Probability sampling protocol of classification maps from spaceborne/airborne image

To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: (i) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. (ii) The inclusion probabilities must be: (a) knowable for nonsampled units and (b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne Very High Resolution (VHR) images, where: (I) an original Categorical Variable Pair Similarity Index (CVPSI, proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and (II) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability sampling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic MapperT (SIAMT) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAMT by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAMT pre-classification maps proposed in this contribution, together with OQIs claimed for SIAMT by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAMT software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems (GEOSS) initiative and the QA4EO international guidelines.

Comparison of NDVI fields obtained from different remote sensors

Satellite image data have become an important source of information for monitoring vegetation and mapping land cover at several scales. Beside this, the distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Various vegetation indices have been developed for qualitative and quantitative assessment of vegetation using remote spectral measurements. In particular, sensors with spectral bands in the red (RED) and near-infrared (NIR) lend themselves well to vegetation monitoring and based on them [(NIR - RED) / (NIR + RED)] Normalized Difference Vegetation Index (NDVI) has been widespread used. Given that the characteristics of spectral bands in RED and NIR vary distinctly from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The spatial resolution also varies significantly between sensors, as well as within a given scene in the case of wide-angle and oblique sensors. As a result, NDVI values will vary according to combinations of the heterogeneity and scale of terrestrial surfaces and pixel footprint sizes. Therefore, the question arises as to the impact of differences in spectral and spatial resolutions on vegetation indices like the NDVI and their interpretation as a drought index. During 2012 three locations (at Salamanca, Granada and Córdoba) were selected and a periodic pasture monitoring and botanic composition were achieved. Daily precipitation, temperature and monthly soil water content were measurement as well as fresh and dry pasture weight. At the same time, remote sensing images were capture by DEIMOS-1 and MODIS of the chosen places. DEIMOS-1 is based on the concept Microsat-100 from Surrey. It is conceived for obtaining Earth images with a good enough resolution to study the terrestrial vegetation cover (20x20 m), although with a great range of visual field (600 km) in order to obtain those images with high temporal resolution and at a reduced cost. By contranst, MODIS images present a much lower spatial resolution (500x500 m). The aim of this study is to establish a comparison between two different sensors in their NDVI values at different spatial resolutions. Acknowledgements. This work was partially supported by ENESA under project P10 0220C-823. Funding provided by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no. MTM2009-14621 and i-MATH No. CSD2006-00032 is greatly appreciated.

Generación de una librería RVC - CAL para la etapa de estimación de abundancias en el proceso de análisis de imágenes hiperespectrales

