80 Euro. ISBN 978-3-944101-20-0. Rhombos-Verlag, Berlin 2014

About this book

This research evaluates the potential of remote sensing for monitoring forest cover change in El Rawashda forest, Sudan, using Landsat ETM and Terra ASTER imagery. This was accomplished by performing eight change detection algorithms. Firstly a simplified post-classification with only 4 forest classes, namely close forest, open forest, bare land and grass land, was used. A RGB-NDVI change detection strategy to detect major decrease or increase in forest vegetation was developed as well. This method was found to be more effective than NDVI image differencing as it distinguishes different change classes by different colour tones. The Tasseled Cap green layer (GTC) composite was proposed to detect the change in vegetation of the study area. This method performs better than RGB-NDVI. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The calculated date to date change vectors contain useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the Principal Components Analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. A recently proposed approach, the Multivariate Alteration Detection (MAD), in combination with a posterior Maximum Autocorrelation Factor Transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. As a final step a quantitative accuracy assessment at the level of change/no change pixels was performed. Among the various investigated methods of forest cover change analysis the highest accuracy was obtained using post-classification comparison based on supervised classification.

The author

Wafa Mohamed Tahir NoriWafa Mohamed Tahir Nori holds a bachelor degree, in forest studies in 1994 from University of Khartoum, Faculty of Agriculture, a master degree in forest pathology in 1996 from University of Khartoum, Faculty of Forestry, and a doctoral degree in remote sensing in 2012 from Technische Universität Dresden, Germany. The author started her work experience during her master research as part time teaching assistant at University of Khartoum and University of Juba from 1994 to 1996, and then was affiliated to University of Kordofan, Faculty of Natural Resources and Environmental Studies, El Obeid, Sudan. Currently she has been promoted to assistant professor and continues research in applications of remote sensing in the monitoring of drylands.

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The editor/Der Herausgeber

Prof. Dr. techn. habil. Elmar Csaplovics leitet den Lehrstuhl Geofernerkundung am Institut für Photogrammetrie und Fernerkundung der Technischen Universiaet Dresden

Kontakt:
TU Dresden, Institut für Photogrammetrie und Fernerkundung, Helmholtzstraße 10, 01062 Dresden
http://www.tu-dresden.de/ipf/

Professur Geofernerkundung

Prof. Dr. Elmar Csaplovics: "In loser Folge sollen in dieser Schriftenreihe wissenschaftliche Arbeiten, die am Lehrstuhl für Fernerkundung bearbeitet und betreut werden, in ansprechender Form veröffentlicht werden. Wir glauben, dass dadurch das Spektrum von Literatur zum Themenkreis projektorientierter angewandter fernerkundlicher Forschung nachhaltig bereichert werden kann und wünschen uns demgemäß eine kritikfreudige Schar von Leserinnen und Lesern."

Editorial

From this station [Quaicha], however, we were entertained with a most magnificent sight. The mountains at a distance towards the banks of the Tacazze (Setit, border river between Eritrea and Ethiopia, called Tekezé in the eastern Sudan, where it joins the Atbarah south of Shuwak), all Debra Haria (mountain region in Tigray, northernmost province of Ethiopia), and the mountains towards Kuara (Quarra, former Ethiopian frontier province towards the Sudan), were in a violent bright flame of fire. The Arabs feed all their flocks upon the branches of trees; no beast in this country eats grass. When therefore the water is dried up, and they cannot longer stay, they set fire to the woods, and to the dry grass below it. The flame runs under the trees, scorches the leaves and new wood, without consuming the body of the tree. After the tropical rains begin, the vegetation immediately returns; the springs increase, the rivers run and the pools are filled with water. All sorts of verdure being now in the greatest luxuriancy, the Arabs revisit their former stations. This conflagration is performed at two seasons; the first by the Shangalla (Shanqella, ethnic groups of the westernmost parts of Ethiopia, with a Nilo-Saharan ethnic background) and hunters on the southern part of this woody country, begins in the month of October, on the return of the sun, the circumstances of which I have already mentioned; the latter which happens in March, and lasts all April, besides providing future sustenance for their flocks, is likewise intended to prevent, at least to diminish, the ravages of the fly; a plague of the most extraordinary kind, already described. (p.265)
Our journey [on the 18th of April 1772, towards Sennar] for the first seven hours was through a barren bare and sandy plain without finding a vestige of any living creature, without water and without grass. (p.333)

Bruce J (1813) Travels to discover the source of the Nile, in the years 1768, 1769, 1770, 1771, 1772 & 1773 by James Bruce of Kinnaird Esq., 3rd ed., vol.6. Constable, Edinburgh and Longman, London.

