No description available.

No description available.

Introduction and objectives In Vietnam, the forest resources have been declining and degrading severely in recent years. The degradation has decreased the natural forest area, changed the forest structure seriously and reduced timber volume and biodiversity. From 1999 to 2005, the rich forest area has decreased 10.2%, whereas the poor secondary forest has increased dramatically by 20.7%. Forest structure plays an important role in forestry research. Understanding forest structure will unlock an understanding of the history, function and future of a forest ecosystem (Spies, 1998). The forest structure is an excellent basis for restoration measures. Therefore, this research is necessary to contribute to improving forest area and quality, reducing difficulties in forest management. The study also enhances the grasp of forest structure, structure changes after harvesting and fills serious gaps in knowledge. In addition, the research results will contribute to improving and rescuing the poor secondary forest and restoring it, approaching the old-growth forest in Vietnam. Material and methods The study was conducted in Kon Ka Kinh national park. The park is located in the Northeastern region of Gia Lai province, 50 km from Pleiku city center to the Northeast. The park is distributed over seven different communes in three districts: K’Bang, Mang Yang and Đăk Đoa. Data were collected from 10 plots of secondary forests (Type IIb) and 10 plots of primeval forests (Type IV). Stratified random sampling was applied to select plot locations. 1 ha plots were used to investigate gaps. 2000 m2 plots were used to measure overstorey trees such as diameter at breast height, total height, crown width and species names. 500 m2 subplots were used to record tree positions. For regeneration, 25 systematic 4 m2 subplots were established inside 1 ha plots. After data were collected in the field, data analyses were conducted by using R and Excel. Firstly, some stand information, such as density, volume and so on, was calculated, and then descriptive statistics were computed for diameter and height variables. Linear mixed effect models were applied to analyze the difference of diameter and height and to check the effect of random factor between the two forest types. Diameter and height frequency distributions were also generated and compared by using permutational analysis of variance (PERMANOVA). Non-linear regression models were analyzed for diameter and height variables. Similar analyses were implemented for gaps. Regarding spatial point patterns of overstorey trees, replicated point pattern analysis techniques were applied in this research. For biodiversity, some calculations were run such as richness and biodiversity indices, comparison of biodiversity indices by using linear mixed models and biodiversity differences between two forest types tested again by permutational analysis of variance. In terms of regeneration, some analyses were implemented such as: height frequency distribution generation, frequency difference testing, biodiversity indices for the regeneration and spatial distribution checking by using a nonrandomness index. Results and discussion After analyzing the data, some essential findings were obtained as follows: Hypothesis H1 “The overstorey structure of secondary forests is more homogeneous and uniform than old-growth forests” is accepted. In other words, the secondary forest density is about 1.8 times higher than the jungle. However, the volume is only 0.56 times as large. The average diameter and height of the secondary forest is smaller by 5.71 cm and 3.73 m than the old-growth forest, respectively. Linear mixed effect model results indicate that this difference is statistically different and the effect of the random factor (Section) is not important. Type IIb has many small trees and the diameter frequency distribution is quite homogeneous. The old-growth forest has more big trees. For both forest stages, the height frequency distribution is positively skewed. PERMANOVA results illustrate that the frequency distribution is statistically different between the two forest types. Regression functions are also more variant and diverse in the old-growth forest, because all standard deviations of the parameters are greater there. Gap analysis results indicate that the number of gaps in the young forest is slightly higher, while the average gap size is much smaller. The gap frequency distribution is statistically different between the two types. In terms of the spatial point pattern of overlayer trees, the G-test and the pair correlation function results show that trees distribute randomly in the secondary forest. In contrast, the spatial point patterns