Las imágenes hiperespectrales permiten extraer información con una gran resolución espectral, que se suele extender desde el espectro ultravioleta hasta el infrarrojo. Aunque esta tecnología fue aplicada inicialmente a la observación de la superficie terrestre, esta característica ha hecho que, en los últimos años, la aplicación de estas imágenes se haya expandido a otros campos, como la medicina y, en concreto, la detección del cáncer. Sin embargo, este nuevo ámbito de aplicación ha generado nuevas necesidades, como la del procesado de las imágenes en tiempo real. Debido, precisamente, a la gran resolución espectral, estas imágenes requieren una elevada capacidad computacional para ser procesadas, lo que imposibilita la consecución de este objetivo con las técnicas tradicionales de procesado. En este sentido, una de las principales líneas de investigación persigue el objetivo del tiempo real mediante la paralelización del procesamiento, dividiendo esta carga computacional en varios núcleos que trabajen simultáneamente. A este respecto, en el presente documento se describe el desarrollo de una librería de procesado hiperespectral para el lenguaje RVC - CAL, que está específicamente pensado para el desarrollo de aplicaciones multimedia y proporciona las herramientas necesarias para paralelizar las aplicaciones. En concreto, en este Proyecto Fin de Grado se han desarrollado las funciones necesarias para implementar dos de las cuatro fases de la cadena de análisis de una imagen hiperespectral - en concreto, las fases de estimación del número de endmembers y de la estimación de la distribución de los mismos en la imagen -; conviene destacar que este trabajo se complementa con el realizado por Daniel Madroñal en su Proyecto Fin de Grado, donde desarrolla las funciones necesarias para completar las otras dos fases de la cadena. El presente documento sigue la estructura clásica de un trabajo de investigación, exponiendo, en primer lugar, las motivaciones que han cimentado este Proyecto Fin de Grado y los objetivos que se esperan alcanzar con él. A continuación, se realiza un amplio análisis del estado del arte de las tecnologías necesarias para su desarrollo, explicando, por un lado, las imágenes hiperespectrales y, por otro, todos los recursos hardware y software necesarios para la implementación de la librería. De esta forma, se proporcionarán todos los conceptos técnicos necesarios para el correcto seguimiento de este documento. Tras ello, se detallará la metodología seguida para la generación de la mencionada librería, así como el proceso de implementación de una cadena completa de procesado de imágenes hiperespectrales que permita la evaluación tanto de la bondad de la librería como del tiempo necesario para analizar una imagen hiperespectral completa. Una vez expuesta la metodología utilizada, se analizarán en detalle los resultados obtenidos en las pruebas realizadas; en primer lugar, se explicarán los resultados individuales extraídos del análisis de las dos etapas implementadas y, posteriormente, se discutirán los arrojados por el análisis de la ejecución de la cadena completa, tanto en uno como en varios núcleos. Por último, como resultado de este estudio se extraen una serie de conclusiones, que engloban aspectos como bondad de resultados, tiempos de ejecución y consumo de recursos; asimismo, se proponen una serie de líneas futuras de actuación con las que se podría continuar y complementar la investigación desarrollada en este documento. ABSTRACT. Hyperspectral imaging collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However, this new field has shown specific requirements; for example, it needs to accomplish strong time specifications, since all the potential applications - like surgical guidance or in vivo tumor detection - imply real-time requisites. Achieving this time requirements is a great challenge, as hyperspectral images generate extremely high volumes of data to process. For that reason, some new research lines are studying new processing techniques, and the most relevant ones are related to system parallelization: in order to reduce the computational load, this solution executes image analysis in several processors simultaneously; in that way, this computational load is divided among the different cores, and real-time specifications can be accomplished. This document describes the construction of a new hyperspectral processing library for RVC - CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This Diploma Project develops the required library functions to implement two of the four stages of the hyperspectral imaging processing chain - endmember and abundance estimations -. The two other stages - dimensionality reduction and endmember extraction - are studied in the Diploma Project of Daniel Madroñal, which complements the research work described in this document. The document follows the classical structure of a research work. Firstly, it introduces the motivations that have inspired this Diploma Project and the main objectives to achieve. After that, it thoroughly studies the state of the art of the technologies related to the development of the library. The state of the art contains all the concepts needed to understand the contents of this research work, like the definition and applications of hyperspectral imaging and the typical processing chain. Thirdly, it explains the methodology of the library implementation, as well as the construction of a complete processing chain in RVC - CAL applying the mentioned library. This chain will test both the correct behavior of the library and the time requirements for the complete analysis of one hyperspectral image, either executing the chain in one processor or in several ones. Afterwards, the collected results will be carefully analyzed: first of all, individual results -from endmember and abundance estimations stages - will be discussed and, after that, complete results will be studied; this results will be obtained from the complete processing chain, so they will analyze the effects of multithreading and system parallelization on the mentioned processing chain. Finally, as a result of this discussion, some conclusions will be gathered regarding some relevant aspects, such as algorithm behavior, execution times and processing performance. Likewise, this document will conclude with the proposal of some future research lines that could continue the research work described in this document.

Predicción espacio-temporal de la irradiancia solar global a corto plazo en España mediante geoestadística y redes neuronales artificiales

