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本研究では，希少猛禽類クマタカの保全・保護対策のために, クマタカの生息適地推定モデルを構築し山形県内における生息適地分布図を作成した．本モデルでは対象地域をグリッドで分割し, 個々のグリッドに対して周辺の地形・植生状況を表現する各種指標値を与えて生息地としての適否を推定した. 従来の研究では, 約5km×5kmのグリッド内の, 平均標高・平均傾斜・最低標高等の地形指標が主に用いられていた. しかし本研究では, クマタカの生息環境をより的確に表現するため, クマタカの生息に重要とされる谷地形の分布状況を表現する指標等, 全部で13種類の地形・植生指標を対象とした. また, 詳細な生息適地分布図を作成するためグリッドサイズを50mとした. なお, 各指標が集計対象とする周辺範囲の広さによって指標の環境説明力が変化するため，既往の研究を参考に, 集計対象範囲を各グリッドから半径0.5km, 1km, 1.5km, 3kmの円内の4通りに設定した. 各指標値の算出には国土地理院の数値地図50mメッシュ(標高)および環境省の現存植生図(1/50, 000)を用いた. 最適モデルの選択においては，ロジスティック回帰分析を用いて各地形・植生指標を組み合わせたモデル式を作成し, 各々の適合度や推定精度等を比較した. その結果, 周辺半径1kmの谷地形の分布状況を表す指標と周辺半径3kmの樹林地の面積割合を表す指標を組み合わせたモデルが最適モデルとして選択された(Overall accuracy 91.8％). さらに, 最適モデルにもとづいて作成した生息適地分布図を5kmグリッド化したものとクマタカ確認情報分布図を比較すると, 概ね高い精度(Overall accuracy 89.0%)で一致し, 推定された生息適地の大部分においてクマタカの確認情報が存在していることが判明した. 以上より, 本研究で作成したモデルは高い精度で生息適地を推定できており, クマタカの生息適地推定には谷地形の分布と樹林面積に着目することが重要であるといえる., The mountain hawk-eagle (Spizaetus nipalensis) is an endangered and an umbrella species ranking high in the food chain of the forest ecosystem. The objective of this study was to estimate the potential habitat of the mountain hawk-eagle in Yamagata prefecture, northern Japan. Habitat suitability of each grid was estimated using values of indices corresponding to terrain and vegetation features of the surroundings. Previous studies have computed terrain indices such as minimum and mean altitude and mean slope gradient within each grid-square of approximately 5km in size. To estimate potential habitat more accurately, we used 13 terrain and vegetation indices, including the valley distribution index that is important for the mountain hawk-eagle, and set a grid resolution of 50m to describe potential habitat map at fine scale. Since these indices would be sensitive to the analyzing window size placed around each grid, we examined this effect by changing radii as 0.5, 1, 2 and 3km. Values of these indices were computed in each grid with a 50m digital elevation model (50m DEM) and a rasterized 1/50, 000 digital vegetation map. By comparing Akaike’s Information Criterion (AIC) values and accuracies among logistic regression models with different combinations from a set of 13 indices, an optimal model with the lowest AIC value and high accuracy was determined. The model used the distribution of valleys within 1km radius and the area of forest within 3km radius, which indicates the importance of valleys and forest for the mountain hawk-eagle’s habitat. The potential habitat areas estimated by this model substantially corresponded (89.0%) to the areas where the mountain hawk-eagle has been observed, with the overall accuracy of 91.8%. We concluded that this model could accurately estimate the potential habitat of the mountain hawk-eagle.

From 1998 through 2000, we tracked the autumnal migrations of 13 oriental white storks (Ciconia boyciana) by satellite in order to identify their important stopover sites. The storks were successfully tracked and provided data on partial (n= 4) or complete (n= 9) autumnal migration between the Russian Far East breeding sites and the wintering sites in southeastern China. Twenty-seven stopover sites were identified, the most important of which were in Tonghe Peat Moor (46.095°N, 128.942°E), Momoge Nature Reserve (45.945°N, 123.939°E), and Jiantuozhi Gley Mire (39.221°N, 118.672°E). The connectedness between each stopover site and its surrounding stay sites was also evaluated; the results suggested that the stopover sites situated on the seashores of Liaodong Bay, Bohai Bay, and Laizhou Bay in eastern China are less connected than the others. We concluded that, among the sites studied, Jiantuozhi Gley Mire on the northern shore of Bohai Bay should have a higher priority for protection for two reasons: it is used by many storks, in common, for relatively long periods; and it is at higher risk of being isolated from the migration route network.

[ABSTRACT] Methane has high global warming potential and its increase may give a serious impact to our future climate and environment. Since wetland is one of the major emission sources of methane estimation of methane emission requires precise characterization of wetland conditions in global or continental scale. This study examined a remote sensing method to estimate methane emission from extensive wetland areas. A scaling method between high spatial resolution data (SPOT HRV) and coarse resolution but wide coverage data (NOAA AVHRR) was investigated to extrapolate local information on land cover mixture condition in wetland from HRV to more extensive area through AVHRR, and to estimate wetland coverage area more precisely. First, in SPOT data, land cover types of the area was classified into four categories including vegetation, water, and two types of bogs which are sources of methane, and next AVHRR image density was statistically regressed with category mixture ratio derived from HRV pixels in each AVHRR pixel. This scaling model from regression analysis provides wetland coverage ratio in sub-pixel level only from AVHRR data. The method was applied to western Siberian wetland, and total emission of methane was calculated by multiplying estimated bog areas with unit emission rate of methane in each bog types.

[ABSTRACT] A method is presented for extracting wetland areas in northern high-latitude zones using Normalized Difference Vegetation Index (NDVI) and land surface temperature (T_s) calculated from midday NOAA/AVHRR imagery. Wetland areas have been distinguished from other land-cover types using signatures on a scattergram of NDVI vs. T_s. The method was applied for extracting wetland areas in the basin of the Ob River in the west Siberian lowland. The result has been verified with ground-truth data and land-cover classification results obtained from high-resolution satellite images.

[ABSTRACT] A SPOT/HRV image was obtained at Plotnikovo shortly after the flight measurements of greenhouse gases in August 1994. The image area was classified into seven categories using spectral features and aerial photographs. Mean methane fluxes for different kind of ecosystems have been obtained from the ground measurements. Methane emission from the SPOT image area was estimated by combining the result of ecosystem classification and the values of mean methane fluxes.

[ABSTRACT] A scaling technique was investigated to extrapolate the local information on landcover type mixtures derived from high spatial resolution data (TM) to more extensive area through low spatial resolution but wide coverage data (AVHRR). Over a combined set of TM and AVHRR, first, landcover types of the area was classified into three categories including vegetation, water and others (non-vegetation) with TM data, and next AVHRR image density was statistically regressed with category mixture conditions derived from TM pixels in each AVHRR pixel. Based on the regression model (scaling model) local information on the category mixture was estimated only from AVHRR data over more extensive area. The method is applied to the wetland type categorization and mapping with AVHRR where landcover is required to be characterized by the complex mixtures of vegetation, water and soil.