Very High Resolution Plant Community Mapping at High Moor, Kushiro Wetland

Transcription

1 APPLICATIONS PAPER Very High Resolution Plant Community Mapping at High Moor, Kushiro Wetland Kunihiko Yoshino, Sayuri Kawaguchi, Fusayuki Kanda, Keiji Kushida, and Fuan Tsai Abstract Frequent monitoring of the state of wetlands is important in order to sustain these valuable ecosystems. Reliable reference maps make this monitoring more effective. A very high resolution and reliable vegetation map with a nominal spatial resolution of 2 cm by 2 cm was created for a study area in the high moor in the Kushiro wetland, Japan. The reference map was created using aerial photography recorded in the summer of 1998 with a 35 mm, non-metric camera mounted on a balloon. The study site contains 40 wetland plant community types covering about 10 ha of the high moor. The resulting map shows that belt-like spatial patterns of typical wetland plant community groups can be clearly distinguished and confirmed through visual interpretation and spatial pattern analysis. The optimal spatial resolution for monitoring vegetation in this area using remote sensing is 0.3 m or smaller. Introduction Wetland ecosystems play various roles in regional natural environments. For example, they function as important entities in regional carbon and nutrient cycles, as hotspots of biodiversity and as tourism landscapes (Dugan, 1993). Wetlands throughout the world are thought to contain precious natural environments and landscapes that should be preserved (Mitsch and Gosselink, 2007). Recently, these valuable wetland landscapes have been reduced by many types of human activities (Williams, 1990). In order to sustain these valuable wetland ecosystems in the long term, it is important to continuously monitor the state of wetland ecosystems, to carefully watch the environment and to provide warnings of any environmental changes (Lyon and McCartby, 1995). A reliable reference map is essential for identifying environmental changes. Using detailed and reliable reference maps, quantitative information on wetland ecosystems, such as the spatial distribution of plant communities and their spatial relationships, will effectively help in monitoring and characterizing the target wetland, and will increase awareness of Kunihiko Yoshino is with the Faculty of Engineering, Information and Systems, University of Tsukuba, Japan, Tennoudai, Tsukuba, Ibaraki , Japan (sky@sk.tsukuba.ac.jp). Sayuri Kawaguchi is with the Kushiro Parks and Greenery Association, 9-34 Ichikawa-Kawakitacho, Kushiro, Hokkaido, , Japan. Fusayuki Kanda is with Hokkaido University of Education Kushiro Campus, Shiroyama, Kushiro, Hokkaido , Japan. Keiji Kushida is with the Department of Bioenvironmental and Agricultural Engineering, College of Bioresource Sciences, Nihon University, 1866 Kameino, Fujisawa, Kanagawa , Japan. Fuan Tsai is with the National Central University, Taiwan, No.300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan (R.O.C.). environmental changes (Johnston, 1989; Roush, 2007). In addition to information related to environmental change, detailed and reliable reference maps provide other types of information, such as spatial patterns of plant communities and average patch sizes of plant communities. Reference maps also assist in establishing appropriate remote sensing techniques. For example, maps can assist in determining the ranges of semi-variograms, which help to determine the optimal ground instantaneous field of view (GIFOV) of remote sensors. The average numbers of plant communities per unit area, i.e., the spatial diversity of classes within a specific search window (Robinove, 1986), is useful in determining the effectiveness of spectral mixture analysis (Somers et al., 2011) when using hyperspectral remote sensing data. Recently, the Kushiro wetland in Japan has been in the spotlight due to a large-scale national conservation project called the Natural Restoration Project in the Kushiro Shitsugen Wetland. This project by the Japanese government has been underway since 2002 in several parts of the Kushiro wetland to restore the wetland environment. However, the Kushiro wetland requires more intensive monitoring, especially in the high moor area near Akanuma, since some local ecologists have claimed there have been environmental changes even in the core area of the wetland. A detailed and reliable reference map of the plant communities in the wetland is necessary to quantitatively determine environmental changes. The present study has created a very detailed map of the wetland plant communities at the high moor in the Kushiro wetland. The map has a nominal spatial resolution of 2 cm by 2 cm and was derived from very high resolution color aerial photography. The map was used to analyze the spatial patterns of wetland plant communities. The specific objectives of