of trees are more regular and diverse in the old-growth forest. The spatial point pattern difference is not significant, and this is proved by a permutational t-test for pair correlation function (pcf). Envelope function results indicate that the variation of pcf in young forests is much lower than in the primary forests. Hypothesis H2 “The overstorey species biodiversity of the secondary forest is less than in the old-growth forest” is rejected. Results show that the number of species of the secondary forest is much greater than in the old-growth forest, especially richness. The richness of the secondary forest is 1.16 times higher. The Simpson and Shannon indices are slightly smaller in the secondary forest. The average Simpson index for both forest stages is 0.898 and 0.920, respectively. However, the difference is not significant. Species accumulation curves become relatively flatter on the right, meaning a reasonable number of plots have been observed. Estimated number of species from accumulation curves in two forest types are 105 and 95/ha. PERMANOVA results show that number of species and proportion of individuals in each species are significantly different between forest types. Hypothesis H3 “The number regenerating species of the secondary forest is less and they distribute more regularly, compared to the old-growth forest” is rejected. There are both similarities and differences between the two types. The regeneration density of the stage IIb is 22,930 seedlings/ha, greater than the old forest by 9,030 seedlings. The height frequency distribution shows a decreasing trend. Similar to overstorey, the richness of the secondary forest is 141 species, higher than the old-growth forest by 9 species. Biodiversity indices are not statistically different between two types. PERMANOVA results indicate that the number of species and the proportion of individuals for each species are also not significantly different from observed forest types. Nonrandomness index results show that the regeneration distributes regularly. Up to 95% of the plots reflect this distribution trend. Hypothesis H4 “Restoration measures (with and without human intervention) could be implemented in the regenerating forest” is accepted. The investigated results show that the secondary forest still has mother trees, and it has enough seedlings to restore. Therefore, restoration solutions with and without human intervention can be implemented. Firstly, forest protection should be applied. This measure is relevant to national park regulations in Vietnam. Rangers and other related organizations will be responsible for carrying out protection activities. These activities will protect forest resources from illegal logging, grazing and tourist activities. Environmental education and awareness-raising activities for indigenous people is also important. Another measure is additional and enrichment planting. It should focus on exclusive species of the overstorey in Type IIb or exclusive species of the primary forest. Selection of these species will lead to species biodiversity increase in the future. This also meets the purpose of the maximum biodiversity solution. Conclusion Forest resources play a very important role in human life as well as maintaining the sustainability of ecosystems. However, at present, they are under serious threat, particularly in Vietnam. Central Highland, Vietnam, where forest resources are still relatively good, is also threatened by illegal logging, lack of knowledge of people and so on. Therefore, it needs the hands of the people, especially foresters and researchers. Through research, scientists can provide the knowledge and understanding of the forest, including the structure and forest restoration. This study has obtained important findings. The secondary forest is more homogeneous and uniform, while the old-growth forest is very diverse. Biodiversity of the overstorey in the secondary forest is more than the primary. The number of regenerating species in the secondary forest is higher, but other indices are not statistically different between two types. The regeneration distribute regularly on the ground. The secondary forest still has mother trees and sufficient regeneration, so some restoration measures can be applied here. Findings of the study contribute to improve people’s understanding of the structure and the structural changes after harvesting in Kon Ka Kinh national park, Gia Lai. That is a key to have better understandings of the history and values of the forests. These findings and the proposed restoration measures address rescuing degraded forests in Central Highland in particular and Vietnam in general. And further, this is a promising basis for the management and sustainable use of forest