初夏の航空ハイパースペクトルセンサデータによる植生のマッピング

摘要

The authors previously made a vegetation map that included beech, walnut, dwarf bamboo and altherbosa, using airborne hyperspectral sensor (HSS) data acquired in early autumn in the Shirakami Mountains of Japan. From the spectral response pattern, high and low radiances were identified for beech and for walnut, so we created four classifications for these : high-radiance beech, high-radiance walnut, low-radiance beech, and low -radiance walnut. However, field observations found no visual difference (e.g., tree age) between the high-radiance and low-radiance areas for each of these vegetations. The difference in radiance may depend on differences in the photosynthetic capacity of foliage immediately before the foliage changes in color in autumn. In the present study, we made a vegetation map for the same area using airborne HSS data from early summer. From the map, differences in radiance were not found in beech vegetation and walnut vegetation. The producer's classification accuracy of walnut increased slightly to 78.5% in early summer, from 76.5% in early autumn, and the producer's classification accuracy of beech increased to 96.9% in early summer, from 80.0% in early autumn. In addition to the improvement of classification accuracy, the shade area in the tree crowns is smaller in summer than in autumn, and it is easier to extract training pixels from airborne HSS data of tree crowns in early summer than in early autumn. Judging from these facts, it was found that airborne vegetation classification maps drawn from HSS data are more accurate when the data are acquired in summer than when the data are acquired in early autumn.%初秋よりも太陽高度の高い初夏の航空HSSデータを使った方が，投影される影の面積も少なく，直達光に照らされる樹冠が多いことから，それだけ教師となる画素を多く選ぶことができた。また，佐藤ほか（2006）が提案した輝度値規格化の手法は，初秋のみならず初夏の航空HSSデータでも植生分類に有効であることが示された。さらに，教師となる画素で分類精度を評価したところ，初秋の航空HSSデータを使った場合に比べて，サワグルミの作成者正解率は微増の78.9%，ブナの作成者正解率は80.0%から96.9%に上昇した。調査区に対するサワグルミ樹冠の面積占有率を分類結果から調べたところ，全体的にみれば，現地で描画した樹冠投影図から判断される面積占有率約30%とほぼ同様の値を得ることができた。