Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)
Abstract
:1. Introduction
- (1)
- determine the best band transformations (vegetation indices) to remotely separate damaged and un-damaged forest stands after a storm and to monitor the vegetation dynamics in the short period after the storm.
- (2)
- employing multispectral data in an image differencing change detection technique for windthrow detection in the whole Friuli Venezia Giulia Rregion.
2. Materials and Methods
2.1. Study Sites
2.2. Remote Sensing Data
- four bands at 10 m: B02, blue (490 nm); B03, green (560 nm); B04, red (665 nm) and B08, near-infrared (NIR) (842 nm).
- six bands at 20 m: B05–B07, three narrow bands in the vegetation red-edge (RE) spectral domain (705 nm, 740 nm, 783 nm); B8A, a narrow band in the near-infrared (NIR) (865 nm) and B11–B12, two large short-wave infrared (SWIR) bands (1610 nm and 2190 nm).
2.3. Statistical Analyses
2.4. Change Detection
2.5. Descriptive Statistics on Regional Windthrows
2.6. Map Validation
3. Results
3.1. Temporal Profiles of Vegetation Indices
3.2. Vegetation Indices Performance
3.3. Spectral Signatures
3.4. NDWI8A VID Windthrow Map
3.5. Map Validation
4. Discussion
4.1. Vegetation Indices Temporal Profiles
4.2. Spectral Reflectance of Vegetation after Windthrow
4.3. NDWI8A VID Windthrow Maps
4.4. Study Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Date | Product | Reference Period |
---|---|---|
21 April 2017 | S2A_MSIL2A_20170421T100031_N0204_R122_T33TUM_20170421T100541.SAFE | 2017.04 |
08 October 2017 | S2A_MSIL2A_20171008T100031_N0205_R122_T33TUM_20171008T100322.SAFE | 2017.10 |
21 April 2018 | S2B_MSIL2A_20180421T100029_N0207_R122_T33TUM_20180421T120642.SAFE | 2018.04 |
26 September 2018 | S2A_MSIL2A_20180926T101021_N0208_R022_T33TUM_20180926T191545.SAFE | 2018.09 |
15 November 2018 | S2A_MSIL2A_20181115T101251_N0210_R022_T33TUM_20181115T114042.SAFE | 2018.11 |
05 March 2019 | S2A_MSIL2A_20190305T101021_N0211_R022_T33TUM_20190305T162851.SAFE | 2019.03 |
21 April 2019 | S2A_MSIL2A_20190421T100031_N0211_R122_T33TUM_20190421T131138.SAFE | 2019.04 |
23 July 2019 | S2A_MSIL2A_20190723T101031_N0213_R022_T33TUM_20190723T125722.SAFE | 2019.07 |
13 September 2019 | S2B_MSIL2A_20190913T100029_N0213_R122_T33TUM_20190913T142855.SAFE | 2019.09 |
Index | Reference | Description |
---|---|---|
Green Normalized Difference Vegetation Index GNDVI = | [23] | GNDVI was suggested to avoid the NDVI saturation effect in dense canopy structures in order to correctly infer chlorophyll canopy content [24]. It uses the green band (B3, ~0.60 µm) as a replacement for the red one. The values range from −1 to 1, with growing values as green biomass increases. |
Inverted Red-Edge Chlorophyll Index IRECI = | [25] | IRECI is one of the best indices for the estimation of leaf area index (LAI), namely the one-sided green leaf area per unit ground surface area. It has been also used to estimate canopy and leaves chlorophyll content. IRECI makes use of all Red-Edge (RE) Sentinel bands (B5, B6 and B7, respectively ~0.7 µm, 0.74 µm and 0.78 µm, the latter corresponding to maximum reflectance) and the red band (B4, ~0.65 µm, minimum reflectance) to characterize the RE slope. By the use of the B5/B6 ratio, IRECI does not put heavy emphasis on the red, which helps to avoid saturation at high chlorophyll concentrations, while utilizing the strong contrast of the B7 and B4 difference which is sensitive to LAI. |
Normalized Difference Red-Edge Blue Index NDREDI = | [26] | NDREDI is a very recent index evolved in a study for windthrow detection in Germany from PSRI. Both NDREDI and PSRI, by applying the RE and blue spectral bands, proved to be suitable measures for the detection of windthrows. |