resources in the future.:TABLE OF CONTENTS ACKNOWLEDGEMENTS I TABLE OF CONTENTS III LIST OF FIGURES VIII LIST OF TABLES XI LIST OF ABBREVIATIONS XII SUMMARY XIII CHAPTER I: INTRODUCTION 1 1.1. The decline of natural forest resources, orientation of difficulty and development in Vietnam 1 1.1.1. Decline of forest resources 1 1.1.2. Difficulties in forestry management 1 1.1.3. Management strategies 2 1.2. Forest structure role 3 1.3. Forest restoration in Vietnam 4 1.4. Importance of old-growth and secondary forests 4 1.5. Aims, scope and hypotheses 6 1.5.1. Aims 6 1.5.1.1. General objective 6 1.5.1.2. Specific objective 6 1.5.2. Scope 6 1.5.3. Hypotheses 6 CHAPTER II: LITERATURE REVIEW 8 2.1. Tropical forest structure analysis 8 2.1.1. History 8 2.1.1.1. Overstorey 8 2.1.1.2. Regeneration 12 2.1.2. Structural attributes of tropical forests 13 2.1.2.1. Overstorey 14 a. Analyzed attributes 14 b. Relevant attributes to this study 15 2.1.2.2. Regeneration 21 2.2. Secondary tropical forest restoration 22 2.2.1. Strategies for secondary forest restoration 23 2.2.1.1. Protection and natural recovery 24 2.2.1.2. Natural regeneration management 24 a. Growing conditions and yield of desirable regeneration improvement 24 b. Desirable regeneration assistance 25 2.2.1.3. Accelerated Natural Regeneration (ANR) 25 2.2.1.4. Enrichment planting 25 2.2.1.5. The framework species method 26 2.2.1.6. Maximum diversity planting method 26 CHAPTER III: MATERIAL 27 3.1. Natural conditions of the study area 27 3.1.1 Geographic location, boundaries and area of Kon Ka Kinh national park 27 3.1.2. Topography, geology and soil 28 3.1.2.1. Topography 28 3.1.2.2. Geology and soil 29 3.1.3. Climate and hydrology 30 3.1.3.1. Climate 30 3.1.3.2. Hydrology 31 3.2. Vegetation in Kon Ka Kinh national park 31 3.2.1. The area of land use types 31 3.2.2. Plant biodiversity 33 3.2.3. The flora and forest vegetation 33 3.2.3.1. Flora 33 3.2.3.2. Forest vegetation 34 3.2.3.3. History of forest exploitation in the park 35 3.3. Assessing the natural conditions and vegetation of the park 37 3.4. Population, ethnicity and labor 38 3.4.1. Population 38 3.4.2. Labor and ethnicity 39 3.4.3. Poverty status 40 3.5. Forest resources classification 40 3.5.1. The Loeschau’s classification system 40 3.5.2. The relationship between forest types with development phases 42 CHAPTER IV: METHODOLOGY 45 4.1. Plot establishment method 45 4.2. Data collection method 47 4.2.1. Data collection for overstorey stem maps 47 4.2.1.1. Tree data collection 47 4.2.1.2. Tree positions 50 4.2.1.3. Gap inventory 51 4.2.2. Data collection for regeneration 52 4.3. Data analysis method 55 4.3.1. Applied methods for the upper layer 55 4.3.1.1. Stand information 55 a. Calculation for each tree 55 b. Calculation for a stand 55 4.3.1.2. Descriptive statistics for height and diameter variables 56 a. Central tendency 56 b. Dispersion and variability 56 c. Measures of distribution shape 57 4.3.1.3. Linear mixed-effects analysis 59 a. Applications with this study and data arrangement 60 b. Homoscedasticity checking 61 c. Checking autocorrelation 63 d. Checking normal distribution of the residuals 66 e. Model selection and information summary 67 4.3.1.4. Frequency distribution 68 a. Generating frequency distributions 68 b. Frequency distribution difference testing 69 4.3.1.5. Diameter-height regression analysis 70 a. Used function forms 70 b. Theoretical calculations 71 c. Model selection 73 4.3.1.6. Gap analysis 74 a. Descriptive statistics for gaps 74 b. Calculating the gap area proportion for each forest type 74 c. Gap size frequency distribution 74 d. Gap size frequency distribution difference testing 75 4.3.1.7. Spatial point patterns of tree species 75 a. Applications 76 b. Tree density analysis 77 c. Testing for randomness 78 d. Comparing point pattern variation 83 e. Testing the difference between forest types 84 4.3.1.8. Overstorey tree species diversity analysis 85 a. Richness and species importance value index (SIVI) 85 b. Species diversity index 86 c. Species accumulation curve 88 d. Biodiversity index comparison 88 e. Tree species diversity comparison 89 4.3.2. Regenerating tree storey structure analysis 90 4.3.2.1. Frequency distribution of regeneration 90 4.3.2.2. Height frequency distribution difference testing 91 4.3.2.3. Biodiversity indices for regeneration 91 4.3.2.4. Biodiversity index comparison by using LMM 91 4.3.2.5. Regeneration species diversity comparison 91 4.3.2.6. Regeneration spatial distribution checking 91 a. Nonrandomness index 91 b. Nonrandomness index value comparison 92 CHAPTER V: RESULTS 93 5.1. Overstorey structure analysis results 93 5.1.1. Stand information 93 5.1.2. Descriptive statistics results 95 5.1.3. Linear mixed effect model results 97 