In recent decades expanding land-use change has caused extensive deforestation of the tropical rainforestinducing large-scale transformation of the landscape patterns across the South American continent. Landscapechange is a modification process of the natural forest cover into fragments which generate various ecologicalimpacts. Habitat loss is identified to be a major threat to biodiversity, as it exposes species to the risk ofextinction. This study investigates 80 locations within tropical rainforest biomes to examine the landscape changewhich has occurred from 1993 – 2014. The intention is to identify the impacts of landscape fragmentation onhummingbird species diversity by spatial landscape analysis in GIS and regression modeling. The analysis foundthat there is no relationship between deforestation and reduction of hummingbird diversity. The results indicatethat hummingbird species are not particularly sensitive to landscape change as they have high resilience in regardto forest fragmentation. A potential threshold value of deforestation degree could be identified, up to whichhummingbird species richness increased, but locations subjected to over 40% fragmentation were estimated tohave lower hummingbird diversity. However, by using the spatial explicit biological data, the analysis indicatethat an extinction debt may exist in the landscape, and that future extinctions may be expected to occur in thefollowing decades as consequence of deforestation. Other factors may be as important determining variables forspecies richness: the spatial scale of the study, the habitat connectivity, hummingbird generalist tendencies.Conclusively, identification of the key factors of deforestation impacts on species diversity is essential for futureefficiency in conservation planning and sustainability of the tropical rainforest biodiversity.

The topic of this study is to evaluate data sources from the perspective of the information about landscape they provide to their users. The aim is comparison of thematic content of Urban Atlas database and data from Czech cadastre of real estate in Prague metropolitan region between years 2006 and 2012 with focus at meaning of classification systems used by both datasets. The data are processed by evaluation of thematic similarity and statistical tools which quantify similarity between researched data. Results are further verified by using validation data. Important results are visualized by charts, tables and maps. The areas with high degree of dissimilarity were found using chosen methods and their thematic characteristics were further examined as well as their major causes. It was proved that differences between both datasets are significant and they share certain characteristics. It was also proved that cadastral data are to high extent out-of-date. Keywords Urban Atlas, cadastre of real estate, land use, land cover, Prague

Cette thèse vise à identifier et évaluer les impacts biogéophysiques des changements d'usage des sols depuis les 150 dernières années jusqu'à la fin du XXIe siècle sur le climat en Afrique de l’Ouest à partir des modèles LUCID et des scénarios CMIP5 utilisés dans le contexte LUCID-CMIP5. Les analyses menées dans cette thèse se sont d'abord basées dans le Sahel et dans le Golfe de Guinée où les changements passés de la couverture terrestre sont supérieurs à 5%. Les simulations LUCID ont été d'abord évaluées dans cette thèse en comparant les précipitations et la température de l'air simulées par les modèles aux données d'observation. Les analyses ont montré que la moyenne et la variabilité inter-annuelle observées des précipitations et de la température sont respectivement sous-estimées et surestimées par la plupart des modèles de climat LUCID même si la température semble mieux simulée que les précipitations. Dans cette étude, les deux simulations actuelles forcées respectivement par une distribution actuelle et pré-industrielle de la couverture terrestre ont été comparées. Les résultats montrent qu'il n'y a pas de différence évidente entre ces deux simulations par rapport aux valeurs moyennes climatiques des précipitations et de la température dans les modèles comme si les changements de la couverture terrestre n'ont pas vraiment d'importance sur la représentation de ces variables. Dans le Golfe de Guinée, les analyses montrent que l'expansion des surfaces cultivées et des pâturages s'est effectuée au détriment d'une déforestation entraînant une diminution du LAI, une augmentation d'albédo et une diminution de la rugosité de surface. Les analyses montrent que les impacts historiques des changements d'occupation des sols sur le climat dans ces régions restent très petits par