Spatial analysis and modelling of fire severity and vegetation recovery on and around Mt Cooke, south-western Australia

The South Western Australian Floristic Region (SWAFR) is an area with high biodiversity and species endemism. Numerous granite outcrops within the area provide specialised ecosystems for these endemic plants that are under threat by changes to the fire regime. This study reviews a fire on Mt Cooke in 2003. Using remote sensing and GIS, the fire is studied in relation to vegetation and fire indices to assess the fire severity and studies if the topography affected the fire severity. The vegetation recovery is monitored for ten years post-fire to assess recovery rates

Interlinkages of Land Degradation, Marginality and Land Use Cover Change in Kenya : Development of an interdisciplinary framework using remote sensing and GIS

Land degradation (LD) is a global problem affecting and being affected by socio-ecological systems. In this thesis the interlinkages of LD, marginality and land use cover change (LUCC) in Kenya based on remote sensing and geographic information systems (GIS) are analyzed. By combining biophysical and socio-economic data we obtain a deeper understanding of internal dynamics and their relationship to processes of decreasing productivity within a coupled Human-Environment System (HES). The simultaneous use of quantitative and qualitative methods supports insights in different disciplines. LD stands for the decrease of soil fertility and, hence, land productivity. Marginality is defined as the root cause of poverty but goes beyond the solely economic perspective of poverty measurement. LUCC represents another interdisciplinary concept where LC refers to the land surface and its biophysical determinants which can be detected with remote sensing while land use (LU) includes an active component referring to activities on land by human impact. The study was conducted on two different scales: the national scale, the country Kenya, and a local scale focusing on western Kenya. With census data and household survey information the socio-economic perspective was presented while biophysical assessment on LD and LUCC was conducted via remote sensing imagery. Time series analysis of vegetation information derived from remotely sensed imagery – NDVI and EVI – lead to the analysis of trends of land productivity from 2001 to 2011. In the national study, based on five indicator groups, different dimensions of marginality such as health, education, access to infrastructure and information but also economy could be analyzed. A set of eight indicators was detected that explains decreasing productivity trends with the use of exploratory regression and ordinary least square regression (OLS) on the national scale. Explaining decreasing productivity trends on the local level using household information for 42 villages and their respective acting scopes made obvious that also qualitative information is needed to validate and interpret results correctly. Trigger events such as the post-election violence in 2007 and 2008, and the world economy crisis in 2008 had a significant impact on decreasing productivity trends in 2009 in the local study area. The national and the local study both showed that variables explaining decreasing and stable productivity trends are in close relationship while increasing productivity is influenced by a different set of variables. Therefore, with regard to the concept of land degradation neutrality (LDN) stable productivity trends need to be taken into account for future research. Identification of biophysical and socio-economic variables influencing productivity trends helps to get a better understanding of coupled HES. The interdisciplinary approach of this study is path leading for the development of food security strategies. Validation of the here presented results on the respective spatial scale can be used to identify areas where a need for action is required to stop ongoing productivity decrease and finally stabilize yields

Développement et validation d’un indice de production des prairies basé sur l’utilisation de séries temporelles de données satellitaires : application à un produit d’assurance en France

Une assurance indicielle est proposée en réponse à l'augmentation des sécheresses impactant les prairies. Elle se base sur un indice de production fourragère (IPF) obtenu à partir d'images satellitaires de moyenne résolution spatiale pour estimer l'impact de l'aléa dans une zone géographique définie. Le principal enjeu lié à la mise en place d'une telle assurance réside dans la bonne estimation des pertes subies. Les travaux de thèse s’articulent autour de deux objectifs : la validation de l'IPF et la proposition d'amélioration de cet indice. Un protocole de validation est construit pour limiter les problèmes liés à l'utilisation de produit de moyenne résolution et au changement d’échelle. L'IPF, confronté à des données de référence de différentes natures, montre de bonnes performances : des mesures de production in situ (R² = 0,81; R² = 0,71), des images satellitaires haute résolution spatiale (R² = 0,78 - 0,84) et des données issues de modélisation (R² = 0,68). Les travaux permettent également d'identifier des pistes d'amélioration pour la chaîne de traitement de l'IPF. Un nouvel indice, basé sur une modélisation semiempirique combinant les données satellitaires avec des données exogènes relatives aux conditions climatiques et à la phénologie des prairies, permet d'améliorer la précision des estimations de production de 18,6 %. L’ensemble des résultats obtenus ouvrent de nombreuses perspectives de recherche sur le développement de l'IPF et ses potentiels d'application dans le domaine assurantiel

