Estimation of above-ground biomass of mangrove forests using high-resolution satellite data

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1 Estimation of above-ground biomass of mangrove forests using high-resolution satellite data Yasumasa Hirata 1, Ryuichi Tabuchi 2, Saimon Lihpai 3, Herson Anson 3*, Kiyoshi Fujimoto 4, Shigeo Kuramoto 5, Yukira Mochida 6 1 Bureau of Climate Change, Forestry and Forest Products Research Institute, Tsukuba, Japan 2 Forestry Division, Japan International Research Center for Agricultural Sciences, Tsukuba, Japan 3 Pohnpei State Government, Federated State Micronesia 4 Faculty of Policy Studies, Nanzan University, Seto, Japan 5 Hokkaido Research Center, Forestry and Forest Products Research Institute, Sapporo, Japan 6 Yokohama National University, Japan * retired Introduction Mangrove forests in tropical and subtropical countries play important roles from the viewpoint of ecosystem services. Several mapping techniques of mangrove area using satellite sensor with a couple of 10-meters ground resolution, i.e. Landsat and SPOT, were developed to protect, restore and monitor costal ecosystem. The new generation of high resolution satellite data of finer ground resolution than 1-m 1-m such as IKONOS and QuickBird opened a new era for taking forest inventories and assessing forest biodiversity with remote sensing at landscape level. Wang et al. (2004a, 2004b) indicated that both IKONOS and QuickBird data were suitable for classification of mangrove species from comparison of the results of texture analysis, likelihood classification and object-oriented classification. Estimating biomass and mapping from texture analysis of high resolution satellite data are also tested for mangrove forests. In this study, we present methods to identify individual crowns in mangrove forests from high-resolution satellite data and to estimate aboveground biomass of mangrove forest from the derived crown area using allometric relationships between crown area and stem diameter, and between stem diameter and biomass. Materials and methods Study area and Plot establishment Study area is located in Pohnpei Island, which is a high volcanic island, and an atoll located at the north of the island, Federated States Micronesia. Pohnpei Island surrounded by barrier reefs and mangrove forests growing on the reef flats fringing the island and some of them are situated in estuaries.

2 Five rectangular plots were established as four different types of mangrove forests to monitor their dynamics. Two 1-ha plots were set up in the eastern island in These plots were characterized by a forest formed on coral reef (PC) and a forest formed in estuary (PE1). They were set up from the vicinity of shoreline with 50 m wide by 200 m long. Additionally, a smaller plot with 30 m wide by 60 m long was also set up at a slightly higher elevation in the estuary type (PE2) nearby PE1. Two other plots, PS with 20 m wide by 100 m long and PR with 20 m wide by 50m long were established in 2002 and 2003 in stands dominated by Sonneratia spp. and Rhizophora spp. All trees taller than 1.3 m were numbered and tagged for repeat measurement. Species, root height and stem diameter were recorded. In the case of Rhizophora spp., stem at 30 cm above root collar was measured, while DBH (diameter at breast height) was measured for other species. Heights of some sample trees in each species were measured and diameter-height curves were derived from them. The positions of all trees within the plots were measured and mapped. All crowns of standing trees were projected and delineated on the map. Each plot was positioned with a GPS at four corners. Satellite data QuickBird multispectral and panchromatic data were acquired for the plots. QuickBird satellite has high ground resolution sensor, 0.61 m panchromatic and 2.44 m multispectral at nadir. The ground resolution of the panchromatic data and the multispectral data after resampling with the nearest neighbor method were 0.7 m and 2.8 m respectively. Both QuickBird panchromatic and multispectral data were extracted for the plots with margin. Data analysis Allometry between crown area and stem diameter We investigated allometry between crown area and stem diameter for each mangrove species to estimate aboveground biomass using forest inventory data and GIS data produced from crown projection maps for the plots. Extraction of individual crown from QuickBird data In high resolution satellite data, reflectance of treetops shows up more strongly than that of crown periphery. This difference is caused by crown shapes and canopy structure. The digital number (DN) of satellite data represents the relative intensity of reflectance. Hence, the DN values at treetops are larger than at the periphery of crowns. A reversal image of QuickBird panchromatic data, obtained by subtracting each DN of the original data from the maximum value of DN in the data for each pixel, was prepared to apply a watershed method for extraction of the individual crowns. The watershed method addresses the influence of terrain

