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

Abstract

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 m 2 m −2 and 1.43 m 2 m −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.

Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network

“…Compared to the mainstream hand-held LAI measurement instruments (e.g., LAI-2200C), LAINet has an advantage in acquiring time-series field LAI automatically. Compared with other existing automated measurement methods using photos and TLS [11][12][13][14][15][16], the file size of WSN-based LAINet is less bandwidth-intensive, hence, post-processing is less computationally demanding. In principle, both LAINet and PASTIS-57 are based on radiation transmittance to measure PAI, without distinguishing leaves with different chlorophyll contents and from other plant tissues [9,11].…”

“…Compared with other existing automated measurement methods using photos and TLS [11][12][13][14][15][16], the file size of WSN-based LAINet is less bandwidth-intensive, hence, post-processing is less computationally demanding. In principle, both LAINet and PASTIS-57 are based on radiation transmittance to measure PAI, without distinguishing leaves with different chlorophyll contents and from other plant tissues [9,11]. Calibration among all sensors deployed above and below the canopy is a must.…”

“…Precisely, LAINet can measure the effective Plant Area Index (PAIe) from multi-point directional transmittance based on the Beer-Lambert Law, as the sensors do not discriminate leaves with different chlorophyll contents from other plant tissues [9]. Other similar automated LAI measurement methods, such as the PAI Autonomous System from Transmittance Instantaneous Sensors at 57 • (PASTIS-57) instrument [11,12], automated digital hemispherical photography (DHP) [13,14], automated digital cover photography [15], and terrestrial laser scanning [16] have been developed.…”

Noninvasive Technologies for Primate Conservation in the 21st Century

“…Traditionally, satellite images have been used for this process and these are still the method of choice for large-scale analyses (Voigt et al 2018). However, new sensors offer comparable data for phenology (Brede et al 2018). At a smaller scale, drones can offer advantages over satellite images, such as flexible data collection, capturing imagery below cloud cover and providing much higher resolution imagery than commercially available satellites (Rodríguez et al 2012;Wich and Koh 2018).…”

Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-to-End Uncertainty Evaluation Framework

“…Using the consolidated data, it should be possible to identify the most common and substantial uncertainty contributors and inform future in situ sampling and measurement protocols accordingly. In addition to further field campaigns, detailed consideration will be given to the use of permanent instrumentation [72][73][74][75][76][77][78][79]. This will include a review of site deployment considerations and an initial plan for the establishment of permanent ESAsupported FRM4VEG 'supersites'.…”