Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

Figures & data

Figure 1. The Bavarian Forest National Park. a) Yearly bark-beetle-infested areas based on aerial imagery. b) the location of the Bavarian Forest National Park and the adjacent Šumava National Park in Central Europe.

Figure 2. Time series of area infested by bark beetles in the BFNP. Columns represent yearly increase in infested areas, and lines/dots the cumulative infested area. The study period is marked by the grey box. Infested areas that have been left standing, which were used in this study, are colored in blue.

Figure 3. Workflow of the methods used in this study. Gray boxes indicate the datasets, and white boxes the applied methods.

Table 1. Overview of the vegetation indices computed from the Landsat and Sentinel-2 data. ρ represents the reflectance in a given band.

Name	Sensor	Formula	Reference
Normalized Difference Vegetation Index (NDVI)	Landsat/ Sentinel-2	$NDVI=\frac{{\rho }_{NIR}-{\rho }_{red}}{{\rho }_{NIR}+{\rho }_{red}}$	Rouse et al. (1973); Tucker (1979)
Normalized Difference Moisture Index (NDMI)	Landsat/ Sentinel-2	$NDMI=\frac{{\rho }_{NIR}-{\rho }_{SWIR1}}{{\rho }_{NIR}+{\rho }_{SWIR1}}$	Gao (1996)
Normalized Burn Ratio (NBR)	Landsat/ Sentinel-2	$NBR=\frac{{\rho }_{NIR}-{\rho }_{SWIR2}}{{\rho }_{NIR}+{\rho }_{SWIR2}}$	López García and Caselles (1991)
Enhanced Vegetation Index (EVI)	Landsat/ Sentinel-2	$EVI=2.5\ast \frac{{\rho }_{NIR}-{\rho }_{red}}{{\rho }_{NIR}+6\ast {\rho }_{red}-7.5\ast {\rho }_{blue}+1}$	Huete et al. (2002)
Tasseled Cap Brightness (TCB)	Landsat/ Sentinel-2	$\mathrm{T}\mathrm{C}\mathrm{B}=\hspace{0.17em}0.2043\ast {\rho }_{blue}+0.4158\ast {\rho }_{green}+\hspace{0.17em}0.5524\ast {\rho }_{red}+0.5741\ast {\rho }_{NIR}\text{break}+\hspace{0.17em}0.312\ast {\rho }_{SWIR1}+\hspace{0.17em}0.2303\ast {\rho }_{SWIR2}$	Crist (1985)
Tasseled Cap Greenness (TCG)	Landsat/ Sentinel-2	$\mathrm{T}CG=-0.1603\ast {\rho }_{blue}-0.2819\ast {\rho }_{green}-0.4934\ast {\rho }_{red}+0.7940\ast {\rho }_{NIR}-0.0002\ast {\rho }_{SWIR1}\mathrm{\setminus }\mathrm{b}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{k}-0.1446\ast {\rho }_{SWIR2}$	Crist (1985)
Tasseled Cap Wetness (TCW)	Landsat/ Sentinel-2	$\mathrm{T}CW=0.2043\ast {\rho }_{blue}+0.4158\ast {\rho }_{green}+0.5524\ast {\rho }_{red}+0.5741\ast {\rho }_{NIR}+0.3124\ast {\rho }_{SWIR1}\mathrm{\setminus }\mathrm{b}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{k}+0.2303\ast {\rho }_{SWIR2}$	Crist (1985)
Tasseled Cap Disturbance Index (TCD); unscaled	Landsat/ Sentinel-2	$TCD=TCB-\left(TCG+TCW\right)$	Frantz (2021); Healey et al. (2005)
