Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
"> Figure 1
<p>(<b>a</b>) Study area in Sarntal Valley with reference data points for mountain pines and other land cover classes. Blue stars indicate the locations of the field photography seen in <b>c</b>,<b>d</b>. Background: Orthophoto 2020 (Autonomous Province of Bolzano); (<b>b</b>) location of study area in South Tyrol, Italy. Basemap and Hillshade: OpenStreetMap, SRTM; (<b>c</b>,<b>d</b>) mountain pine fields in Durnholz Valley with visible clear-cuts. Pictures were taken during a field visit in July 2021 and (<b>e</b>) typical appearance of young, regrowing mountain pine. Picture was taken near location (<b>d</b>).</p> "> Figure 2
<p>(<b>a</b>) Study area (in yellow) and settlement areas (in red) in the Sarntal Alps with a black rectangle showing the extent of the example imagery in (<b>b</b>–<b>d</b>). Background: Orthophoto 2020; (<b>b</b>) Orthophoto 2020 (20 cm) (Autonomous Province of Bolzano); (<b>c</b>) PlanetScope (5 July 2020) (3 m); (<b>d</b>) Sentinel-2 (7 July 2020) (10 m) and (<b>e</b>) Spectral signatures of grassland, mountain pines and coniferous forest. Spectral signatures are mean reflectance values from reference polygons within the extent of <b>b</b>–<b>d</b> based on PlanetScope bands (5 July 2020).</p> "> Figure 2 Cont.
<p>(<b>a</b>) Study area (in yellow) and settlement areas (in red) in the Sarntal Alps with a black rectangle showing the extent of the example imagery in (<b>b</b>–<b>d</b>). Background: Orthophoto 2020; (<b>b</b>) Orthophoto 2020 (20 cm) (Autonomous Province of Bolzano); (<b>c</b>) PlanetScope (5 July 2020) (3 m); (<b>d</b>) Sentinel-2 (7 July 2020) (10 m) and (<b>e</b>) Spectral signatures of grassland, mountain pines and coniferous forest. Spectral signatures are mean reflectance values from reference polygons within the extent of <b>b</b>–<b>d</b> based on PlanetScope bands (5 July 2020).</p> "> Figure 3
<p>Methodology workflow for mountain pine mapping in the Sarntal Alps. A detailed description of the different feature spaces is given in <a href="#remotesensing-14-03190-t001" class="html-table">Table 1</a>. Each individual feature space of the respective classification schemes is classified using a Random Forest model. A subsequent majority filter generates the respective final output for the different feature spaces for the four classification schemes. For identifying the optimal GLCM window size (WS) for PS and S2 feature space, each GLCM WS feature space is classified and validated. The GLCM WS feature space with the highest overall accuracy is chosen as GLCM<sub>MAX</sub> for PlanetScope and Sentinel-2, respectively.</p> "> Figure 4
<p>Overall accuracies of mountain pine classifications based on PS and S2 (<a href="#remotesensing-14-03190-t001" class="html-table">Table 1</a>) feature spaces combined with different GLCM window sizes (in meter). GLCM window sizes were calculated from 3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13, 15 × 15, 17 × 17, 23 × 23, 29 × 29, 35 × 35, 43 × 43 and 51 × 51 pixels for both PlanetScope and Sentinel-2 data (<a href="#sec2dot3dot1-remotesensing-14-03190" class="html-sec">Section 2.3.1</a>.).</p> "> Figure 5
<p>Overall accuracies of classification results for each feature space in: (<b>a</b>) classification scheme 1; (<b>b</b>) classification scheme 2; (<b>c</b>) classification scheme 3; (<b>d</b>) classification scheme 4 and (<b>e</b>) overall accuracy of PlanetScope and Sentinel-2 feature spaces in classification schemes 1–4.</p> "> Figure 6
<p>Mountain pine stands in the Durnholz Valley, classified by different feature space combinations. (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) Classification results based on PlanetScope feature spaces. Mountain pine areas derived from PlanetScope feature spaces are colored in red. (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) 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).</p> "> Figure 7
<p>ROC curves and AUC for different feature spaces of each classification scheme.</p> "> Figure 8
<p>(<b>a</b>) Classified mountain pine stands in the Sarntal Alps, South Tyrol. Displayed classification based on PS + GLCM<sub>MAX</sub> + Topo + CHM feature space (OA 91%). Background: Orthophoto 2020 (Autonomous Province of Bolzano); (<b>b</b>–<b>d</b>) Zoom-in examples of classification result with indication of omission and commission errors.</p> "> Figure 9
<p>Landscape metrics for mountain pine stands in the Sarntal Alps, South Tyrol, based on final classification result (PS + GLCM<sub>MAX</sub> + Topo + CHM). (<b>a</b>) Surface area covered by mountain pines at different altitudes; (<b>b</b>) relative land cover of mountain pines at different altitudes; (<b>c</b>) surface area covered by mountain pines by cardinal direction; (<b>d</b>) surface area by mountain pine patches at different patch sizes.</p> ">
Abstract
:1. Introduction
- (i)
- To create an accurate map of the overall coverage and spatial distribution of mountain pines in the Sarntal Alps,
- (ii)
- To quantify how the spatial resolutions of open-access Sentinel-2 (10 m) and commercial PlanetScope (3 m) data influence the mapping accuracy of mountain pines,
- (iii)
- To analyze which of the features previously used for alpine vegetation classification are the most useful for the delineation of mountain pines.