El Rawasha Forest is one of the most important relics of the former pasture woodland of the East African Sub-Saharan regions. Transhumants between the Butana region and the northwestern Ethiopian borderland followed livestock corridors three of which intersect at El Rawasha and Wad Kabo Forests. As rainfed agriculture supported by mechanisation increased, the forests became more and more the last resting places for pastoralists moving from north to south and vice versa. Traditionally during the months of June and July as well as of October and November the herds enter the forests for grazing. Forest products and fodder are demanded, but the quantity and quality of range species growing on the forest floor immediately after the rains cannot satisfy the needs of increasing sizes of herds and flocks, and thus browsing more and more affects the forest per se (El Dool 1994).
Nevertheless the increasing pressure of villagers on the forest resources has not to be underestimated. Rainfed agricultural schemes occupy all the land around the forests and the needs for charcoal have led to illegal felling especially of Talh (Acacia seyal) and subsequently to the expansion of bare land inside the forest. Unprotected soils are affected by insolation, are thus deteriorated and exposed to erosion (Gibreab 1997)
El Dool (1994) stated that the forests (in the early 1990s) „still represent the original natural cover characteristic of the area and comprise the last existing forest area in the dry savannah left above the 14th degree north latitude in the whole African region .. The (two) forests are therefore od prime importance from the environmental and genetic biodiversity point of view and should be managed and protected on a sustained yield basis. ..“ (p.134)
Since the late eighties several attempts were undertaken by the Sudanese Forest National Coorporation (FNC) to cope with the multiple problems affecting forest resources by integrated management plans involving the participation of local people. The El Rawashda model 1 management system was set up by the FAO Fuel Wood Development for Energy Project from 1983 until 1989. A forest committee consisting of representatives of the local communities participated in the rehabilitation and the protection (against illegal felling) of the respective forest areas. The El Rawashda model 2 management system was developed by the forestry branch of the Agricultural Development Project for the Eastern Sudan (ADES) and resembles the FAO approach except the fact that the local people are not involved in the actions of controlled felling.
Anyhow, Salah El Shazali (1995) commented that „The current practice of so-called integrated land use planning, exemplified by its „model“ of Rawashda Wad Kabo forests in Gedarif State, seems to be little more than a mockery of the concept.“ (p.133)
Therefore attempts to combat against the progressive degradation and deterioriation of the forest were not successful. The loss of high-quality forest areas as well as the continuous diminution of the diversity of the forest floor with special reagrd to palatable pasture species by overgrazing as well as by the subsequent impact on trees by browsing – which was estimated appr. 70% of the green biomass consumption by livestock in the early 1990s (Gaiballa 1992) – continued to decrease the ecological (biodiversity) and economical (biomass) quality of the forest reserve. Recent studies on the dynamics of change inside the forest need both a sound methodology of spatio-temporal as well as of balanced socio-ecological analysis. The assessment of spatial dimensions of change depends on full-coverage monitoring of patterns of change and thus on the integration of remote sensing land use and land cover change (LUCC) detection. Few projects have up to now focused on the assessment of gradients of change by the problem-oriented application of earth observation data collected by remote sensing satellite systems.
The PhD research of Wafa Nori contributes to urgently needed scientific action to close the gap between local to sub-regional studies mainly based on socio-economic inventories - with limited significance concerning the regional spatial dimension - and medium-scale to small-scale mapping of land use and land cover (change) based on operational earth observation data with low information value concerning the local to regional impact patterns which are caused by heterogeneous interrelations between different demands of different ethnic groups.
In general a sound (spatial) SWOT analysis of El Rawashda Forest Reserve depends on the application of the concept of regionalisation and thus on the integration of LUCC investigations of not only the focus area (El Rawashda) but also of the surrounding subregions, explicitely of the rainfed agricultural schemes and their development, of the historical distribution and routes of livestock corridors (analysed by aerial photography of the 1950s and later) as well as of the interrelations with the Butana region and the degradation processes which occured there during the last 50 years, thus successively changing the oscillations of transhumance towards the south.
The PhD thesis of Wafa Nori deals with an in-depth analysis of the reliability of spaceborne remote sensing earth observation for multi-temporal assessment of dynamics of land use and land cover change (LUCC) of the El Rawashda Forest Reserve with specific regard to a comparative research into the accuracy, thus effectiveness and apporpriateness of different methods of change detection based on time series of satellite imagery. The selected time frame covers the period 2000-2003-2006, which is a crucial period for monitoring and assessing the degree of impact of several management projects coordinated by the Sudanese FNC. The aim of the research is a methodological analysis of remote sensing image classification approaches on the one hand and the application-oriented analysis of change towards the evaluation of influences of management regulations on the status of conservation and the sustainable use of the forest on the other hand. In-depth research into the reliability of pixel-based comparative multi-temporal image analysis of forest cover change is provided. The application of Tasselled Cap Transformation (TCT), especially of RGB synthesis of the greenness component, and of TCT-based Change Vector Analysis represent a valuable baseline – as does the Multivariate Alteration Detection together with Maximum Autocorrelation Factor Analysis (MAD/MAF components) - for further research and application-oriented adaptation towards operational regional monitoring. Remarkably both visual on-screen interpretation of specific RGB- visualisation together with subsequent visual segmentation as well as traditional post classification comparison show an undiminished potential for in-depth change detection assessment with specific regard to class-to-class change analysis.
Evidently spatial monitoring and assessment of forest cover change in El Rawashda Forest remain fragmentary as long as the interdependencies between seasonal and annual land use and land cover changes in the forest both in space and time and changes in land use around the forest which are driven by the implementation of large-scale rainfed agricultural schemes and subsequently by an increase of local population and thus by increasing demand of (forest) resources are not thoroughly investigated. North-south migration routes of transumhumants and related livestock corridors are deteriorated due to the closure of former migration routes which is caused by a tremendous increase of agricultural land at the cost of grazing land during the last five decades. Changes in cycles of migration are driven by increasing impacts of local to regional degradation and desertification as a result of interrelated effects of drougth and overexploitation (Crummey 2005, UNDP 2006).
The PhD thesis of Wafa Nori represents an important contribution to the improvement and urgently needed further development of regional monitoring and assessment of anthropozooic impact patterns in El Rawashda Forest Reserve. Low cost and easy-to-handle appropriate technologies of multi-temporal remote sensing image analysis implemented in regional officies responsible for land and forest management should pave the way for a significant improvement of measures of conservation and sustainable development as well as of management of forest resources with participation of the respective spectrum of stakeholders.