Kernel NDVI (kNDVI)	Landsat/ Sentinel-2	$kNDVI=\frac{1-k}{1+k}$ with $k=\mathrm{e}\mathrm{x}\mathrm{p}\frac{-{\left({\rho }_{NIR}-{\rho }_{red}\right)}^2}{2\ast {\sigma }^2}$ with $\sigma =0.5\ast \left({\rho }_{NIR}+{\rho }_{red}\right)$	Camps-Valls et al. (2021); Frantz (2021)
Normalized Difference Tillage Index (NDTI)	Landsat/ Sentinel-2	$NDT=\frac{{\rho }_{SWIR1}-{\rho }_{SWIR2}}{{\rho }_{SWIR1}+{\rho }_{SWIR2}}$	van Deventer et al. (1997)
Normalized Difference Red Edge Index 1 (NDRE1)	Sentinel-2	$NDRE1=\frac{{\rho }_{rededge2}-{\rho }_{rededge1}}{{\rho }_{rededge2}+{\rho }_{rededge1}}$	Gitelson and Merzlyak (1998)
Normalized Difference Red Edge Index 2 (NDRE2)	Sentinel-2	$NDRE2=\frac{{\rho }_{rededge3}-{\rho }_{rededge1}}{{\rho }_{rededge3}+{\rho }_{rededge1}}$	Ju et al. (2010)
Red Edge Chlorophyll Index (CRE)	Sentinel-2	$CLRE=\frac{{\rho }_{rededge3}}{{\rho }_{rededge1}}-1$	Gitelson et al. (2003)
Normalized Difference Vegetation Index Red Edge 1 (NDVIre1)	Sentinel-2	$NDVIre1=\frac{{\rho }_{BNIR}-{\rho }_{rededge1}}{{\rho }_{BNIR}+{\rho }_{rededge1}}$	Gitelson and Merzlyak (1998)
Normalized Difference Vegetation Index Red Edge 2 (NDVIre2)	Sentinel-2	$NDVIre2=\frac{{\rho }_{BNIR}-{\rho }_{rededge2}}{{\rho }_{BNIR}+{\rho }_{rededge2}}$	Fernández-Manso, Fernández-Manso, and Quintano (2016)
Normalized Difference Vegetation Index Red Edge 3 (NDVIre3)	Sentinel-2	$NDVIre3=\frac{{\rho }_{BNIR}-{\rho }_{rededge3}}{{\rho }_{BNIR}+{\rho }_{rededge3}}$	Fernández-Manso, Fernández-Manso, and Quintano (2016)
Normalized Difference Vegetation Index Red Edge 1 narrow (NDVIre1n)	Sentinel-2	$NDVIre1n=\frac{{\rho }_{NIR}-{\rho }_{rededge1}}{{\rho }_{NIR}+{\rho }_{rededge1}}$	Fernández-Manso, Fernández-Manso, and Quintano (2016)
Normalized Difference Vegetation Index Red Edge 2 narrow (NDVIre2n)	Sentinel-2	$NDVIre2n=\frac{{\rho }_{NIR}-{\rho }_{rededge2}}{{\rho }_{NIR}+{\rho }_{rededge2}}$	Fernández-Manso, Fernández-Manso, and Quintano (2016)
Normalized Difference Vegetation Index Red Edge 3 narrow (NDVIre3n)	Sentinel-2	$NDVIre3n=\frac{{\rho }_{NIR}-{\rho }_{rededge3}}{{\rho }_{NIR}+{\rho }_{rededge3}}$	Fernández-Manso, Fernández-Manso, and Quintano (2016)
Modified Simple Ratio Red Edge (MSRre)	Sentinel-2	$MSRre=\frac{\frac{{\rho }_{BNIR}}{{\rho }_{rededge1}}-1}{\sqrt{\frac{{\rho }_{BNIR}}{{\rho }_{rededge1}}}+1}$	Chen (1996)
Modified Simple Ratio Red Edge narrow (MSRren)	Sentinel-2	$MSRre=\frac{\frac{{\rho }_{NIR}}{{\rho }_{rededge1}}-1}{\sqrt{\frac{{\rho }_{NIR}}{{\rho }_{rededge1}}}+1}$	Fernández-Manso, Fernández-Manso, and Quintano (2016)

Table 2. Overview of the indices computed from the SAR imagery. σ represents SAR backscatter in dB and $\gamma$ linearly scaled SAR backscatter.

