Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Meteorological Data and scPDSI
2.2.2. MODIS Derived Time Series of Vegetation Indices
2.2.3. Ancillary Data—Forest Mask
2.3. PCA Analyses Design
2.3.1. scPDSI Based Meteorological Variability
2.3.2. MODIS Derived NDVI and NDII7 Time Series
- NDVI and NDII7 time series, where the former relates to the photosynthetic activity, while the latter approximates foliage water content;
- removal of seasonality through a per-pixel z-score normalization
- two different lengths of NDVI and NDII7 time series (applied to the original and z-score normalized datasets alike) where beside the complete MODIS time series comprising all 23 annual composites (1–23), a vegetation season time series focusing on a period between end of April and mid-October (corresponding with 8th to 18th MODIS annual composites (8–18)) were also exploited as time series restricted to vegetation season allow excluding dormancy state signal and potential impact of snow cover; and
3. Results
3.1. Meteorological Conditions in South Tyrol
3.2. Forest Photosynthetic Activity Captured by PCA of MODIS NDVI Time Series
3.3. Forest Foliage Water Content Indicated by PCA of Vegetation Season MODIS NDII7 Time Series
3.4. Loadings Rotation
3.5. Comparison of Identified Potential Forest Responses to Meteorological Drought Conditions
4. Discussion
4.1. Drought Conditions in South Tyrol
4.2. Evaluation of Multiple S-mode PCA Decomposition Approaches and Data Setups
4.3. Forest Drought Response Identified through PCA Decomposition of MODIS NDVI and NDII7 Time Series
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
NDII7 | Normalized Difference Infrared Index based on MODIS band 7 |
NDVI | Normalized Difference Vegetation Index |
PC | Principal component |
PCA | Principal Component Analysis |
scPDSI | self-calibrated Palmer Drought Severity Index |
Appendix A
- Kaiser’s stopping rule [101], which proposes to rotate all loadings with eigenvalue ≥1.
- Cattell’s scree test [68], in which the selection is based on a visual interpretation of the eigenvalues plot and identification of a transition point between incline and leveled line. Because the transition point belongs to the leveled part, only loadings of a lower order than the transition point are rotated. Cattell’s scree test can be recognized as simplified graphical solution of the N rule [44].
- A priori criterion, where a number of rotated factors is set beforehand.
- Non-trivial factors approach, in which only these loadings are rotated that have at least three variables loadings above a certain threshold (customary 0.3).
- Percent of cumulative variance criterion, in which rotated are these foremost loadings that eigenvalues sum up to a predefined value.
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Index | z-Score Data Normalization | Annual Time Window (MODIS Composites) | Dataset Short Name | EOF Matrix | Rotation (Retained Scores) |
---|---|---|---|---|---|
NDVI | no | full year (1–23) | NDVI1–23 | cor. | no |
no | veg. season (8–18) | NDVI8–18 | cor. | no | |
yes | full year (1–23) | nNDVI1–23 | cov. | no | |
yes | veg. season (8–18) | nNDVI8–18 | cov. | yes (1–5) | |
NDII7 | no | veg. season (8–18) | NDII78–18 | cor. | no |
yes | veg. season (8–18) | nNDII78–18 | cov. | yes (1–4) |
PCs | |||||||
---|---|---|---|---|---|---|---|
a | b | c | d | e | f | g | |
1scPDSI | −0.310 | 0.736 * | 0.717 * | 0.608 * | −0.590 * | 0.751 * | 0.772 * |
2scPDSI | 0.360 | −0.172 | −0.199 | 0.502 | 0.186 | 0.301 | 0.241 |
3scPDSI | 0.632 * | −0.576 * | −0.559 | −0.023 | 0.607 * | −0.331 | −0.397 |
4scPDSI | 0.257 | 0.030 | −0.010 | 0.583* | 0.029 | 0.466 | 0.400 |
Index | PC Name | Score Order | EOF Matrix Rotation | Short Name of Original Dataset | Scores Retained for Rotation | Rotation Approach |
---|---|---|---|---|---|---|
a | 3COVnNDVI8–18 | 3 | cov. | nNDVI8–18 | - | - |
b | 1CORNDII78–18 | 1 | cor. | NDII78–18 | - | - |
c | 1COVnNDII78–18 | 1 | cov. | nNDII78–18 | - | - |
d | 4COVnNDII78–18 | 4 | cov. | nNDII78–18 | - | - |
e | 3COVnNDVI8–18ROT5P | 3 | cov. | nNDVI8–18 | (1–5) | Promax |
f | 4COVnNDII78–18ROT4V | 4 | cov. | nNDII78–18 | (1–4) | Varimax |
g | 4COVnNDII78–18ROT4V | 4 | cov. | nNDII78–18 | (1–4) | Promax |
Index | PC Name | PCs | ||||||
---|---|---|---|---|---|---|---|---|
a | b | c | d | e | f | g | ||
a | 3COVnNDVI8–18 | 1.000 * | ||||||
b | 1CORNDII78–18 | −0.207 * | 1.000 * | |||||
c | 1COVnNDII78–18 | −0.422 * | 0.487 * | 1.000 * | ||||
d | 4COVnNDII78–18 | 0.267 * | 0.032 | 0.000 | 1.000 * | |||
e | 3COVnNDVI8–18ROT5P | 0.714 * | −0.352 * | −0.674 * | −0.031 * | 1.000 * | ||
f | 4COVnNDII78–18ROT4V | 0.320 * | 0.187 * | 0.339 * | 0.617 * | 0.025 | 1.000 * | |
g | 4COVnNDII78–18ROT4V | 0.285 * | 0.226 * | 0.497 * | 0.659 * | −0.051 | 0.924 * | 1.000 * |
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Lewińska, K.E.; Ivits, E.; Schardt, M.; Zebisch, M. Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series. Remote Sens. 2016, 8, 639. https://doi.org/10.3390/rs8080639
Lewińska KE, Ivits E, Schardt M, Zebisch M. Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series. Remote Sensing. 2016; 8(8):639. https://doi.org/10.3390/rs8080639
Chicago/Turabian StyleLewińska, Katarzyna Ewa, Eva Ivits, Mathias Schardt, and Marc Zebisch. 2016. "Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series" Remote Sensing 8, no. 8: 639. https://doi.org/10.3390/rs8080639