5.1.3.1. Box plots for the diameter and height variables 97 5.1.3.2. Model analysis and adaptation 97 5.1.3.3. Model parameter estimation 100 5.1.4. Frequency distributions 101 5.1.4.1. Frequency distribution results for both types 101 5.1.4.2. Frequency distribution difference 107 5.1.5. Diameter-height regression results 107 5.1.5.1. Estimated parameters 107 5.1.5.2. Model selection 110 5.1.5.3. Regression charts 110 5.1.6. Gap analysis 116 5.1.6.1. Gap descriptive information 116 5.1.6.2. Gap area ratio 117 5.1.6.3. Gap size frequency distribution 117 5.1.6.4. Gap size frequency distribution difference testing results 120 5.1.7. Spatial distribution analysis 120 5.1.7.1. Density testing results 120 5.1.7.2. Randomness checking results 122 5.1.7.3. Variation difference between two types 123 5.1.7.4. Point pattern difference testing between two types 124 5.1.8. Overstorey species diversity analysis results 125 5.1.8.1. Richness, SIVI and biodiversity indices 125 5.1.8.2. Biodiversity index comparison by using LMM 127 5.1.8.3. Tree species diversity comparison 127 5.2. Regeneration storey structure analysis results 128 5.2.1. Height frequency distribution 128 5.2.2. Height frequency distribution difference testing 130 5.2.3. Biodiversity index for regeneration 131 5.2.4. Biodiversity index difference comparison 133 5.2.5. Regeneration species diversity comparison 133 5.2.6. Regeneration spatial distribution 134 5.2.6.1. Nonrandomness index results 134 5.2.6.2. Nonrandomness index value testing results 134 CHAPTER VI: DISCUSSION 135 6.1. Overstorey structure differentiation 135 6.1.1. Structure and spatial distribution difference 135 6.1.1.1. Stand information 135 6.1.1.2. Statistical descriptions for diameter and height 136 6.1.1.3. Diameter and height growth difference testing by linear mixed effect models 137 6.1.1.4. Frequency distribution dissimilarity 138 6.1.1.5. Diameter-height regression 139 6.1.1.6. Canopy gaps 140 6.1.1.7. Spatial distribution patterns 141 6.1.2. Biodiversity distinction of overstorey trees 143 6.2. Regeneration dissimilarity 145 6.2.1. Density and frequency distribution 145 6.2.2. Biodiversity indices 146 6.2.3. Spatial distribution of regeneration 147 6.3. Proposing restoration measures 147 6.4. Improved points in this research 150 CHAPTER VII: CONCLUSION AND RECOMMENDATION 152 7.1. Conclusion 152 7.2. Suggestions for further research 154 REFERENCES 156 APPENDIX 180

L'un des problèmes les plus complexes dans l'analyse des images histologiques est l'évaluation de l¿organisation spatiale des structures histologiques dans le tissu. En fait, les sections histologiques peuvent contenir un très grand nombre de cellules de différents types et irrégulièrement réparties dans le tissu, ce qui rend leur contenu spatial indescriptible d'une manière simple. Les méthodes fondées sur la théorie des graphes ont été largement explorées dans cette direction, car elles sont des outils de représentation efficaces ayant la capacité expressive de décrire les caractéristiques spatiales et les relations de voisinage qui sont interprétées visuellement par le pathologiste. On peut distinguer trois familles principales de méthodes des graphes utilisées à cette fin: analyse de structure syntaxique, analyse de réseau et analyse spectrale. Cependant, un autre ensemble distinctif de méthodes basées sur la morphologie mathématique sur les graphes peut être développé et adapté pour ce problème. L'objectif principal de cette thèse est le développement d'un outil capable de fournir une évaluation quantitative des arrangements spatiaux des structures histologiques en utilisant la morphologie mathématique basée sur les graphes. / One of the most challenging problems in histological image analysis is the evaluation of the spatial organizations of histological structures in the tissue. In fact, histological sections may contain a very large number of cells of different types and irregularly distributed, which makes their spatial content indescribable in a simple manner. Graph-based methods have been widely explored in this direction, as they are effective representation tools having the expressive ability to describe spatial characteristics and neighborhood relationships that are visually interpreted by the pathologist. We can distinguish three main families of graph-based methods used for this purpose: syntactic structure analysis, network analysis and spectral analysis. However, another distinctive set of methods based on mathematical morphology on graphs can be additionally developed for this issue. The main goal of this dissertation is the development of a framework able to provide quantitative evaluation of the spatial arrangements of histological structures using graph-based mathematical morphology.

