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1995

Sammendrag

In Norway, about 5500 km2 are surveyed annually for forest management planning. Approximately 50 % of that area is recorded by means of aerial photo interpretation. A method for determination of gross value for individual forest stands by means of aerial photo interpretation has previously been developed (Nsset 1990, 1991a). The logging costs can be calculated by means of cost functions (Anon. 1994) utilizing stand characteristics related to the trees and characteristics related to the terrain, such as slope and skidding distance. Some properties related to the terrain can be derived from digital elevation models (DEM). The objective of the present work was to develop an integrated method for determination of gross value and logging costs for individual mature forest stands by means of aerial photo interpretation, DEMs, and geographical information systems (GIS). Computation of gross value as well as logging costs were based on the so-called mean tree of a stand, i.e. a tree defined by mean diameter by basal area and Loreys mean height. The models for computation of gross value and logging costs are shown in Fig. 1 and Fig. 2, respectively. In addition to stand characteristics related to the trees, Fig. 2 shows that slope and skidding distance must be derived in order to compute the logging costs. The method for computation of gross value and logging costs was demonstrated by means of a case study from a forest area in Froland municipality, South Norway, of about 710 ha. Black-and-white aerial photographs at an approximate scale of 1:15000 and a stereoplotter of the second order (Wild B8) were used to delineate the stands, measure stand mean height, and interpret crown closure, site quality, and tree species distribution of the individual stands. For all the mature stands within the area, information on topography and ground conditions interpreted in the aerial photos were used to suggest skid paths for timber transportation from the stands via existing skid roads to landings along the forest roads or public roads.The stand boundaries, the suggested skid paths, existing skid roads, existing forest roads and public roads, and contour lines taken from the official Economic Map Series at the scale 1:5000 with 5 m contour interval were digitized into separate map layers applying the pcARC/INFO software. All the forest stand characteristics were related to the stand map layer.The stand characteristics required for the gross value computation were exported to the SAS package (Anon. 1985), which was used for calculation of gross value as well as logging costs. The data flow is shown in Fig. 4. The gross value was calculated according to the model displayed in Fig. 1. The average slope and skidding distance of the individual stands were required for the logging costs calculation. The grid based GIS package IDRISI (Eastman 1992) was used for determination of slope and distance (Fig. 4) before the logging costs could be computed by means of the model shown in Fig. 2. A DEM was generated with IDRISI from the digitized contour lines (Fig. 5).The slope was computed (Fig. 6), and average slope of the individual stands was found and exported to the SAS package via pcARC/INFO (Fig. 4). The skidding distance for a stand was defined as the average distance on the terrain from the individual grid cells of a stand via the digitized skid paths and skid roads to landings along the forest roads or public roads.The crossings between the skid paths/roads and the forest/public roads were used for landing points. However, IDRISI provides procedures for calculation of ground plane distances only. A method for computation of approximate on terrain distances was therefore developed. The average extension of distance due to slope for traversing a grid cell was found (Eq. 3). A grid map where the grid cells were assigned the values of the average extensions due to slope was generated. A stand boundary layer where the stand boundaries were assigned the value -1 was produced. The average extension layer was overlaid the stand boundaries and the various path and road maps (Fig. 7 and Fig. 8), such that all stand boundary cells, except the crossings between the boundaries and the roads, attained the value -1. All other cells, including the crossing cells, attained the average extension values. The resulting layer (Fig. 10) was used as friction layer and the forest/public road layer was used as source layer to indicate the cells from which skidding distances should be determined utilizing the COST procedure (COSTGROW algorithm) of IDRISI. The cells of the friction layer assigned the value -1 (the stand boundary cells) served as absolute transportation barriers. The computed skidding distances (Fig. 11) were, however, based on an on the terrain straight line distance within the individual stands (Fig. 3). The computed distance for each grid cell was therefore multiplied by an average ground plane winding factor of 1.16 found for 28 of the stands within the study area (Table 1). Finally, the average corrected skidding distance for each stand was derived and exported to pcARC/INFO and SAS for logging costs computation. Computed gross value, logging costs, and gross profit are displayed in Figs. 12, 13, and 14, respectively. The suggested method for computation of gross value of mature stands is likely to give satisfying accuracy.Assuming that some of the calculations of the logging costs model (Hkl and n, Fig. 2) be corrected by means of sample plot inventories, a random error (standard deviation) of about 10 % should be expected. At present, practical large scale application of the presented method is hindered by a lack of proper digital elevation data. Implementation of the method requires also integrated computer programs on more powerful computer platforms than the one used here.

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