rapport aux changements induits par l'augmentation des gaz à effet de serre dans l’atmosphère. Le LAI simulé par les modèles de surface LUCID et leur relation avec le climat en Afrique de l'Ouest ont été évalués, les résultats montrent que les précipitations sont fortement et positivement corrélées à la densité de feuillage avec des valeurs supérieures ou égales à 0.8 dans les deux régions. La plupart des modèles de climat montrent que la corrélation entre le LAI et la température de l'air est positive dans le Sahel et négative dans le Golfe de Guinée et suggèrent que plus de LAI dans le golfe de Guinée conduit plus d'évapotranspiration et donc une surface plus froide, alors que dans le Sahel l'effet d'albédo de l'augmentation du LAI peut dominer et augmenter la température de surface.Dans un second temps, l'impact biophysique des changements futurs de la couverture terrestre sur le climat de surface du XXIe siècle a été évalué à l'aide des simulations spécifiques similaires aux scénarios RCP8.5 mais avec une végétation fixe en 2006. Les analyses révèlent qu'à l’échelle régionale, les impacts biophysiques des changements d'occupation des sols dans les scénarios ont été globalement faibles mais statistiquement significatifs au Sahel et en Afrique centrale où la déforestation est prescrite dans le futur (>10%), mais avec une large dispersion sur la réponse du climat résultant aux différentes paramétrisations de la surface terrestre dans les modèles de climat. / By climate models developed in the LUCID project and CMIP5 models used in the LUCID-CMIP5 projet, this thesis aims to identify and evaluate biogeophysical impacts of LULCC of the past 150 years and the end of XXIst century on surface climate in West Africa. Focusing analysis in two contrasted regions of West Africa: Sahel and Guinea where land cover change is above 5% since pre-industrial times, results reveal expansion of crops and pasture and deforestation in Guinea in all LUCID models. In this work, simulations of present-day rainfall and surface air temperature have been compared with observed datasets. Results show that the observed mean and inter-annual variability of rainfall are respectively underestimated and overestimated by most of the seven climate models. Overall surface air temperature is better simulated than precipitation.Two simulations of rainfall and surface air temperature, forced respectively with present-day and pre-industrial land cover distribution are also compared. Results show that there is no obvious/visible difference between the two simulations with respect to mean climatic values of both rainfall and temperature as if the changes in land cover did not really matter for the good representation of those variables. Finally, this thesis evaluates leaf area index (LAI) in the LUCID models and its relationships with surface climate. Observations reveal that precipitation is highly and positively correlated to foliage density with values larger or equal to 0.8 in both the Sahel and Guinea. Five out of seven models show positive correlations, but not as large as in the observations. However none of the models is able to capture a larger correlation between precipitation and LAI in Guinea than in the Sahel. Most of climate models show that correlation between LAI and surface air temperature is positive in the Sahel and negative in Guinea. It suggests that more LAI in Guinea will lead to more evapotranspiration and therefore cooler surface, while in the Sahel the albedo effect of increased LAI may dominate and increase surface temperature. Finally, analysis reveals that historical effects of land-use changes are not regionally significant among the seven climate models due to a small land-cover change prescribed in these regions compared to the changes induced by large scale forcing such as sea surface temperatures changes and CO2 concentration increase.Furthermore, biogeophysical impact of land-use change in the XXIst Century climate were evaluated using specific simulations similar to RCP8.5 scenarios but with a prescribed fixed land cover map on 2006. The analysis reveals, that in contrast of last 150 years, deforestation continues in the coming years in tropical region in scenarios resulting from the extension of the cultivated area reaching 15 million km2 in 2100 over tropical Africa. Regionally, the biogeophysical impacts of projected changes in land cover in RCP8.5 scenarios were generally small but statistically significant in the Sahel and Central Africa regions where deforestation is more than 10% with a wide dispersion of climate response due to differents parameterizations of land surface in climate models.

No description available.

