Assessing, monitoring and mapping forest resources in the Blue Nile region of Sudan using an object-based imageanalysis approach

Volume 14 of the series Remote Sensing and Applied Geoinformatics

Mustafa Mahmoud El-Abbas


Following the hierarchical nature of forest resource management, this research focuses on the monitoring and assessment of forest cover at various abstraction levels based upon categorical land use/land cover (LU/LC) classification and change detection as well as empirical estimation of changes at local operational levels. An approach of object-based image analysis (OBIA) based on optical sensor data has been adapted and applied in the destabilized Blue Nile region of Sudan in order to gather the required spatial information in support of future forest planning and decision making. At the categorical level rules have been developed and optimal features have been extracted for each segment. Based on thematic LU/LC maps series of optimised algorithms have been created to depict the dynamics of change of LU/LC entities. Detailed change classes as well as change statistics have been produced. Moreover hot-spot areas have been investigated and aggregated to the community-level.
ISBN: 9783937231716
Veröffentlicht: 12/2018, Band 14. Auflage, Einband: Broschur, Abbildung und Tabellen: zahlr. Tabellen und Fotos, viele davon farbig, Seiten 226, Format 176 x 250 mm, Gewicht 0.5 kg
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Mustafa Mahmoud El-Abbas

Assessing, monitoring and mapping forest resources in the Blue Nile region of Sudan using an object-based imageanalysis approach

Volume 14 of the series "Remote Sensing and Applied Geoinformatics" / Band 14 der Reihe „Fernerkundung und angewandte Geoinformatik“

Published by Univ. Prof. Dr. habil. Elmar Csaplovics, Lehrstuhl Remote Sensing, FR Geowissenschaften, TU Dresden

(Herausgegeben von Univ. Prof. Dr. habil. Elmar Csaplovics, Lehrstuhl Remote Sensing, FR Geowissenschaften, TU Dresden)

226 pages, format DIN B5 (176 x 250 mm), weight 0.5 kg, cover: paperback, numerous illustrations, many of them colored. Language: English. Price: 38,00 Euro. ISBN 9783937231716. Publishing house: Rhombos Verlag, Berlin 2018

226 Seiten, Format DIN B5 (176 x 250 mm), Gewicht 0,5 kg, Einband: Broschur, zahlreiche Abbildungen, viele davon farbig. Sprache: Englisch. Preis: 38,00 Euro. ISBN 9783937231716. Rhombos-Verlag, Berlin 2018


A thesis submitted in partial fulfilment of the requirements of the Faculty of Environmental Sci-ences of Technische Universität Dresden for awarding the academic degree Doctor of Natural Science (Dr. rer. Nat.)

Academic Supervisors:

Prof. Dr.habil. Elmar Csaplovics, Institute of Photogrammetry and Remote Sensing, TU Dresden

Prof. Dr. Elnour Abdalla Elsiddig, Faculty of Forestry, Department of Forest Management , University of Khartoum

Dr. habil. Hannelore Kusserow, Institute of Geographical Sciences, FU Berlin


The Editor of the series/Der Herausgeber der Schriftenreihe

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

Prof. Dr. techn. habil. Elmar Csaplovics,  Research Group Remote Sensing, Department of Geosciences, Technische Universität Dresden

Prof. Dr. Elmar Csaplovics obtained a doctorate in remote sensing  at TU Wien in 1982. He was a post-doc research fellow at  the French National Institute for Agricultural Research (INRA) in Montpellier and at the Department of Geology, Geophysics and Geoinformatics, FU Berlin 1988-1992. After habilitation in remote sensing at TU Wien he is professor of Remote Sensing at TU Dresden since 1993. He was visiting professor at University College London (2007), at Université Paris VII Denis Diderot (2014) and at TU Wien (2015). His research focus is on remote sensing and applied geoinformation analysis for monitoring and assessment of land use land cover with emphasis on wetlands, arid lands, landscape history and natural heritage.

TU Dresden, Institut für Photogrammetrie und Fernerkundung, Helmholtzstraße 10, 01062 Dresden

Homepage:, Professur Geofernerkundung

Prof. Dr. Elmar Csaplovics: "In a unregulary order, scientific papers being edited and supervised at the Department of Remote Sensing will be published in an appealing way in this series, which we believe will enrich the range of literature on the subject of project-oriented applied remote sensing research and we wish accordingly a critical crowd of readers."