Elmar Csaplovics

References
Crummey D (2005) Land, Ethnicity, and Political Legitimacy in Eastern Sudan. Red Sea Press, Trenton NJ
El Dool YMA (1994) Integrated management plan for Rawashda and Wad Kabo forest reserves, Gedaref State, 1995-1999, Part 1, Description. FAO, Rome
Gaiballa AK (1992) Nomads and Forest Management. Sudan Fuel Wood Development for Energy Project, GCP/SUD/047/NET. FAO, Khartoum
Gibreab G (1997) People on the Edge in the Horn: Displacement, Land Use and the Environment in Gedaref Region, Sudan. Red Sea Press, Trenton NJ
Salah El Shazali I (1995) The Importance of Forest Resource Managment in Eastern Sudan: The Case of El Rawashda and Wad Kabo Forest reserves, in: OSSREA (ed) Managing Scarcity: Human Adaptation in East African Drylands, OSSREA, Addis Ababa, pp. 131-138
UNDP (ed) (2006) Share the Land or Part the Nation: The Patoral Land Tenure System in Sudan. UNDP, Khartoum

Contents

1    Introduction    1
1.1    Background    1
1.1.1    Forest in Sudan    1
1.1.2    Remote Sensing for Ecosystem Management    3
1.2    Problem definition    4
1.3    Motivation    5
1.4    Current status of research    7
1.5    Objectives    9
1.6    Structure of the thesis    10