Hydro-geomorphological Attributes and Distribution of Urban Land and Population in River Basins

Global urbanization is a major trend in the 21st century and imposes a significant impact on the hydrologic cycle, climate, and biodiversity. In particular, Asia and Africa are expected to experience the fastest urbanization rate in the following decade, manifested by impervious land cover construction and urban population growth. Therefore, accurate information on spatial distribution of urban land and population at the present stage can attribute to a better understanding of urbanization processes in the future. Meanwhile, the knowledge of hydrological responses on the land use and land cover (LULC) change can provide valuable insights into sustainable development strategies. This dissertation aims to answer three fundamental questions: (1) How do natural environmental factors that relate to water resource, climate, and geomorphological attributes constrain the distribution of current urban land and population? (2) How will the global urban growth in the near future affect the urban exposure to natural disasters such as fluvial flood, drought, and ecosystem degradation? (3) How will land change impact hydrologic processes within a river basin? To solve the research questions, studies presented in this dissertation are organized into three parts. Part one analyzes the spatial distribution of urban ratio, population density, and urban population density in 11 river basins in Asia and Africa, considering average annual precipitation, surface freshwater availability, and access to coastal zone as three influencing factors. Then, a set of regression models is conducted for the Yangtze River Basin as a more comprehensive investigation. Part two examines the global and regional patterns of urban growth from 2000 to 2030 as well as the change of urban area’s exposure to floods and droughts. Part three is an assessment of streamflow in the Chao Phraya Basin based on different precipitation and LULC scenarios. The Soil and Water Assessment Tool (SWAT) is used to develop the hydrological model. The results reveal that higher urban ratio and human concentration occur in the vicinity of a stream network and a coastal zone; while precipitation does not effectively influence the distribution of urban land and population as expected. The emerging coastal metropolitan regions in Africa and Asia will be larger than those in the developed countries and will have larger areas exposed to flood and drought. The case study of Chao Phraya River Basin demonstrates that land change will increase both the risk of drought and flood hazards

Forestry and Arboriculture Applications Using High-Resolution Imagery from Unmanned Aerial Vehicles (UAV)

Forests cover over one-third of the planet and provide unmeasurable benefits to the ecosystem. Forest managers have collected and processed countless amounts of data for use in studying, planning, and management of these forests. Data collection has evolved from completely manual operations to the incorporation of technology that has increased the efficiency of data collection and decreased overall costs. Many technological advances have been made that can be incorporated into natural resources disciplines. Laser measuring devices, handheld data collectors and more recently, unmanned aerial vehicles, are just a few items that are playing a major role in the way data is managed and collected. Field hardware has also been aided with new and improved mobile and computer software. Over the course of this study, field technology along with computer advancements have been utilized to aid in forestry and arboricultural applications. Three-dimensional point cloud data that represent tree shape and height were extracted and examined for accuracy. Traditional fieldwork collection (tree height, tree diameter and canopy metrics) was derived from remotely sensed data by using new modeling techniques which will result in time and cost savings. Using high resolution aerial photography, individual tree species are classified to support tree inventory development. Point clouds were used to create digital elevation models (DEM) which can further be used in hydrology analysis, slope, aspect, and hillshades. Digital terrain models (DTM) are in geographic information system (GIS), and along with DEMs, used to create canopy height models (CHM). The results of this study can enhance how the data are utilized and prompt further research and new initiatives that will improve and garner new insight for the use of remotely sensed data in forest management