the present study are: (a) to rectify aerial photos using a sufficient number of ground control points and to mosaic the rectified photos using digital photogrammetry, (b) to interpret plant communities and to delineate a wetland plant community map using an object-based segmentation method, and (c) to analyze the spatial pattern of plant communities using GIS-based spatial analysis techniques. The term very high spatial resolution is used to indicate that the pixel size of the imagery is in the order of several centimeters. Study Area and Methods High Moor Study Area near Akanuma in the Kushiro Wetland The Kushiro wetland is the largest wetland in Japan and is located in the eastern part of Hokkaido Island, northern Japan (43 5'N, 'E and 5 m in elevation). Figure 1 shows a map Photogrammetric Engineering & Remote Sensing Vol. 80, No. 9, September 2014, pp /14/ American Society for Photogrammetry and Remote Sensing doi: /PERS PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING September

2 Figure 1. Location of the Akanuma study area in the Kushiro wetland, Hokkaido Island, Japan. of the Kushiro wetland and Akanuma, the study area in the present study. This region is categorized as a cool temperate zone with a mean annual temperature of 5.7 C and an annual precipitation of 1,040 mm. The Kushiro wetland has abundant herbaceous vegetation and a large area of high moor. This wetland has been designated as a preservation area in the National Park since 1987 (Tsuyuzaki et al., 2004). The Akanuma study area (43 6'N, 'E) is a small lake surrounded by high moor in the western part of the Kushiro wetland. It represents the climax stage of the transition to wetland vegetation. As a result, the area around Akanuma is a valuable conservation area. This area has been designated as a special protected area. Recently, however, the landscape of the Kushiro wetland has started to look different compared to several decades ago. The wetland has become drier (Nakamura, 2002), the habitat of alders (Alnus japonica) has expanded (Kanda, 1996; Oki, 2002; Kanda, 2010), and the extent of the Kushiro wetland itself has decreased due to urbanization and development of agricultural lands in surrounding areas (Tsuyuzaki et al., 2004). Moreover, some local researchers have pointed out that small changes in the distribution of plant communities on the high moor may be occurring. This indicates the need for detailed mapping of the spatial pattern of plant communities in the high moor near Akanuma. This area shows a typical landscape of high moor: numerous hummocks and hollows form mosaic landscapes. A hummock is a dry condition with poor nutrition, while a hollow is a wet condition and sometimes becomes a pool with rainwater. Sphagnum spp. and Carex middendorffii grow on hummocks. Carex limosa and Scheuchzeria palustris usually grow in hollows (Iwasa et al., 2003). Aerial Photography Balloon and Camera System Plant communities in the high moor are spatially very complex and high in terms of variety. The use of low altitude aerial photography is more effective to accurately cover a large area compared to ground surveys for creating a detailed vegetation map, including details on individual plants, (Lyon, 1993). In the present study, color aerial photographs were taken using a balloon flown at low altitude over the Akanuma study area. A non-metric, radio-controlled 35 mm color film camera was mounted on this balloon. In order to make the balloon compact and light, the camera system did not include a video camera. A ground operator released the shutter at irregular intervals following his best estimates of balloon height and position. More than one hundred exposures were taken using three film cartridges at the study site on the morning of 04 and 05 July, 1998 (Miyamoto et al., 2004). Ground Control Points Prior to acquiring the aerial photographs, thirty ground control points (GCPs) made of white paper or plastic panels were arranged on the wooden path over the study area. The geographic coordinates of these GCPs were measured using a pair of differential global positioning system (DGPS) receivers. After development, printing, and enlargement of the color film, only sixteen GCPs were identified on the photos. The geographic coordinates measured using DGPS were converted to coordinates of the 13 th Zone of the Japan plane orthogonal coordinate system. The geometric error of the DGPS measurement is less than 1 to 2 cm, according to the operations manual. Since these sixteen GCPs were not sufficient to rectify the southernmost photos, in the summer of 2004, one more GCP was established on the wooden path, which was clearly identified on aerial photographs taken in 1998, and its geographic coordinates were measured with the same DGPS unit as the one used in In total, seventeen GCPs were used for the image rectification in this analysis. Orthorectified Digital Color Mosaicked Image Original Color Photos A total of 24 aerial photographs of high visual quality and covering the study site were selected from the approximately 100 original photos. These were enlarged and printed to 4.8 times the size of a 35 mm file frame of 36 mm by 24 mm; the final photographs measured approximately 173 mm by 115 mm. The photographs were digitized using a flatbed color image scanner at a resolution of 1,200 dpi, image by image. 896 September 2014 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

