Canopy structure in a temperate old-growth evergreen forest analyzed by using aerial photographs

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1 Plant Ecology 168: 23 29, Kluwer Academic Publishers. Printed in the Netherlands. 23 Canopy structure in a temperate old-growth evergreen forest analyzed by using aerial photographs T. Fujita 1, A. Itaya 1, M. Miura 1, T. Manabe 2 and S. Yamamoto 1, * 1 Laboratory of Forest Ecology and Physiology, Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, , Japan; 2 Kitakyushu Museum and Institute of Natural History, 6-1, Nishihonmachi-3, Yahatahigashi-ku, Kitakyushu, , Japan; *Author for correspondence Received 14 May 2001; accepted in revised form 11 April 2002 Key words: Canopy gaps, Canopy height profile, Digital elevation model, Forest dynamics, Tatera Forest Reserve Abstract We used aerial photographs to create a digital elevation model of the canopy surface of a 10-ha study area in a temperate old-growth evergreen forest. A topographic map of the ground surface in a 4-ha permanent plot within the study area was also drawn from ground measurements. The difference between the two elevation values (i.e., canopy surface ground surface) at each point in a 5-m grid was considered to be the canopy height, and a canopy height profile was constructed from these data. The canopy structure in the 4-ha plot that was estimated in this way was compared with that obtained by two ground observation methods, i.e., the canopy (vegetation) height profile method and the canopy coverage census method. Large gaps were adequately detected by the aerial photograph method, but small gaps were less often detected. Gap size distribution obtained by the aerial photograph method was similar to that observed on the ground, and was a function of gap depth. This study indicates that if a detailed topographic map can be made, the canopy height profile derived from aerial photography can be effective in analyzing the canopy structure of evergreen forests, such as tropical rain forests, over large areas. Introduction Forest canopy gaps are formed by the death of one or several canopy trees (Runkle 1982; Brokaw 1985; Yamamoto 1989), and these gaps provide trees with important opportunities for regeneration (Denslow 1987). Gap size influences microenvironmental conditions, especially light condition (Canham 1989). Large gaps which keep open long time permit the regeneration of light demanding species and in small gaps shade-tolerant species can regenerate steadily. As a result, the composition of regenerating plant species within gaps is related to gap size (Yamamoto 1992). The frequency of gap formation is also important for regeneration. Repeated canopy disturbance is required to reach canopy layer for intermediate light demanding species (Runkle and Yetter 1987). Thus, investigating the size and frequency of gap events is crucial to understanding the structure and dynamics of forest communities. Most research on gaps has been conducted by ground surveys over large areas (e.g., Runkle (1982) and Yamamoto (1989, 1992)). However, the definition of gaps is a problem; different definitions have been used, depending both on the researchers and on the forest structure (Lieberman et al. 1989; Runkle 1992), and this prevents accurate comparisons of canopy gap disturbance regimes among different forest types. Arbitrary definitions of canopy gaps can be avoided by using the canopy height profile method (CHPM) (vegetation height profile technique, Karr (1971) and Hubbell and Foster (1986), Brokaw and Grear (1991)) as a ground surveying technique. Data obtained with this method are more objective than those obtained by other methods that have been used to census gaps, which have been defined arbitrarily. The CHPM also makes it possible to compare canopy structures between forests. Another, less objective,

