Sam B. Coggins, Nicholas C. Coops, Michael A. Wulder. (2010). Improvement of low level bark beetle damage estimates with adaptive cluster sampling. Silva Fennica vol. 44 no. 2 article id 456. https://doi.org/10.14214/sf.456

Keywords: forest inventory; adaptive cluster sampling; mountain pine beetle; object-based classification; high spatial resolution; satellite; digital aerial imagery

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Detection of low level infestation in forest stands is of principle importance to determine effective control strategies before the attack spread to large areas. Of particular concern is the ongoing mountain pine beetle, Dendroctonus ponderosae (Hopkins) epidemic, which has caused approximately 14 million hectares of damage to lodgepole pine (Pinus contorta Dougl. ex. Loud var. latifolia Engl.) forests in western Canada. At the stand level attacked trees are often difficult to locate and can remain undetected until the infestation has become established beyond a small number of trees. As such, methods are required to detect and characterise low levels of attack prior to infestation expansion, to inform management, and to aid mitigation activities. In this paper, an adaptive cluster sampling approach was applied to very fine-scale (20 cm) digital aerial imagery to locate mountain pine beetle damaged trees at the leading edge of the current infestation. Results indicated a mean number of 7.36 infested trees per hectare with a variance of 18.34. In contrast a non-adaptive approach estimated the mean number of infested trees in the same area to be 61.56 infested trees per hectare with a variance of 41.43. Using a relative efficiency estimator the adaptive cluster sampling approach was found to be over two times more efficient when compared to the non-adaptive approach.

Coggins, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, B.C., Canada V6T 1Z4 E-mail: scoggins@interchange.ubc.ca
Coops, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, B.C., Canada V6T 1Z4 E-mail: ncc@nn.ca
Wulder, Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Rd., Victoria, B.C., Canada V8Z 1M5 E-mail: maw@nn.ca

Category : Research note

article id 9986, category Research note

Ninni Saarinen, Joanne C. White, Michael A. Wulder, Annika Kangas, Sakari Tuominen, Ville Kankare, Markus Holopainen, Juha Hyyppä, Mikko Vastaranta. (2018). Landsat archive holdings for Finland: opportunities for forest monitoring. Silva Fennica vol. 52 no. 3 article id 9986. https://doi.org/10.14214/sf.9986

Keywords: National Forest Inventory; satellite; Landsat time series

Highlights: The 45-year Landsat archive contained 30 076 images for Finland by December 31, 2017; 16.3% of these were acquired within ±30 days of August 1 (northern hemisphere summer), have <70% cloud cover, and a 30 m spatial resolution; Using time series analyses, these data provide unique information that complements other datasets available for forest monitoring and assessment in Finland.

Abstract | Full text in HTML | Full text in PDF | Author Info

There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30 076 images (1972–2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 ±30 days, <70% cloud cover, 30 m spatial resolution). Data from the Landsat archive can augment forest monitoring efforts in Finland, provide new information for science and applications, and enable retrospective, systematic analyses to characterize the development of Finnish forests over the past three decades. The capacity to monitor trends based upon this multi-decadal record with the addition of new measurements is of benefit to multisource inventories and offers nationally comprehensive spatially-explicit datasets for a wide range of stakeholders and applications.

Saarinen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@helsinki.fi
White, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada http://orcid.org/0000-0003-4674-0373 E-mail: joanne.white@canada.ca
Wulder, Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada https://orcid.org/0000-0002-6942-1896 E-mail: mike.wulder@canada.ca
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: annika.kangas@luke.fi
Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
Kankare, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ville.kankare@helsinki.fi
Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi
Hyyppä, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02431 Masala, Finland E-mail: juha.hyyppa@nls.fi
Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi

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