The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1457–1461, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1457-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1457–1461, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1457-2020

  22 Aug 2020

22 Aug 2020

SYNERGISTIC USE OF SENTINEL-1 AND SENTINEL-2 TIME SERIES FOR POPLAR PLANTATIONS MONITORING AT LARGE SCALE

Y. Hamrouni1,2, É. Paillassa3, V. Chéret1, C. Monteil1, and D. Sheeren1 Y. Hamrouni et al.
  • 1Université de Toulouse, INRAE, UMR DYNAFOR, Castanet-Tolosan, France
  • 2Conseil National du Peuplier, Paris, France
  • 3Centre National de la Propriété Forestière, Institut pour le Développement Forestier, Bordeaux, France

Keywords: Poplar plantations, Active learning, Large scale, Mapping, SAR, Stand age

Abstract. The current context of availability of Earth Observation satellite data at high spatial and temporal resolutions makes it possible to map large areas. Although supervised classification is the most widely adopted approach, its performance is highly dependent on the availability and the quality of training data. However, gathering samples from field surveys or through photo interpretation is often expensive and time-consuming especially when the area to be classified is large. In this paper we propose the use of an active learning-based technique to address this issue by reducing the labelling effort required for supervised classification while increasing the generalisation capabilities of the classifier across space. Experiments were conducted to identify poplar plantations in three different sites in France using Sentinel-2 time series. In order to characterise the age of the identified poplar stands, temporal means of Sentinel-1 backscatter coefficients were computed. The results are promising and show the good capacities of the active learning-based approach to achieve similar performance (Poplar F-score ≥ 90%) to traditional passive learning (i.e. with random selection of samples) with up to 50% fewer training samples. Sentinel-1 annual means have demonstrated their potential to differentiate two stand ages with an overall accuracy of 83% regardless of the cultivar considered.