Stratification by ancillary data in multisource forest inventories employing k-nearest-neighbour estimation

Abstract

The Finnish multisource national forest inventory (MS-NFI) utilizes optical area satellite images and digital maps in addition to field plot data to produce georeferenced information, thematic maps, and small-area statistics. In the early version, forestry land (FRYL) was taken directly from the numerical map data. Such data may be outdated and can contain significant errors, for example, the FRYL area is typically overestimated and the mean volume is underestimated. A statistical calibration method has been introduced to reduce the map errors on multisource forest resource estimates. It is based on large-area estimates of map errors, a confusion matrix among land-use classes of the field sample plots, and corresponding map information. The method has some drawbacks: calculations are more complicated than in the original MS-NFI and some field plots may have negative expansion factors. The paper presents a new stratified MS-NFI method to reduce the effect of inaccurate map data on the forest-resource estimates. In this method, the k-nearest-neighbour (k-NN) estimation is applied by strata. All the field plots within each map stratum, independently of their land-use classification by field crew, are used to estimate the areas of land-use classes and forest variables of that stratum. The method was tested on two large areas containing three Landsat 5 TM scenes and field-inventory data from the ninth NFI. The stratified MS-NFI is essentially a different estimation method compared with the calibrated MS-NFI, which calibrates the MS-NFI estimates more or less systematically in one direction. The stratified MS-NFI was found to be statistically simpler and there were fewer significant errors in the estimates than in the calibrated MS-NFI.