Index	Sensor	Formula	Reference
VV Backscatter (VV)	Sentinel-1	${\sigma }_{VV}^0$	Holtgrave et al. (2020)
VH Backscatter (VV)	Sentinel-1	${\sigma }_{VH}^0$	Holtgrave et al. (2020)
VV/VH Ratio (RA)	Sentinel-1	$VVVH={\gamma }_{VH}^{\hspace{0.17em}}/{\gamma }_{VV}^{\hspace{0.17em}}$	Holtgrave et al. (2020)
Radar Vegetation Index (RVI)	Sentinel-1	$RVI=\frac{4{\gamma }_{VH}^{\hspace{0.17em}}}{{\gamma }_{VV}^{\hspace{0.17em}}+{\gamma }_{VH}^{\hspace{0.17em}}}$	Charbonneau, Trudel, and Fernandes (2005); Holtgrave et al. (2020); Nasirzadehdizaji et al. (2019)

Figure 4. Sampling design as exemplified for one pixel representing an infested area in 2018. The red line represents the changes in NBR values for one example pixel infested in 2018 over time. The left (right) gray column indicates the period covering the sampling of values representing healthy (infested) forest. The infestation occurred during the period represented by the orange box.

Table 3. Infestation Onsets for the Investigated Years, as derived by using PhenIps.

Year	Date of Infestation Onset
2016	7 May 2016 (day of year: 128)
2017	15 May 2017 (day of year: 135)
2018	21 April 2018 (day of year: 111)
2019	18 May 2019 (day of year: 138)

Figure 5. Jeffries-Matusita distance for all computed vegetation indices. For the abbreviations of the indices, see Tables 1 and 2.

Figure 6. Value distributions between healthy and infested forests for the three indices selected for processing: a) CRE, b) NBR, c) VH. Dashed lines represent the probability density functions derived from the data for the Bayesian probability computations.

Figure 7. Overall accuracy (a), omission error (b), omission error of the infested class (c) commission error for the infested class based on the Bayesian approach.

Figure 8. Overall accuracy (a), omission error (b), omission error of the infested class (c) commission error for the infested class based on the random forest approach.

Table 4. Confusion Matrix for the Classification using the Sentinel-2 configuration with the Bayesian approach based on the CRE and a probability threshold of 0.98.

		Reference
		Infested	Healthy	Total
Prediction	Infested	43.93%	1.71%	45.64%
	Healthy	4.44%	49.92%	54.36%
	Total	48.37%	51.63%	100%

Table 5. Optimal thresholds and overall accuracies (OA) for every sensor configuration and approach.

Sensor configuration	Bayesian approach	RF approach
Landsat	Index: NBR; Threshold: .93, OA: .89	Threshold: .88, OA: .86
Sentinel-2	Index: CRE; Threshold: .98, OA: .93	Threshold: .90, OA: .78
Landsat/Sentinel-2	Index: NBR; Threshold: .96, OA: .87	Threshold: .96, OA: .87
Sentinel-1	Index: VH; Threshold: .67, OA: .62	Threshold: .93, OA: .58
Combination	Indices: NBR, VH; Threshold: .77, OA: .67	Threshold: .94, OA: .67

Figure 9. Patch-size-specific spatial accuracies for the Sentinel-2 configuration based on the Bayesian approach: a) overall accuracy, b) omission error for the infested class, c) commission error for the infested class. The gray dashed lines represent the accuracy/errors regardless of patch size, as shown in Figure 7.

Figure 10. Distribution of values regarding the timeliness of detection for all sensor configurations based on the Bayesian approach (a) and the random forest approach (b). All dates are standardized to days since the beginning of the bark beetle infestation in the respective year.

Figure 11. Distribution of values regarding the timeliness of detection for the years 2016–2019, as determined using the Sentinel-2 random forest configuration.

Figure 12. Distribution of values regarding the timeliness of detection in patches of different sizes for the years 2016–2019, as determined using Sentinel-2 random forest configuratio

Figure 13. Distribution of values regarding the timeliness of detection according to three different probability thresholds (0.5, 0.75, 0.9), as determined using the Sentinel-2 random forest configuration.

Figure 14. Final infestation map for the Bavarian Forest National Park. Colors represent the detection date based on the Sentinel-2 configuration using the CRE and the Bayesian approach with a probability threshold of 0.98. The black box indicates the subset illustrated in Figure 15.

Figure 15. Subset of the infestation map for an area in the northwest region of the BFNP. Polygons represent the disturbance patches in the reference data, labeled according to the infestation year in each one. The three red rectangles delineate areas that were not successfully detected. The satellite-based assessment agreed well with the ground data.

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Data availability statement

The data that support the findings of this study are available from the corresponding author, S.K., upon reasonable request.