The study area is part of the campus forest of the Faculty of Agriculture at Shinshu University, Nagano Prefecture, central Honshu Island, Japan (35°51′N, 138°56′E, Figure 2). The campus forest, which is in the village of Minamiminowa, Kamiina District, Nagano Prefecture, is located at an altitude of 770 m above sea level and the area consists of undulating terrain and flat land. The campus covers 52.7 ha, 15 ha of which is occupied by high density conifer plantations, in which the dominant tree species are C. obtusa (Co), C. pisifera (Cp), P. densiflora (Pd), L. kaempferi (Lk), C. japonica (Cj), and broad-leaved trees [16]. The age of plantations ranges from 24 to 86 years old and trees are typically 8 to 36 m in height and 2.1 to 13.5 m in crown diameter (Table 1).

The airborne sensor system used in the present study was developed by PASCO Corporation and contains an UltraCamD (UCD) constructed by Vexcel Co. Image data were acquired on 28 July 2005 during clear weather conditions. The multispectral data of the full-size image consisted of four bands (visible blue, green, red, and near infrared) of 3,680 lines × 2,400 pixels at nadir, stored as 16-bit data. The original images with sensor orientation, topographic relief displacement, and systematic errors were rectified by ortho-correction based on X, Y, and Z values for sensor positions derived from a GPS system and the principal points and focal length of the sensor. The geographic projection was converted to Japan JGD2000 Zone 8. The ground resolution of the multi-image was 50 cm after correction. Image processing and analysis were performed using ortho-correction processing with ERDAS IMAGINE 8.6 [17] and a tree-based stand map on field data, which was processed with ArcGIS 9.3 [18]. Treetop detection and tree-crown delineations were performed using the Individual Tree Crown (ITC)-Suite [19] and Geomatica 9 [20].

The field investigation was conducted from April 2005 to June 2010. Detailed stand information was available for forest compartments 1–7, where all trees ≥8 cm in diameter at breast height (DBH) were measured. The survey noted species, DBH, height (H), tree position, and strata (upper, intermediate, or understory layer). The two-story plantations, in which young C. obtusa or C. japonica were planted under older P. densiflora stands, were high density. Species, age, compartment area, count, average DBH and height, timber volume, and growth per hectare were summarized by forest compartment (Table 2). C. obtusa, P. densiflora, and L. kaempferi stands ranged in age from 24 to 86 years; in timber volume from 426 to 509 m³; and in crown density from seven to nine, with high timber volume and high tree density.

Forest compartment boundaries, forest roads, forest survey data, and geographic data such as contour lines on the forest base map (scale 1:5,000) of the campus research forest were included in the forest database. All tree positions in forest compartments 1–7 from the field survey were transferred and registered in the forest database using GIS [16].

The flowchart in Figure 3 provides an overview of methods. The general steps used in this study were as follows:

Forested areas were extracted using forest boundaries from the GIS database to separate them from non-forested areas, such as roads, grass areas, and artificial structures.

Homogeneous cover-type regions were separated by textures or gray levels, which were reflecting tree species and crown sizes. The diameters of the tree crowns ranged from 3 to 10 m; small tree areas were designated as those with tree crowns of 3 to 5 m, and large tree areas were those with crowns of 5 or more meters. Next, small tree-crown areas were smoothed on the image with a 3 × 3 average pixel filter; and large tree areas with an additional 5 × 5 pixel filter.

Homogeneous cover type analysis separated regions with different textures or gray levels by tree species and crown size. Small tree areas were extracted by blocking out large tree areas (Figure 4). Small tree crown areas were smoothed separately with a 3 × 3 average filter, and large tree areas were smoothed with a 3 × 3 and then a 5 × 5 averaging filter.

The ITC isolation image was produced using valley-following procedures. The ITC isolation image and multi-spectral images were fed into a supervised classification process to identify species [16]. Treetops were overlain onto the ITC classification image (Figure 5(a)) and then associated with a species semi-automatically based on their correspondence to the ITC classification image. Through this process, spurious treetops in the valley and canopy gaps were excluded as they did not correspond to a class. An example classification showing the distribution of treetops by species and relative differences in density is given in Figure 5(b). Patches of C. obtusa, P. densiflora, and L. kaempferi were clear, indicating a good representation of conifer plantations in the area.