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. PlanetScope Imagery
2.2.2. Sentinel-2 Imagery
2.2.3. LiDAR Data Derivatives
2.2.4. Reference Dataset
2.3. Methods
2.3.1. Feature Extraction
2.3.2. Feature Spaces and Classification Schemes
2.3.3. Random Forest Classification and Validation
3. Results
3.1. GLCM Window Size Optimization
3.2. Classification Results
4. Discussion
4.1. Influence of Spatial Resolution
4.2. Feature Selection and Importance
4.3. Classification Evaluation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Confusion Matrices and Accuracy Statistics for Classification Schemes
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 346 | 66 | 412 | |
Class 1 | 59 | 193 | 252 | |
Total | 405 | 259 | 664 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.8117 | 0.6024 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.8543 | 0.8398 | ||
Class 1 | 0.7452 | 0.7659 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 370 | 49 | 419 | |
Class 1 | 36 | 208 | 244 | |
Total | 406 | 257 | 663 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.8718 | 0.7274 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.9113 | 0.8831 | ||
Class 1 | 0.8093 | 0.8525 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 302 | 140 | 442 | |
Class 1 | 93 | 130 | 223 | |
Total | 395 | 270 | 665 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.6496 | 0.253 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.7646 | 0.6833 | ||
Class 1 | 0.4815 | 0.5830 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 280 | 123 | 403 | |
Class 1 | 115 | 147 | 262 | |
Total | 395 | 270 | 665 | |
Overall Accuracy and Kappa coefficient | ||||
OA | OA | Kappa | ||
0.6421 | 0.6421 | 0,2545 | ||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.7089 | 0.6948 | ||
Class 1 | 0.5444 | 0.5611 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 386 | 51 | 437 | |
Class 1 | 19 | 208 | 227 | |
Total | 405 | 259 | 664 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.8946 | 0.7734 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.9531 | 0.8833 | ||
Class 1 | 0.8031 | 0.9163 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 387 | 47 | 434 | |
Class 1 | 19 | 210 | 229 | |
Total | 406 | 257 | 663 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.9005 | 0.786 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.9532 | 0.8917 | ||
Class 1 | 0.8171 | 0.9170 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 395 | 59 | 454 | |
Class 1 | 10 | 200 | 210 | |
Total | 405 | 259 | 664 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.8961 | 0.7739 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.9753 | 0.8700 | ||
Class 1 | 0.7722 | 0.9524 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 385 | 51 | 436 | |
Class 1 | 21 | 206 | 227 | |
Total | 406 | 257 | 663 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.8914 | 0.7662 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.9483 | 0.8830 | ||
Class 1 | 0.8016 | 0.9075 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 381 | 36 | 417 | |
Class 1 | 24 | 223 | 247 | |
Total | 405 | 259 | 664 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.9096 | 0.8085 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.9407 | 0.9137 | ||
Class 1 | 0.8610 | 0.9028 |
Reference | ||||
---|---|---|---|---|
Classification | Class 0 | Class 1 | Total | |
Class 0 | 382 | 38 | 420 | |
Class 1 | 24 | 219 | 243 | |
Total | 406 | 257 | 663 | |
Overall Accuracy and Kappa coefficient | ||||
OA | Kappa | |||
0.9065 | 0.801 | |||
Class Statistics | ||||
PA | UA | |||
Class 0 | 0.9409 | 0.9095 | ||
Class 1 | 0.8521 | 0.9012 |
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Classification Schemes | Feature Spaces | Features | Number of Layers | Spatial Resolution |
---|---|---|---|---|
Scheme 1 | PS | Multi-temporal PlanetScope + NDVI | 20 | 3 m |
S2 | Multi-temporal Sentinel-2 + NDVI | 20 | 10 m | |
Topo | Elevation + slope + aspect | 3 | 3 m | |
CHM | Canopy height model | 1 | 3 m | |
Scheme 2 | PS + GLCMMAX | PS + GLCM statistics for optimal window size | 40 | 3 m |
S2 + GLCMMAX | S2 + GLCM statistics for optimal window size | 40 | 10 m | |
Scheme 3 | PS + GLCMMAX + Topo | PS + GLCM statistics for optimal window size + Topo | 43 | 3 m |
S2 + GLCMMAX + Topo | S2 + GLCM statistics for optimal window size + Topo | 43 | 10 m | |
Scheme 4 | PS + GLCMMAX + Topo + CHM | PS + GLCM statistics for optimal window size + Topo + CHM | 44 | 3 m |
S2 + GLCMMAX + Topo + CHM | S2 + GLCM statistics for optimal window size + Topo + CHM | 44 | 10 m |
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Rösch, M.; Sonnenschein, R.; Buchelt, S.; Ullmann, T. Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy. Remote Sens. 2022, 14, 3190. https://doi.org/10.3390/rs14133190
Rösch M, Sonnenschein R, Buchelt S, Ullmann T. Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy. Remote Sensing. 2022; 14(13):3190. https://doi.org/10.3390/rs14133190
Chicago/Turabian StyleRösch, Moritz, Ruth Sonnenschein, Sebastian Buchelt, and Tobias Ullmann. 2022. "Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy" Remote Sensing 14, no. 13: 3190. https://doi.org/10.3390/rs14133190
APA StyleRösch, M., Sonnenschein, R., Buchelt, S., & Ullmann, T. (2022). Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy. Remote Sensing, 14(13), 3190. https://doi.org/10.3390/rs14133190