Abstract
Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.
Highlights
Global change in the natural environment is caused by different factors
Our previous work was devoted to consideration of the applicability of vegetation indices derived from satellite imagery to tracking the dynamics of vegetation cover, and to indirect assessment of the climate dynamics
At the different stages of the study we used for analysis 8-day MOD09A1 (Vermote, 2015) and 1-day MOD09GA (Vermote, Wolfe, 2015) Moderate Resolution Imaging Spectroradiometer (MODIS) composites. 1-day resolution datasets potentially give a possibility to ensure monitoring of vegetation cover dynamics and indirect estimation of climate parameters change with a ground observations discreetness
Summary
Global change in the natural environment is caused by different factors. It is needed to detect and explore dependencies between the environment components to understand and forecast change trends. Study of global change and change in climate and vegetation cover involves the analysis of retrospective data collected using both ground-based observation networks and remote sensing (satellite) measurements over the past 30-40 years and more (if available). Our research is conducted to investigate the possibilities for common application of remote sensing and ground-based monitoring data, and aimed on production of some synergy by fusion of ground-based data accuracy with spatial resolution of satellite observations when studying dynamics and change in climate and vegetation cover parameters. Studies performed earlier by other authors (Medvedeva et al, 2008; Miklashevich, Bartalev, 2016), as well as some results of our previously conducted research (Panidi et al, 2017; 2018; 2019), demonstrate a feasibility of integrating approach This integration assumes investigation of change in the climatevegetation system instead of separated analysis of climate and(or) vegetation cover. Involvement of vegetation indices ( NDWI) makes it possible to detect regional-scale differentiation (i.e., spatial heterogeneity) of growing season characteristics, while the ground observation network can be sparse and hardly applicable to solve this problem
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