Dr. Aris Kyparissis Profile

Dr. Aris Kyparissis

at Univ of Ioannina

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Proceedings Article | 6 August 2018 Paper

Effects of satellite spatial resolution on gross primary productivity estimation through light use efficiency modeling

Theofilos Vanikiotis, Stavros Stagakis, Aris Kyparissis

Proceedings Volume 10773, 107731R (2018) https://doi.org/10.1117/12.2326605

KEYWORDS: Spatial resolution, MODIS, Satellites, Data modeling, Sensors, Ecosystems, Data analysis, Vegetation, Photosynthesis, Reflectivity

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Terrestrial Gross Primary Productivity (GPP) describes the total amount of CO2 assimilated by plants in an ecosystem during photosynthesis and is considered the largest flux component of the global carbon cycle. One of the most prominent techniques for estimating GPP at ecosystem scale is the Light Use Efficiency (LUE) approach, taking advantage of the spatiotemporal capabilities that satellite data provide. LUE expresses GPP as the product of absorbed photosynthetically active radiation (APAR) and the efficiency (ε) that APAR is converted to biomass. Although satellite imagery is the key component of such models, the effects of image spatial resolution on model performance have not been thoroughly investigated. The emergence of new satellite instruments with enhanced spatial, spectral and temporal capabilities (i.e. Copernicus Sentinels) provides the opportunity for GPP estimation in high spatial resolution and comparison with low resolution GPP products (i.e. MODIS). In this study, a LUE model is applied to three satellite instruments with different spatial resolution: MODIS (500 m), Sentinel-3 (300 m) and Sentinel-2 (10 m). The GPP estimates of the three instruments are compared over six forest sites in Greece: two deciduous (Quercus sp., Fagus sylvatica), two coniferous (Pinus nigra, Pinus halepensis) and two mixed (Pinus nigra with Fagus sylvatica). The results demonstrate that spatial resolution is not a crucial parameter for LUE modeling in wide, homogenous and fully covered forested areas. The spatial resolution is more important when applying LUE in mixed canopies or partially covered forested areas due to the effects of the different land cover types. To that purpose, Sentinel-2 presents a unique potential for accurate characterization of the land cover type and dynamics, due to the increased spatial resolution and frequent coverage, appearing as a prominent tool for future large scale GPP monitoring.

Proceedings Article | 10 October 2007 Paper

Ecosystem productivity and dynamics issued from multispectral and hyperspectral satellite imagery

Aris Kyparissis, Nikos Markos, Stavros Stagakis, Efi Levizou, Olga Sykioti

Proceedings Volume 6742, 67420I (2007) https://doi.org/10.1117/12.737688

KEYWORDS: MODIS, Satellites, Ecosystems, Vegetation, Photosynthesis, Reflectivity, Data modeling, Atmospheric modeling, Hyperspectral imaging, Sensors

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Low resolution images from MODIS multispectral sensor are used for extracting indices correlated with major parameters of productivity, for two deciduous forests (Fagus sylvatica, Quercus sp.) and one shrubland dominated by the semi-deciduous Phlomis fruticosa. Ground ecophysiological measurements were conducted for three growing periods (2005-2007) and are used for indices evaluation as well as input parameters for an ecosystem productivity model. The results of the ground-based productivity model are compared to the 8day MODIS GPP product, showing that MODIS algorithm underestimates productivity and does not closely follow ecosystem dynamics. In an attempt for a more precise productivity product a new light-use efficiency model based on satellite and meteorological data is designed and presented. Moreover, hyperspectral images from CHRIS/PROBA are used for a more detailed study of the semi-decidual Phlomis fruticosa ecosystem. Ground ecophysiological measurements from two growing periods (2006-2007) are used for evaluation purposes. Images are geometrically corrected and atmospherically adjusted. The reflectance spectra obtained are used for extracting indices related to numerous plant physiological parameters. Fast responsive plant processes, such as the function of the photosynthetic apparatus, the photoprotective response to stress factors (low or high temperature, lack of precipitation) and the detailed pigment content of leaves (chlorophyll a, chlorophyll b, carotenoids) may well be followed by such indices issued from hyperspetral data, offering great advantage over multispectral images for ecosystem remote sensing.

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