3 Each digital image consists of approximately 8,200 5,500 pixels in TIFF format using 24-bit RGB color. Flowers on some individual plants such as Iris laevigata could be recognized on these enlarged images by their color and shape. Image Registration and Rectification Registration and rectification of each aerial photograph is essential for making a mosaicked orthophoto. Using triangulation of the six external parameters for each photo, describing the camera position relative to the ground coordinates (X, Y, Z) and the camera rotation angles (omega, phi, kappa) around three axes, the mean altitude of the camera was estimated, taking also into account other internal image parameters related to image distortion, such as displacement of the principal point of the film, lens distortions, film flatness, etc. (Kasser et al., 2002). Image registration and rectification were carried out on all digital images using ERDAS Imagine 9.1 and LPS 9.1 (ERDAS, Inc.). First, the internal parameters of the camera were determined based on photogrammetry algorithms: the focal length of the lens of the non-metric camera and the principal point location of the film along the horizontal and vertical axes on the film (Leica, 2000; Wolf and Dewitt, 2000). Other internal image parameters related to image distortion due to film flatness and lens distortion can be estimated image by image using ground coordinates from the GCPs (Murai et al., 1980; Murai et al., 1984) when the ground coordinates of more than nine GCPs are known for each photo. However, these internal image parameters were assumed to be constant among all the photos in the present study due to insufficient number of GCPs. These image parameters were estimated using other photos taken with the same camera. Second, image orientation was computed using LPS 9.1 (Leica Photogrammetric Suite, ERDAS, Inc.) in order to remove image distortion as much as possible. Using 17 GCPs for which 3D ground coordinates were measured using DGPS and more than 100 tie-points which were digitized on overlapping photos, a bundle adjustment was applied to compute exterior and interior orientation parameters of each image, because some photos had several GCPs while others had no GCPs. After 20 iterations, the computations for image orientation for the selected 24 photographs converged. The final RMSE values of the image registration using 17 GCPs were smaller than 0.3 m on average. This error level is acceptable for the present study due to the relatively small number of GCPs and the 35 mm non-metric film camera used in the photography of the wetland. The height of the camera attached to the balloon from the ground surface over the Akanuma study site ranged from 41 m to 181 m. Third, each digital image was rectified using the estimated interior and exterior parameters of each image in order to remove image distortion. The resampling grid size employed was 2 cm by 2 cm as corresponding to the DGPS accuracy. Consequently, very detailed, rectified and georeferenced color images were obtained. Mosaicked Image of the Study Area Following registration and rectification, the images were mosaicked using ERDAS Imagine and LPS in order to obtain a final mosaicked image, corrected in terms of color balance and contrast. The final mosaicked color image covered an area of about 10 ha (200 m in the east-west direction and 500 m in the north-south direction) from the southern edge of Akanuma to the embankment (Figure 2). When the imagery appears quite accurate, minor differences in color, contrast and texture can be observed. In terms of georeferencing, misalignment at the boundaries of images was small in the east-west direction, but substantial in the north-south direction, especially at the margins of the study area. This is due to irregular image distortions that could not be rectified by the registration algorithm and the relatively small number of GCPs. The maximum geometric error is 0.5 m in the final orthophoto mosaic. Although this orthophoto has a certain amount of geometric errors, it provides valuable data for the delineation of plant communities in the summer of Results Image Classification Using Object-based Segmentation The first objective of the present study was to create a detailed vegetation map of the high moor near Akanuma in the Kushiro