Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps
"> Figure 1
<p>The Viote del Monte Bondone RGB image derived from AisaEAGLE hyperspectral data including the five Eddy Covariance towers positions (indicated by letters from A–E) and the thirty 60 × 60 m regions of interest (ROIs) used in this study for optical diversity assessment. Reflectance values for each 0.9 m pixel (in grey) and the ROI mean reflectance value (in red) are indicated as an example of spectral variability within a single ROI. Optical diversity and productivity relationships were analyzed at the thirty 60 × 60 m ROIs at an increasing pixel size window (from 0.9–20 m; see the upper right corner). At the bottom, a list of the three flights carried out during the campaign including the adapted solar time window for averaging Net Ecosystem Exchange (NEE) and photosynthetically active radiation (PAR) data.</p> "> Figure 2
<p>Variable importance of projection (VIP) statistics for the modeling coefficients of partial least squared regression (7 components; R<sup>2</sup> = 0.95; RMSE 10.25%) based on AisaEAGLE ROI reflectance data (circular ROIs with a radius of 30m), fitted to the Eddy Covariance measured Net Ecosystem Exchange (NEE). The Sentinel-2 bands used in the study are highlighted, as well as the chlorophyll absorption, the leaf and canopy scattering ranges [<a href="#B45-remotesensing-11-00614" class="html-bibr">45</a>], and the NIR water absorption peak [<a href="#B46-remotesensing-11-00614" class="html-bibr">46</a>].</p> "> Figure 3
<p>Across-scale optical diversity and productivity relationships (calculated for the 30 60 × 60 m ROIs) expressed as the R<sup>2</sup> values of the linear regression between CV (calculated from continuum-removed reflectance) and spectral vegetation indices at increasing pixel sizes (0.9–30 m). Solid color bars indicate the R<sup>2</sup> values when the CV was calculated from the AisaEAGLE full spectrum continuum-removed reflectance, while the striped-line bars indicate the R<sup>2</sup> values when the CV was calculated only from the continuum-removed reflectance values of the Sentinel-2 simulated bands. The bars highlighted with a bold line are indicating the R<sup>2</sup> values obtained with the full simulation (both spectral and spatial resolution) of Sentinel-2 data.</p> "> Figure 4
<p>Map of the productivity (expressed as MTCI, on the left) and optical diversity (expressed as CV, on the right) values for the 30 60 × 60 m ROIs located on the different grasslands associations. The spectral and spatial resolutions of the image used to process the map were achieved resampling the AisaEAGLE hyperspectral imagery and simulate the resolutions which can be obtained using Sentinel-2 data to calculate all the investigated SVIs. Darker green colors correspond to higher levels of productivity (expressed as MTCI, on the left) and optical diversity (expressed as CV, on the right).</p> ">
Abstract
:1. Introduction
- (i)
- can we identify optical diversity and ecosystem productivity relationships (for a wide range of productivity) using remotely-sensed proxies?
- (ii)
- are such relationships scale-dependent?
- (iii)
- are the S2 data spatial and spectral resolutions suitable to detect such relationships?