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."

About the book

Following the hierarchical nature of forest resource management, this research focuses on the monitoring and assessment of forest cover at various abstraction levels based upon categorical land use/land cover (LU/LC) classification and change detection as well as empirical estimation of changes at local operational levels. An approach of object-based image analysis (OBIA) based on optical sensor data has been adapted and applied in the destabilized Blue Nile region of Sudan in order to gather the required spatial information in support of future forest planning and decision making. At the categorical level rules have been developed and optimal features have been extracted for each segment. Based on thematic LU/LC maps series of optimised algorithms have been created to depict the dynamics of change of LU/LC entities. Detailed change classes as well as change statistics have been produced. Moreover hot-spot areas have been investigated and aggregated to the community-level. The study utilised a well-designed questionnaire to address the factors affecting LU/LC dynamics and the possible solutions based on the perception of local communities. At the operational structural forest stand level correlation and regression analyses have been applied to identify the relations between a wide range of spectral and textural metrics and the field-derived forest attributes. Furthermore the best fitting models have been cross-validated with an independent set of field samples, which revealed a high degree of precision. The research concludes that OBIA shows a significant capability to serve as an efficient approach to gaining accurate knowledge about land-related features, whether at the level of operational structural forest attributes or at the level of categorical LU/LC classification and change detection. Moreover, the developed methodological framework exhibits a potential solution to attain precise facts and figures about the dynamics of change and their driving forces.

About this author

Mustafa M. El-Abbas is an assistant professor at the Faculty of Forestry, Department of Forest Management, University of Khartoum, where he obtained his B.Sc. (Hons.) and M.Sc. degrees (2001 and 2006 respectively). Moreover, he has got a diploma of forest ecology and forest resource management from the University of Helsinki, Finland, in 2006. Dr. El-Abbas took his doctoral degree in remote sensing at TU Dresden. Currently, he has been working as a post-doc researcher at the Department of Geosciences, RG Remote Sensing and Applied Geoinformation Analysis, at TU Dresden. He has professional experience in the fields of remote sensing and GIS in general and object-based approaches of image analysis in particular with emphasis on natural resources management. Dr. El-Abbas has contributed to many international symposia and workshops as well as other scientific fora and has published a large number of scientific papers in international congress proceedings and journals.


Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to up-date the desired information.

An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporat-ing expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes.

Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA.

Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high.

Aggregated to the community-level, social survey of household data provides a comprehensive perspective addi-tionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception.

At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attrib-ute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used af-fect the quality of the developed model this was also discussed based on the different sensors examined.

To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forces.


Da das Waldressourcenmanagement hierarchisch strukturiert ist, beschäftigt sich die vorliegende Arbeit mit der natürlichen Waldbedeckung auf verschiedenen Abstraktionsebenen, das heißt insbesondere mit der Ebene der kategorischen Landnutzung / Landbedeckung (LU/LC) sowie mit der kontinuierlichen empirischen Abschätzung auf lokaler operativer Ebene. Da zurzeit kein Sensor die Anforderungen aller Ebenen der Bewertung von Waldressourcen und von Multisource-Bildmaterialien (d.h. RapidEye, TERRA ASTER und LANDSAT TM) erfüllen kann, wurden zusätzlich andere Formen von Daten und Wissen untersucht und in die Arbeit mit eingebracht. Es wurde eine objekt-basierte Bildanalyse (OBIA) in einer destabilisierten Region des Blauen Nils im Sudan eingesetzt, um nach möglichen Lösungen zu suchen, erforderliche Informationen für die zukünftigen Waldplanung und die Entscheidungsfindung zu sammeln. Außerdem wurden die räumliche Heterogenität, sowie die sehr schnellen Änderungen in der Region untersucht. Dies motiviert nach effizienteren, flexibleren und genaueren Methoden zu suchen, um die gewünschten aktuellen Informationen zu erhalten.

