Assessment of organic matter temporal dynamics in the klyazma basin using remote sensing and qgis trends.earth

Т А Трифонова ,Н В Мищенко ,Pavel Shutov

doi:10.5377/nexo.v34i02.11624

Т А Трифонова , Н В Мищенко + Show 1 more

Open Access

https://doi.org/10.5377/nexo.v34i02.11624

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Abstract

The article addresses the dynamics of biological processes in various landscapes within a holistic natural geosystem—a catchment area. The Klyazma river (the fourth order tributary to the Volga) was selected as the object of study. The natural complex of the Klyazma river basin is a combination of different landscapes, each marked by a diverse composition of geomorphological and soil-vegetation structures. The study is based on remote sensing data and the Trends.Earth Land Degradation Monitoring Project (Land Cover Dataset, European Space Agency 2015, 300 m spatial resolution) implemented using the open-source Quantum GIS 2.18. Four landscape provinces and eight site were identified in the studied catchment area according to the geomorphological structure and the soil and vegetation cover. The ecosystem parameters Gross Primary Productivity, Net Primary Productivity, and Ecosystem Respiration were measured in the identified sites. In different landscapes the biological processes, characterizing the organic matter dynamics in the form of plant production and organic matter accumulation, differ in both rate and intensity, and variously respond to the changes in climate parameters and land use. The river basin, as a holistic ecosystem, showed sufficient stability of the dynamic processes. This suggests that holistic natural ecosystems, such as catchment areas, have internal compensatory mechanisms that maintain the development stability over long period of time, while irrational land use remains the main damaging factor.

Highlights

Practical implications of environmental research on a planetary scale have become more substantial due to the development of space-borne remote sensing, which allows examining the surface conditions of vast areas with high spatial and temporal resolution
The open-source global data on gross primary productivity (GPP) collected by the MODIS sensor are increasingly used to examine the carbon cycle associated with terrestrial ecosystems (Turner et al, 2006; Wu et al, 2010)
Some works have addressed the relationship between the change in land use and the spatiotemporal dynamics of carbon flows (Novick et al, 2015)

Summary

Introduction

Practical implications of environmental research on a planetary scale have become more substantial due to the development of space-borne remote sensing, which allows examining the surface conditions of vast areas with high spatial and temporal resolution. There have appeared ecosystem productivity evaluation models that use remote sensing data and operate indicative measurable indices, such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) (Hashimoto et al, 2012; Chen et al, 2019). Despite this interest in remote sensing applications, there is still a need for commonly accepted approaches to assessing, analyzing, and forecasting the biological productivity of ecosystems (Robinson et al, 2018; Li et al, 2019; Varghese, & Behera, 2019). Some works have addressed the relationship between the change in land use and the spatiotemporal dynamics of carbon flows (Novick et al, 2015)

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