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Tree Species Classification with Multi-Temporal Sentinel-2 Data

Persson, Magnus ; Lindberg, Eva ; Reese, Heather

Remote sensing (Basel, Switzerland), 2018-11, Vol.10 (11), p.1794 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Tree Species Classification with Multi-Temporal Sentinel-2 Data
Autor: Persson, Magnus ; Lindberg, Eva ; Reese, Heather
Assuntos: Accuracy ; Betula ; Boreo-nemoral ; Cellulose ; Classification ; Fjärranalysteknik ; Forest Science ; Forestry and Wood Technology ; Forests ; Image classification ; Infrared imagery ; Larix marschlinsii ; Leaves ; Lignin ; Model accuracy ; Multi-temporal ; Phenology ; Picea abies ; Pine trees ; Pinus sylvestris ; Quercus robur ; Rainforests ; Random Forest ; Remote Sensing ; Senescence ; Sentinel-2 ; Short wave radiation ; Skog och träteknik ; Skogsvetenskap ; Species ; Species classification ; Spectral resolution ; Terrestrial ecosystems ; Terrestrial environments ; Tree species classification ; Trees ; Variable selection
É parte de: Remote sensing (Basel, Switzerland), 2018-11, Vol.10 (11), p.1794
Descrição: The Sentinel-2 program provides the opportunity to monitor terrestrial ecosystems with a high temporal and spectral resolution. In this study, a multi-temporal Sentinel-2 data set was used to classify common tree species over a mature forest in central Sweden. The tree species to be classified were Norway spruce (Picea abies), Scots pine (Pinus silvestris), Hybrid larch (Larix × marschlinsii), Birch (Betula sp.) and Pedunculate oak (Quercus robur). Four Sentinel-2 images from spring (7 April and 27 May), summer (9 July) and fall (19 October) of 2017 were used along with the Random Forest (RF) classifier. A variable selection approach was implemented to find fewer and uncorrelated bands resulting in the best model for tree species identification. The final model resulting in the highest overall accuracy (88.2%) came from using all bands from the four image dates. The single image that gave the most accurate classification result (80.5%) was the late spring image (27 May); the 27 May image was always included in subsequent image combinations that gave the highest overall accuracy. The five tree species were classified with a user’s accuracy ranging from 70.9% to 95.6%. Thirteen of the 40 bands were selected in a variable selection procedure and resulted in a model with only slightly lower accuracy (86.3%) than that using all bands. Among the highest ranked bands were the red edge bands 2 and 3 as well as the narrow NIR (near-infrared) band 8a, all from the 27 May image, and SWIR (short-wave infrared) bands from all four image dates. This study shows that the red-edge bands and SWIR bands from Sentinel-2 are of importance, and confirms that spring and/or fall images capturing phenological differences between the species are most useful to tree species classification.
Editor: Basel: MDPI AG
Idioma: Inglês

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Tree Species Classification with Multi-Temporal Sentinel-2 Data

Persson, Magnus ; Lindberg, Eva ; Reese, Heather

Basel: MDPI AG

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