El enriquecimiento del conocimiento sobre la Irradiancia Solar (IS) a nivel de superficie terrestre, así como su predicción, cobran gran interés para las Energías Renovables (ER) - Energía Solar (ES)-, y para distintas aplicaciones industriales o ecológicas. En el ámbito de las ER, el uso óptimo de la ES implica contar con datos de la IS en superficie que ayuden tanto, en la selección de emplazamientos para instalaciones de ES, como en su etapa de diseño (dimensionar la producción) y, finalmente, en su explotación. En este último caso, la observación y la predicción es útil para el mercado energético, la planificación y gestión de la energía (generadoras y operadoras del sistema eléctrico), especialmente en los nuevos contextos de las redes inteligentes de transporte. A pesar de la importancia estratégica de contar con datos de la IS, especialmente los observados por sensores de IS en superficie (los que mejor captan esta variable), estos no siempre están disponibles para los lugares de interés ni con la resolución espacial y temporal deseada. Esta limitación se une a la necesidad de disponer de predicciones a corto plazo de la IS que ayuden a la planificación y gestión de la energía. Se ha indagado y caracterizado las Redes de Estaciones Meteorológicas (REM) existentes en España que publican en internet sus observaciones, focalizando en la IS. Se han identificado 24 REM (16 gubernamentales y 8 redes voluntarios) que aglutinan 3492 estaciones, convirtiéndose éstas en las fuentes de datos meteorológicos utilizados en la tesis. Se han investigado cinco técnicas de estimación espacial de la IS en intervalos de 15 minutos para el territorio peninsular (3 técnicas geoestadísticas, una determinística y el método HelioSat2 basado en imágenes satelitales) con distintas configuraciones espaciales. Cuando el área de estudio tiene una adecuada densidad de observaciones, el mejor método identificado para estimar la IS es el Kriging con Regresión usando variables auxiliares -una de ellas la IS estimada a partir de imágenes satelitales-. De este modo es posible estimar espacialmente la IS más allá de los 25 km identificados en la bibliografía. En caso contrario, se corrobora la idoneidad de utilizar estimaciones a partir de sensores remotos cuando la densidad de observaciones no es adecuada. Se ha experimentado con el modelado de Redes Neuronales Artificiales (RNA) para la predicción a corto plazo de la IS utilizando observaciones próximas (componentes espaciales) en sus entradas y, los resultados son prometedores. Así los niveles de errores disminuyen bajo las siguientes condiciones: (1) cuando el horizonte temporal de predicción es inferior o igual a 3 horas, las estaciones vecinas que se incluyen en el modelo deben encentrarse a una distancia máxima aproximada de 55 km. Esto permite concluir que las RNA son capaces de aprender cómo afectan las condiciones meteorológicas vecinas a la predicción de la IS. ABSTRACT ABSTRACT The enrichment of knowledge about the Solar Irradiance (SI) at Earth's surface and its prediction, have a high interest for Renewable Energy (RE) - Solar Energy (SE) - and for various industrial and environmental applications. In the field of the RE, the optimal use of the SE involves having SI surface to help in the selection of sites for facilities ES, in the design stage (sizing energy production), and finally on their production. In the latter case, the observation and prediction is useful for the market, planning and management of the energy (generators and electrical system operators), especially in new contexts of smart transport networks (smartgrid). Despite the strategic importance of SI data, especially those observed by sensors of SI at surface (the ones that best measure this environmental variable), these are not always available to the sights and the spatial and temporal resolution desired. This limitation is bound to the need for short-term predictions of the SI to help planning and energy management. It has been investigated and characterized existing Networks of Weather Stations (NWS) in Spain that share its observations online, focusing on SI. 24 NWS have been identified (16 government and 8 volunteer networks) that implies 3492 stations, turning it into the sources of meteorological data used in the thesis. We have investigated five technical of spatial estimation of SI in 15 minutes to the mainland (3 geostatistical techniques and HelioSat2 a deterministic method based on satellite images) with different spatial configurations. When the study area has an adequate density of observations we identified the best method to estimate the SI is the regression kriging with auxiliary variables (one of them is the SI estimated from satellite images. Thus it is possible to spatially estimate the SI beyond the 25 km identified in the literature. Otherwise, when the density of observations is inadequate the appropriateness is using the estimates values from remote sensing. It has been experimented with Artificial Neural Networks (ANN) modeling for predicting the short-term future of the SI using observations from neighbor’s weather stations (spatial components) in their inputs, and the results are promising. The error levels decrease under the following conditions: (1) when the prediction horizon is less or equal than 3 hours the best models are the ones that include data from the neighboring stations (at a maximum distance of 55 km). It is concluded that the ANN is able to learn how weather conditions affect neighboring prediction of IS at such Spatio-temporal horizons.

Sistemas multiespectrales e hiperespectrales para la observación del territorio : análisis y aplicación a la prospección de hidrocarburos.

