Improving satellite image based forest inventory by using a priori site quality information.

Abstract

Tokola, T. & Heikkilä, J. 1997. Improving satellite image based forest inventory by using a priori site quality information. Silva Fennica 31(1): 67-78.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 standwise 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 volume estimations, but it did not contribute to Scots pine volume estimation procedure.

Training Area Concept in a Two-Phase Biomass Inventory Using Airborne Laser Scanning and RapidEye Satellite Data

“…The RMSE at the independent validation plots was lower than in most previous studies of boreal coniferous and mixed forest. Our study had a relative RMSE of 19%-20% for volume, whereas previous studies using the optical sensor had a relative RMSE of 42%-82% for volume, for instance, studies by Tokola and Heikkilä [72] (82%), Tomppo et al [73] (59%), Kilpeläinen and Tokola [74] (56%), Hyyppä et al [75] (50%), Mäkelä and Pekkarinen [76] (47%), and Hyvönen [77] (42%). Similar to the volume, the relative RMSE (20%) for AGB was also noticeably lower in the present study than the corresponding statistics of Tomppo et al [73] (36%), and Muukkonen and Heiskanen [71] (41%).…”

Multi-source forest inventory data for forest production and utilization analyses at different levels

“…Model-based approaches to error estimation have been developed (Kim and Tomppo 2006;Magnussen et al 2009;2010;McRoberts et al 2011), but the methods are not yet operational. Consequently, the accuracy of knn estimates for small areas have been assessed empirically using independent datasets based, for example, on aerial photographs or intensive field sampling (Tokola and Heikkilä 1997;Hyyppä et al 2000;Katila 2006). The bias of small area estimates in the Finnish MS-NFI have been studied comparing them with the estimates based on NFI field data in sub-regions (groups of municipalities), which are large enough to enable the estimation of forest variables and their standard errors (Katila et al 2000).…”

“…For example, to reduce the effect of map errors a calibration method based on the confusion matrix between land use classes of the field sample plots and corresponding map information has been developed (Katila et al 2000). Further, ancillary data such as site fertility and peat land maps can be used as a priori information for the stratification of data to improve the accuracy of knn predictions (Tokola and Heikkilä 1997;Katila and Tomppo 2002). In Sweden the knn was applied to produce nationwide raster maps, but the small area (sub-county) statistics were estimated using poststratification, where the knn maps were used for stratification (Reese et al 2003;Fridman et al 2014).…”

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

“…For example, some of the plots were subject to considerable positional errors (exceeding 20 m in RMSE). To avoid modeling errors due to positional inaccuracy between ETM+ data and field plot data, efforts to eliminate or to replace potentially erroneous data (e.g., sample plots falling near the forest/non-forest boundary) were employed in some previous studies [6,15,[40][41][42]. In this study, we eliminated potentially erroneous data by the following steps: (1) we generated circular areas (radius of 50 m, corresponding to approximately 3 × 3 pixels of ETM+ data) based on the position coordinates of FRMS plots on the geographic information system (GIS) software; (2) the forest cover of the circular area was visually checked using the mosaicked aerial photograph and ETM+ data; (3) plots whose forest cover ratio for the circular area was less than 50% were eliminated; and (4) plots that were seemingly subject to dramatic changes by clear-cutting or other disturbance were excluded from the analysis [3,40].…”

“…For example, soil type (peat land and mineral soil) [14], forest site quality [15], the age of each stand and the year of the last thinning operation [16], geographical distance [17] and large-area variation of forest variables [18] were used as a priori information to stratify forests and/or as explanatory variables of the k-NN technique, in combination with satellite image data, to estimate stand volume. All of these studies revealed that adding and/or using some spatial information alongside satellite image data have the potential to improve the accuracy of the estimates in the k-NN technique.…”