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Articles containing the keyword 'Landsat'

Category : Article

article id 5610, category Article
Timo Tokola, Juho Heikkilä. (1997). Improving satellite image based forest inventory by using a priori site quality information. Silva Fennica vol. 31 no. 1 article id 5610. https://doi.org/10.14214/sf.a8511
Keywords: forest inventory; stand characteristics; remote sensing; forest surveys; site factors; ancillary data; Landsat TM
Abstract | View details | Full text in PDF | Author Info

The purpose of this study was to test the benefits of a forest site quality map, when applying satellite image-based forest inventory. By combining field sample plot data from national forest inventories with satellite imagery and forest site quality data, it is possible to estimate forest stand characteristics with higher accuracy for smaller areas. The reliability of the estimates was evaluated using the data from a stand-wise survey for area sizes ranging from 0.06 ha to 300 ha. When the mean volume was estimated, a relative error of 14 per cent was obtained for areas of 50 ha; for areas of 30 ha the corresponding figure was below 20 per cent. The relative gain in interpretation accuracy, when including the forest site quality information, ranged between 1 and 6 per cent. The advantage increased according to the size of the target area. The forest site quality map had the effect of decreasing the relative error in Norway spruce (Picea abies) volume estimations, but it did not contribute to Scots pine (Pinus sylvestris) volume estimation procedure.

  • Tokola, E-mail: tt@mm.unknown (email)
  • Heikkilä, E-mail: jh@mm.unknown
article id 5307, category Article
Simo Poso, Raito Paananen, Markku Similä. (1987). Forest inventory by compartments using satellite imagery. Silva Fennica vol. 21 no. 1 article id 5307. https://doi.org/10.14214/sf.a15464
Keywords: forest inventory; stand characteristics; remote sensing; Landsat 5 TM; forest inventory and monitoring; two phase sampling; ancillary information
Abstract | View details | Full text in PDF | Author Info

A method for using satellite data in forest inventories and updating is described and tested. The stand characteristics estimated by the method showed high correlation with the same characteristics measured in the field. The correlation coefficients for volume, age and mean height were about 0.85. It seems that the method is applicable to practical forestry. Extensive work in programming, however, is required.

The PDF includes an abstract in Finnish.

  • Poso, E-mail: sp@mm.unknown (email)
  • Paananen, E-mail: rp@mm.unknown
  • Similä, E-mail: ms@mm.unknown

Category : Research article

article id 10347, category Research article
Matti Katila, Tuomas Rajala, Annika Kangas. (2020). Assessing local trends in indicators of ecosystem services with a time series of forest resource maps. Silva Fennica vol. 54 no. 4 article id 10347. https://doi.org/10.14214/sf.10347
Keywords: National Forest Inventory; satellite images; k-nearest neighbour estimation; Landsat; Mann-Kendall test; multitemporal inventory data; temporal trend analysis
Highlights: Untitled Document Contextual Mann-Kendall test detects significant trends in time-series of forest maps; Trends become more consistent as the areal unit size used for test input increases; Changes in different scales reflect different phenomena in forests; Significant trends were detected even after multiple testing correction.
Abstract | Full text in HTML | Full text in PDF | Author Info

Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric k-nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m2 pixel size and units of 240 × 240 m2, 1200 × 1200 m2 and 12  000 × 12  000 m2 aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12  000 × 12  000 m2. The multiple testing corrections decreased the amount of significant p-values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.

  • Katila, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland; ORCID https://orcid.org/0000-0001-6946-5736 E-mail: matti.katila@luke.fi (email)
  • Rajala, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: tuomas.rajala@luke.fi
  • Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland ORCID https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
article id 431, category Research article
Pauline Stenberg, Miina Rautiainen, Terhikki Manninen, Pekka Voipio, Heikki Smolander. (2004). Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica vol. 38 no. 1 article id 431. https://doi.org/10.14214/sf.431
Keywords: Landsat ETM ; Leaf Area Index; spectral vegetation indices; boreal coniferous forests
Abstract | View details | Full text in PDF | Author Info
Estimation of leaf area index (LAI) using spectral vegetation indices (SVIs) was studied based on data from 683 plots on two Scots pine and Norway spruce dominated sites in Finland. The SVIs studied included the normalised difference vegetation index (NDVI), the simple ratio (SR), and the reduced simple ratio (RSR), and were calculated from Landsat ETM images of the two sites. Regular grids of size 1 km2 with gridpoints placed at 50 m intervals were established at the sites and measurements of LAI using the LAI-2000 instrument were taken at the gridpoints. SVI-LAI relationships were examined at plot scale, where the plots were defined as circular areas of radius 70 m around each gridpoint. Plotwise mean LAI was computed as a weighted average of LAI readings taken around the gridpoints belonging to the plot. Mean LAI for the plots ranged from 0.36 to 3.72 (hemisurface area). All of the studied SVIs showed fair positive correlation with LAI but RSR responded more dynamically to LAI than did SR or NDVI. Especially NDVI showed poor sensitivity to changes in LAI. RSR explained 63% of the variation in LAI when all plots were included (n = 683) and the coefficient of determination rose to 75% when data was restricted to homogeneous plots (n = 381). Maps of estimated LAI using RSR showed good agreement with maps of measured LAI for the two sites.
  • Stenberg, Department of Forest Ecology, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: pauline.stenberg@helsinki.fi (email)
  • Rautiainen, Department of Forest Ecology, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: mr@nn.fi
  • Manninen, Finnish Meteorological Institute, Meteorological research, Ozone and UV radiation research, P.O. Box 503, FIN-00101 Helsinki, Finland E-mail: tm@nn.fi
  • Voipio, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: pv@nn.fi
  • Smolander, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: hs@nn.fi

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 ORCID https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@helsinki.fi (email)
  • 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 ORCID 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 ORCID 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 ORCID https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi
article id 324, category Research note
Matti Katila. (2006). Empirical errors of small area estimates from the multisource National Forest Inventory in Eastern Finland. Silva Fennica vol. 40 no. 4 article id 324. https://doi.org/10.14214/sf.324
Keywords: k-nearest neighbours estimation; Landsat ETM ; multisource forest inventory; synthetic estimation
Abstract | View details | Full text in PDF | Author Info
The precision of multisource national forest inventory (MS-NFI) estimators and simple synthetic estimators based on NFI field data only was assessed employing an independent inventory data set of several small areas in Eastern Finland. There were seven test units of size 100 km2 and three test units of size 1 km2 for which a systematic field sampling was carried out. The ‘improved’ MS-NFI method yielded the most precise estimates for mean volume and mean volume of pine and spruce: relative root mean square errors (RMSE*) were 5%, 12% and 15% for 100 km2 test units and 13%, 27% and 40% for 1 km2 test units respectively. The stratified MS-NFI method was best for broad-leaved volume estimation. Synthetic estimation based on the NFI9 field plots post-stratified with coarse scale forest variable maps from NFI8 resulted in RMSE*s comparable to those of the ordinary MS-NFI in areas of 100 km2 for mean volume and mean volume of pine and spruce. The amount of variation between the field inventory estimates for the test units explained by the MS-NFI estimators remained the same or increased when the size of the area increased from of 1 km2 to 100 km2 and up to 2000 km2. The validation of the largest areas was made against the NFI9 field inventory estimates for groups of municipalities in the study area.
  • Katila, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: mk@nn.fi (email)

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