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High Resolution Forest Maps from Interferometric TanDEM-X and Multitemporal Sentinel-1 SAR Data

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Abstract

In this study, a workflow for a semi-automated forest/non-forest detection is proposed that is based on multitemporal Sentinel-1 ground range detected (GRD) C-band backscatter and TanDEM-X Coregistered Single look Slant range Complex (CoSSC) X-band imagery and an unsupervised random forest classification approach. Therefore, numerous features that refer to frequency, polarisation, and texture were extracted from SAR data of different seasons. The aim was to develop a processing scheme that is feasible for semi-automated forest mapping and monitoring from SAR data at high spatial resolution and on annual scale. It was tested for seven study sites in Germany and Canada which represent different biomes and forest types. Results were validated against field observations and existing forest maps. The best performance for the German study sites was achieved with multitemporal Sentinel-1 backscatter data from the onset of the growing season with small incidence angle and VH polarisation, together with extracted textural features and TanDEM-X data. Producer’s accuracies for the forest class of the different study sites ranged from 88.4 to 98.0%. User’s accuracies ranged from 85.5 to 87.0%. Using Sentinel-1 data covering the whole growing season at a 12 day repetition rate, ascending and descending orbits and VV and VH polarisations led to comparable results. Limited data availability for the Canadian study sites resulted in on average to less reliable results than at the German sites with a higher range of producer’s (62.4–98.8%) and user’s accuracies (46.2–90.2%).

Keywords

Sentinel-1 TanDEM-X Forest mask Random forest Classification 

Zusammenfassung

Großmaßstäbige Forstkartierung auf der Grundlage von interferometrischen TanDEM-X und multitemporalen Sentinel-1-SAR Daten. Diese Studie schlägt einen Arbeitsablauf zur halbautomatischen Wald/nicht-Wald Detektion vor, der auf multitemporalen Sentinel-1 ground range detected (GRD) C-Band Rückstreuwerten und TanDEM-X Coregistered Single look Slant range Complex (CoSSC) X-Band Bildern und einer unüberwachten Random Forest Klassifikation basiert. Dabei wurden unterschiedliche Parameterkombinationen von SAR Frequenz, Polarisation, Textur und Jahreszeit getestet. Ziel war es, ein Prozessierungsschema zu entwickeln, welches eine (halb-) automatische Waldkartierung und -überwachung mit hochaufgelösten SAR Daten auf jährlicher Basis möglich macht. Das Verfahren wurde für sieben Untersuchungsgebiete in verschiedenen Biomen und Waldtypen in Deutschland und Kanada getestet. Das beste Ergebnis für die deutschen Untersuchungsgebiete wurde mit multitemporalen Sentinel-1 Daten vom Beginn der Wachstumsperiode in einem absteigenden Orbit und VH Polarisationen, zusammen mit Texturmerkmalen und TanDEM-X Daten erreicht. Die Produzenten-Genauigkeit in den unterschiedlichen Untersuchungsgebieten lag für die Waldklasse zwischen 88,4 und 98,0%. Die Nutzer-Genauigkeit lag zwischen 85,5 und 87,0%. Die Verwendung eines Sentinel-1 Datensatzes aller verfügbaren Aufnahmen für die gesamte Wachstumsperiode lieferte vergleichbare Ergebnisse. Die eingeschränkte Datenverfügbarkeit für die kanadischen Gebiete führte im Vergleich zu den deutschen Gebieten zu weniger verlässlichen Ergebnissen, wobei sowohl die Produzenten-Genauigkeit (62,4 bis 98,8%) als auch die Nutzer-Genauigkeit (46,1 bis 90,2%) eine erhebliche Spannweite aufweisen.

Notes

Acknowledgements

This work is part of the DLR/BMWi funded project “BoDEM: Processing of digital terrain models from X- and C-band SAR data for the derivation of high resolution surface layers for soil and ecosystem mapping” (Grant no. 50EE1509).

Supplementary material

41064_2017_40_MOESM1_ESM.xlsx (11 kb)
Supplementary material 1 (xlsx 10 KB)
41064_2017_40_MOESM2_ESM.xlsx (13 kb)
Supplementary material 2 (xlsx 12 KB)

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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2017

Authors and Affiliations

  1. 1.Institute of GeographyGeorg-August-Universität GöttingenGöttingenGermany

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