Multi-model approach for remote sensing data processing and interpretation is described. The problem of satellite data
utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Observation,
measurement and modeling data utilization method in the framework of multi-model approach is described.
Methodology and models of risk assessment in framework of decision support approach are defined and described.
Method of water quality assessment using satellite observation data is described. Method is based on analysis of spectral
reflectance of aquifers. Spectral signatures of freshwater bodies and offshores are analyzed. Correlations between
spectral reflectance, pollutions and selected water quality parameters are analyzed and quantified. Data of MODIS,
MISR, AIRS and Landsat sensors received in 2002-2014 have been utilized verified by in-field spectrometry and lab
measurements. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed.
Decision on water quality category is making based on fuzzy algorithm using limited set of uncertain parameters. Data
from satellite observations, field measurements and modeling is utilizing in the framework of the approach proposed. It
is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Problems of construction
of spatial and temporal distribution of calculated parameters, as well as a problem of data regularization are discussed.
Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated and
discussed.
Problem of remote sensing data harnessing for decision making in conflict territories is considered. Approach for analysis of socio-economic and demographic parameters with a limited set of data and deep uncertainty is described. Number of interlinked techniques to estimate a population and economy in crisis territories are proposed. Stochastic method to assessment of population dynamics using multi-source data using remote sensing data is proposed. Adaptive Markov’s chain based method to study of land-use changes using satellite data is proposed. Proposed approach is applied to analysis of socio-economic situation in Donbas (East Ukraine) territory of conflict in 2014-2015. Land-use and landcover patterns for different periods were analyzed using the Landsat and MODIS data . The land-use classification scheme includes the following categories: (1) urban or built-up land, (2) barren land, (3) cropland, (4) horticulture farms, (5) livestock farms, (6) forest, and (7) water. It was demonstrated, that during the period 2014-2015 was not detected drastic changes in land-use structure of study area. Heterogeneously distributed decreasing of horticulture farms (4-6%), livestock farms (5-6%), croplands (3-4%), and increasing of barren land (6-7%) have been observed. Way to analyze land-cover productivity variations using satellite data is proposed. Algorithm is based on analysis of time-series of NDVI and NDWI distributions. Drastic changes of crop area and its productivity were detected. Set of indirect indicators, such as night light intensity, is also considered. Using the approach proposed, using the data utilized, the local and regional GDP, local population, and its dynamics are estimated.
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