Retrieval and assessment of CO2 uptake by Mediterranean ecosystems using remote sensing and meteorological data

El IPCC (Intergovernmental Panel onClimateChange) apunta que, sin una reducción de las emisiones antropogénicas de gases de efecto invernadero, la temperatura media del planeta aumentaría y el sistema climático mundial experimentaría durante el siglo XXI cambios muy probablemente mayores a los ya observados durante el siglo XX. Los ecosistemas terrestres desarrollan un papel fundamental en el ciclo del carbono a través de la fotosíntesis, la respiración, combustión de biomasa y la descomposición. La energía es fijada mediante fotosíntesis y es directamente empleada por la vegetación para su crecimiento produciendo materia orgánica que será posteriormente consumida por microorganismos y resto de seres vivos de manera directa o indirecta. La producción primaria bruta (GPP), i.e., el carbono fijado por la vegetación a través de la fotosíntesis, se puede estimar utilizando el modelo clásico de Monteith. Según el mismo, la GPP viene dada por el producto de tres variables: la radiación incidente fotosintéticamente activa (PAR), la fracción de PAR absorbida por la cubierta vegetal (fAPAR) y la eficiencia en el uso de la radiación (LUE). En el trabajo de tesis realizado se ha tratado la problemática de la obtención de estimaciones diarias de GPP para España. Esto involucra la investigación y mejora de las variables que componen el modelo de Monteith. Para ello se han adaptado, mejorado y desarrollado nuevas metodologías para la obtención de la LUE, la PAR y la fAPAR. Para la obtención de la PAR se han aplicado dos metodologías complementarias: (i) La primera estima la radiación a partir de datos de estación de otras variables meteorológicas (como temperatura y precipitación) mediante la construcción de diversos modelos (redes neuronales, procesos regresión mediante kernels,…), y obtiene los mapas a partir de la espacialización de dichas variables puntuales. (ii) La segunda obtiene el PAR a partir de las imágenes de irradiancia del satélite MSG (Meteosat Segunda Generación), e incorpora además un remuestreo de dichas imágenes y una corrección topográfica (por elevación). Para la obtención fAPAR se han aplicado algoritmos operacionales avalados y se han post-procesado para la corrección de huecos y ruido en las series temporales para aumentar la consistencia de las mismas. Finalmente, para la obtención de la LUE se han empleado cartografías híbridas del tipo de cubierta vegetal adaptadas al área de estudio, se han aplicado estimadores a partir de variables meteorológicas (coeficientes de estrés hídrico y por bajas temperaturas) y se ha evaluado el potencial de índices espectrales a partir de datos de satélite como el índice de reflectividad fotoquímico (PRI) u otros índices espectrales sensibles al contenido en agua de la cubierta. Finalmente los resultados de las estimaciones de GPP se han validado de forma directa sobre datos de estaciones terrestres (torres Eddy covariance) y de forma indirecta por comparación con otros productos de satélite (productos de la NASA obtenidos mediante MODIS y Copernicus DMP). Adicionalmente se ha realizado un análisis del potencial explicativo de las variables de entrada para de esta forma observar patrones espaciales relacionados con la relevancia de su variabilidad temporal en las estimaciones del modelo optimizado en el trabajo de tesis.Photosynthesis is a process by which carbon and energy enter ecosystems. The knowledge of where,when, and how carbon dioxide (CO2) is exchanged between terrestrial ecosystems and atmosphere is crucial to close the Earth's carbon budget and predict feedbacks in a likely warming climate. Gross photosynthesis (uptake of CO2) by vegetation is responsible for the gross primary production (GPP) of the ecosystem. Normally GPP refers to the sum of the photosynthesis by all leaves measured at the ecosystem scale. John Monteith proposed in 1972 a simple approach that has become the paradigm for understanding GPP. It considers GPP as proportional to the incident short wave radiation (PAR), the fractional absorption of that flux (fAPAR) and the radiation use conversion efficiency, also known as light-use efficiency (LUE). This simple equation involves a great deal of biological and biophysical complexity. Photosynthesis requires that the plant replace the water that inevitably escapes from its leaves when CO2 is taken up from the atmosphere. Plants also require a supply of nutrients. Physiological and developmental mechanisms operate to adjust the GPP to the availability of resources. Thus, different types of stresses can affect the efficiency. The different terms in Monteith's equation are emphasized by different scientists. Crop physiologists focus on the PAR term, which explains the seasonal growth of crops and year-to-year variation in yield. Early work within the remote