An automated GIS-based technique for evaluation of indirect growing season estimations

• Main Authors: Tsepelev, V. Yu., Panidi, Evgeny, Rykin, Ivan S.
• Format: Article in Journal/Newspaper
• Language: English
• Published: FOSS4G, Open Source Geospatial Foundation (OSGeo) 2019
• Subjects: Information Technology; Academic; taiga; Tundra
• Online Access: https://dx.doi.org/10.5446/43575; https://av.tib.eu/media/43575

A number of climate change research projects discover dependencies between dynamics of vegetation indexes and dynamics of meteorological parameters, which make possible estimation and monitoring of growing season parameters using remote sensing data. In our study, we use Normalized Difference Water Index (NDWI) that can be derived automatically from the daily satellite imagery collected by MODIS sensor. The NDWI indicates amount of liquid water in plant tissue, and then reflects change of vegetation growing conditions and particularly growing season change. To ensure monitoring of growing season parameters we elaborated an automated software complex that incorporates desktop Geographic Information System (GIS) software (QGIS was used), geospatial database and complex of computational tools. The GIS is used as an infrastructure element for operating and visualization purposes, while the database together with computational tools enable storage and multipurpose processing of meteorological and remote sensing data. The meteorological data is collected for the past period of 130 years and NDWI data for the 20 years. Developed complex is tested on the example of Republic of Komi (Northern part of European Russia) that is covered by Taiga and Tundra natural zones and impacted by different climate forming factors. Currently we describe architecture of the elaborated complex and design of data processing chains. Elaborated complex ensure automation of downloading raw remote sensing data and reprocessing it into gridded NDWI maps. In this context, it can be underlined that daily collected MODIS imagery can be discovered as big geospatial data, due to this we were needed to resolve a number of optimization tasks to implement its processing. Subsequently, NDWI data is used to produce gridded map series that reflects time and spatial dynamics of growing season characteristics. Produced data have a special significance for areas with sparse meteorological network.