Current issue: 55(3)
Under compilation: 55(4)
Two different pulse density airborne laser scanning datasets were used to develop a quality assessment methodology to determine how airborne laser scanning derived variables with the use of reference surface can determine forest road quality. The concept of a reference DEM (Digital Elevation Model) was used to guarantee locally invariant topographic analysis of road roughness. Structural condition, surface wear and flatness were assessed at two test sites in Eastern Finland, calculating surface indices with and without the reference DEM. The high pulse density dataset (12 pulses m–2) gave better classification results (77% accuracy of the correctly classified road sections) than the low pulse density dataset (1 pulse m–2). The use of a reference DEM increased the precision of the road quality classification with the low pulse density dataset when the classification was performed in two-steps. Four interpolation techniques (Inverse Weighted Distance, Kriging, Natural Neighbour and Spline) were compared, and spline interpolation provided the best classification. The work shows that applying a spline reference DEM it is possible to identify 66% of the poor quality road sections and 78% of the good ones. Locating these roads is essential for road maintenance.
The fusion of optical satellite imagery, strips of lidar data and field plots is a promising approach for the inventory of tropical forests. Airborne lidars also enable an accurate direct estimation of the forest canopy cover (CC), and thus a sample of lidar strips can be used as reference data for creating CC maps which are based on satellite images. In this study, our objective was to validate CC maps obtained from an ALOS AVNIR-2 satellite image wall-to-wall, against a lidar-based CC map of a tropical forest area located in Laos. The reference CC values which were needed for model training were obtained from a sample of four lidar strips. Zero-and-one inflated beta regression (ZOINBR) models were applied to link the spectral vegetation indices derived from the ALOS image with the lidar-based CC estimates. In addition, we compared ZOINBR and logistic regression models in the forest area estimation by using >20% CC as a forest definition. Using a total of 409 217 30 × 30 m population units as validation, our model showed a strong correlation between lidar-based CC and spectral satellite features (root mean square error = 12.8%, R2 = 0.82). In the forest area estimation, a direct classification using logistic regression provided better accuracy than the estimation of CC values as an intermediate step (kappa = 0.61 vs. 0.53). It is important to obtain sufficient training data from both ends of the CC range. The forest area estimation should be done before the CC estimation, rather than vice versa.
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.