Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm

This paper suggested simulation approaches for quantifying and reducing the effects of National Forest Inventory (NFI) plot location error on aboveground forest biomass and carbon stock estimation using the k-Nearest Neighbor (kNN) algorithm. Additionally, the effects of plot location error in pre-GPS and GPS NFI plots were compared. Two South Korean cities, Sejong and Daejeon, were chosen to represent the study area, for which four Landsat TM images were collected together with two NFI datasets established in both the pre-GPS and GPS eras. The effects of plot location error were investigated in two ways: systematic error simulation, and random error simulation. Systematic error simulation was conducted to determine the effect of plot location error due to mis-registration. All of the NFI plots were successively moved against the satellite image in 360° directions, and the systematic error patterns were analyzed on the basis of the changes of the Root Mean Square Error (RMSE) of kNN estimation. In the random error simulation, the inherent random location errors in NFI plots were quantified by Monte Carlo simulation. After removal of both the estimated systematic and random location errors from the NFI plots, the RMSE% were reduced by 11.7% and 17.7% for the two pre-GPS-era datasets, and by 5.5% and 8.0% for the two GPS-era datasets. The experimental results showed that the pre-GPS NFI plots were more subject to plot location error than were the GPS NFI plots. This study’s findings demonstrate a potential remedy for reducing NFI plot location errors which may improve the accuracy of carbon stock estimation in a practical manner, particularly in the case of pre-GPS NFI data.

[1]  Merja Halme,et al.  Improving the accuracy of multisource forest inventory estimates to reducing plot location error — a multicriteria approach , 2001 .

[2]  Arnt Kristian Gjertsen,et al.  Accuracy of forest mapping based on Landsat TM data and a kNN-based method , 2007 .

[3]  Marian Muste,et al.  SUMMARY OF EXPERIMENTAL UNCERTAINTY ASSESSMENT METHODOLOGY WITH EXAMPLE , 1999 .

[4]  A. Lister,et al.  A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data , 2012 .

[5]  Erkki Tomppo,et al.  Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach , 2004 .

[6]  C. Ghilani,et al.  Adjustment Computations: Statistics and Least Squares in Surveying and GIS , 1987 .

[7]  M. Nilsson,et al.  Combining national forest inventory field plots and remote sensing data for forest databases , 2008 .

[8]  M. Nilsson,et al.  Applications using estimates of forest parameters derived from satellite and forest inventory data , 2002 .

[9]  Joon Heo,et al.  An integrated methodology for estimation of forest fire-loss using geospatial information , 2008, Environmental monitoring and assessment.

[10]  P. S. Roy,et al.  Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest , 1996, Journal of Biosciences.

[11]  M. Nilsson,et al.  Countrywide Estimates of Forest Variables Using Satellite Data and Field Data from the National Forest Inventory , 2003, Ambio.

[12]  Y. Youn.  Use of forest resources, traditional forest-related knowledge and livelihood of forest dependent communities: Cases in South Korea , 2009 .

[13]  Rudy Pastel,et al.  An overview of importance splitting for rare event simulation , 2010 .

[14]  Ronald E. McRoberts,et al.  The effects of rectification and Global Positioning System errors on satellite image-based estimates of forest area , 2010 .

[15]  Tonny J. Oyana,et al.  Uncertainties of mapping aboveground forest carbon due to plot locations using national forest inventory plot and remotely sensed data , 2011 .

[16]  Y. Ronen.  Uncertainty Analysis , 1988 .

[17]  C. Kleinn,et al.  Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory , 2009 .

[18]  Joon Heo,et al.  Timber age verification using historical satellite image analysis , 2006 .

[19]  Ronald E. McRoberts,et al.  Estimating and Circumventing the Effects of Perturbing and Swapping Inventory Plot Locations , 2005, Journal of Forestry.

[20]  A. Ramachandran,et al.  Carbon sequestration : estimation of carbon stock in natural forests using geospatial technology in the Eeastern Ghats of Tamil Nadu, India , 2007 .

[21]  E. Tomppo,et al.  Selecting estimation parameters for the Finnish multisource National Forest Inventory , 2001 .

[22]  Dengsheng Lu,et al.  Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin , 2004 .

[23]  Jaehoon Jung,et al.  A synergetic approach to estimating timber age using integrated remotely sensed optical image and InSAR height data , 2012 .

[24]  Barbara Koch,et al.  Satellite estimation of forest carbon using regression models , 2008 .

[25]  R. Fournier,et al.  A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland , 2006 .

[26]  Juha Hyyppä,et al.  Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables , 2010, Remote. Sens..

[27]  J. Hyyppä,et al.  Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes , 2000 .

[28]  Hui Li,et al.  A framework for creating and validating a non-linear spectrum-biomass model to estimate the secondary succession biomass in moist tropical forests , 2010 .

[29]  Ronald E. McRoberts,et al.  Estimating forest attribute parameters for small areas using nearest neighbors techniques , 2012 .

[30]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[31]  Frédéric Achard,et al.  Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics , 2011 .

[32]  Joon Heo,et al.  Quality improvement of loblolly pine (Pinus taeda) plantation inventory GIS using Shuttle Radar Topography Mission (STRM) and the National Elevation Dataset (NED) , 2006 .