wetland using very high spatial resolution color aerial photography. Most of the vegetation maps in the region have been delineated by specialists through visual interpretation of aerial photography. However, the very high spatial resolution the aerial photography hindered the efforts of vegetation specialists to identify and delineate plant communities by hand based on color differences because delineation of the boundaries is so complex. In order to delineate the boundaries of plant communities and to create an initial vegetation classification using a polygon-based supervised classification, a computational algorithm based on object-based segmentation was employed using ecognition 3 software (Definiens, Inc.), image by image. This image segmentation is based on local texture, color contrast, and shape parameters of polygon edges. It produces polygons with homogeneous characteristics of these factors in vector form, depending on predefined thresholds for color and shape indices. The object-based segmentation was applied to each individual image in order to obtain consistent classification results. The thresholds for color and shape indices for ecognition were 0.5 and 0.5, respectively. The spatial size of segmentation was decided using trial and error image by image so that the width of the wooden path (about 30 cm) which runs from the bank to Akanuma was divided into several polygons. Interpretation of Plant Communities Following the segmentation of images, each polygon was classified using supervised classification based on training data of plant communities image by image. These training data were obtained through intensive ground truthing of vegetation by vegetation specialists. Major plant communities in this study area have been investigated by Kanda (2010). In total, 40 different plant communities were identified during ground truthing, excluding four other categories such as water surfaces, deer trails, wooden paths, GCPs, and pasture. The geographical coordinates of training sites were recorded using GPS. The names of the plant communities in the study area are listed in Table 1. They were categorized into ten groups in terms of water condition or typical plant species as follows: Group I Area of very high ground water level near water surface or aquatic plants (1-2), Group II Area of high ground water level (3 to 10), Group III Wet area with Alnus japonica(11 to 12), Group IV Relatively dry area (13), Group V Relatively low moor plant communities (14 to 17), Group VI Transitional moor with Myrica gale var. tomentosa (Yachiyanagi) (18 to 24), Group VII Transitional moor with Eriophorum vaginatum (Watasuge) (25 to 29), Group VIII Lightly humid high moor plant communities (30 to 32), Group IX Typical high moor plant communities (33 to 40), and Group X Other (41 to 44). Based on the training data, all polygons delineated by PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING September

4 ecognition were classified using the maximum likelihood (ML) classification. The parameters for classification were only color variables and not texture, because plant ecologists mainly used color difference for visual interpretation. In this manner, the classes of plant communities were assigned to more than 300,000 polygons, image by image. The polygons for each plant community were merged together, i.e., polygons adjoining to polygons with the same plant community were combined into one polygon. After that, the total number of polygons was 225,504 (hereafter referred to as the Akanuma Vegetation Map or AVM_2cm). The very detailed vegetation map in vector form was also converted to raster format using a grid size 2 cm by 2 cm. Some inconsistencies were observed in the classification results for the images. Adjusting each image relative to the others is made somewhat difficult due to differences in contrast, viewing angle, sun illumination, and thin cloud cover at the time of exposure. As a result, although the ML classification was conducted on each individual image using training data, the vegetation classification was not very reliable. Intensive re-interpretation of plant communities and careful manual validation over the extent of the study site were required. Almost one year was dedicated to re-analysis of the images, incorporating more than 30 revisions and re-interpretations image by image. Two plant ecologists who are very familiar with vegetation of this area replaced incorrect plant community codes with correct ones when double-checking their interpretation. This effort made use of editing tools in Arc- GIS, following reference data provided by Tsujii et al.