2 24 ground survey method is the canopy coverage census method (CCCM). In this method, a large plot is divided into smaller contiguous quadrats of equal area, and the canopy cover above each of the smaller quadrats is then estimated (Yamamoto et al. 1995; Manabe et al. 2000). For example, Yamamoto et al. (1995) and Manabe et al. (2000) estimated canopy gaps in every 5 5-m quadrat within a 4-ha permanent plot in an old-growth evergreen forest. They considered canopy cover of < 30% to be a canopy gap. A significant problem with these ground-surveying methods is that they require a substantial investment of time and labor to obtain data over large areas. The canopy height profile method using aerial photography (APM), however, both avoids the arbitrary definition of canopy gaps and makes it possible to obtain data over large areas relatively efficiently. Using this method, Nakashizuka et al. (1995) created digital elevation models of both ground and canopy surfaces in a 5-m grid. These investigators used aerial photographs that were taken in winter (without foliage) and in summer (with foliage) of a 60-ha area of a temperate deciduous forest. They considered the difference between the two elevation values (canopy surface ground surface) at each point on the grid to be the canopy height, and constructed a canopy height profile. The APM developed by Nakashizuka et al. (1995) is a very effective tool for the analysis of canopy structure over large areas. One major drawback of this method, however, is that unless detailed topographic maps are available, it cannot usefully be applied to evergreen forests. The objective of this study was to apply the APM to investigate the canopy structure of a temperate old growth evergreen broad-leaved forest. The principal goals of this study were to examine the canopy structure of an old-growth evergreen forest by ground surveying (using the CHPM and CCCM), and by the APM, and to discuss the problems and applicability of these various methods. Study site Evergreen broad-leaved forest, dominated by Castanopsis spp. and Quercus spp., is considered to be the climax forest of the East Asian warm-temperate region (Kira 1991; Tagawa 1995). This forest type once covered most of the lowland and foothill areas of western Japan. At present, most forests are secondary, Figure 1. Location of the study site. The 4-ha permanent plot was set at the foot of Mt Tatera. resulting from human disturbance; however, relatively undisturbed primary forest stands remain in mountainous regions and on islands of the Kyushu district in western Japan. The Tatera Forest Reserve is located at the center of the South Island of Tsushima, at 34 9 N and E, between the Japanese Archipelago and the Korean Peninsula (Figure 1). This 100-ha reserve is situated on the north-facing slope of Mt. Tatera, 120 to 560 m above sea level. The topography is flat and gentle at lower altitudes, and rather steep at high altitudes. The well-developed evergreen broad-leaved forest is dominated by Castanopsis cuspidata and Distylium racemosum at low altitudes, and by Quercus acuta at higher altitudes (Itow 1991). At low altitudes, some canopy trees exceed > 1.0 m in diameter at breast height (dbh), and the height of the canopy is approximately m. The reserve is a truly primeval evergreen broad-leaved forest and has been free from human disturbance for centuries. Itow (1977, 1991) and Manabe et al. (2000) provide details of the environment and vegetation. Only the flat and gentle topography at the low altitudes of this reserve is entirely suitable for analysis by aerial photography, because the degree of error in estimating canopy height by aerial photography increases in areas of high topographic relief (Nakashizuka et al. 1995).

3 25 Methods Ground survey A 4-ha ( m) permanent sampling plot was established in 1990 on a site with flat and gentle topography at a low altitude within the reserve. The geographic locations of the base points of the plot were positioned by GPS (Global Positioning System) measurements in order to correlate ground data precisely with aerial photographic data. Topographic data of the ground surface in this permanent plot were obtained in a grid at 10-m intervals using a transit compass in Since that time, topographic changes have not occurred in the plot. Elevations at 10-m intervals were interpolated to 5 -m intervals to evaluate canopy height at the same resolution. In 2000, the CHPM was conducted to obtain canopy height profile data from measurements for four canopy height classes ( 5, 5 10, 10 15,>15m)at each corner of every m quadrat within the 4-ha plot. Using the canopy height classes as a 5 5-m grid, the 2.5-m grid was converted into a 5-m grid to evaluate canopy heights at the same resolution. A measuring pole (15 m maximum length) was used to measure canopy height. The CHPM is more accurate than other methods. To obtain data by the CCCM, canopy gaps were recorded in the fall of 1997 by visual estimation in each 5-m quadrat; canopy cover < 30% above about 10 m, was considered to be a canopy gap. When a canopy gap extended across many contiguous quadrats, the size of the canopy gap (gap size) was evaluated by computing the number of quadrats that it covered. Aerial photography A natural color aerial photograph (1:8000) was taken from an aircraft in November, The elevations at the corners of every 2.5-m quadrat were digitized for the 10-ha area using a stereoplotter (AVIOLYT BC/LMT, Leica), and the value for the center of each 5 5 -m quadrat was used as the canopy height of that quadrat. Three images were used to obtain stereo-images covering the area. Elevational data were digitized by an aerial measuring company (Chuoh Consultants, Nagoya, Japan). The difference between the elevation of the canopy and the ground surface was considered to be the canopy height (Figure 2). When any part of the canopy lower than a given height was defined as a gap, gaps could easily be detected as holes in the canopy plane. Since a different grid size was used by the three methods, the medium grid size (5 m) was used for comparisons between the different methods, because data from the canopy coverage census method could not be modified; ground DEM (10-m grid) was interpolated into a 5-m grid. The accuracy of the CCCM and the APM was estimated by using the error matrix. Both the overall accuracy as well as Kappa statistics (Congalton 1991) were computed. The Kappa statistic is an indicator of the extent to which the percentage correct values of an error matrix are due to true agreement versus chance agreement. Results Spatial distribution of gaps and gap characteristics The spatial distribution of gaps assessed by the APM corresponded well, in general, with that detected by ground surveying (Figure 3). The spatial distribution assessed by the APM was more closely correlated to the CHPM than to the CCCM. However, the gap characteristics (except mean gap area) detected by the APM were closer to those detected by the CCCM than to the gaps determined by the CHPM (Table 1). Values for the number of gaps, the total gap area, gap area percentages, and the maximum gap area detected by the APM and by the CCCM were smaller than those detected by the CHPM using ground surveys. The values of the mean gap size detected by the CHPM and by the APM were the same, but that detected by the CCCM was smaller. The overall accuracy of the grids (i.e., the proportion of quadrats detected as gaps [closed] by the CHPM that coincided with gaps [closed] detected by the CCCM) was 83.5%. The ratio was higher (86.3%) between the CHPM and the APM (Table 2). The values of Kappa statistics were not as high. The differences in height class distribution between the CHPM and the APM suggest that aerial photography detected far fewer deep canopy gaps (those sections of the forest that have a low canopy) than ground surveying did (Table 3). Gap size distribution Gap size distributions detected by the three methods showed a similar pattern (Figure 4). The frequency