3 on surface water hydrology by modeling the movement of water over the land surface and it computes the local directions of flow and the gradual accumulation of water moving downslope across the landscape. If we regard the reversal image as a digital elevation model (DEM), the crowns of individual trees appear to be depressions in the image. As a result, each crown area is extracted as a watershed using the method. Non-tree areas on the image should be removed during image processing because they lead to an overestimation of crown area. The threshold between tree crown and non-tree area in DN of the satellite data was determined by comparison of the satellite data and a field investigation. A mask of the non-tree areas was generated as binary data using a threshold. Identification of species The reflectance from surface of crown in each band of QuickBird multispectral data depends on biochemical properties of species as well as crown shape and canopy structure. Using this characteristic, it is expected that mangrove species would be classified. The highest DN of each band of QuickBird multispectral data within each polygon was extracted and reassigned to each polygon to classify mangrove species. K-means method, one of unsupervised classification, was used for the analysis. Estimation of aboveground biomass Komiyama et al. (1988) investigated the weight of each organ for mangrove species from some sampling trees and introduced formulas as bellows; ws, wb, wl, wpr and wf = a(d 2 H) b (1) where, ws :weight of stem wb :weight of branches wl :weight of leaves wpr :weight of prop roots wf :weight of fruits and a and b are estimated parameters. The weight of each organ by species was estimated from Eq. (1) using two variables, i.e. stem diameter and tree height, while tree height was introduced from the obtained diameter-height curves. We calculated aboveground biomass (B a ) of a tree as the sum of these weights. B a =ws+wb+wl +wpr+wf (2)

4 We calculated total aboveground biomass at stand level from field data, canopy surface maps, and QuickBird data using these formulas and compared their results to evaluate the method to estimate aboveground biomass from high-resolution satellite data. Results Allometry between crown area and stem diameter Crown area of each tree was calculated from the map using GIS software automatically. The relationships between crown area and stem diameter for main species were estimated. These equations were used to obtain stem diameter from the canopy surface maps and QuickBird data. Extraction of crown and Identification of species from QuickBird data We acquired polygons of individual crowns that appear on the surface of canopy from QuickBird panchromatic data applying watershed method to its reversal data. Gaps were removed before the crown extraction procedure. Thresholds to remove gap area were decided to 245 for the data on PC, PE1 and PE2, and 275 for the data on PS and PR by verification of histogram and interpretation of the images. We assigned the highest values of digital number in each band of QuickBird multispectral data to the polygons that were generated in the crown extraction procedure. We did not measure the reflectance of each mangrove species in the field. Therefore, we could not classify polygons of crowns from the information of reflectance in each band. Instead of it, statistical method was used to identify the species after unsupervised classification. Using ratio of total crown area of all study plots from canopy surface map, species were assigned to clusters of unsupervised classification. Estimation of aboveground biomass The aboveground biomass was calculated from stem diameter obtained from the forest inventory to evaluate the method to estimate aboveground biomass from high-resolution satellite data. Compared with the stands on coral reef and in estuary, the aboveground biomass was extremely small in the stands in front of the sea. We compared the results from three different sources and 7 27 % of aboveground biomass was underestimated with QuickBird data. Except PE2, % of aboveground biomass was underestimated with the canopy surface map. Discussion Mangrove forest normally has stratification, therefore, a part of crown is only observed from the space except some dominant trees. The accuracy of the biomass estimation seems to depend on the amount of the suppressed trees. Indeed, tree numbers of study plots, which

5 were extracted from the QuickBird data respectively, were smaller than real tree numbers. Particularly, if trees of second layer are hidden by canopy layer, uncounted biomass occupy quit large part of estimation. Here, we used the watershed method to extract individual crowns. This is one of most common method for this purpose, however, we should recognize that crown conditions are obviously different by individual trees. Some large trees are regards as multiple trees because of some divided crowns. When canopy surface is comparatively flat, canopy is not divided suitably for the limitation of the method. Nevertheless these problems, the results indicated the possibility of utilization of high-resolution satellite data to estimate the aboveground biomass of mangrove forest. The extent of mangroves is comprehensible along with the estimation of the biomass with the data. I would now like to go on to obtain generalized parameters to estimate the biomass. Komiyama, A., Moriya, A., Suhardjono, P., Toma, T. and Ogino, K Forest as an ecosystem: its structure and function. In Biological System of Mangrove. K. Ogino and Chihara (eds.). Ehime University, Ehime, Matsuyama, pp Wang, L., Sousa, W.P. and Gong, P. 2004a Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing 25, Wang, L., Sousa, W.P., Gong, P. and Biging, G.S. 2004b Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment 91, Fig.1. Estimation of abovegrond biomass