(2002). Finally, a mosaicked vegetation map was double-checked and validated by two specialists with extensive knowledge of the plant ecology in the Kushiro wetland. Very Detailed Vegetation Map All the rectified and classified images were mosaicked together, producing a final, consistent image. Plate 1 shows the very detailed vegetation map of the high moor near Akanuma. The comparison of the mapping results with the field surveys resulted in a number of observations. The map of plant communities around Akanuma shows the spatial transition of plant communities indicated by the imagery. The plant communities represent three main categories: the first is near the bank, the second is in the middle area, and the third is near Akanuma. They appear to be belts extending east and west. Ledum palustre var. diversipilosum - Chamaedaphne-calyculata (33) was mainly found near the bank. Empetrum nigrum var. japonicum - Ledum palustre var. diversipilosum -Thelypteris nipponica (38), Thelyterisnipponica - Ledum palustre var. diversipilosum - Carex limosa (39), and Sphagnum magellanicum - Phragmites australis - Chamaedaphne calyculata (40) were found in the middle area. Vaccinium oxycoccus - Equisetum fluviatile - Calamagrostis langsdorffii (30), Sphagnum sp. - Mirica gale var. tomentosa - Vaccinium oxycoccus (31), and Phragmites australis - Calamagrostis langsdorffii - Vaccinium oxycoccus (32) were found near Akanuma. Visually, the final map appears very good and reflects the actual spatial pattern of plant communities near Akanuma. The very detailed vegetation map shows three regions where typical wetland plant communities are dominant in three different color bands from north to south, i.e., from Akanuma to the embankment. Figure 2. Black and white versions of the very high resolution mosaicked orthophoto of the high moor near Akanuma. 898 September 2014 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

5 Table 1. Plant Communities in the High Moor Near Akanuma PC# Group Japanese name of plant community Scientific name of plant community 1 Nemurokouhone Nuphar pumilum I 2 Nemurokouhone-Mitsugashiwa Nuphar pumilum - Menyanthes trifoliata 3 Mitsugashiwa Menyanthes trifoliata 4 Mitsugashiwa-Kakitsubata Menyanthes trifoliata - Iris laevigata 5 Mitsugashiwa-Mujinasuge-Yoshi Menyanthes trifoliata - Carex lasiocarpa var. occultans - Phragmites australis 6 Kakitsubata-Mizugoke-Mitsugashiwa Iris laevigata - Sphagnum sp. - Menyanthes trifoliata II 7 Sagishuge-Kakitsubata-Yachiyanagi Eriophorum gracile - Iris laevigata - Myrica gale var. tomentosa 8 Yachisuge-Mizudokusa-Kakitsubata Carex limosa - Equisetum fluviatile - Iris laevigata 9 Mujinasuge-Mizudokusa-Kakitsubata Carex lasio carpa var. occultans - Equisetum fluviatile - Iris laevigata 10 Yachisuge-Kakitsubata Carex limosa - Iris laevigata 11 Hannnoki Alnus japonica III 12 Hannnoki-Himeshida-Yamadorizennmai Alnus japonica - Thelypteris palustris - Osumunda cinnamomea 13 IV Himeshida-Yamadorizennmai Thelypteris palustris - Osumunda cinnamomea 14 Yoshi Phragmites australis 15 Chishimagariyasu-Iwanogariyasu Calamagrostis neglecta - Calamagrostis langsdorffii V 16 Iwanogariyasu-Yoshi Calamagrostis langsdorffii - Phragmites australis 17 Yoshi-Yachisuge Phragmites australis - Carex limosa 18 Yachiyanagi Myrica gale var. tomentosa 19 Yachiyanagi-Mujinasuge Myrica gale var. tomentosa - Carex lasio carpa var. occultans 20 Himewatasuge-Yachiyanagi Scirpus hudsonianus - Myrica gale var. tomentosa 21 VI Ipponnsuge-Iwanogariyasu-Yachiyanagi Carex tenuiflora - Calamagrostis langsdorffii - Myrica gale var. tomentosa 22 Iwanogariyasu-Yachisuge-Yachiyanagi Calamagrostis langsdorffii - Carex limosa - Myrica gale var. tomentsa 23 Yoshi-Yachiyanagi Phragmites australis- Myrica gale var. tomentosa 24 Yoshi-Yachiyanagi-Kakitsubata Phragmites australis - Myrica gale var. tomentosa - Iris lasiocarpa 25 Watasuge Eriophorum vaginatum 26 Watasuge-Iwanogariyasu Eriophorum vaginatum - Calamagrostis langsdorffii 27 VII Watasuge-Yachisuge-Kakitsubata Eriophorum vaginatum - Carex limosa - Iris laevigata 28 Kotanukimo-Watasuge-Tachigiboushi Utricularia intermedia - Eriophorum vaginatum - Hosta rectifolia 29 Mujinasuge-Watasuge-Chishimagariyasu 30 Tsurukokemomo-Mizudokusa-Iwanogariyasu Carex lasio carpa var. occultans - Eriophorum vaginatum - Calamagrostis neglecta Vaccinium oxycoccus - Equisetum fluviatile - Calamagrostis langsdorffii 31 VIII Mizugoke-Yachiyanagi-Tsurukokemomo Sphagnum sp. - Mirica gale var. tomentosa - Vaccinium oxycoccus 32 Yoshi-Iwanogariyasu-Tsurukokemomo Phragmites australis - Calamagrostis langsdorffii - Vaccinium oxycoccus 33 Isotsutsuji-Yachitsutsuji(Horomuitsutsuji) 34 Isotsutsuji-Mujinasuge-Sugigoke Ledum palustre var. diversipilosum - Chamaedaphne calyculata Ledum palustre var. diversipilosum - Carex lasio carpa var. occultans - Polytrichum juniperinum 35 Isotsutsuji-Watasuge Ledum palustre var. diversipilosum - Eriophorum vaginatum 36 Chamizugoke-Isotsutsuji-Yachisuge Sphagnum fuscum - Ledum palustre var. diversipilosum - Carex limosa 37 IX Chamizugoke-Murasakimizugoke-Yachiyanagi Sphagnum fuscum - Sphagnum magellanicum - Myrica gale var. tomentosa 38 Gannkourann-Isotsutsuji-Nikkoushida Empetrum nigrum var. japonicum - Ledum palustre var. diversipilosum - Thelypteris nipponica 39 Nikkoushida-Isotsutsuji-Yachisuge Thelyteris nipponica - Ledum palustre var. diversipilosum - Carex limosa Murasaki_mizugoke-Yoshi-Yachitsutsuji (Horomuitsutsuji) Pools or Deer_trails 42 Akanuma pond X 43 Wooden path or Landmark 44 Bank vegetation(grass) PC#: Plant Community Code Sphagnum magellanicum - Phragmites australis - Chamaedaphne calyculata PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING September