Normalized Difference Vegetation Index NDVI = | [27] | In NDVI, the combination of the red band (B4, ~0.65 µm), sensible to chlorophyll content, and NIR band (B8, ~0.85 µm), sensible to leaf structure, permit the detection of vegetation. NDVI ranges between −1 and 1. Naked soils have values around 0, negative values are retrieved from water surfaces while positive values are found in vegetated areas, the higher values the greater is the biomass. It has been used for estimation of Leaf Area Index, biomass, chlorophyll concentration in leaves and plant productivity [28]. |
Normalized Difference Water Index 11 NDWI = | [29] | NDWI combines NIR (~0.85 µm) and SWIR (~1.6 µm) bands for water stress monitoring in plants. SWIR reflectance reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies. NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of NIR and SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. Due to the high sensitivity of NDWI and NDWI8A to changes in water content of leaves, this index is efficient in detecting damage, monitoring drought periods and estimating yield depletion [30]. Index values vary from −1 to 1, they are negative when the vegetation is dry or on bare soils, while they are positive when the vegetation is green and rich in water [31,32]. |
Normalized Difference Water Index 11 with 8A band NDWI8A = | ||
Plant Senescence Reflectance Index PSRI = | [33] | PSRI was introduced to estimate the onset, the stage, relative rates and kinetics of the senescence process. An increase of reflectance between 0.55 and 0.74 µm (sensed by B4 and B6) accompanied senescence-induced degradation of chlorophyll, whereas in the range 0.4–0.5 µm (B2) it remains low due to retention of carotenoids. The index is sensitive to the carotenoids and chlorophyll ratio and it has been used as a quantitative measure of leaf senescence. |
Specific Leaf Area Vegetation Index SLAVI = | [34] | SLAVI was developed to estimate the specific leaf area (SLA), namely the one side area of the leaf divided by its dry weight. It is based on red and near-infrared reflections, which have a high correlation with net photosynthesis, net primary production and leaf area index [35], and on SWIR reflection, which guarantees leaves water content estimation. SLAVI is negatively correlated with cellulose and lignin content. It is also sensitive to the volume and height of vegetation [36]. |
Index | Coefficient | SE | p-Value | R2 | |
---|---|---|---|---|---|
NDWI8A | Intercept | 0.49 | 0.007 | *** | 0.80 |
Treatment | −0.388 | 0.011 | *** | ||
NDWI | Intercept | 0.473 | 0.021 | *** | 0.77 |
Treatment | −0.4 | 0.006 | *** | ||
NDVI | Intercept | 0.841 | 0.01 | *** | 0.76 |
Treatment | −0.29 | 0.007 | *** | ||
PSRI | Intercept | 0.025 | 0.003 | *** | 0.69 |
Treatment | 0.163 | 0.005 | *** | ||
SLAVI | Intercept | 4.122 | 0.062 | *** | 0.65 |
Treatment | −2.813 | 0.106 | *** | ||
GNDVI | Intercept | 0.774 | 0.003 | *** | 0.60 |
Treatment | −0.145 | 0.005 | *** | ||
IRECI | Intercept | −0.048 | 0.003 | *** | 0.32 |
Treatment | −0.108 | 0.006 | *** | ||
NDREDI | Intercept | 0.55 | 0.006 | *** | 0.03 |
Treatment | −0.043 | 0.01 | *** |
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Share and Cite
Olmo, V.; Tordoni, E.; Petruzzellis, F.; Bacaro, G.; Altobelli, A. Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy). Remote Sens. 2021, 13, 1530. https://doi.org/10.3390/rs13081530
Olmo V, Tordoni E, Petruzzellis F, Bacaro G, Altobelli A. Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy). Remote Sensing. 2021; 13(8):1530. https://doi.org/10.3390/rs13081530
Chicago/Turabian StyleOlmo, Valentina, Enrico Tordoni, Francesco Petruzzellis, Giovanni Bacaro, and Alfredo Altobelli. 2021. "Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)" Remote Sensing 13, no. 8: 1530. https://doi.org/10.3390/rs13081530