Paysages emblématiques des campagnes de l’Ouest, les bocages ont connu une série de transformations rapides et importantes ces quarante dernières années : érosion du linéaire de haies, changements des usages du sol, rationalisation et agrandissement du parcellaire, banalisation et massification des bâtiments agricoles, enfrichement des secteurs les plus difficiles à exploiter. L’ampleur des ajustements observés souligne le décalage entre ces formes paysagères héritées d’une longue histoire agraire, et l’évolution des systèmes agricoles qui contribuent à les produire.Si la transformation des paysages de bocage est généralement attribuée au tournant « productiviste » du modèle agricole français, les processus socio-techniques à l’origine de ces évolutions sont plus rarement explicités. L’objet de ce travail est d’évaluer plus spécifiquement la contribution de l’agrandissement des exploitations agricoles à la dynamique d’évolution des paysages bocagers de l’Ouest de la France.Ce travail de géographie s’appuie dans un premier temps sur une analyse statistique à l’échelle du grand Ouest de la France, visant à étudier l’effet des transformations foncières sur les structures spatiales agricoles (assolements, parcellaire, linéaires boisés…). Dans un second temps, les dynamiques paysagères et foncières de quatre espaces d’étude (Bessin, Bocage Virois, Sud Manche, Pays d’Auge), situés en domaine laitier et bocager mais présentant des configurations agricoles variées, ont été étudiées entre 2003 et 2016. Ce travail s’appuie notamment sur un dispositif méthodologique original, articulant au sein d’un système d’information géographique à échelle parcellaire (1) la construction d’un suivi à échelle spatio-temporelle fine des dynamiques paysagères (évolution du maillage bocager, de la trame parcellaire et de l’occupation du sol) et (2) la reconstitution de l’évolution de la mosaïque des parcellaires d’exploitations par appariement de plusieurs millésimes du registre parcellaire graphique. L’exploitation de cette base de données spatio-temporelle a permis de mieux comprendre le rôle de la transformation foncière des exploitations agricoles dans la dynamique des paysages bocagers. Enfin, une enquête de terrain a été réalisée auprès de 150 agriculteurs équitablement répartis dans les quatre espaces d’étude, afin d’appréhender les conséquences sociales, techniques et productives de l’agrandissement à l’échelle des exploitations agricoles, qui diffèrent singulièrement selon le type de trajectoire foncière suivie. / Symbolic landscapes of the countryside of the West of France, the bocage landscapes have undergone a series of transformations these last forty years : decrease in hedgerow density, land uses changes, plots extension, normalization and enlargement of farm buildings, spatial extension of wilderness… The intensity of landscape transformations highlights the contradiction between these landscape forms produced by a long agrarian history and the contemporary evolutions of farming systems. The transformation of hedgerow landscapes is generally attributed to the "productivist" turn of the French agricultural model. However, the socio-technical processes behind these changes are more rarely explained.The first purpose of this geography work is to study the effects of changing agricultural systems on agricultural spatial structures, based on a statistical analysis at the scale of the West of France. In a second step, the landscape and land dynamics of four study areas (Bessin, Bocage Virois, Sud Manche, Pays d'Auge), located in the dairy and bocage domain, have been studied between 2003 and 2016. This work is based on an original methodological device, set up in a geographical information system. This structuration of geographic information makes possible to (1) monitor the landscape dynamics (evolution of the hedgerow density, land cover and plot morphology changes) at a fine spatial and temporal scale and (2) to reconstruct the evolution of the mosaic of farm plots, by matching land-parcell identification systems across the time (2007, 2011, 2013). From this spatio-temporal database, we characterized the coevolution of landscape structures and farm territories across the time, in order to better understand the landscape consequences of farm enlargment.Finally, a field survey was carried out among 150 farmers equitably distributed in the four study areas, in order to apprehend the social, technical and productive consequences of the farms enlargment, which differ singularly according to the type of land trajectory followed.

Land-cover and land-use change are highly dynamic and contribute to changes in the water balance. The most common changes are urbanisation, deforestation and desertification. This dissertation deals with the topic of projecting land cover (LC) into the near future with the help of the scenario technique. The aim of the thesis is the projection of the urban and rural land-cover change (LCC) till 2025. Two research questions are addressed in this work: (1) Which integrated concept can be developed to combine different methods to project urban and rural LCC into the future based on past LCC? (2) Is it possible to implement the developed concept and does the implementation deliver plausible results? To answer the research questions, a 4-step concept is adopted which serves as workflow for projecting the LCC: (i) the definition of the scenario context, and with that the definition of the study area, (ii) the identification of spatial and dynamic drivers for LCC, consisting of spatial drivers that are location-dependent, such as slope or soil type, and dynamic drivers of LCC, such as demographic and economic development, (iii) scenario formulation and projection of identified drivers, and (iv) scenario-based projections of future LCC, which means its quantitative and spatial modification (demand and allocation). For implementation and testing, the Upper Western Bug River catchment in Ukraine serves as the study site. The extent of the study area reaches from the source of the Western Bug to the Dobrotvir gauging station and is thus entirely located in Ukraine. This presents the first step of the developed concept of the projection of LCC. The existing