La observación de la Tierra es una herramienta de gran utilidad en la actualidad para el estudio de los fenómenos que se dan en la misma. La observación se puede realizar a distintas escalas y por distintos métodos dependiendo del propósito. El actual Trabajo Final de Grado persigue exponer la observación del territorio mediante técnicas de Teledetección, o Detección Remota, y su aplicación en la exploración de hidrocarburos. Desde la Segunda Guerra Mundial el capturar imágenes aéreas de regiones de la Tierra estaba restringido a usos cartográficos en el sentido estricto. Desde aquellos tiempos, hasta ahora, ha acontecido una serie de avances científicos que permiten deducir características intrínsecas de la Tierra mediante mecanismos complejos que no apreciamos a simple vista, pero que, están configurados mediante determinados parámetros geométricos y electrónicos, que permiten generar series temporales de fenómenos físicos que se dan en la Tierra. Hoy en día se puede afirmar que el aprovechamiento del espectro electromagnético está en un punto máximo. Se ha pasado del análisis de la región del espectro visible al análisis del espectro en su totalidad. Esto supone el desarrollo de nuevos algoritmos, técnicas y procesos para extraer la mayor cantidad de información acerca de la interacción de la materia con la radiación electromagnética. La información que generan los sistemas de captura va a servir para la aplicación directa e indirecta de métodos de prospección de hidrocarburos. Las técnicas utilizadas en detección por sensores remotos, aplicadas en campañas geofísicas, son utilizadas para minimizar costes y maximizar resultados en investigaciones de campo. La predicción de anomalías en la zona de estudio depende del analista, quien diseña, calcula y evalúa las variaciones de la energía electromagnética reflejada o emitida por la superficie terrestre. Para dicha predicción se revisarán distintos programas espaciales, se evaluará la bondad de registro y diferenciación espectral mediante el uso de distintas clasificaciones (supervisadas y no supervisadas). Por su influencia directa sobre las observaciones realizadas, se realiza un estudio de la corrección atmosférica; se programan distintos modelos de corrección atmosférica para imágenes multiespectrales y se evalúan los métodos de corrección atmosférica en datos hiperespectrales. Se obtendrá temperatura de la zona de interés utilizando los sensores TM-4, ASTER y OLI, así como un Modelo Digital del Terreno generado por el par estereoscópico capturado por el sensor ASTER. Una vez aplicados estos procedimientos se aplicarán los métodos directos e indirectos, para la localización de zonas probablemente afectadas por la influencia de hidrocarburos y localización directa de hidrocarburos mediante teledetección hiperespectral. Para el método indirecto se utilizan imágenes capturadas por los sensores ETM+ y ASTER. Para el método directo se usan las imágenes capturadas por el sensor Hyperion. ABSTRACT The observation of the Earth is a wonderful tool for studying the different kind of phenomena that occur on its surface. The observation could be done by different scales and by different techniques depending on the information of interest. This Graduate Thesis is intended to expose the territory observation by remote sensing acquiring data systems and the analysis that can be developed to get information of interest. Since Second World War taking aerials photographs of scene was restricted only to a cartographic sense. From these days to nowadays, it have been developed many scientific advances that make capable the interpretation of the surface behavior trough complex systems that are configure by specific geometric and electronic parameters that make possible acquiring time series of the phenomena that manifest on the earth’s surface. Today it is possible to affirm that the exploitation of the electromagnetic spectrum is on a maxim value. In the past, analysis of the electromagnetic spectrum was carry in a narrow part of it, today it is possible to study entire. This implicates the development of new algorithms, process and techniques for the extraction of information about the interaction of matter with electromagnetic radiation. The information that has been acquired by remote sensing sensors is going to be a helpful tool for the exploration of hydrocarbon through direct and vicarious methods. The techniques applied in remote sensing, especially in geophysical campaigns, are employed to minimize costs and maximize results of ground-based geologic investigations. Forecasting of anomalies in the region of interest depends directly on the expertise data analyst who designs, computes and evaluates variations in the electromagnetic energy reflected or emanated from the earth’s surface. For an optimal prediction a review of the capture system take place; assess of the goodness in data acquisition and spectral separability, is carried out by mean of supervised and unsupervised classifications. Due to the direct influence of the atmosphere in the register data, a study of the minimization of its influence has been done; a script has been programed for the atmospheric correction in multispectral data; also, a review of hyperspectral atmospheric correction is conducted. Temperature of the region of interest is computed using the images captured by TM-4, ASTER and OLI, in addition to a Digital Terrain Model generated by a pair of stereo images taken by ASTER sensor. Once these procedures have finished, direct and vicarious methods are applied in order to find altered zones influenced by hydrocarbons, as well as pinpoint directly hydrocarbon presence by mean of hyperspectral remote sensing. For this purpose ETM+ and ASTER sensors are used to apply the vicarious method and Hyperion images are used to apply the direct method.