6 Plate 1. Very detailed vegetation map of the high moor near Akanuma in the summer of Spatial Pattern Analysis Landscape Metric Analysis The very detailed vegetation map of Akanuma provides ecological information on plant communities, including their spatial patterns. Using AVM_2cm in vector format, quantitative landscape metrics related to patch size, density, shape, and edge were determined using Patch Analyst for ArcGIS (Esri, Inc.). This tool computes a set of principal characteristics of the target landscape, including mean patch size, patch density, mean shape index, total edge length, mean patch edge length, etc. Table 2 shows the results of the patch analysis and provides a characterization of the dominant and minor plant communities. Examining Table 2 more in detail reveals some interesting observations. In terms of the total area of each plant community, (30), (33), and (40) exceed 10,000 m2. The total area of (33) is 16,514 m2 and represents the largest plant community in this area. (24), (25), and (32) exceed 5,057 m2. These six plant communities combined occupy 44 percent of the total area. The total areas of (4), (15), (17), (20), (22), (26), (29), (36), (37), (38), and (39) fall between 1,121 m2 and 4,047 m2, occupying 22 percent of the total area. The areas of all other plant communities are less than 1,000 m2. Area (13) has the smallest area of only 11.6 m2. The plant communities of which each individual area is less than 1,000 m2 occupy 30 percent of the total. These trends support the results of the visual interpretation mentioned earlier. Areas (30) (Vaccinium oxycoccus - Equisetum fluviatile - Calamagrostis langsdorffii), (33) (Ledum palustre var. diversipilosum - Chamaedaphne calyculata), (40) (Sphagnum magellanicum - Phragmites australis - Chamaedaphne calyculata), (24) (Phragmites australis - Myrica gale var. tomentosa - Iris lasiocarpa), (25) (Eriophorum vaginatum), and (32) 900 Septem b er (Phragmites australis Calamagrostis langsdorffii - Vaccinium oxycoccus) are the primary dominant plant communities in this area. Areas (4), (15), (17), (20), (22), (26), (29), (36), (37), (38), and (39) are the secondary dominant plant communities. All other ones are rare plant communities in this area. Composition of Plant Communities The results of the patch analysis do not describe the spatial distribution of plant communities. In order to study the characteristics of their spatial distribution, the AVM_2cm with a 2 cm resolution was spatially aggregated. A simple low-pass filter with majority-rule was applied to the raster version of AVM_2cm using a window size, which is equal to a grid size of 30 cm 30 cm. This filter assigns the most dominant plant community in each search window (15 15 cells) to the new large pixel of 30 cm by 30 cm. Only very small differences were observed when comparing the total areas of individual plant communities in the AVM_2cm and AVM_30cm maps. A transect analysis was conducted on the AVM_30 cm map. Figure 3 shows nine transects: three (Twest, Tcenter, Teast) from north to south and six (A, B, C, D, E, and F) from east to west. The composition of plant communities at 5-meter intervals was determined along the transects by calculating the percentages of plant communities for each 5 5 m grid cell. The results characterize the unique spatial patterns of wetland vegetation in the high moor area of the Kushiro wetlands. Figure 4a shows the changes in the composition of the primary plant communities along the transects from north to south. Figure 4b shows the changes along the transects from east to west. In order to make the Figure 4 readable, percentages of each 10 plant community groups are shown in Figure 4. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

7 Table 2. Landscape Metrics Derived from the Very Detailed Vegetation Map Group PC# Polygon Area(m^2) Perimeter(m) MSI Sum Max. Mean SD Sum Max. Mean SD I , , , , , , , II , , , , , , , , , III , IV , V 15 2, , , , , , , , , , VI , , , , , , , , , , , VII 27 1, , , , , , , , VIII 31 3, , , , , , , , , , , , IX 36 18, , , , , , , , , , , , , X , , , Total 225, MSI: Mean Shape Index PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING September

