Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sites Description, Instrument Set-Up, and Data Sources
2.2. EC Data Processing and Flux Partitioning to GPP
2.3. Calculation of Vegetation Indexes from PhenoCam
2.4. Data Filtering and to Compute Daily VIs and GPP
- We discarded VIs measured with PAR below 600 µmol m−2 s−1. This procedure was used, on one hand, to filter out the VI values measured during adverse meteorological conditions (i.e., rainy, foggy, or overcast half-hours [34,48]), and on the other hand, Petach et al. [29] suggests to apply a threshold on PAR to reduce the variability of CamNDVI due to changes in illumination conditions. Here, we selected a more conservative threshold than Petach et al. [29].
- A max.density filter method was developed to filter and retrieve daily VIs. We constructed the probability density function (PDF) of VIs in 3-day moving windows (30 observations), and assigned the value that has highest probability density as the filtered daily value. We did not apply the widely used max method [22,36], which uses the 90th percentile of the VIs value from a 3-day moving window as the filtered daily value. This is because the variability of NIRDN is larger compared to other channels (i.e., RDN, GDN, BDN) in the PhenoCam, which would result in large variability of VIs (i.e., CamNDVI, CamNIRv and CamRVI) that is especially obvious for Mediterranean ecosystems compared to other ecosystems (some comparison using data retrieved from [34], results are not shown in this study). Hence, we chose to apply the max.density filter to retrieve time series of VIs with less variability, which were not always retrieved by applying max filter in our study. We used an example to demonstrate the better filter performance of max.density compare to max filter methods in our study (Figure A1).
- Daily VIs were gap-filled using the Singular Spectrum Analysis (SSA) method implemented in the “spectral.methods” R package [60].
- Similar to the processing of VIs, the daily GPP was derived from half-hourly data following the step (2) and step (3).
2.5. Phenological Transition Dates (PTDs) Extraction
- Data were smoothed using the spline method [20,36]; PTDs were extracted using the derivatives of smoothed seasonal cycle [61] and applying thresholds (i.e., 50%) of amplitude of VIs [62]. As the start and end of the season are extremely important to characterize the phenology, we defined two sets of PTDs in the start (UD, SOStrs; Table 2) and end of season (RD, EOStrs; Table 2) for intercomparison and better characterizing the phenology. These two sets of PTDs are derived based on different perception and methodology. UD and RD are retrieved as the intersection dates between steepest slope and minimum value in the Green-up and Dry-down periods, respectively [61]. In contrast, SOStrs and EOStrs are retrieved by using the thresholds of 50% amplitude [62]; i.e., they are defined when 50% of amplitudes are reached in the Green-up and Dry-down periods, respectively. Other extracted PTDs and the phenological periods analyzed in this study were summarized in Figure 2 and Table 2. The detailed procedures and corresponding code related to the extraction of PTDs are provided in Appendix B.
- Uncertainty of extracted PTDs was assessed by extracting PTDs repeatedly (100 times) from an ensemble of time series constructed by summing original data and random noise as described by Filippa et al. [36].
