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Article

Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe

Remote Sensing and Geospatial Analytics Division, GMV, Isaac Newton 11, P.T.M. Tres Cantos, E-28760 Madrid, Spain
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Remote Sens. 2020, 12(19), 3159; https://doi.org/10.3390/rs12193159
Received: 1 September 2020 / Revised: 23 September 2020 / Accepted: 24 September 2020 / Published: 26 September 2020
The spatial and temporal dynamics of the forest cover can be captured using remote sensing data. Forest masks are a valuable tool to monitor forest characteristics, such as biomass, deforestation, health condition and disturbances. This study was carried out under the umbrella of the EC H2020 MySustainableForest (MSF) project. A key achievement has been the development of supervised classification methods for delineating forest cover. The forest masks presented here are binary forest/non-forest classification maps obtained using Sentinel-2 data for 16 study areas across Europe with different forest types. Performance metrics can be selected to measure accuracy of forest mask. However, large-scale reference datasets are scarce and typically cannot be considered as ground truth. In this study, we implemented a stratified random sampling system and the generation of a reference dataset based on visual interpretation of satellite images. This dataset was used for validation of the forest masks, MSF and two other similar products: HRL by Copernicus and FNF by the DLR. MSF forest masks showed a good performance (OAMSF = 96.3%; DCMSF = 96.5), with high overall accuracy (88.7–99.5%) across all the areas, and omission and commission errors were low and balanced (OEMSF = 2.4%; CEMSF = 4.5%; relBMSF = 2%), while the other products showed on average lower accuracies (OAHRL = 89.2%; OAFNF = 76%). However, for all three products, the Mediterranean areas were challenging to model, where the complexity of forest structure led to relatively high omission errors (OEMSF = 9.5%; OEHRL = 59.5%; OEFNF = 71.4%). Comparing these results with the vision from external local stakeholders highlighted the need of establishing clear large-scale validation datasets and protocols for remote sensing-based forest products. Future research will be done to test the MSF mask in forest types not present in Europe and compare new outputs to available reference datasets. View Full-Text
Keywords: forest mask; validation; probability sampling; remote sensing; earth observations; forestry; accuracy assessment; forest classification forest mask; validation; probability sampling; remote sensing; earth observations; forestry; accuracy assessment; forest classification
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MDPI and ACS Style

Fernandez-Carrillo, A.; Franco-Nieto, A.; Pinto-Bañuls, E.; Basarte-Mena, M.; Revilla-Romero, B. Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe. Remote Sens. 2020, 12, 3159. https://doi.org/10.3390/rs12193159

AMA Style

Fernandez-Carrillo A, Franco-Nieto A, Pinto-Bañuls E, Basarte-Mena M, Revilla-Romero B. Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe. Remote Sensing. 2020; 12(19):3159. https://doi.org/10.3390/rs12193159

Chicago/Turabian Style

Fernandez-Carrillo, Angel, Antonio Franco-Nieto, Erika Pinto-Bañuls, Miguel Basarte-Mena, and Beatriz Revilla-Romero. 2020. "Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe" Remote Sensing 12, no. 19: 3159. https://doi.org/10.3390/rs12193159

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