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PhenoSat – A Tool for Remote Sensing Based Analysis of Vegetation Dynamics

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Multitemporal Remote Sensing

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 20))

Abstract

PhenoSat is a software tool that extracts phenological information from satellite based vegetation index time-series. This chapter presents PhenoSat and tests its main characteristics and functionalities using a multi-year experiment and different vegetation types – vineyard and semi-natural meadows. Three important features were analyzed: (1) the extraction of phenological information for the main growing season, (2) detection and estimation of double growth season parameters, and (3) the advantages of selecting a sub-temporal region of interest. Temporal NDVI satellite data from SPOT VEGETATION and NOAA AVHRR were used. Six fitting methods were applied to filter the satellite noise data: cubic splines, piecewise-logistic, Gaussian models, Fourier series, polynomial curve-fitting and Savitzky-Golay. PhenoSat showed to be capable to extract phenological information consistent with reference measurements, presenting in some cases correlations above 70 % (n = 10; p ≤ 0.012). The start of in-season regrowth in semi-natural meadows was detected with a precision lower than 10-days. The selection of a temporal region of interest, improve the fitting process (R-square increased from 0.596 to 0.997). This improvement detected more accurately the maximum vegetation development and provided more reliable results. PhenoSat showed to be capable to adapt to different vegetation types, and different satellite data sources, proving to be a useful tool to extract metrics related with vegetation dynamics.

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Acknowledgments

The authors would like to thank JOINT RESEARCH CENTRE (Community Image Data portal) for providing access to the SPOT_VGT and AVHRR images.

Arlete Rodrigues would like to thank to Fundação para a Ciência e a Tecnologia (FCT) for the Doctoral Grant (SFRH/BD/62189/2009).

Part of this project was supported by European Regional Development Fund (ERDF), programme COMPETE and National funds by FCT – Fundação para a Ciência e a Tecnologia, project PTDC/AGR-AAM/67182/2006, LAMESAT_XXI.

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Authors and Affiliations

  1. Faculdade de Ciências da Universidade do Porto, Department of GAOT & Centro de Investigação em Ciências Geo-Espaciais, Universidade do Porto, Porto, Portugal

    Arlete Rodrigues & Mário Cunha

  2. Faculdade de Ciências da Universidade do Porto, Department of Matemática & INESC TEC, Universidade do Porto, Porto, Portugal

    André R. S. Marcal

Authors
  1. Arlete Rodrigues
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  2. André R. S. Marcal
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Correspondence to Arlete Rodrigues .

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Editors and Affiliations

  1. Division of Geoinformatics, KTH Royal Institute of Technology, Stockholm, Sweden

    Yifang Ban

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Rodrigues, A., Marcal, A.R.S., Cunha, M. (2016). PhenoSat – A Tool for Remote Sensing Based Analysis of Vegetation Dynamics. In: Ban, Y. (eds) Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-47037-5_10

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