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Article

Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57

1
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
2
Centre d’Etudes Spatiales de la BIOsphere, Toulouse University, CNES, CNRS, IRD, UPS, (Toulouse), 31401 Toulouse, France
3
Institut National de la Recherche Agronomique–Université d’Avignon et des Pays du Vaucluse (INRA-UAPV), 228 Route de l’Aérodrome, 84914 Avignon, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1032; https://doi.org/10.3390/rs10071032
Received: 15 May 2018 / Revised: 22 June 2018 / Accepted: 27 June 2018 / Published: 30 June 2018
(This article belongs to the Special Issue Land Surface Phenology )
Land Surface Phenology (LSP) and Leaf Area Index (LAI) are important variables that describe the photosynthetically active phase and capacity of vegetation. Both are derived on the global scale from optical satellite sensors and require robust validation based on in situ sensors at high temporal resolution. This study assesses the PAI Autonomous System from Transmittance Sensors at 57° (PASTiS-57) instrument as a low-cost transmittance sensor for simultaneous monitoring of LSP and LAI in forest ecosystems. In a field experiment, spring leaf flush and autumn senescence in a Dutch beech forest were observed with PASTiS-57 and illumination independent, multi-temporal Terrestrial Laser Scanning (TLS) measurements in five plots. Both time series agreed to less than a day in Start Of Season (SOS) and End Of Season (EOS). LAI magnitude was strongly correlated with a Pearson correlation coefficient of 0.98. PASTiS-57 summer and winter LAI were on average 0.41 m2m−2 and 1.43 m2m−2 lower than TLS. This can be explained by previously reported overestimation of TLS. Additionally, PASTiS-57 was implemented in the Discrete Anisotropic Radiative Transfer (DART) Radiative Transfer Model (RTM) model for sensitivity analysis. This confirmed the robustness of the retrieval with respect to non-structural canopy properties and illumination conditions. Generally, PASTiS-57 fulfilled the CEOS LPV requirement of 20% accuracy in LAI for a wide range of biochemical and illumination conditions for turbid medium canopies. However, canopy non-randomness in discrete tree models led to strong biases. Overall, PASTiS-57 demonstrated the potential of autonomous devices for monitoring of phenology and LAI at daily temporal resolution as required for validation of satellite products that can be derived from ESA Copernicus’ optical missions, Sentinel-2 and -3. View Full-Text
Keywords: Land Surface Phenology; Leaf Area Index; ground-based; forest; validation; radiative transfer model; DART model Land Surface Phenology; Leaf Area Index; ground-based; forest; validation; radiative transfer model; DART model
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MDPI and ACS Style

Brede, B.; Gastellu-Etchegorry, J.-P.; Lauret, N.; Baret, F.; Clevers, J.G.P.W.; Verbesselt, J.; Herold, M. Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57. Remote Sens. 2018, 10, 1032. https://doi.org/10.3390/rs10071032

AMA Style

Brede B, Gastellu-Etchegorry J-P, Lauret N, Baret F, Clevers JGPW, Verbesselt J, Herold M. Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57. Remote Sensing. 2018; 10(7):1032. https://doi.org/10.3390/rs10071032

Chicago/Turabian Style

Brede, Benjamin, Jean-Philippe Gastellu-Etchegorry, Nicolas Lauret, Frederic Baret, Jan G.P.W. Clevers, Jan Verbesselt, and Martin Herold. 2018. "Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57" Remote Sensing 10, no. 7: 1032. https://doi.org/10.3390/rs10071032

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