8 area, a reduction in the number of endmembers for SMA in a pixel is useful in trying to reduce the computation load of SMA for hyperspectral images such as those from Compact Airborne Spectrographic Imager (CASI) (JAXA EORC Special Data Sets,1994). Determining the approximate number of endmembers in a GIFOV is helpful to better estimate the fractions of elements. Diversity analysis of AVM provides this kind of information for the study area. Figure 5 shows two plant community diversity maps in grids of approximately 1 m 1 m and 2 m 2 m derived from AVM_30cm. These maps show the maximum number of endmembers in a pixel GIFOV. In the case of remotely sensed imagery for which GIFOV is about 1 m 1 m, modeling five or fewer elements using SMA is sufficient, while in the case of 2 m 2 m, ten or fewer elements are needed. Generally, SMA based on a linear mixture model requires more spectral bands than the number of mixed elements in a mixed pixel. The numbers of five and ten elements in GIFOVs of about 1 m 1 m and 2 m 2 m, respectively, in this area shows that it is not necessary to apply a SMA model to multi or hyper spectral imagery with a spatial resolution of 1 to 2 m using more than ten mixture elements if the combination of mixture elements in a mixed pixel can be locally estimated well during computation. Figure 3. Location of transect lines for composition analysis. The composition of primary plant communities along the transects from north to south in Figure 4a show the very distinct transition of plant communities from the mixed area with marsh and high moor plant communities to transitional plant communities. For example, Group II, V, and VIII are dominant near Akanuma, while Group VI, VII, and IX are becoming more dominant closer to the bank. In the middle area, the dominant plant communities are Group VI, VII, VIII, and IX, reflecting a mix of those of the high moor plant communities and the transitional plant communities. These characteristics are also shown in Figure 4b, which show well-defined strata of plant communities along every transect from east to west. The graph for transect A shows typical dominant plant communities near ponds. The graphs for transects B and C show the typical plant communities of the high moor. The graph of transect D shows the plant community of the transitional zone mixed with the high moor plant communities. The graphs of transects E and F show the composition of transitional plant communities. Since transect A was set near Akanuma and transect F was set near the bank, this means that the three types of wetland plant communities are distributed parallel to Akanuma and the bank. Diversity of Plant Communities The diversity of plant communities in the study area is so high that almost all the pixels of remotely sensed imagery over this area are thought to be mixed pixels. Strahler et al. (1986) has defined this as a L-resolution scene model in which the elements are smaller than the individual cells and are not individually detectable. Cracknell (1998) has illustrated some research on mixed pixels, trying to estimate fractions of elements in a GIFOV. Somers (2011) has identified Spectral Mixture Analysis (SMA) as a well-established and effective technique to address the problem of mixed pixels if the remote sensing image to analyze has more spectral bands than the number of expected endmembers. Considering that there could be more than 40 plant communities of significance in terms of wetland ecology in this Discussion The very detailed map of plant communities in the high moor near Akanuma in the Kushiro wetland created in the present study provides a number of interesting spatial characteristics of wetland plant communities and provides useful information for wetland ecosystem conservation in the near future for additional environmental monitoring. Furthermore, the map could serve as the baseline map for detecting environmental changes. Lechner et al. (2009) have suggested that the spatial resolution of a raster should be many times smaller than the often fragmented and linear vegetation patches in order to extract them accurately. Following their suggestion, the desirable vegetation map for the Akanuma high moor area requires images with a spatial resolution of only a few centimeters. The map with a spatial resolution of 2 cm delineated in the present study is in the same order as Lechner et al. (2009) have suggested. However, the procedures and labor costs associated with creating this type of detailed vegetation map make it impractical to map the whole extent of the Kushiro wetland. The very detailed vegetation maps at a spatial resolution of several centimeters may only be feasible for hotspot areas, such as valuable high moor areas in wetlands. In order to create very detailed vegetation maps for larger areas, other methods need to be developed or applied; for example, SMA using hyperspectral remotely sensed imagery. At