Das Konzept von OBIA wurde als Substitution-Analyse-Rahmen vorgeschlagen, um die Mängel vom früheren pixel-basierten Konzept abzumildern. In diesem Sinne untersucht die Studie die beliebtesten Maximum-Likelihood-Klassifikatoren des pixel-basierten Konzeptes als Beispiel für das Verhalten der spektralen Klassifikatoren in dem jeweiligen Datenbereich und der Region. Im Gegensatz dazu analysiert OBIA Fernerkundungsdaten durch den Einbau von Wissen des Analytikers sowie kostenlose Zusatzdaten in einer Art und Weise, die menschliche Intelligenz für die Bildinterpretation als eine reale Darstellung der Funktion simuliert. Als ein Segment einer Basisverar-beitungseinheit wurden verschiedene Kombinationen von Segmentierungskriterien getestet um ähnliche spektrale Werte in Gruppen von relativ homogenen Pixeln zu trennen. An der kategorische Subtraktionsebene wurden Regeln entwickelt und optimale Eigenschaften für jede besondere Klasse extrahiert. Zwei Verfahren (Rule Based (RB) und Nearest Neighbour (NN) Classifier) wurden zugeteilt um die segmentierten Objekte der entsprechenden Klasse zuzuweisen.

Außerdem versucht die Studie die Fragen zu beantworten, ob OBIA in feiner räumlicher Auflösung grundsätzlich genauer ist als eine gröbere Auflösung, und wie beide, das pixel-basierte und das OBIA Konzept sich in einer relativen Genauigkeit als eine Funktion der räumlichen Auflösung vergleichen lassen. Diese Arbeit zeigt insbesondere, dass das OBIA Konzept eine fortschrittliche Lösung für die Bildanalyse ist, da die Genauigkeiten - an den verschiedenen Skalen angewandt - im Vergleich mit denen der pixel-basierten Konzepte verbessert wurden. Unterdessen waren die berichteten Ergebnisse der feineren räumlichen Auflösung nicht nur für die beiden Ansätze konsequent hoch, sondern durch das OBIA Konzept deutlich verbessert.

Die schnellen Veränderungen und die Heterogenität der Region sowie die unterschiedliche Datenherkunft haben dazu geführt, dass die Umsetzung von Post- Klassifizierungs-Änderungserkennung besser geeignet ist als radio-metrische Transformationsmethoden. Basierend auf thematische LU/LC Karten wurden Serien von optimierten Algorithmen entwickelt, um die Dynamik in LU/LC Einheiten darzustellen. Deshalb wurden für Detailänderung "von-bis"-Informationsklassen sowie Veränderungsstatistiken erstellt. Ferner wurden die erzeugten Änderungskarten bewertet, was zeigte, dass die Genauigkeit der Änderungskarten konstant hoch ist. Aggregiert auf die Gemeinde-Ebene bieten Sozialerhebungen der Haushaltsdaten eine umfassende zusätzliche Sichtweise auf die Fernerkundungsdaten. Die vorher festgelegten degradierten und erfolgreich wiederhergestellten Hot Spots wurden untersucht. Die Studie verwendet einen gut gestalteten Fragebogen um Faktoren die die Dynamik der Änderung der Landbedeckung und mögliche Lösungen, die auf der Wahrnehmung der Gemeinden basieren, anzusprechen.

Auf der Ebene des operativen strukturellen Waldbestandes wird die Begründung für die Einbeziehung dieser Analysen angegeben um semiautomatische OBIA Metriken zu schätzen, die aus dem Wald-Attribut durch automatisierte Segmentierungsalgorithmen in den Baumkronen abgegrenzt oder Cluster von Kronen Ebenen erworben wird. Korrelations- und Regressionsanalysen wurden angewandt, um die Beziehungen zwischen einer Vielzahl von spektralen und strukturellen Metriken und den aus den Untersuchungsgebieten abgeleiteten Waldattributen zu identifizieren. Die Ergebnisse des OBIA Rahmens zeigen starke Beziehungen und präzise Schätzungen. Die bes-ten Modelle waren mit einem unabhängigen Satz von kreuzvalidierten Feldproben ausgestattet, welche hohe Genauigkeiten ergaben. Eine wichtige Frage ist, wie die räumliche Auflösung und die verwendete Bandbreite die Qualität der entwickelten Modelle auch auf der Grundlage der verschiedenen untersuchten Sensoren beeinflussen.

Schließlich zeigt die Studie, dass OBIA in der Lage ist, als ein effizienter und genauer Ansatz Kenntnisse über die Landfunktionen zu erlangen, sei es bei operativen Attributen der Waldstruktur oder auch auf der kategorischen LU/LC Ebene. Außerdem zeigt der methodischen Rahmen eine mögliche Lösung um präzise Fakten und Zahlen über die Veränderungsdynamik und ihre Antriebskräfte zu ermitteln.


