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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 38, 2012 - Issue 1
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Research Article

Monitoring the state of a large boreal forest region in eastern Canada through the use of multitemporal classified satellite imagery

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Pages 91-108 | Received 06 Apr 2011, Accepted 07 Mar 2012, Published online: 04 Jun 2014
 

Abstract

Multitemporal classification of Landsat imagery was used to measure and monitor the state of the forest over a large area (11.6 million ha) of boreal forest in eastern Canada using four criteria for a 20 year period (1985–2005). The Enhancement-Classification Method was used in this study. Forty-eight thematic classes based on Canada's National Forest Inventory were identified, then grouped into 13 indicators, and reorganized within four main criteria: (i) forest versus nonforest land cover, (ii) forest development stage, (iii) forest cover type, and (iv) forest cover density. Validation based on 2973 high-resolution geo-referenced digital aerial colour photos of the 2005 classified images showed an overall accuracy of the four criteria of 83%, 68%, 58%, and 62%, respectively. The change in each indicator between 1985 and 2005 could be summarized as: (i) a decrease in productive forest area of 0.4% (approx. 43 000 ha); (ii) a 4.6% decrease in mature stand area, with a concomitant increase in areas classified as vegetated (1.3%) and regenerated (3.4%); (iii) concentration of harvesting pressure on coniferous and mixed stands with respective reductions of 8.2% and 0.8%, due to their conversion to deciduous stands; and (iv) an increase in low-density stands (3.1%) and a decrease in high-density stands (8.3%). These results demonstrate that medium-resolution (30 m) remote sensing tools can be used both to monitor the state of the boreal forest and to produce key indicators, which were extracted from the multidate Landsat satellite imagery.

Une classification multi temporelle d'images Landsat a été utilisée afin d’évaluer l’état de la forêt sur une période de 20 ans (1985–2005) dans une grande région (11,6 millions ha) de la forêt boréale de l'est du Canada à l'aide de quatre critères. La méthode de classification améliorée ECM a été utilisée pour cette étude. Quarante-huit classes thématiques basées sur l'inventaire forestier national ont été identifiées et regroupées en treize indicateurs selon quatre critères: (i) forêt non-forêt; (ii) stade de développement forestier; (iii) type de couvert forestier; (iv) densité du couvert forestier. La précision globale à l'aide de 2973 photos couleur à haute résolution pour les images classifiées en 2005 selon les quatre critères a été de 83%, 68%, 58% et 62% respectivement. Les changements observés pour chaque indicateur entre 1985 et 2005 peuvent être décrits comme suit: (i) une diminution de la superficie de forêt productive de 0,4% (~43000 ha); (ii) une diminution de 4,6% de la superficie des peuplements matures et une augmentation de la superficie des zones revégétées (1.3%) et régénérées (3,4%); (iii) une diminution de la superficie des peuplements résineux et mixtes respectivement de 8,2% et 0,8% et une augmentation du couvert feuillu due principalement à la succession végétale suite à la récolte forestière et (iv) une augmentation de la superficie des peuplements ouverts (3,1%) et une diminution des peuplements denses (8,3%). Ces résultats démontrent que les outils de la télédétection multi-spectrale à moyenne résolution (30 m) peuvent être utilisés à la fois pour faire un suivi opérationnel de l’état de la forêt et pour produire des indicateurs clés extraits à partir des images satellites classifiées.

Acknowledgements

This research was enabled through funding from the Economic Development Agency of Canada for the Regions of Quebec and the NSERC/UQAT/UQAM Industrial Chair in Sustainable Forest Management. The work was also supported by the Canadian Space Agency through the EOSD project led by the Canadian Forest Service. The authors are grateful to Philippe Villemaire, Stephen Côté, Luc Guindon, and Guy Simard from Laurentian Forestry Centre (LFC) for their scientific and technical contributions. The digital aerial photos were collected by the Géo-3D company. We also would like to thank Dr. P.Y. Bernier from the LFC and Dr. W.F.J. Parsons from the Centre for Forest Research for their valuable comments and for the English-language review of the manuscript.

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