4 26 Figure 2. The procedure to detect gaps in the 4 ha plot using aerial photographs. The topographic data of the ground surface (a) were obtained by ground measurement. The digital elevation model was made from aerial photographs for (b) canopy surface. The difference between the two elevation values was regarded as (c) canopy height. When the grid whose height is higher than a certain height (15 m in this case) was considered as a plane, gaps can be detected as (d). decreased with gap size increased, with two modes at smaller range (> 25 m 2 ) and at 150 m 2 of gap size. Ground surveys could detect more gaps smaller than 50 m 2 compared with the aerial photography. But the larger the gap size > 50 m 2, the power of gap detection was similar among three methods. Gap-size distribution was a function of gap depth (Figure 5); the deeper the gaps were, the less frequent they were. Number of gaps larger than 100 m 2 was only one in the canopy height lower than 11 m. Gap size distribution was different in canopy height lower than 11 and 13 m from that lower than 15 m, and they showed monotonic decline. Discussion Generally, spatial distribution of canopy gaps detected by the APM corresponded well with the CHPM and the values of gap characteristics corresponded with the CCCM. The gap size distribution detected by the APM was similar to those by the ground survey. Thus, we can estimate where canopy gaps occurred, the configuration of gaps and various gap characteristics in natural evergreen broad-leaved forest using aerial photographs. The availability of aerial photographs for the analysis of canopy structure was reported by Nakashizuka et al. (1995) in the deciduous broad-leaved forest. Although we require the detailed topographic data, this method is useful for evergreen broad-leaved forest as well as deciduous forest.

5 27 Table 1. Characteristics of gaps observed by the ground survey (canopy height profile method [CHPM] and canopy coverage census method [CCCM]) and the canopy height profile method using aerial photographs (APM). The gaps were defined as the parts of the canopy lower than 15 m high in the canopy height profile method. In the canopy coverage census method, above about 10 m higher from the ground, canopy cover < 30% was considered to be a gap. Ground survey CHPM CCCM APM Number of gaps Total gap area (m 2 ) Percentage gap area (%) Mean gap area (m 2 ) Maximum gap area (m 2 ) Table 2. Error matrices representing accuracies of each category. Agreement (from closed to closed or from gap to gap) and disagreement (from closed to gap or from gap to closed) of each grid state detected by the ground survey (canopy height profile method [CHPM] and canopy coverage census method [CCCM]) and the canopy height profile method using aerial photographs (APM). The gaps were defined as the parts of the canopy lower than 15 m high in the canopy height profile method. In the canopy coverage census method, above about 10 m higher from the ground, canopy cover < 30% was considered to be a gap. CHPM Closed Gap total CCCM Closed Gap total Kappa = 0.34 APM Closed Gap total Kappa = 0.53 Figure 3. Spatial distribution of gaps detected by (a) canopy height profile method (CHPM) and (b) canopy coverage census method (CCCM) by ground survey, and (c) aerial photographs (APM). In figures (a) and (c), the darker grids indicate deeper gaps; black grids indicate 0 5 m, dark grids indicate 5 10 m, and light gray grids indicate m, in height. In (b), above about 10 m higher from the ground, canopy cover < 30% was considered to be a gap. However, although estimators of the aerial photography were corresponded with those of ground survey, there were a few weak points of the aerial photography. It is difficult to detect the deeper or smaller gaps by this method. In this result, smaller gaps by the APM were less than those by the CHPM, and the canopy height lower than 11 m were seldom detected as canopy gap. Canopy trees surrounding canopy gaps cast shadows into the gaps, so the image of smaller deeper canopy gaps in the aerial photographs were dark, resulting in underestimating the values of gap characteristics. Topography also influences the detec-