Nothing is more pleasant than the country around Sennaar, in the end of August and beginning of September [ …]; instead of that barren, bare waste, which it appeared on our arrival in May (1772, editor’s note), the corn now sprung up, and covered the ground, made the whole this immense plain appear a level, green land, inters-persed with great lakes of water and ornamented at certain intervals with groups of villages, the conical tops of the houses presenting, at a distance, the appearance of small encampments. Through this immense, extensive plain, winds the Nile, a delightful river there, above a mile broad, full to the very brim, but never overflowing. Every where on these banks are seen numerous herds of the most beautiful cattle of various kinds, the tribute recently extorted from the Arabs, who, freed from all their vexa-tions, return home with the remainder of their flocks in peace, at as great a distance from the town, country, and their oppressors, as they possibly can.
Bruce of Kinnaird J (1791) Travels to discover the sources of the Nile, in the years 1768, 1769, 1770, 1771, 1772, and 1773, in six volumes, vol V, ch IX, p 231f

Sub-Saharan semiarid regions suffer from increasing local impact on vegetation canopies in terms of overgrazing by herds and flocks, overexploitation of soils by rainfed agriculture neglecting fallow cycles and fuelwood consumption as well as illegal timber extraction. Subregional to local variations in extent and severity of impact are due to varying patterns of socioeconomic and socio-political pressure on the local people and are furthermore driven by gradients of migration of people displaced from their homelands due to ethnical and economic reasons. Mapping state and changes of land use and land cover is often focusing on monitoring and analysing the effects of longterm to mediumterm climatic variations towards their impact on specific ecoclimatic zones. This approach is more and more blamed for its neglect of regional and subregional variations of drought phenomena via arguing that the whole mechanism is much more complex in terms of causes and reasons of land cover changes. Local to regional patterns of land use land cover change have to be assessed and analysed much more intensively. Besides parameters of climatic change such as variations in periodicity and decline of amount of rainfall, increasing impact of strong winds and of temperature variations, spatiotemporal information on regional and local land use and land cover change as a mirror of anthropozooic impact has to be collected in a much more holistic way. It is obvious that the latter is responsible for severe land degradation, thus creating a circulus vitiosus which more and more deteriorates the livelihood of indigenous people. Local patterns of vegetation status and change are to be assessed in high spatio-temporal resolution in order to get a better idea about the interrelated effects of human impact on land degradation, deterioration of livelihood and subsequent abandonment of the land. Remote sensing of the environment by (very) high resolution spaceborne imagery provides the perfect tool and methodological background for collecting, maintaining, analysing and visualising spatiotemporal data which are capable to describe land use and land cover change in different scales both quantitatively as well as qualitatively.

Semi-arid regions of sub-Saharan Africa differ significantly in terms of their environment, their equipment with natural resources and their socio-political condition. Availability of water throughout the year is an advantage which attracts both displaced as well as migrating people and their herds and flocks. Conflicts with regard to rights of water and land use between local and migrating people are inevitable. Thus besides eco-climatic conditions the far more significant driving force of land degradation is resulting from socio-economic and socio-political instabilities, which change both in time (from seasonal to multi-annual) as well as in space (from the water hole level to the local land management level).