the same time, intensive ground surveys and accurately recording ground conditions are essential for accurate and detailed mapping of plant communities in large area. One of the most important tasks for continuous environmental monitoring is to establish enough GCPs as survey markers to cover the area of interest. Insufficient GCPs result in substantial errors in the registration and rectification of imagery. Ground truthing is also essential to establish reference data for the accuracy assessment of vegetation classification using very high resolution images. Ideally, intensive ground truthing is carried out at the same time as the imagery is recorded, but this also takes time and effort. In some case, the very high spatial resolution images could be used in place of ground truthing because they provided great detail. Researchers may not need to go the study site as often, consequently, very high spatial resolution aerial color photographs are very useful. Few studies have used very high spatial resolution data to map plant communities. Some of the reasons for this are the difficulties in image rectification, mosaicking, and 902 September 2014 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

9 Figure 4a. Plant composition along the transect lines from north to south. interpretation when creating vegetation maps. While the present study has demonstrated some of the disadvantages in using very high spatial resolution images for wetland vegetation studies, very interesting spatial characteristics and useful knowledge of the plant communities in the high moor in the Kushiro wetland were derived from the vegetation maps. Very high spatial resolution aerial photographs of wetlands taken by unmanned aerial vehicles (UAV) (Strecha, 2012) have substantial potential for environmental monitoring. Conclusions The present study as used aerial photography to describe the status of the wetland ecosystem near the high moor area around Akanuma in the Kushiro wetland, Hokkaido, Japan. The relationship between the spatial resolution of remote sensing imagery and the actual mean patch sizes of plant communities in the wetland were determined, as well as the spatial distribution of wetland plant communities using plant community analysis and landscape metrics. The present study used very high spatial resolution color photographs taken in the summer of 1998 with a 35 mm, non-metric camera mounted on a balloon. A very detailed and reliable vegetation map was created with a nominal spatial resolution of 2 cm by 2 cm, including 40 plant community types for summer The study area consists of approximately 10 ha of high moor and provides a baseline map for ecological studies. The optimal spatial resolution for monitoring vegetation in this area using remote sensing is 0.3-meter or smaller. A belt-like spatial pattern of typical wetland plant community groups can be clearly distinguished in the very detailed vegetation map through visual interpretation and spatial analysis. Patch and composition analysis of plant communities was applied to understand the spatial pattern of wetland plant communities. These analyses explain the general spatial pattern of plant communities as well as the more detailed spatial characteristics, such as ratios of dominant plant communities, their flourishing areas, their total area and general distribution. Results of diversity analysis using grid sizes of 1 m 1 m and 2 m 2 m demonstrate that it is not necessary to use more than ten elements in SMA modeling if the combination of mixture elements in a mixed pixel can be well estimated in computation. Results of the present study show that remote sensing using very high spatial resolution is invaluable for wetland monitoring and wetland studies. Specifically, this type of data collected over many years will be very helpful to detect small but serious environmental changes in wetlands. While a number of improvements in the methodology are desirable, the results are very useful for further wetland environment monitoring and conservation. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING September

10 Figure 4b. Plant composition along transect lines (A through F) from east to west. Acknowledgments This research was partly funded by a Kurita Water and Environment Foundation Grant in 2009 and by JSPS grant in aid for the ground survey in 2010 (No ) and in 2012 (No ). References Cracknell, A.P Synergy in Remote Sensing - What s in a pixel?, International Journal of Remote sensing, 19(11): Dugan, P. (editor), Wetlands In Danger, Oxford University Press, New York, 187 p. Iwasa, Y., T. Matsumoto, K. Kikuzawa, and the Ecological Society of Japan (editors), Encyclopedia of Ecology, Kyoritsu Publication, Inc., Japan, 682 p. 904 September 2014 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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