6 28 Table 3. Canopy height distribution measured by the canopy height profile method (CHPM) by ground survey and by aerial photographs (APM). The percentage value of observation grids belonging to each height class are shown. Canopy height class (m) CHPM (%) APM (%) < Figure 5. Size distributions of gaps with different depths detected from aerial photographs. The parts of the canopy lower than 15, 13 and 11 m are considered as gaps in each case. Figure 4. Gap size distribution detected by canopy height profile method (CHPM) and canopy coverage census method (CCCM) by ground survey, and aerial photographs (APM). The gaps defined as the parts of the canopy lower than 15 m high in the canopy height profile method. In the canopy coverage census method, above about 10 m higher from the ground, canopy cover < 30% was considered to be a gap. tion of canopy gaps; estimation at steeper slope is more difficult than that at gentler slope. In this stand, the topography is considerably gentle and steep slope site is rare. In contrast to smaller gaps, larger gaps can be well detected by the aerial photography. The size distribution patterns were common in three methods, with bimodal distribution, indicating site-specific gap regime of this stand. In this forest, dominant canopy trees were lager than 100 m 2 in crown area, and multiplefallen tree gaps were formed. Most of these gaps might be formed by typhoons (Manabe et al. 2000). The result of gap-size pattern may reflect this forest gap regime. Although there are some weak points in aerial photography such as difficulty in detecting deeper or smaller gaps and requirement of detailed topographic data, aerial photography is useful tool for the analysis of canopy structure of evergreen broad-leaved forest. Zenner (2000) also reported three dimensional characters of forest structure were useful tool for understanding forest canopy sturucture. Our method is different from his method, but we can also deal with forest structure by using three dimensional characters. Aerial photographs have been taken for several decades. So once we obtain the detailed topographic map, we can rebuild the past forest structure, estimate the variation of biomass or understand the gap-regime using this method. Acknowledgements Financial support was provided by Grant in Aids for Scientific Research ( and ) from

7 29 the Ministry of Education, Science, Sports and Culture. We thank the Tsushima District Forest Office for permitting this study, and A. Nakanishi, N. Tomaru, S. Ueno and H. Yoshimaru for their help in the field. We also thank T. Nakashizuka and N. Tomaru for his kind advice for this study, and S. Walsh and an anonymous reviewer for their valuable comments and informations. References Brokaw N.V.L Treefalls, regrowth, and community structure in tropical forests. In: Pickett S.T.A. and White P.S. (eds), The Ecology of Natural Disturbance and Patch Dynamics. Academic Press, New York, USA, pp Brokaw N.V.L. and Grear J.S Forest structure before and after hurricane Hugo at three elevations in the Luquillo Mountains, Puerto Rico. Biotropica 23: Canham C.D Different responses to gaps among shade-tolerant tree species. Ecology 70: Congalton R.G A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37: Denslow J.S Tropical rainforest gaps and tree species diversity. Annual Review of Ecology and Systematics 18: Hubbell S.P. and Foster R.B Canopy gaps and the dynamics of a neotropical forest. In: Crawley M.J. (ed.), Plant Ecology. Blackwell Scientific Publications, Oxford, pp Itow S Phytosociological studies on forest vegetation in western Kyushu, Japan. VI. Natural forests of Castanopsis cuspidata in Tsushima. (in Japanese with English summary). Hikobia 8: Itow S Species turnover and diversity patterns along an evergreen broad-leaved forest coenocline. Journal of Vegetation Science 2: Karr J.R Structure of avian communities in selected Panama and Illinois habitats. Ecol. Monogr. 41: Kira T Forest ecosystems of east and southeast Asia in grobal perspective. Ecol. Res. 6: Lieberman M., Lieberman D. and Peralta R Forests are not just Swiss cheese: Canopy stereogeometry of non-gaps in tropical forests. Ecology 70: Manabe T., Nishimura N., Miura M. and Yamamoto S Population structure and spatial patterns for trees in a temperate old-growth evergreen broad-leaved forest in Japan. Plant Ecology 151: Nakashizuka T., Katsuki T. and Tanaka H Forest canopy structure analyzed by using aerial photographs. Ecological Research 10: Runkle J.R Patterns of disturbance in some old-growth mesic forests of eastern North America. Ecology 63: Runkle J.R Guidelines and Sample Protocol for Sampling Forest Gaps. General Technical Report, PNW-GTR-283, USDA Forest Service. Pacific Northwest Research Station, Portland, 44p. Runkle J.R. and Yetter T.C Treefalls revisited: gap dynamics in the southern Appalachians. Ecology 68: Tagawa H Distribution of lucidophyll oak-laurel forest formation in Asia and other areas. Tropics 5: Yamamoto S Gap dynamics in climax Fagus crenata forests. Botanical Magazine, Tokyo 102: Yamamoto S Gap characteristics and gap regeneration in primary evergreen broad-leaved forests of western Japan. Botanical Magazine, Tokyo 105: Yamamoto S., Nishimura N. and Matsui K Natural disturbance and tree species coexistence in an old-growth beech-dwarf bamboo forest, southwestern Japan. Journal of Vegetation Science 6: Zenner E.K Do residual trees increase structural complexity in Pacific Northwest coniferous forest? Ecological Applications 10:

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