# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 3 Results
### Classification Results  
Classification accuracies of scheme 1 varied strongly among the different feature spaces and showed the overall lowest accuracy among all schemes (Figure 5, Tables A1-A10). LiDAR derived feature spaces resulted in overall accuracies of 65.0% (Topo) and 64.2 % (CHM), while the multi-temporal satellite feature spaces performed significantly better. Thereby, the overall accuracy of PS (81.2%) was lower than S2 (87.2%). For PS also, the PA (74.5%) and UA (76.6%) were considerably lower compared to the PA (80.9%) and UA (85.3%) of the S2 feature space. Particularly visible are false positive pixels (commission error) in the forest areas on the south-facing slopes of Durnholz Valley (Figure 6a), which are also visible in the results of the S2 feature space (Figure 6b). For PS, additional false negative pixels (omission error) occurred in some large, contiguous patches, where mountain pine was incorrectly not detected (Figure 6a). Mountain pine classifications of scheme 2 showed overall higher OAs than scheme 1 and a slightly higher accuracy for S2 + GLCMMAX (90.0%) than for PS + GLCMMAX (89.5%). The inclusion of GLCM textural features led to an increase in the PA and UA of PS + GLCMMAX (80.3% and 91.6%) and S2 + GLCMMAX (81.7% and 91.7%), which is also visible in the decrease of false positive pixels (Figure 6c,d). The addition of GLCMMAX also resulted in a reduction of the omitted mountain pine pixels in the PS + GLCMMAX feature space, while some boundary areas still experienced some confusion (Figure 6c). The OAs of mountain pine classifications did not change  
Figure 4: Overall accuracies of mountain pine classifications based on PS and S2 (Table 1) feature spaces combined with different GLCM window sizes (in meter). GLCM window sizes were calculated from \(3\times 3\), \(5\times 5\), \(7\times 7\), \(9\times 9\), \(11\times 11\), \(13\times 13\), \(15\times 15\), \(17\times 17\), \(23\times 23\), \(29\times 29\), \(35\times 35\), \(43\times 43\) and \(51\times 51\) pixels for both PlaneScope and Sentinel-2 data (Section 2.3.1.). significantly when topographical features were added to the feature space scheme 3). However, our visual assessment showed that false positive pixels in the valley were almost completely eliminated (Figure 6e,f). The inclusion of CHM (scheme 4) slightly improved both classification results and led to the best results for PS + GLCM\({}_{\text{MAX}}\) + Topo + CHM (91.0%) and S2 + GLCM\({}_{\text{MAX}}\) + Topo + CHM (90.6%). Figure 5: Overall accuracies of classification results for each feature space in: (**a**) classification scheme 1; (**b**) classification scheme 2; (**c**) classification scheme 3; (**d**) classification scheme 4 and (**e**) overall accuracy of PlanetScope and Sentinel-2 feature spaces in classification schemes 1–4. Figure 6: Mountain pine stands in the Durnholz Valley, classified by different feature space combinations. (**a**,**c**,**e**,**g**) Classification results based on PlanetScope feature spaces. Mountain pine areas derived from PlanetScope feature spaces are colored in red. (**b**,**d**,**f**,**h**) Classification results based on Sentinel-2 feature spaces. Mountain pine areas derived from Sentinel-2 feature spaces are colored in blue. Commission errors occur where pixels of other land cover classes are classified as mountain pines (false positive). Omission errors occur where mountain pine pixels are incorrectly not detected (false negative). Background: Orthophoto 2020 (Autonomous Province of Bolzano). ROC and AUC values confirmed the accuracy assessments and our visual evaluation of the different mountain pine classifications (Figures 5 and 6). For scheme 1, the performance of the S2 classification model based on ROC curves indicated a higher true positive rate compared to PS (Figure 7). However, PS performed much better in predicting true positive pixels than Topo or CHM. Likewise, the AUC of S2 (0.94) indicated that the class separability performance is significantly higher compared to the PS classification model (0.90).