Literature DB >> 15049351

Countrywide estimates of forest variables using satellite data and field data from the National Forest Inventory.

Heather Reese1, Mats Nilsson, Tina Granqvist Pahén, Olle Hagner, Steve Joyce, Ulf Tingelöf, Mikael Egberth, Håkan Olsson.   

Abstract

About 55% of the land area in Sweden is covered by forest. Presently, there is no uniform and geographically explicit description of the forest cover for Sweden. Independent data sources already existing are satellite data from Landsat TM and SPOT HRV, map masks and forest inventory plots. Together, they provide the possibility of computing estimates of forest variables such as stem volume and stand age. The accuracy for these estimations will be low on the pixel level, but higher on a stand level. This type of raster-based forest data is useful for authorities when planning how timber resources should be utilized or for monitoring purposes. The production line that was developed to create a countrywide database of forest variable estimates in a cost-efficient way is described. Accuracy of forest variable estimates for an area in southwestern Sweden was assessed at stand level. Results showed 33% overall root mean square error for the estimates of total wood volume, and 23% for the age estimates.

Mesh:

Year:  2003        PMID: 15049351     DOI: 10.1579/0044-7447-32.8.542

Source DB:  PubMed          Journal:  Ambio        ISSN: 0044-7447            Impact factor:   5.129


  13 in total

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Authors:  H Gibb; J Hilszczański; J Hjältén; K Danell; J P Ball; R B Pettersson; O Alinvi
Journal:  Oecologia       Date:  2007-10-31       Impact factor: 3.225

2.  Improving the precision of sample-based forest damage inventories through two-phase sampling and post-stratification using remotely sensed auxiliary information.

Authors:  Cornelia Roberge; Sören Wulff; Heather Reese; Göran Ståhl
Journal:  Environ Monit Assess       Date:  2016-03-08       Impact factor: 2.513

3.  Evaluating common drivers for color, iron and organic carbon in Swedish watercourses.

Authors:  Johan Temnerud; Julia K Hytteborn; Martyn N Futter; Stephan J Köhler
Journal:  Ambio       Date:  2014       Impact factor: 5.129

4.  Aluminium concentrations in Swedish forest streams and co-variations with catchment characteristics.

Authors:  Stefan Löfgren; Neil Cory; Therese Zetterberg
Journal:  Environ Monit Assess       Date:  2009-06-20       Impact factor: 2.513

5.  Forests regenerating after clear-cutting function as habitat for bryophyte and lichen species of conservation concern.

Authors:  Jörgen Rudolphi; Lena Gustafsson
Journal:  PLoS One       Date:  2011-04-07       Impact factor: 3.240

6.  Evaluating citizen science data for forecasting species responses to national forest management.

Authors:  Louise Mair; Philip J Harrison; Mari Jönsson; Swantje Löbel; Jenni Nordén; Juha Siitonen; Tomas Lämås; Anders Lundström; Tord Snäll
Journal:  Ecol Evol       Date:  2016-12-20       Impact factor: 2.912

7.  Forest Edge Regrowth Typologies in Southern Sweden-Relationship to Environmental Characteristics and Implications for Management.

Authors:  Björn Wiström; Anders Busse Nielsen
Journal:  Environ Manage       Date:  2017-04-10       Impact factor: 3.266

8.  Small-scale population divergence is driven by local larval environment in a temperate amphibian.

Authors:  Patrik Rödin-Mörch; Hugo Palejowski; Maria Cortazar-Chinarro; Simon Kärvemo; Alex Richter-Boix; Jacob Höglund; Anssi Laurila
Journal:  Heredity (Edinb)       Date:  2020-09-21       Impact factor: 3.821

9.  Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor.

Authors:  Hieu Cong Nguyen; Jaehoon Jung; Jungbin Lee; Sung-Uk Choi; Suk-Young Hong; Joon Heo
Journal:  Sensors (Basel)       Date:  2015-07-31       Impact factor: 3.576

10.  Predicting grey-sided vole occurrence in northern Sweden at multiple spatial scales.

Authors:  Magnus Magnusson; Arvid Bergsten; Frauke Ecke; Orjan Bodin; Lennart Bodin; Birger Hörnfeldt
Journal:  Ecol Evol       Date:  2013-10-06       Impact factor: 2.912

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