sensing community focused on the fAPAR term, which is linked to canopy structure and condition (i.e. to green biomass). It has a clear seasonal evolution in deciduous species and shows limited variability in evergreen forest ecosystems. The fAPAR is a common biophysical product derived from different remote sensing missions through the inversion of radiative transfer models or from empirical relations with vegetation indices. More recently the strong influence of the LUE term on productivity --particularly in strongly seasonal and nutrient-limited and/or water stressed vegetation canopies-- has been recognized. Variation in LUE is significant over shorter time scales when water or temperature stress develop. The LUE has been shown to vary spatially between biomes, ecosystems, and plant species, and to vary temporally during the growing season, due to environmental and physiological limitations. LUE responds more rapidly than fAPAR to different environmental factors related to the energy balance, water availability and nutrient levels. For operational applications, LUE can be expressed as the product of a LUEmax (maximum light-use efficiency), which depends on cover type, and different terms accounting for the reduction in efficiency due to different types of stress. The computation of these terms frequently requires meteorological data, which are seldom available at the needed spatial and temporal scales. The Monteith's approach provides the theoretical basis for most production efficiency models (PEMs), also known as light-use-efficiency (LUE) models: the MODIS-GPP model describes the global terrestrial photosynthesis at 1 km spatial scale and various time steps; the parametric model C-Fix has been applied to estimate forest GPP in several European countries and the modified C-Fix also takes into account the short-term water stress, a typical feature of the hot and dry Mediterranean summer. These models use remotely sensed data as well as meteorological data. In most PEMs, fAPAR is the only satellite-derived variable and, as such, it provides the link between ecosystem function and structure. Validation of satellite-derived GPP products is problematic. The development of eddy covariance (EC) as a method for quantifying the carbon, water, and energy balance over so-called "flux sites" has provided observational data to test and calibrate models; but the EC towers measure net CO2 exchange. GPP is obtained from these measurements after correcting them for respiratory losses (about half). The density of sampling is never enough to get regional or continental scale GPP. This is the domain of models. The modeling approaches also have specific limitations concerning: (i) the uncertainties of vegetation indices due to the presence of soil background mainly in sparse areas, and due to cloud and aerosol contamination problems, (ii) errors in the re-analysis of meteorological data, and (iii) difficulty constraining the light-use-efficiency term. The quality assessment of GPP products is rather complicated by the fact that GPP cannot be measured directly on a geographically relevant scale. In this Thesis, a model to estimate GPP for Mediterranean ecosystems at regional scale is proposed. The three terms in Monteith's equation have been obtained following procedures optimized for the study area, Spain (excluding Canary Islands). The "optimized model" is driven by meteorological and satellite data (MODIS/TERRA and SEVIRI/MSG). Considering the peculiarities of the study area, i.e., the diversity of the vegetation type dynamics and its spatial heterogeneity, the algorithm has been developed to run at a daily time step (to capture the dynamics even in agro-ecosystems) and 1 km spatial resolution (to assure that the spatial resolution of the remote sensing estimates is comparable to the footprint of ground estimates). Thus, the inputs of the model have been retrieved at these temporal and spatial resolutions. The daily GPP product obtained as explained above is difficult to validate due to the lack of ground GPP data. Nevertheless, GPP estimations from several eddy covariance (EC) towers have been used. These towers belong to the European Fluxes Database Cluster (http://www.europe-fluxdata.eu). By chance, these EC towers are mainly located in the semi-arid areas, which are more difficult to model due to their larger soil background effects. Thus, this direct validation of the GPP product serves to establish its upper uncertainty level. Moreover, an indirect validation, by means of an inter-comparison with two other operational products (from MODIS and Copernicus), is carried out. The results have been highly satisfactory and promising. A further analysis of the percentage of variance associated with each input of the Monteith's equation clearly evidences the role of the water stress in the inter-annual variation of GPP in Mediterranean ecosystems