No description available.

La compréhension des processus écologiques qui sous-tendent l’assemblage des communautés végétales et la coexistence des espèces est un objectif central en écologie. Ces processus sont potentiellement nombreux et de natures contrastées. Ainsi, la composition d’une communauté de plantes dépend de processus déterministes liés aux conditions environnementales abiotiques (climat, conditions physiques et chimiques du sol, lumière) et d’interactions biotiques complexes, positives (facilitation, symbioses) comme négatives (compétition, prédation, pathogènes). En outre, les communautés sont influencées par des processus stochastiques (capacité de dispersion limitée, dérive écologique). Si les mécanismes à l’origine de ces processus sont très différents, ils ont néanmoins en commun la génération de motifs (patterns) spatiaux de distribution d’espèces dans les communautés. L’analyse de la structure spatiale des communautés permet ainsi une étude indirecte des processus régissant les communautés. La nature complexe de ces patterns spatiaux a mené au développement de nombreuses méthodes statistiques de détection et de description de patterns. Les méthodes basées sur des vecteurs propres spatiaux sont parmi les plus puissantes et précises pour détecter des patterns complexes et multi-échelles. Ces vecteurs propres, utilisés comme prédicteurs spatiaux, peuvent être combinés à un ensemble de variables environnementales dans un cadre de partition de variation. Celui-ci permet, en théorie, de démêler les effets uniques et l’effet conjoint des variables environnementales et spatiales sur la variation de composition d’une communauté. Il mène ainsi à une quantification de l’action des processus déterministes et des processus stochastiques sur l’assemblage de la communauté. Néanmoins, je montre dans cette thèse qu’un certain flou méthodologique concernant deux étapes déterminantes des analyses basées sur les vecteurs propres spatiaux a mené une proportion élevée d’études à utiliser ces méthodes de manière sous-optimale, voire fortement biaisée. Ceci compromet la fiabilité des patterns spatiaux détectés et des processus écologiques inférés. Une autre limitation de ce cadre d’analyse concerne la fraction de la partition de variation décrivant l’effet environnemental spatialement structurés qu’aucune méthode ne permet de tester.Cette thèse présente des solutions non biaisées, puissantes et précises à ces différentes limitations méthodologiques et permet d’élargir le cadre de l’inférence de processus écologique à partir de patterns spatiaux de communautés. Les différentes étapes d’amélioration de ces méthodes ont également été illustrées dans la thèse au travers de trois cas d’études fournis par deux communautés d’arbres tropicale et tempérée et une communauté de champignons symbiotiques des arbres. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

No description available.