geo-database for implementation is scarce. LC data is available for the territory of the EU (e.g. CORINE Land Cover) but not for Ukraine. Therefore, the implementation of the second step had to focus on the derivation of LC data for three-time steps to get the basis for the LCC. A classification of satellite scenes of Landsat and SPOT are done for the time steps 1989, 2000, and 2010. The two decades show a huge development of LCC. The increase of ‘artificial surface’ and unmanaged ‘grassland’ is visible with the decrease of ‘arable land’ and ‘forests’. An extended statistical analysis considering the systematic LCC reveals stable transition pathways, which in turn are the basis of the projection of future land cover. This refers to the second step of the concept: change detection. One transition pathway is that ‘arable land’ is not used and converted in settlement areas, but rather changes into ‘grassland’. With the derived LC and the analysed LCC as a basis of the work, the search for spatial and dynamic drivers start at the third time step. A list of dynamic drivers is first compiled, via literature research, and then tested for effect on LCC with statistical analyses. The dynamic driving forces are the ‘Gross Domestic Product’ (GDP) and ‘population development’. Spatial driving forces are laws/planning practices, fertility, slope, distance to the city Lviv, settlements, roads, or rivers. As a result, population development has an effect on the change in the LC class to 'artificial surface' from 'grassland' and 'arable land' from ‘grassland'. The implementation of the third step is done with the help of four storylines where the overall development of the dynamic drivers are included towards 2025. With that it is possible to project them into the future. The fourth step includes the calculation of the demand for each LC class with the projected dynamic drivers. The areas that have a high probability to change into another LC class are determined in suitability maps (allocation) which are derived by translating the transition pathways into GIS algorithms including the spatial driving forces. The class of 'artificial surface' changes the most under scenario A until 2025 and less under scenario D — the sustainable scenario. The LC class 'arable land' decreases in scenario A and B, but has the strongest development in scenario D. The LC class of unmanaged ‘grassland’ is quite stable under scenario A and B, but decreases in C and D. The results of systematic changes in ‘arable land’ that changes into ‘grassland’ are different compared to developments in other countries like Germany. The protection and conservation of arable land is not seen as strongly in other Eastern European countries as it is in the Upper Western Bug River catchment. In turn, the identified spatial and dynamic drivers fit other studies in Eastern Europe. The applied concept of projecting LCC with these steps are highly flexible for implementation in other study sites. However, the volume of work can differ within the steps because of the available databases. In Ukraine the available LCC data was not detailed enough to carry out a future projection. So, a main part of the work is dedicated to the derivation of past LC for different time steps. The involvement of regional experts helped to gain detailed knowledge of processes of LCC. The advantage of the presented concept with the mixture of quantitative (e.g. satellite analyses, statistical analyses) and qualitative methods can overcome methodological knowledge gaps. In addition, the retrospective analyses, as starting points, for the projection of future LCC carves out the site-specific allocation of change.:Acknowledgements..............................................................................................................................III Abstract..................................................................................................................................................IV Zusammenfassung..............................................................................................................................VII Contents..................................................................................................................................................X Abbreviations......................................................................................................................................XIV 1.Introduction.................................................................................................................................1 1.1Background................................................................................................................................1 1.2Objectives and Research Questions.......................................................................................3 1.3Structure....................................................................................................................................3 2.Basics of the Work......................................................................................................................5 2.1Land Cover, Land Use and Land-cover Change....................................................................5 2.2Projection of Land-Cover Change...........................................................................................6 2.3Drivers of Land-Cover and Land-Use Change.....................................................................10 2.4Basics of Scenario Methods..................................................................................................12 3.Conceptual Framework............................................................................................................15 3.1Step1: Definition of the Scenario Context...........................................................................17 3.2Step 2: Identification of Spatial and Dynamic Drivers of Land-Cover Change...............18 3.3Step 3: Scenario Formulation and Projection of Identified Drivers.................................18 3.4Step 4: Scenario-based Projections of Future Land-Cover Change.................................19 4.Implementation and Testing of the Framework..................................................................20 