2.6. Statistical Analysis
3. Results
3.1. Time Series of VIs (GCC, CamNDVI, CamNIRv, CamRVI), GPP, and Their Relationship with Meteorological Conditions
3.2. Comparison of Phenological Transition Dates (PTDs) Derived from Different VIs
3.3. Comparison of Phneological Transition Dates (PTDs) Derived from VIs and GPP
3.4. Comparison of Growing Season Length (GSL)-Derived VIs and GPP
4. Discussion
4.1. Characterizing Variatoin and Drivers of Structural and Physiological Phenology
4.2. Utilizing Different PhenoCam-Based VIs to Represent Structural Phenology
4.3. Combing Different PhenoCam-Based VIs to Represent Physiological Phenology
5. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
Appendix A
Autumn | Winter | Spring | Summer | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
(September–November) | (December–February) | (March–May) | (June–July) | |||||||||
Ta | Prec | GPP | Ta | Prec | GPP | Ta | Prec | GPP | Ta | Prec | GPP | |
(°C) | (mm) | (µmol m−2 s−1) | (°C) | (mm) | (µmol m−2 s−1) | (°C) | (mm) | (µmol m−2 s−1) | (°C) | (mm) | (µmol m−2 s−1) | |
Hydro-2014 | 17.5 | 333.4 | 4.7 | 6.4 | 105.6 | 4.3 | 16.3 | 94.9 | 9.4 | 27.5 | 67.7 | 4.2 |
Hydro-2015 | 16.8 | 296.0 | 3.6 | 9.4 | 181.6 | 5.7 | 13.5 | 281.7 | 11.7 | 26.4 | 14.1 | 7.0 |
Hydro-2016 | 17.2 | 272.7 | 3.6 | 8.0 | 205.9 | 5.2 | 16.4 | 94.0 | 8.9 | 27.3 | 46.4 | 4.2 |
Green-Up Period | Dry-Down Period | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GCC | CamNDVI | CamNIRv | CamRVI | GCC | CamNDVI | CamNIRv | CamRVI | |||||||||
Stats | UD | SOStrs | UD | SOStrs | UD | SOStrs | UD | SOStrs | EOStrs | RD | EOStrs | RD | EOStrs | RD | EOStrs | RD |
N | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
MAE (day) | 8.0 | 13.1 | 14 | 17.5 | 4.8 | 2.8 | 16 | 20.2 | 7.0 | 7.1 | 4.9 | 5.5 | 8.7 | 8.8 | 6.9 | 4.4 |
RMSE (day) | 10.6 | 10.4 | 14.5 | 19 | 6.3 | 3.3 | 16.6 | 21.6 | 9.6 | 10.0 | 6.0 | 6.3 | 10.2 | 9.9 | 8.0 | 5.5 |
r | 0.61 | 0.64 | 0.94 | 0.77 | 0.91 | 0.98 | 0.91 | 0.75 | 0.90 | 0.91 | 0.94 | 0.94 | 0.91 | 0.95 | 0.92 | 0.95 |
p-value | * | * | *** | * | ns | ns | *** | ** | * | * | ns | ns | * | * | ns | ns |
Appendix B. The Procedures and Code for Extracting Phenological Transition Dates (PTDs)
- Try to find interminD. The linking point of two peaks (interminD) is the minimum between the two peaks between the Julian day of the year (Doy) 150–250.
- Once we found the interminD, the time series is split into two parts and for each part the main PTDs are computed. The date of POS is calculated by determining the date at which the maximum value of the time series (using f(t) to refer to the time series hereafter) is reached. Baseline and maxline are the minimum and maximum value in each part of f(t), respectively.
- The maximum and minimum of the first derivative of the f(t) (f’(t)) represent the maximum slopes of the upward and downward period (dashed lines). The intersections between these lines and the baseline are defined as upward day (UD) and recession day (RD). UD stands for the value when the f(t) begins to increase during the Green-up period. RD stands for the value when the f(t) stops decreasing during the Dry-down period. The intersections between these lines and maxline are the saturating day (SD) and downward day (DD). The SD indicates when the plants begin to reach full greenness or maximum photosynthesis, while DD stands for the date when plants begin to senesce [61].
- SOStrs and EOStrs are retrieved by computing the date when the value reaches 50% of the maximum in the upward and downward period, respectively [62].