A Simple RVoG Test for PolInSAR Data

In this paper, we present a simple algorithm for assessing the validity of the RVoG model for PolInSAR-based inversion techniques. This approach makes use of two important features characterizing a homogeneous random volume over a ground surface, i.e., the independence on polarization states of wave propagation through the volume and the structure of the polarimetric interferometric coherency matrix. These two features have led to two different methods proposed in the literature for retrieving the topographic phase within natural covers, i.e., the well-known line fitting procedure and the observation of the (1, 2) element of the polarimetric interferometric coherency matrix. We show that differences between outputs from both approaches can be interpreted in terms of the PolInSAR modeling based on the Freeman-Durden concept, and this leads to the definition of a RVoG/non-RVoG test. The algorithm is tested with both indoor and airborne data over agricultural and tropical forest areas.

Earth observations : applications, research, training, services.

"A NASA industrial applications center"--P. [4] of cover.

Satellite earth resources data : a users assistance publication /

"Module U-3."

Annual earth resources program review.

Mode of access: Internet.

The Earth from orbit : Apollo-Soyuz pamphlet no. 5.

Bibliography: p. 56-57.

Open-source mapping and services for Web-based land-cover validation

Monitoring land-cover changes on sites of conservation importance allows environmental problems to be detected, solutions to be developed and the effectiveness of actions to be assessed. However, the remoteness of many sites or a lack of resources means these data are frequently not available. Remote sensing may provide a solution, but large-scale mapping and change detection may not be appropriate, necessitating site-level assessments. These need to be easy to undertake, rapid and cheap. We present an example of a Web-based solution based on free and open-source software and standards (including PostGIS, OpenLayers, Web Map Services, Web Feature Services and GeoServer) to support assessments of land-cover change (and validation of global land-cover maps). Authorised users are provided with means to assess land-cover visually and may optionally provide uncertainty information at various levels: from a general rating of their confidence in an assessment to a quantification of the proportions of land-cover types within a reference area. Versions of this tool have been developed for the TREES-3 initiative (Simonetti, Beuchle and Eva, 2011). This monitors tropical land-cover change through ground-truthing at latitude / longitude degree confluence points, and for monitoring of change within and around Important Bird Areas (IBAs) by Birdlife International and the Royal Society for the Protection of Birds (RSPB). In this paper we present results from the second of these applications. We also present further details on the potential use of the land-cover change assessment tool on sites of recognised conservation importance, in combination with NDVI and other time series data from the eStation (a system for receiving, processing and disseminating environmental data). We show how the tool can be used to increase the usability of earth observation data by local stakeholders and experts, and assist in evaluating the impact of protection regimes on land-cover change.

Rubric-Q:adding quality-related elements to the GEOSS clearinghouse datasets

Geospatial data have become a crucial input for the scientific community for understanding the environment and developing environmental management policies. The Global Earth Observation System of Systems (GEOSS) Clearinghouse is a catalogue and search engine that provides access to the Earth Observation metadata. However, metadata are often not easily understood by users, especially when presented in ISO XML encoding. Data quality included in the metadata is basic for users to select datasets suitable for them. This work aims to help users to understand the quality information held in metadata records and to provide the results to geospatial users in an understandable and comparable way. Thus, we have developed an enhanced tool (Rubric-Q) for visually assessing the metadata quality information and quantifying the degree of metadata population. Rubric-Q is an extension of a previous NOAA Rubric tool used as a metadata training and improvement instrument. The paper also presents a thorough assessment of the quality information by applying the Rubric-Q to all dataset metadata records available in the GEOSS Clearinghouse. The results reveal that just 8.7% of the datasets have some quality element described in the metadata, 63.4% have some lineage element documented, and merely 1.2% has some usage element described. © 2013 IEEE.

Theory and Practice in Sustainability Science: Influence of Urban Form on the Urban Heat Island and Implications for Urban Systems

As the world population continues to grow past seven billion people and global challenges continue to persist including resource availability, biodiversity loss, climate change and human well-being, a new science is required that can address the integrated nature of these challenges and the multiple scales on which they are manifest. Sustainability science has emerged to fill this role. In the fifteen years since it was first called for in the pages of Science, it has rapidly matured, however its place in the history of science and the way it is practiced today must be continually evaluated. In Part I, two chapters address this theoretical and practical grounding. Part II transitions to the applied practice of sustainability science in addressing the urban heat island (UHI) challenge wherein the climate of urban areas are warmer than their surrounding rural environs. The UHI has become increasingly important within the study of earth sciences given the increased focus on climate change and as the balance of humans now live in urban areas.