2    Review of forest change detection techniques    13
2.1    Introduction    13
2.1.1    Image acquisition    13
2.1.2    Pre-processing    14
2.2    Visual interpretation    14
2.3    Pixel-based methods    15
2.3.1    Single-date indices    15
2.3.1.1    Spectral reflectance    15
2.3.1.2    Vegetation indices    16
2.3.1.3    Principal Components    16
2.3.1.4    Tasseled cap components    17
2.3.1.5    Spectral Mixture Analysis    17
2.3.1.6    Classification    18
2.3.2    Multi-date transformation    18
2.3.2.1    Image differencing    18
2.3.2.2    Change vector analysis    19
2.3.2.3    Multitemporal principal component analysis    20
2.3.2.4    Multitemporal Kauth-Thomas transformation    21
2.3.2.5    The Multivariate Alteration Detection    21
2.3.2.6    Chi square transformation    22
2.3.3    Change detection algorithms    22
2.3.3.1    Post-classification comparison (PCC)    23
2.3.3.2    Thresholding    23
2.3.3.3    Classification    24
2.4    Object-based methods    24
2.4.1    GIS-data    25
2.4.2    Image segmentation    26
2.5    Change mapping    27
2.6    Method comparison    28

3    Study area and research data acquisition    29
3.1    Study area    29
3.1.1    Location of the study area    29
3.1.1.1    Topography    29
3.1.1.2    Geology    29
3.1.1.3    Soil    29
3.1.2    Physical Characteristics of El Rawashda Reserved Forest    31
3.1.2.1    Climate    31
3.1.2.2    Vegetation    32
3.1.3    Social and land-use characteristics    33
3.1.3.1    Population in Gedarif State with special reference to El Rawashda    33
3.1.3.2    Land-use, land use changes and their influence on the forests    34
3.1.3.3    Socio-economic background of the nomad    35
3.1.4    Management Units    35
3.1.4.1    El Rawashda model I    35
3.1.4.2    El Rawashda Model II    36
3.2    Research data acquisition    36
3.2.1    Field sampling    36
3.2.2    Data collection for study area    37
3.2.2.1    Landsat Imagery    37
3.2.2.2    Aster Imagery    38
3.2.3    Image pre-processing    38
3.2.3.1    Atmospheric correction    38
3.2.3.2    Image to image registration    39

4    Methods of Change Detection    41
4.1    Descriptions of land cover classes    41
4.2    Classification of images using Maximum Likelihood Classifier    41
4.3    Accuracy assessment of classified images    43
4.4    Calculation of Vegetation Indices as independent variables    43
4.4.1    NDVI    44
4.4.2    NDVI-RGB    44
4.4.3    SAVI    44
4.4.4    TDVI    45
4.5    Index Differencing    45
4.6    Tasseled Cap Transformation    45
4.6.1    RGB-TCG    45
4.7    Change Vector Analysis    46
4.8    Mapping with Conventional and Selective Principal Component Analysis    46
4.8.1    Multitemporal PCA: the conventional approach    46
4.8.2    Selective PCA    46
4.9    Change Detection by Multivariate Alteration Detection    47
4.10    Accuracy assessments for change detection techniques    47
4.11    Object-Oriented Classification of reference data by eCognition    48

5    Results    51
5.1    Classification and Change Detection Accuracy    51
5.2    Post Classification Change Detection    53
5.3    Change detection based on Vegetation Indices    58
5.3.1    Vegetation Indices differences    58
5.3.2    RGB-NDVI    59
5.4    Application of Tasseled Cap for change detection    61
5.4.1    Derivation of Tasseled Cap Transformation    62
5.4.2    Derivation of Greenness component for change detection    63
5.5    Change Vector Analysis: An Approach for Detecting Forest Changes    65
5.5.1    Analysis of the change image 2000/2003    65
5.5.2    Analysis of the change image 2003/2006    66
5.6    Mapping with Conventional and Selective Principal Component Analysis    66
5.6.1    Generation of PCA Components    66
5.6.2    Change detection with Multitemporal PCA: the conventional approach    68
5.6.3    Change detection with Selective PCA    72
5.7    Change Detection based on Multivariate Alteration Detection (MAD)    73
5.7.1    MAD Interpretation    74
5.7.2    Maximum autocorrelation factor (MAF) analysis    77
5.8    Accuracy assessments for change detection techniques    79

6    Discussion    85

7    Conclusions and outlook    93
7.1    Conclusions    93
7.2    Outlook    94

8    References    97

 

 

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