It is self-evident that especially the riverine environments along the large rivers crossing the sub-Saharan drylands are and have been the focal points of settlement and land exploitation, the Nile and Niger rivers being both the most significant ancient as well as recent representatives. The kingdom and town of Sennar came into power after the invasion of the Funj people from nowadays southern Sudan (Sudd) at the beginning of the 16th century. It found its peak during the 17th century when territorial expansion reached its apogee by extending the territory as far as to Southern Kordofan in the west and Dongola in the north. The Funj Sultanate of Sennar (Sinnar) lasted till 1821 when Turkish influence which grew since the late 18th century culminated in a peaceful invasion. After the resignation of the last king the territory was assimilated into the Ottoman Egypt. The old capital of Sennar extended approximately 20km north-northwest from the new town. A milestone in rural development driven by colonial policies was the establishment of the Sennar dam in 1925 by the English colonial administration, aiming at fostering irrigation agriculture (dominantly cotton, then more and more replaced by wheat) in the Gezira scheme (founded 1911) bordering Sennar State in the northwest. Till a few years ago the town of Sinja was the capital of Sennar State, but Sennar itself was always and is still the largest town in terms of the number of inhabitants. Population has multiplied by five from 1973 to 2007, thus proving significant migration. Reasons are closely related to socio-political threats such as the Ethiopian-Eritrean and the South Sudan conflicts, associated with socio-economic pressure due to overexploitation of resources caused by accumulation of different, often controversial demands on land use. Severe impacts are driven by an ongoing trend towards mechanised farming based on rainfed agriculture which threatens sensible soil strata and leads to degradation and subsequent decline of crop yields. At the same time riverine forests along the Blue Nile are affected by often illegal fuelwood and timber extraction as well as grazing activities. The monitoring and assessment of forest resources along the Blue Nile is a matter of highest priority under the tasks of the Sudanese Forest National Cooperation (FNC). The multitude and heterogeneity of socio-ecological and socio-economic driving factors of soil and vegetation degradation calls for a sound and reliable methodology of spatio-temporal monitoring of patterns of land use and land cover changes as well as a subsequent critical analysis of parameters of efficiency and integrability of spaceborne remote sensing for management and planning of protective and restorative measures of policies of forest management and forest conservation. Mustafa El-Abbas focuses on that issue by implementing multisource and multi-resolution operational earth observation via (very) high resolution imagery of Terra Aster and RapidEye sensor systems and by analysing the potential of object-based image analysis for multiscale monitoring from the level of categorical land use land cover classes to the level of forest structural attributes such as average stand height and especially forest stand volume. Mustafa El-Abbas provides an extensive and at the same time in-depth research into the innovative assessment and evaluation of up-to-date imagery and image analysis for forest management purposes in the semiarid environment of forests along the Blue Nile. It is obvious that Mustafa El-Abbas is perfectly capable to accumulate, condense and extend as well as to apply a wealth of target-oriented knowledge of the local and regional characteristics of land cover and land use change in the Blue Nile region in general and of forest cover change and forest structure on the stand level with specific regard to pure riverine Acacia nilotica stands in particular.

Balboch is on the eastern bank of the Nile, not a quarter of a mile from the ford below. The river here runs north and south; towards the sides it is shallow, but deep in the middle of the current, and in this part it is much infested with crocodiles, Sennaar is two miles and a half S.S.W. of it. We heard the evening drum very distinctly […]

Bruce of Kinnaird J (1791) Travels …, vol V, ch VII, p 177

 The banks of the Nile about Sennaar resemble the pleasantest parts of Holland in the summer season; but soon after, when the rains cease, and the sun exerts its utmost influence, the dora [millet] begins to ripen, the leaves to turn yellow and to rot, the lakes to putrify, smell, and be full of vermin, all this beauty suddenly disappears.

Bruce of Kinnaird J (1791) Travels …, vol V, ch IX, p 232

The overall scientific value and reliability of the research presented by Mustafa Elabbas is evident. It is challenging to realise that based on the well-structured, detailed and in-depth work of Mustafa El-Abbas a clear perspective for a multi-level regionalisation of multi-sensor and multi-resolution earth observation data analysis for purposes of forest management from the categorical land use land cover level to the local forest stand level is made possible. The presented performance of operational satellite imagery of very high (geometric) resolution and of object-based image analysis for monitoring and assessment of forest canopy characteristics in different scales is providing a promising baseline for a significant improvement of forest management issues in drylands of semi-arid sub-Saharan Africa, where accessibility of terrain is limited and dynamics of change both resulting from migration gradients as well as from subsequent anthropozooic impact on natural resources, especially on ligneous layers of vegetation, are highly frequent. The research of Mustafa El-Abbas contributes significantly to a better understanding of causes and reasons of degradation of forest canopies in semiarid environments at a regional to local scale. The complexity of driving factors of forest degradation along the Blue Nile valley has strong spatio-temporal characteristics. It is evident that impacts are driven by complex factors which are connected in a multifold thematic network of interrelations of both economic as well as political and ecological, climatic, geomorphological and biogeographical parameters. Mustafa El-Abbas provides an important and innovative contribution to an improvement of the monitoring of forest cover and forest use at the multi-scale and multi-temporal level which represents a cornerstone for establishing a sound forest management in the Blue Nile region of the Sudan and beyond.

