Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57
<p>The PASTiS-57 instrument installed in the Speulderbos site: (<b>A</b>) With the opened data-logger box, the six sensors can be seen on top, a spare cable is curled up at the back of the data-logger, and two D-cell batteries for power supply in the box; and (<b>B</b>) two PASTiS-57 installed at the centre of Plot C (centre marker not visible).</p> "> Figure 2
<p>Map of the study site with the scaffold tower where reference instruments were placed and the five sampling plots. Background is an airborne false-colour composite of 2013. The location of the study site within The Netherlands is marked on the inset.</p> "> Figure 3
<p>DART sample scene: (<b>A</b>) True colour image sample for below-canopy sensor in DART. The viewing zenith angle is 57.5°, but the field of view is extended compared to the sensors used in the modelling to give an overview of the scene. (<b>B</b>) Top view of the created mock-up with 50 trees.</p> "> Figure 4
<p>Recordings of two sampling days for one device in Plot A. Upper panels are the observations below the canopy, lower panels are reference readings from above the canopy. DN axis is on log-scale. Dotted horizontal line is saturation point at 4000 DN. Discontinued lines on lower panel reach saturation. Only pairs for which the observation did not reach 0 Digital Number were considered.</p> "> Figure 5
<p>All campaigns of one instrument in Plot C before (Naive) and after application of filtering (Filtered).</p> "> Figure 6
<p>Comparison of PASTiS-57 and TLS derived PAI for single sensors (coloured) and Land and Xiang clumping correction (LX), and MODIS LAI for five plots during the spring 2016 and summer 2017 campaigns.</p> "> Figure 7
<p>DART model results for turbid canopy representation for west facing sensors. Positive errors mean over-estimation by the retrieval. Light grey and darker grey areas are the 20% and 5% accuracy requirement of GCOS [<a href="#B54-remotesensing-10-01032" class="html-bibr">54</a>].</p> "> Figure 8
<p>DART model results of discrete canopy representation for five different tree densities (horizontal panels in number of trees). Retrieval without (None) and with clumping correction after Lang and Xiang (LX) [<a href="#B44-remotesensing-10-01032" class="html-bibr">44</a>].</p> "> Figure 9
<p>Sensitivity of PASTiS-57 PAI due to digitisation at low observation readings (DN) for two levels of reference readings.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. PASTiS-57 Instrument
2.2. Field Experiment
2.2.1. Study Area and Field Data Collection
2.2.2. Plant Area Index (PAI) Retrieval
2.2.3. Reference Datasets
2.2.4. Phenological Model Fitting
2.3. Radiative Transfer Model Experiments
3. Results
3.1. Field Experiment
3.2. Radiative Transfer Model Experiments
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Values | Unit |
---|---|---|
Leaf Area Index (LAI) | 1, 2, …, 10 | m2m−2 |
Leaf Angle Distribution (LAD) | spherical, erectrophile, planophile, extremophile, plagiophile | - |
Chlorophyll a and b () | 20, 50, 80 | μg cm−2 |
Solar Zenith Angle (SZA) | 0, 57.5, 80 | ° |
Parameter | A | B | C | D | E |
---|---|---|---|---|---|
5.31 (±0.04) | 5.65 (±0.04) | 5.46 (±0.05) | 5.09 (±0.05) | 5.67 (±0.05) | |
5.82 (±0.03) | 6.10 (±0.03) | 5.72 (±0.04) | 5.62 (±0.04) | 5.99 (±0.03) | |
5.63 (±0.15) | – | – | – | – | |
1.63 (±0.03) | 1.94 (±0.03) | 1.85 (±0.04) | 1.57 (±0.03) | 1.75 (±0.04) | |
3.10 (±0.02) | 3.51 (±0.03) | 3.02 (±0.03) | 3.03 (±0.03) | 3.32 (±0.02) | |
0.80 (±0.09) | – | – | – | – | |
0.43 (±0.03) | 0.49 (±0.04) | 0.54 (±0.06) | 0.48 (±0.04) | 0.44 (±0.03) | |
0.41 (±0.03) | 0.52 (±0.05) | 0.47 (±0.04) | 0.43 (±0.04) | 0.31 (±0.02) | |
0.77 (±0.26) | – | – | – | – | |
129.5 (±0.2) | 129.3 (±0.2) | 129.3 (±0.2) | 129.2 (±0.2) | 129.1 (±0.2) | |
130.0 (±0.3) | 128.6 (±0.4) | 129.3 (±0.4) | 129.5 (±0.4) | 130.9 (±0.3) | |
126.0 (±0.5) | – | – | – | – | |
117.9 | 119.5 | 121.0 | 119.6 | 118.4 | |
118.2 | 119.9 | 119.7 | 119.2 | 115.8 | |
120.9 | – | – | – | – |
Parameter | A | B | C | D | E |
---|---|---|---|---|---|
5.69 (±0.04) | 5.72 (±0.03) | 5.64 (±0.05) | 5.49 (±0.05) | 5.79 (±0.04) | |
6.06 (±0.08) | 6.07 (±0.08) | 6.00 (±0.13) | 6.05 (±0.12) | 6.15 (±0.10) | |
5.06 (±0.11) | – | – | – | – | |
1.59 (±0.04) | 1.80 (±0.04) | 1.69 (±0.05) | 1.55 (±0.05) | 1.64 (±0.04) | |
3.05 (±0.10) | 3.43 (±0.14) | 2.74 (±0.17) | 2.78 (±0.18) | 3.34 (±0.16) | |
0.65 (±0.09) | – | – | – | – | |
−0.08 (±0.00) | −0.08 (±0.00) | −0.07 (±0.00) | −0.08 (±0.01) | −0.09 (±0.01) | |
−0.08 (±0.01) | −0.12 (±0.02) | −0.07 (±0.01) | −0.10 (±0.02) | −0.09 (±0.02) | |
−0.16 (±0.02) | – | – | – | – | |
307.6 (±0.7) | 307.8 (±0.7) | 307.5 (±1.0) | 309.8 (±1.0) | 311.7 (±0.7) | |
307.2 (±1.9) | 317.4 (±2.0) | 307.4 (±3.0) | 314.4 (±2.6) | 316.7 (±2.6) | |
292.5 (±1.7) | – | – | – | – | |
250.0 | 250.4 | 243.2 | 256.2 | 262.8 | |
258.8 | 288.4 | 263.7 | 280.8 | 281.4 | |
269.0 | – | – | – | – |
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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
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 StyleBrede, 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
APA StyleBrede, B., Gastellu-Etchegorry, J. -P., Lauret, N., Baret, F., Clevers, J. G. P. W., Verbesselt, J., & Herold, M. (2018). Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57. Remote Sensing, 10(7), 1032. https://doi.org/10.3390/rs10071032