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Site | VIs | N | Intercept | Slope | R2 |
---|---|---|---|---|---|
ES-LM1 | NDVI | 4000 | 0.60 | 1.51 | 0.69 |
NIRv | 3282 | 0.06 | 0.01 | 0.60 | |
RVI | 3389 | −2.03 | 6.61 | 0.54 | |
ES-LM2 | NDVI | 4340 | 0.56 | 1.12 | 0.77 |
NIRv | 4223 | 0.04 | 0.01 | 0.55 | |
RVI | 4268 | −0.14 | 3.55 | 0.57 | |
ES-LMa | NDVI | 3549 | 0.67 | 0.89 | 0.89 |
NIRv | 3522 | 0.06 | 0.01 | 0.73 | |
RVI | 3515 | −0.20 | 5.17 | 0.84 | |
ES-Abr | NDVI | 3560 | 0.64 | 1.18 | 0.83 |
NIRv | 3361 | 0.06 | 0.01 | 0.82 | |
RVI | 3564 | −0.78 | 5.39 | 0.80 |
Terms | Description | |
---|---|---|
Phenological Transition Dates (PTDs) | UD | Upturn day in the green up period in the autumn |
SOStrs | When 50% of amplitude in the green up period in the autumn is reached | |
POS1 | When the first peak of season is reached | |
POS2 | When the second peak of season is reached | |
EOStrs | When 50% of amplitude in the senescent period in the summer is reached | |
RD | Recession day at the end of senescent period in the summer | |
Phenological periods | Green-up | Greenness/GPP increasing period in the autumn (including UD, SOStrs, and POS1) |
Dry-down | Greenness/GPP decreasing period in the summer (including POS2, EOStrs and RD) | |
GSLRD-UD | Growing season length defined in the hydrological year (day length between UD and RD) | |
GSLEOS-SOS | Growing season length defined for comparison with GSLRD-UD (day length between EOStrs and SOStrs, which is widely used in land surface phenology) |
Site | VIs-GPP | N | r |
---|---|---|---|
ES-LM1 | GCC | 1096 | 0.90 |
CamNDVI | 1096 | 0.91 | |
CamNIRv | 1096 | 0.93 | |
CamRVI | 1096 | 0.91 | |
ES-LM2 | GCC | 1096 | 0.86 |
CamNDVI | 1096 | 0.87 | |
CamNIRv | 1096 | 0.87 | |
CamRVI | 1096 | 0.87 | |
ES-LMa | GCC | 607 | 0.89 |
CamNDVI | 607 | 0.90 | |
CamNIRv | 607 | 0.85 | |
CamRVI | 607 | 0.90 | |
ES-Abr | GCC | 635 | 0.86 |
CamNDVI | 635 | 0.91 | |
CamNIRv | 635 | 0.86 | |
CamRVI | 635 | 0.91 |
Season Summary | ||||||||
---|---|---|---|---|---|---|---|---|
GCC | CamNDVI | CamNIRv | CamRVI | |||||
Stats | Green-Up | Dry-Down | Green-Up | Dry-Down | Green-Up | Dry-Down | Green-Up | Dry-Down |
N | 24 | 30 | 24 | 30 | 24 | 30 | 24 | 30 |
MAE (day) | 9.4 | 6.3 | 17.4 | 4.6 | 7.0 | 7.4 | 20.5 | 5.0 |
RMSE (day) | 12.8 | 8.6 | 20.4 | 5.5 | 11.0 | 8.9 | 22.7 | 6.3 |
p-value | *** | *** | *** | ns | ns | *** | *** | * |
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Luo, Y.; El-Madany, T.S.; Filippa, G.; Ma, X.; Ahrens, B.; Carrara, A.; Gonzalez-Cascon, R.; Cremonese, E.; Galvagno, M.; Hammer, T.W.; et al. Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems. Remote Sens. 2018, 10, 1293. https://doi.org/10.3390/rs10081293
Luo Y, El-Madany TS, Filippa G, Ma X, Ahrens B, Carrara A, Gonzalez-Cascon R, Cremonese E, Galvagno M, Hammer TW, et al. Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems. Remote Sensing. 2018; 10(8):1293. https://doi.org/10.3390/rs10081293
Chicago/Turabian StyleLuo, Yunpeng, Tarek S. El-Madany, Gianluca Filippa, Xuanlong Ma, Bernhard Ahrens, Arnaud Carrara, Rosario Gonzalez-Cascon, Edoardo Cremonese, Marta Galvagno, Tiana W. Hammer, and et al. 2018. "Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems" Remote Sensing 10, no. 8: 1293. https://doi.org/10.3390/rs10081293
APA StyleLuo, Y., El-Madany, T. S., Filippa, G., Ma, X., Ahrens, B., Carrara, A., Gonzalez-Cascon, R., Cremonese, E., Galvagno, M., Hammer, T. W., Pacheco-Labrador, J., Martín, M. P., Moreno, G., Perez-Priego, O., Reichstein, M., Richardson, A. D., Römermann, C., & Migliavacca, M. (2018). Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems. Remote Sensing, 10(8), 1293. https://doi.org/10.3390/rs10081293