# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 5 Results
### Coconut Damage Evaluation  
To validate our large-scale predictions we obtained high-resolution images from Worldview-2(r) 2021 Maxar Technologies with a GSD of 50 cm. Images were selected in various parts of the study area. We visually analysed the high-resolution images and marked changes between images before and after the typhoon. Although this is a manually difficult task, we focused on areas with clear changes on the ground. In total we annotated 87 different change points including changes on coconut plantations, and added background  
Figure 5: Overview of Typhoon Goni’s estimated damages to coconut crops.  
points that showed no explicit change. See Figure 6 for examples of annotated points from each class.  
In Figure 7 we present example areas located close to the typhoon path in northern Albay. Validation images show that most tree trunks remain rooted in ground, i.e., lower density found with our method corresponds mostly to canopy loss, not fallen trees.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**State** & **Before** & **After** & **Relative** & **Total** & **Recall** \\  & **[M]** & **[M]** & **Damage [\%]** & **[\%]** & **[\%]** \\ \hline Quezon & 12.2 & 9.3 & 23.7 & 20.9 & 76.8 \\ Northern Samar & 6.2 & 4.2 & 32.9 & 10.7 & 84.0 \\ Camarines Sur & 4.6 & 1.1 & 77.1 & 7.9 & 70.5 \\ Sorsogon & 4.5 & 4.8 & \(-\)5.5 & 7.8 & 73.1 \\ Eastern Samar & 4.4 & 2.9 & 33.0 & 7.5 & 84.0 \\ Masbate & 3.9 & 4.6 & \(-\)18.4 & 6.6 & 96.0 \\ Samar & 3.3 & 2.3 & 30.3 & 5.7 & 92.3 \\ Leyte & 3.3 & 2.0 & 40.2 & 5.7 & 87.0 \\ Albay & 2.4 & 1.1 & 52.8 & 4.1 & 89.3 \\ Camarines Norte & 2.4 & 1.3 & 44.1 & 4.1 & 52.3 \\ Aurora & 1.8 & 1.4 & 21.7 & 3.1 & 82.7 \\ Oriental Mindoro & 1.3 & 1.3 & 2.1 & 2.2 & 62.4 \\ Cebu & 1.1 & 1.1 & \(-\)7.7 & 1.8 & 97.5 \\ Biliran & 1.0 & 1.1 & \(-\)6.0 & 1.7 & 90.5 \\ Romblon & 1.0 & 1.1 & \(-\)12.3 & 1.6 & 89.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top-15 provinces in the Philippines in terms of total coconut tree count. Total [%] represents the percentage with respect to the total coconut tree count of the area of study. Recall [%] is the percentage of covered area with respect to the land area of each province.  
Figure 6: Left: examples of manually annotated validation points. Right: geographical overview of all the very high resolution (VHR) areas used for validation.  
Figures 8 and 9 show examples of areas in the provinces of Aurora and Samar, respectively, which exhibited only minor damages. Most of the areas there show no changes, which appears consistent with our high-resolution validation images. However, we observed very localised areas where damages are predicted. From the high-resolution validation images, there is no clear sign of damaged trees in these areas. We speculate that these predictions are due to subtle changes of the tree foliage, co-registration errors, or inaccurate predictions from the radiometrically more challenging post-tyhoon images. The phenomenon is localised and does not appear systematically, therefore it does not significantly affect the large-scale analysis.  
Figure 8: Comparison of density estimations and high resolution reference data in Aurora, Philippines. Images before and after were sensed on 19 September 2020 and 24 December 2020 respectively.  
Figure 7: Comparison of density estimations and high resolution reference data in Northern Albay, Philippines. Images before and after were sensed on 9 May 2020 and 8 March 2021 respectively.