Dresden, April 2017

Elmar Csaplovics

Table of contents

List of tables    XIII
List of figures    XV
Acronyms and abbreviations    XVII
Kurzfassung    3
1    Introduction    5
1.1    Background and motivation    5
1.1.1    Overview    5
1.1.2    Deforestation and land degradation    6
1.1.3    Remote sensing and forest resource assessment    7
1.2    Research hypotheses    9
1.3    Objectives    10
2    Study area    13
2.1    Overview    13
2.2    Introduction to the study area    13
2.2.1    General description    13
2.2.2    Vegetation    14
2.2.3    Climate    15
2.3    Test sites    17
2.3.1    LU/LC level    17
2.3.2    Forest stand level.    18
3    Theoretical background    19
3.1    Overview    19
3.2    Introduction    19
3.3    Object based image analysis    20
3.3.1    Image segmentation    21
3.3.2    Image objects hierarchy    23
3.3.3    Image object information extraction and feature measures    24
3.3.4    Nearest neighbour classification    28
3.3.5    Rule based classification    29
3.3.6    An  application  of  object-based  approach  for   mapping   earth surface features    30
3.3.7    Quality assessment    37
4    Data and research approaches    39
4.1    Overview    39
4.2    Data    39
4.2.1    Earth observation data    39
4.2.2    Field survey data    42
4.3    Methodology    45
4.3.1    Atmospheric correction    45
4.3.2    Geometric correction    46
4.3.3    Image classification    47
4.3.4    Change detection    56
4.3.5    Classification accuracy assessment    57
4.3.6    Forest parameters estimation and model validation    57
5    Land use/ land cover analyses    63
5.1    General overview    63
5.2    Pixel-based classification of Aster imagery by maximum likelihood (ML) classifier    64
5.3    OBIA approaches    66
5.3.1    Segmentation result    66
5.3.2    Hierarchical classification    67
5.3.3    Object-based classification of Aster imagery by Nearest Neighbor
(NN) classifier    69
5.3.4    Object-based classification of Aster imagery by rule-based  (RB) method    72
5.4    OBIA vs. per pixel classification    74
5.4.1    Classification of agricultural land    76
5.4.2    Classification of residential areas    78
5.4.3    Discrimination of vegetation classes    80
5.5    Scale issue and hierarchical OBIA for mapping LU/LC    82
5.5.1    Overview    82
5.5.2    Pixel-based classification of higher resolution RapidEye scene    83
5.5.3    Object-based classification of higher resolution RapidEye scene    85
5.5.4    Overall assessment of the approaches based on the two selected scales    87
5.6    Summary    88
6    Spatiotemporal analyses and driving forces    91
6.1    General overview    91
6.2    Land use/ land cover classification and accuracy assessment    92
6.2.1    Land use/ land cover classification    92
6.2.2    Accuracy assessment based on error matrix    94
6.2.3    Accuracy assessment based on best classification result    95
6.3    Distribution of LU/LC during the periods 1990, 1999 and 2009    97
6.3.1    Trend, rate and magnitude of land use/ land cover changes    98
6.4    Spatiotemporal object-based post-classification analysis    101
6.4.1    Forest cover change dynamics 1990- 1999    103
6.4.2    Forests cover change dynamics 1999- 2009    106
6.4.3    Overall  evaluation  of  the  adopted  approach  in  mapping  forest cover change dynam-ics    111
6.5    Major driving forces of forest cover loss and restoration problems    115
6.5.1    Deterioration of forest cover in the area    115
6.5.2    Driving forces of forest cover loss    115
6.5.3    Restoration of destroyed forests    117
6.6    Summary    119
7    Object-based texture measures and forest inventory    121
7.1    General overview    121
7.2    Segmentation    122
7.3    Descriptive analysis of field data    123
7.4    Image metrics and forest inventory    125
7.4.1    Volume    125
7.4.2    Height    125
7.4.3    Density    126
7.4.4    Basal area    127
7.5    Development of the regression model to estimate Acacia nilotica stand  volume    128
7.5.1    Model validation    131
7.6    Mapping forest stand volume in Alambwa Acacia nilotica pure stand    133
7.7    Summary    134
8    Conclusions, recommendations and future work    137
8.1    Conclusions    137
8.1.1    Overview    137
8.1.2    Land Use/ Land cover categorical level    137
8.1.3    Integrated framework of change analysis    138
8.1.4    Continuous forest structural attributes level    139
8.2    Recommendations    140
8.3    Limitations    141
8.4    Future work    141
References    143
Appendixes    163


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