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Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2

  • Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaved, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.

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Metadaten
Author:Melanie Brauchler, Johannes Stoffels
URN:urn:nbn:de:hbz:385-1-18645
DOI:https://doi.org/10.3390/rs14030727
Parent Title (English):Remote Sensing
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of completion:2022/02/04
Date of publication:2022/02/04
Publishing institution:Universität Trier
Contributing corporation:The publication was funded by the Open Access Fund of Universität Trier and the German Research Foundation (DFG)
Release Date:2022/05/04
Tag:aerial imagery; foss; geobia; segmentation; sentinel-2
GND Keyword:Luftbild; Luxemburg; Satellitenfernerkundung; Waldinventur
Volume (for the year ...):2022
Issue / no.:Band 14, Heft 3 (2022)
Number of pages:32
Institutes:Fachbereich 6 / Geographie und Geowissenschaften
Dewey Decimal Classification:9 Geschichte und Geografie / 90 Geschichte / 900 Geschichte und Geografie
Licence (German):License LogoCC BY: Creative-Commons-Lizenz 4.0 International

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