Mr. Daniel Carcereri Profile

Daniel Carcereri

at Univ degli Studi di Trento

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Proceedings Article | 7 October 2019 Presentation + Paper

Identification of non-photosynthetic vegetation areas in Sentinel-2 satellite image time series

Yady Tatiana Solano-Correa, Daniel Carcereri, Francesca Bovolo, Lorenzo Bruzzone

Proceedings Volume 11155, 111550Y (2019) https://doi.org/10.1117/12.2533761

KEYWORDS: Vegetation, Earth observing sensors, Remote sensing, MODIS, Satellite imaging, Satellites, Photovoltaics, Sensors, Spatial resolution, Landsat

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Information regarding both the spatial distribution and the quantity of vegetation components is of great relevance in different fields. Of particular interest is the detection of Non-Photosynthetic Vegetation (NPV) against Photosynthetic Vegetation (PV) and Bare Soil (BS). In-situ approaches exist that identify NPV, but are time and cost expensive. In this context, remote sensing is useful thanks to its ability to provide information at different temporal, spatial and spectral scales. While commonly used vegetation indexes, such as the Normalized Difference Vegetation Index (NDVI), provide robust information to highlight PV, the distinction of NPV from BS is less trivial. Some indices designed for Landsat and Sentinel-2 (S2) bands identify only a part of NPV, the one related to tillage, but they do not provide the proper differentiation of NPV and BS. The Cellulose Absorption Index (CAI) exists that may highlight the presence of NPV. Nevertheless, available broadband multispectral sensors like MODIS, Landsat or S2 do not spectrally resolve these narrow wavelength features, thus CAI cannot be directly extracted. This paper presents a surrogated index for the identification and differentiation of NPV, PV and BS in high spatial resolution S2 Satellite Image Time Series (SITS). To do so, inspiration is taken from the paper presented by Guerschman et al., where a surrogated CAI (CAI*) for MODIS sensor is presented, and moves one step forward in order extend it to high spatial resolution sensors. The S2 CAI* was qualitatively analysed on four different climate zones covering grassland and cropland areas.

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