4.1Step 1: Definition of the Scenario Context..........................................................................20 4.2Step 2: Identification of Spatial and Dynamic Drivers for Land-Cover Change..............24 4.3Step 3: Scenario Formulation and Projection of Drivers...................................................28 4.4Step 4: Scenario-based Projections of future Land-Cover Change..................................32 5.Discussion..................................................................................................................................36 5.1Discussion of the Methods....................................................................................................36 5.2Discussion of the Empirical Results.....................................................................................42 6.Conclusions and Outlook........................................................................................................47 7.Reference List............................................................................................................................49 8.Appendix....................................................................................................................................58 8.1Position and Affiliation of the Interviewed Experts...........................................................59 8.2Suitability Maps.......................................................................................................................60 8.3Research Articles.....................................................................................................................66 8.3.1Research Article 1: Retrospective Analysis of Systematic Land-Cover Change in the......... Upper Western Bug River catchment, Ukraine.....................................................................67 8.3.2Research article 2: Cross-Sectoral Projections of Future Land-Cover Change for the........ Upper Western Bug River catchment, Ukraine.....................................................................80

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18556号 / 工博第3917号 / 新制||工||1602(附属図書館) / 31456 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 田村 正行, 准教授 須﨑 純一, 教授 小池 克明 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This paper aims to assess land cover change by high-resolution remote satellite images in Bo Trach district, Quang Binh province. The study used eCognition Developer 9.1 to classify SPOT and Sentinal-2 images of the study area. Objects on the images are characterized by values of Channels, including Red, Green, Blue, NIR, Brightness, NDVI, and RIV. Since then, maps of land cover status in the year of 2005, 2010, and 2017 were created with high accuracy 92.22%, 91.28%, 94.22%, respectively. Overlaid three land cover maps to develop the land cover change maps of two periods 2005-2010 (period 1) and 2010-2017 (period 2). The results show that there is a variation in the area of land cover types, especially agriculture and forest land. Of which, agrarian land increased by 7.7% in period 1 and 9.95% in period 2. Whereas, forest land decreased by 0.6% in period 1 and 1.5% in period 2. / Bài báo này nhằm mục đích đánh giá biến động sử dụng đất bằng viễn thám độ phân giải cao tại huyện Bố Trạch, tỉnh Quảng Bình. Nghiên cứu sử dụng phần mềm eCognition Developer 9.1để phân loại ảnh ảnh SPOT và Sentinal-2 trên địa bàn nghiên cứu. Các đặc trưng của đối tượng trên ảnh được xác định dựa trên giá trị độ sáng các Kênh 1, Kênh 2, Kênh 3, Kênh 4, giá trị độ sáng trung bình (Brightness), chỉ số khác biệt thực vật (NDVI) và tỷ số thực vật (RIV). Từ đó xây dựng được các bản đồ lớp phủ mặt đất các năm 2005, 2010, 2017 với độ chính xác lần lượt là 92,22%, 91,28%, 94.22%. Chồng ghép các bản đồ lớp phủ mặt đất, xây dựng bản đồ biến động sử dụng đất giữa hai thời kỳ 2005-2010 và 2010-2017. Kết quả nghiên cứu cho thấy có sự thay đổi giữa các loại hình lớp phủ gồm: đất nông nghiệp tăng khoảng 7,7% giai đoạn 1 và 9,95% giai đoạn 2. Đất lâm nghiệp giảm khoảng 0,6% giai đoạn 1 và 1,5% giai đoạn 2.

No description available.

Land surfaces have changed as a result of human and natural processes, such asdeforestation, urbanization, desertification and natural disasters like wildfires. Land use and landcover change impacts local and regional climates through various bio geophysical processes acrossmany time scales. More realistic representation of land surface parameters within the land surfacemodels are essential to for climate models to accurately simulate the effects of past, current andfuture land surface processes. In this study, we evaluated the sensitivity and accuracy of theWeather Research and Forecasting (WRF) model though the default MODIS land cover data andannually updated land cover data over southeast of United States. Findings of this study indicatedthat the land surface fluxes, and moisture simulations are more sensitive to the surfacecharacteristics over the southeast US. Consequently, we evaluated the WRF temperature andprecipitation simulations with more accurate observations of land surface parameters over thestudy area. We evaluate the model performance for the default and updated land cover simulationsagainst observational datasets. Results of the study showed that updating land cover resulted insubstantial variations in surface heat fluxes and moisture balances. Despite updated land use andland cover data provided more representative land surface characteristics, the WRF simulated 2- m temperature and precipitation did not improved due to use of updated land cover data. Further,we conducted machine learning experiments to post-process the Noah-MP land surface modelsimulations to determine if post processing the model outputs can improve the land surfaceparameters. The results indicate that the Noah-MP simulations using machine learning remarkablyimproved simulation accuracy and gradient boosting, and random forest model had smaller meanerror bias values and larger coefficient of determination over the majority of stations. Moreover,the findings of the current study showed that the accuracy of surface heat flux simulations byNoah-MP are influenced by land cover and vegetation type.