Research Methodology


        Satellite remote sensing is an important modeling tool which offers precise spatio-temporal information on bio-geophysical urban environment variables as well as of health and condition state of urban green.
In frame of the project, to solve these problem different statistical-empirical and physically based methods that can be used to derive biophysical and biochemical variables of urban environment from optical remote sensing data in the VNIR-SWIR regions will be developed. Subsequently, the prevailing techniques of assimilating remote sensing data into urban ecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing the dynamical functioning of urban ecosystems has significantly increased computational demands. However, the study of Bucharest urban ecosystem dynamics model will be  achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with complementary data of different origin, such as radar/microwave available on cloudy and during night periods.

        Satellite remote sensing used in this project as tool for urban hot areas defining is higher performant comparable with other techniques because is generating data and urban spatial georeferenced extensions by

-Physical characterization of urban systems (delineation, land use classification. Intern urban structures identification, etc), demographyc/socio-economic;

-Measuring and monitoring of bio-geophysical urban systems properties (vegetation, air quality, termal aspects, surface waters, soils, etc.);

-Impact and vulnerability analysis (includung water management and waste land or ilegal urban habitats ) by co-analysis of physical and demographyic/socio-economic urban environment;

-Urban changes monitoring and spatio-temporal dynamics.

        The proposal project will develop and use spectral radiative/ regional climatic models based on satellite series-time data from LANDSAT (MSS, TM, ETM), MODIS, ASTER, AVHRR, SPOT, SAR -ERS, INKONOS and Quick Bird, of numerical simulations over different periods of time represents a new investigation and forecasting method of urban land use/cover changes due to anthropogenic and climatic stressors. Predictive modeling of anthropogenic and climatic stressors impact on urban ecosystems by satellite remote sensing and in situ biogeophysical data is aimed for optimal decision policies that may ensure the risk-reduction of global warming due to anthropogenic radiative forcing.
        Prediction of both natural variability and human impacts on urban ecosystems is inherently probabilistic, due to uncertainties in forecast initial conditions, representation of key processes within models, and climatic forcing factors. Hence, reliable estimates of anthropogenic and climatic risk on urban systems can only be made through ensemble integrations of Earth - System Models in which these uncertainties are explicitly incorporated. For the first time ever, a common ensemble forecast system will be developed for use across a range of timescales (seasonal, decadal, and longer) and spatial scales (global, regional, and local). These models will be used to construct integrated scenarios of future urban system change, due to urban growth, pollution and climatic factors including both non-intervention and stabilization scenarios.
        This will provide a basis for quantitative risk assessment of urban change and variability, with emphasis on changes in extremes, including changes in severe pollution episodes, storminess and precipitation, and the severity and frequency of drought. Most importantly, the models will be extensively validated with in situ monitoring meteorological, biogeophysical and spectroradiometric data, laser and satellite remote sensing data, being compared against quality-controlled, high-resolution girded datasets for Europe. Probability forecasts for Bucharest urban system made with the model system on the seasonal and decadal timescales will also be validated against the existing data.
        The exploitation of the results will be maximized by linking the outputs of the ensemble prediction system to a wide range of applications. In turn, feedbacks from these impact areas back to the climate system will also be addressed.
        Climate variability and extreme climatic events are risk factors for climate sensitive activities such as urban ecosystems and urban vegetation considered like the town's lungs. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. temperature, rainfall, runoff) is provided in probabilistic terms (e.g. via cumulative distribution functions- CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. This project will provide "inferential "statistical approaches needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events.
        Precise geo-spatial information provided by satellite remote sensing, GIS monitoring integrated systems about the spatial distribution of changes in vegetation landcover, floods, draught, etc. will be used to estimate the impacts of urban ecosystem degrading and fragmentation.
        The spatial pattern of landuse/landcover in these areas will consider the different levels of fragmentation of areas in urban and periurban systems. Fragmented agro-forest patches and forest near clearance edges in periurban areas are susceptible to an array of human and bio-climatological impacts.

        Based on satellite and in situ spectroradiometric data and other geophysical and simulated data, in frame of this  project will be  developed an original spectral model of vegetation reflected field in the visible and near infrared spectral domains, which will allow to quantify the changes assessments of urban stressed vegetation under different conditions and extreme climatic events in Romania. Will be establishing connections between the biophysical parameters of a vegetation cover (the reflected radiation flux in different spectral channels) and the geometrical and radiometric factors that characterize the interaction processes in the atmosphere-vegetation-soil system. The proposed vegetation reflectance model, based
on the radiative transfer theory and on the concepts developed by the turbid models will allow the computing of bidirectional reflectances as a function of the optical proprieties of urban vegetation.
        Through the development of a model of Integrated System for monitoring, predictive assessment and risk alarm of urban  ecosystem Bucharest will be addressed such problems as real-time monitoring of green urban vegetation condition, weather impact on ecosystem and the environment, estimating soil moisture, etc.  This system can be used for long-term analysis as well, providing information about urban ecosystem resources, changes in the environment and biodiversity, addressing such important issues as deforestation.
        Climatic modeling will be achieved through adapting Climatic Regional Models of very fine resolution (RCM) approximately 10 km, to perform model calibration and validation on different scale urban areas.
        Project will apply AutoRegressive-Moving Average (ARMA) time series models to a different time period sequences of images of selected ecosystems test areas in Romania. ARMA models are shown to be useful in deriving a spatial summary of the temporal characteristics of the modeled phenomenon, for identifying changes within that system, and for forecasting into the future with statistical confidence.

        Since the launches of Landsat-1 in 1972, we have amassed a nearly continuous record of electromagnetic radiation reflected and emitted from the Earth's surface at visible, near-IR and microwave wavelengths. The images returned from these satellites have given us unique opportunities to monitor the Earth's environment and look for changes over time. We are quickly approaching the point at which we can use remote sensing data to derive proxy indicators of 30-year climatologically normals.
        Thus remote sensing helps in spectral modeling of ecosystems having a critical role to play in climate changes and impacts of its feedbacks on landuse/landcover monitoring and modeling especially in the light of the recent evidence of global warming. If we are to effectively use remotely sensed imagery for climate change studies, extend the conventional change analysis paradigm.
        In frame of this project will be developed for the development of a new suite of analysis procedures named hyper temporal image analysis, for identifying change and variability across long temporal sequences of images image trend analysis for next 50 years, principal components analysis and temporal mixture analysis.

        In frame of this project will be developed:










PREDICTIVE MODELING OF BUCHAREST URBAN SYSTEM DYNAMICS AND QUANTIFICATION OF ANTHROPOGENIC AND CLIMATE FACTORS IMPACTS THROUGH SATELLITE REMOTE SENSING AND BIO-GEOPHYSICAL IN-SITU DATA