Die Windenergie an Land (Onshore-Windenergie) ist neben der Photovoltaik eine der tragenden Säulen der Energiewende in Deutschland. Wie schon in der Vergangenheit wird auch zukünftig der Ausbau der Onshore-Windenergie, mit dem Ziel eine umweltgerechte und sichere Energieversorgung für zukünftige Generationen aufzubauen, durch die Politik massiv vorangetrieben. Für eine planvolle Umsetzung der Energiewende, insbesondere im Bereich der Windenergie, müssen Kenntnisse über den zur Verfügung stehenden Raum und der Wirkungsweise standortspezifischer Faktoren auf planungsrechtlicher Ebene vorhanden sein. In der vorliegenden Arbeit wurde die Region Deutschland auf das für dieWindenergie an Land nutzbare Flächenpotenzial analysiert, von diesem allgemein gültige Energiepotenziale abgeleitet und in einer Sensitivitätsanalyse die Einflüsse verschiedener Abstände zwischen den Windenergieanlagen und Siedlungsstrukturen auf das ermittelte Energiepotenzial untersucht. Des Weiteren wurden für die beobachteten Zusammenhänge zwischen den Distanz- und Energiepotenzialänderungen mathematische Formeln erstellt, mit deren Hilfe eine Energiepotenzialänderung in Abhängigkeit von spezifischen Siedlungsdistanzänderungen vorhersagbar sind. Die Analyse des Untersuchungsgebiets (USG) hinsichtlich des zur Verfügung stehenden Flächenpotenzials wurde anhand eines theoretischen Modells, welches die reale Landschaft mit ihren unterschiedlichen Landschaftstypen und Infrastrukturen widerspiegelt, umgesetzt. Auf Basis dieses Modells wurden so genannte „Basisflächen“ sowie für die Onshore-Windenergie nicht nutzbare Flächen (Tabu- oder Ausschlussflächen) identifiziert und mittels einer GIS-Software (Geographisches Informationssystem) verschnitten. Die Identifizierung der Ausschlussflächen erfolgte über regionalisierte beziehungsweise im gesamten USG geltende multifaktorielle Bestimmungen für die Platzierung von Windenergieanlagen (WEA). Zur Gewährleistung einer einheitlichen Konsistenz wurden die verschiedenen Regelungen, welche aus den unterschiedlichsten Quellen stammen, vereinheitlicht, vereinfacht und in einem so genannten „Regelkatalog“ festgeschrieben. Die Berechnung des im USG maximal möglichen Energiepotenzials erfolgte durch eine Referenzanlage, welche im USG räumlich verteilt platziert wurde. Die Energiepotenziale (Leistungs- und Ertragspotenzial) leiten sich dabei aus der Kombination der räumlichen Lage der WEA, den technischen Leistungsspezifikationen der Referenzanlage und dem regionalem Windangebot ab. Eine wesentliche Grundvoraussetzung für die Berechnung der Energiepotenziale lag in der im Vorfeld durchzuführenden Windenergieanlagenallokation auf den Potenzialflächen begründet. Zu diesem Zweck wurde die integrierte Systemlösung „MAXPLACE“ entwickelt. Mit dieser ist es möglich, WEA unter Berücksichtigung von anlagenspezifischen, wirtschaftlichen und sicherheitstechnischen Aspekten in einzelnen oder zusammenhängenden Untersuchungsregionen zu platzieren. Im Gegensatz zu bereits bestehenden Systemlösungen (Allokationsalgorithmen) aus anderen Windenergie-Potenzialanalysen zeichnet sich die integrierte Systemlösung „MAXPLACE“ durch eine sehr gute Effizienz, ein breites Anwendungsspektrum sowie eine einfache Handhabung aus. Der Mindestabstand zwischen den WEA und den Siedlungsstrukturen stellt den größten Restriktionsfaktor für das ermittelte Energiepotenzial dar. Zur Bestimmung der Einflussnahme von Siedlungsdistanzänderungen auf das Energiepotenzial wurde mit Hilfe des erstellten Landschaftsmodells eine Sensitivitätsanalyse durchgeführt. In dieser wurden die vorherrschenden Landschafts- und Infrastrukturen analysiert und daraus standortbeschreibende Parameter abgeleitet. Neben der konkreten Benennung der Energiepotenzialänderungen, wurden für das gesamte USG mathematische Abstraktionen der beobachteten Zusammenhänge in Form von Regressionsformeln ermittelt. Diese Formeln ermöglichen es, ohne die in dieser Arbeit beschriebene aufwendige Methodik nachzuvollziehen, mit nur wenigen Parametern die Auswirkungen einer Siedlungsdistanzänderung auf das Energiepotenzial innerhalb des Untersuchungsgebiets zu berechnen.