In Chapter 2 a novel contribution to the historical context of sustainability is argued. Sustainability as a concept characterizing the relationship between humans and nature emerged in the mid to late 20th century as a response to findings used to also characterize the Anthropocene. Emerging from the human-nature relationships that came before it, evidence is provided that suggests Sustainability was enabled by technology and a reorientation of world-view and is unique in its global boundary, systematic approach and ambition for both well being and the continued availability of resources and Earth system function. Sustainability is further an ambition that has wide appeal, making it one of the first normative concepts of the Anthropocene.

Despite its widespread emergence and adoption, sustainability science continues to suffer from definitional ambiguity within the academe. In Chapter 3, a review of efforts to provide direction and structure to the science reveals a continuum of approaches anchored at either end by differing visions of how the science interfaces with practice (solutions). At one end, basic science of societally defined problems informs decisions about possible solutions and their application. At the other end, applied research directly affects the options available to decision makers. While clear from the literature, survey data further suggests that the dichotomy does not appear to be as apparent in the minds of practitioners.

In Chapter 4, the UHI is first addressed at the synoptic, mesoscale. Urban climate is the most immediate manifestation of the warming global climate for the majority of people on earth. Nearly half of those people live in small to medium sized cities, an understudied scale in urban climate research. Widespread characterization would be useful to decision makers in planning and design. Using a multi-method approach, the mesoscale UHI in the study region is characterized and the secular trend over the last sixty years evaluated. Under isolated ideal conditions the findings indicate a UHI of 5.3 ± 0.97 °C to be present in the study area, the magnitude of which is growing over time.

Although urban heat islands (UHI) are well studied, there remain no panaceas for local scale mitigation and adaptation methods, therefore continued attention to characterization of the phenomenon in urban centers of different scales around the globe is required. In Chapter 5, a local scale analysis of the canopy layer and surface UHI in a medium sized city in North Carolina, USA is conducted using multiple methods including stationary urban sensors, mobile transects and remote sensing. Focusing on the ideal conditions for UHI development during an anticyclonic summer heat event, the study observes a range of UHI intensity depending on the method of observation: 8.7 °C from the stationary urban sensors; 6.9 °C from mobile transects; and, 2.2 °C from remote sensing. Additional attention is paid to the diurnal dynamics of the UHI and its correlation with vegetation indices, dewpoint and albedo. Evapotranspiration is shown to drive dynamics in the study region.

Finally, recognizing that a bridge must be established between the physical science community studying the Urban Heat Island (UHI) effect, and the planning community and decision makers implementing urban form and development policies, Chapter 6 evaluates multiple urban form characterization methods. Methods evaluated include local climate zones (LCZ), national land cover database (NCLD) classes and urban cluster analysis (UCA) to determine their utility in describing the distribution of the UHI based on three standard observation types 1) fixed urban temperature sensors, 2) mobile transects and, 3) remote sensing. Bivariate, regression and ANOVA tests are used to conduct the analyses. Findings indicate that the NLCD classes are best correlated to the UHI intensity and distribution in the study area. Further, while the UCA method is not useful directly, the variables included in the method are predictive based on regression analysis so the potential for better model design exists. Land cover variables including albedo, impervious surface fraction and pervious surface fraction are found to dominate the distribution of the UHI in the study area regardless of observation method.

Chapter 7 provides a summary of findings, and offers a brief analysis of their implications for both the scientific discourse generally, and the study area specifically. In general, the work undertaken does not achieve the full ambition of sustainability science, additional work is required to translate findings to practice and more fully evaluate adoption. The implications for planning and development in the local region are addressed in the context of a major light-rail infrastructure project including several systems level considerations like human health and development. Finally, several avenues for future work are outlined. Within the theoretical development of sustainability science, these pathways include more robust evaluations of the theoretical and actual practice. Within the UHI context, these include development of an integrated urban form characterization model, application of study methodology in other geographic areas and at different scales, and use of novel experimental methods including distributed sensor networks and citizen science.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.