Characterization and Modeling Agricultural and Forest Trajectories in the Northern Ecuadorian Amazon: Spatial Heterogeneity, Socioeconomic Drivers and Spatial Simulations

This research shows that agricultural frontier regions are heterogeneous and complex entities. This dissertation links four interconnected questions that seek to generate new insights into the processes of land use and land cover change in the Northern Ecuadorian Amazon (NEA). The research uses household survey data collected in the study area in 1990 and 1999 and a set of classified Landsat images for 1973, 1986, 1999, 1996, and 2002. This study, first, analyzes the composition and spatial configuration of the Land Use and Land Cover (LULC) trajectories in the NEA. Land trajectories are built using image algebra and stratified by deforestation stage and census sector. The analysis of LULC trajectories has suggested a core and periphery pattern of transitions in the NEA and shows the complexity of land changes in the region. Second, this research characterizes secondary forest succession, its extent and the socioeconomic, demographic, and biophysical factors that control forest generation. The analysis, using logistic regression, shows how improvements in accessibility and off-farm employment contribute positively to forest regeneration. Third, this research analyzes the spatial heterogeneity and spatial dependence of the relationships between socioeconomic, demographic, and biophysical drivers and LULC. The intent of this question is to find the spatial non-stationarity of the relationships between factors and LULC change using Geographically Weighted Regression and Spatial Lag Models. There is also an emphasis on new spatial representations of the parameters resulting from the regression analysis. This research component determined that the intensity of the drivers of LULC change is heterogeneous across space. Four, this research develops a cellular automata model that simulates LULC trajectories using pixels, neighborhoods, and spatial regimes that interact to produce broad LULC patterns. LULC patterns emerge from rules that control interactions among cells, cell neighborhoods and other spatial regimes created using GWR models. The aim of this research is to clarify the spatial and temporal nature of the relationship between population and land change and to predict positive and negative feedbacks between social, geographical, and biophysical factors that have implications for environmental management and policy

Examining the extent to which hotspot analysis can support spatial predictions of crime

The premise that where crime has occurred previously, informs where crime is likely to occur in the future has long been used for geographically targeting police and public safety services. Hotspot analysis is the most applied technique that is based on this premise – using crime data to identify areas of crime concentration, and in turn predict where crime is likely to occur. However, the extent to which hotspot analysis can accurately predict spatial patterns of crime has not been comprehensively examined. The current research involves an examination of hotspot analysis techniques, measuring the extent to which these techniques accurately predict spatial patterns of crime. The research includes comparing the prediction performance of hotspot analysis techniques that are commonly used in policing and public safety, such as kernel density estimation, to spatial significance mapping techniques such as the Gi* statistic. The research also considers how different retrospective periods of crime data influence the accuracy of the predictions made by spatial analysis techniques, for different periods of the future. In addition to considering the sole use of recorded crime data for informing spatial predictions of crime, the research examines the use of geographically weighted regression for determining variables that statistically correlate with crime, and how these variables can be used to inform spatial crime prediction. The findings from the research result in introducing the crime prediction framework for aiding spatial crime prediction. The crime prediction framework illustrates the importance of aligning predictions for different periods of the future to different police and prevention response activities, with each future time period informed by different spatial analysis techniques and different retrospective crime data, underpinned with different theoretical explanations for predicting where crime is likely to occur