2. Materials and Methods
2.1. Study Area
2.2. Eddy Covariance Measurements
2.3. The Hyperspectral Flight Campaign and Imagery Processing
2.4. Models for NEE Estimation
2.5. Ecosystem Function and Diversity Relationships: Spatial Dynamics
3. Results
3.1. NEE Estimations
3.2. Ecosystem Optical Diversity and Productivity
4. Discussion
4.1. Chlorophyll and Structural Controls on Ecosystem Function
4.2. Optical Diversity and Productivity Across-Scale Observations
4.3. Interpretation of the Diversity Measures Detectable with Optical Sampling
4.4. The Potential of Sentinel-2 Optical Data to Analyze Ecosystem BPRs at the Global Scale
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Band Number | Central Wavelength (nm) | Bandwidth (nm) | Spatial Resolution (m) |
---|---|---|---|
B1 | 443 | 20 | 60 |
B2 | 490 | 65 | 10 |
B3 | 560 | 35 | 10 |
B4 | 665 | 30 | 10 |
B5 | 705 | 15 | 20 |
B6 | 740 | 15 | 20 |
B7 | 783 | 20 | 20 |
B8 | 842 | 115 | 10 |
B8a | 865 | 20 | 20 |
B9 | 945 | 20 | 60 |
B10 | 1380 | 30 | 60 |
B11 | 1610 | 90 | 20 |
B12 | 2190 | 180 | 20 |
SVI | Formulation | Reference |
---|---|---|
NDVI | (R865-R665)/(R865+R665) | [37] |
NDVIg | (R865-R560)/(R865+R560) | [38] |
NDVIre | (R865-R705)/(R865+R705) | [38] |
MTCI | (R865-R705)/(R705+R665) | [39] |
CIre | (R865/R705)-1 | [40] |
CIg | (R865/R561)-1 | [40] |
NIDI1 | (R865-R740)/(R865+R740) | Proposed in this study |
NIDI2 | (R865-R783)/(R865+R783) | [26] |
EVI | 2.5*(R865-R665)/(1+R865+6*R665-7.5*R490) | [41] |
SVI | Model 1 | Model 2 | ||||||
---|---|---|---|---|---|---|---|---|
F1 | F2 | F3 | F1F2F3* | |||||
R2 | PRMSE (%) | R2 | PRMSE (%) | R2 | PRMSE (%) | R2 | PRMSE (%) | |
NDVI | 0.26 | 13.21 | 0.25 | 37.68 | 0.89 | 6.84 | 0.77 | 22.07 |
NDVIg | 0.67 | 8.85 | 0.70 | 23.90 | 0.72 | 10.89 | 0.80 | 20.61 |
NDVIre | 0.64 | 9.18 | 0.41 | 33.36 | 0.60 | 12.91 | 0.80 | 20.47 |
MTCI | 0.75 | 7.68 | 0.52 | 30.20 | 0.79 | 9.40 | 0.85 | 17.89 |
CIre | 0.65 | 9.02 | 0.42 | 33.23 | 0.61 | 12.81 | 0.82 | 19.34 |
CIg | 0.70 | 8.37 | 0.66 | 25.28 | 0.73 | 10.52 | 0.81 | 20.17 |
NIDI1 | 0.73 | 7.93 | 0.52 | 30.22 | 0.75 | 10.19 | 0.90 | 14.54 |
NIDI2 | 0.00 | 15.33 | 0.20 | 39.05 | 0.00 | 20.43 | 0.75 | 23.14 |
EVI | 0.84 | 6.21 | 0.25 | 37.65 | 0.74 | 10.51 | 0.80 | 20.84 |
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Sakowska, K.; MacArthur, A.; Gianelle, D.; Dalponte, M.; Alberti, G.; Gioli, B.; Miglietta, F.; Pitacco, A.; Meggio, F.; Fava, F.; et al. Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps. Remote Sens. 2019, 11, 614. https://doi.org/10.3390/rs11060614
Sakowska K, MacArthur A, Gianelle D, Dalponte M, Alberti G, Gioli B, Miglietta F, Pitacco A, Meggio F, Fava F, et al. Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps. Remote Sensing. 2019; 11(6):614. https://doi.org/10.3390/rs11060614
Chicago/Turabian StyleSakowska, Karolina, Alasdair MacArthur, Damiano Gianelle, Michele Dalponte, Giorgio Alberti, Beniamino Gioli, Franco Miglietta, Andrea Pitacco, Franco Meggio, Francesco Fava, and et al. 2019. "Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps" Remote Sensing 11, no. 6: 614. https://doi.org/10.3390/rs11060614
APA StyleSakowska, K., MacArthur, A., Gianelle, D., Dalponte, M., Alberti, G., Gioli, B., Miglietta, F., Pitacco, A., Meggio, F., Fava, F., Julitta, T., Rossini, M., Rocchini, D., & Vescovo, L. (2019). Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps. Remote Sensing, 11(6), 614. https://doi.org/10.3390/rs11060614