A comparison of the locations of the stems of C. obtusa trees identified using the tree top counting technique within seven forest compartments with those mapped through field survey indicated a close correspondence (for those ≥8 cm DBH) (Table 3). Three counts of trees were compared against the field data; treetops identified using local maxima filtering within the smoothed image, crowns identified using the ITC delineation and both treetops and crowns identified using a combined technique. The number of stems ranged from 399 to 1,065; treetops, from 647 to 1,381; crowns, from 201 to 387; and treetops generated by the new technique, from 416 to 938.

The treetop counts were more than that of the field stems, whereas the crown counts were less than that of the field stems. The error rate between the treetop counts and the stems counted in the field ranged from 22.4% to 62.2%. The error rate between the crown counts and the stems counted in the field ranged from −38.6% to −74.5%. On the contrary, the error rate between the numbers of treetops extracted with the new technique (NT) and stems from the field investigation ranged from 2.3% to −15.8%, showing that this method leads to improved accuracy over treetop and crown delineation used separately in stands 1, 3 and 7.

In stands 2, 4, 5, and 6, the number of new technique treetops was slightly lower than that of stems from field survey data. These data indicate that it is difficult to extract treetops using image analysis, because these are two-storied stands with upper-story trees of L. kaempferi and P. densiflora and understory trees of C. obtusa and broad-leaved trees, which occurred as small-diameter crowns under the upper tree crowns. Stand 7 had the lowest error rate (2.3%), probably because the sharp, conical crowns of the dominant upper-story trees, C. obtusa and C. pisifera, were easier to extract using image analysis compared with crowns of other tree species [16].

Table 4 lists the numbers of stems and tree crowns, and the results of the New Treetop Technique by tree species (NTT). Generally, the number of crowns from the image analysis was fewer than that of stems from the field survey, primarily because some trees in the image were misidentified as one large tree crown rather than a cluster of trees. However, the new image analysis yielded treetops closer to the number of stems from field survey. Stem errors were derived from these data for accuracy verification.

Two types of stem errors were derived from these data for accuracy assessments, the later one (NTT-error 2) excluding understory stems. From the field survey, 118 of the 399 trees in stand 1 were L. kaempferi, and 252 of the 837 trees in stand 2 were P. densiflora. Of the 684 trees in stand 3, 186 were L. kaempferi and 152 were P. densiflora. In stands 4, 5, 6, and 7, C. obtusa and C. pisifera were dominant, with numbers of trees ranging from 352 to 554. Of the numerous broad-leaved trees, the majority was understory and intermediate trees; the numbers of broad-leaved understory trees ranged from 26 to 252 in all stands. The numbers of planted C. obtusa trees ranged from 29 to 194 in stands 2, 4, 5, 6, and 7, which were two-storied plantations. Planted C. japonica trees were mainly understory trees in stands 3 and 5 with 73 trees and 35 trees.

Compared with the respective numbers of stems from the survey, the numbers of C. obtuse, C. pisifera, and broad-leaved treetops determined using the new technique were lower, and the numbers of P. densiflora and L. kaempferi treetops were higher.

The error rates for treetops by tree species between NTT and stems from the field investigation are shown as NTT errors in Table 4. The error rates for C. obtusa and C. pisifera ranged from −16.0% to −89.6%, except in stands 1 and 3, where fewer of these trees grew. The error rates ranged from 15.7% to 183.6% for P. densiflora, from 11.9% to 487.1% for L. kaempferi, and from −33.3% to −100.0% for C. japonica. For the broad-leaved trees, the error rates ranged from −19.0% to −82.4%.

In stands 2, 4, 5, 6, and 7, there were fewer C. obtusa and C. pisifera treetops compared with the numbers of stems from the field survey because many of these were understory trees. However, there were more P. densiflora and L. kaempferi treetops compared with the numbers of stems because, owing to their large crowns, one crown was sometimes misidentified as a cluster of crowns. The error rate of the new technique, excluding understory stems, was obviously improved, as shown by NTT error 2 in Table 4. The error rates for total numbers of C. obtusa and C. pisifera trees per stand, excluding understory stems, ranged from −6.2% to −55.8%, and error rates for broad-leaved trees ranged from 8.2% to 63.1%. The results for multilayered or two-storied stands were inferior to those of single layer stands or pure conifer plantations because multilayered stands have some intermediate or understory trees that are difficult to extract using image analysis.

These results show that the numbers of tree by species determined by the new treetop technique were less accurate than the total number of trees using the new technique. This may be due to the overestimation of P. densiflora, L. kaempferi, and broad-leaved trees compensating for the underestimation of C. obtusa and C. pisifera. Thus, this technique can be used effectively to extract the numbers of stems of upper and intermediate canopy trees in pure conifer plantations when multiple layers or high densities are not involved.