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'https://openalex.org/W2355568752', 'https://openalex.org/W34466898', 'https://openalex.org/W4300989607', 'https://openalex.org/W4388228270', 'https://openalex.org/W904877077', 'https://openalex.org/W985156132'], 'related_works': ['https://openalex.org/W4387330978', 'https://openalex.org/W2351695895', 'https://openalex.org/W2101541756', 'https://openalex.org/W2096384328', 'https://openalex.org/W2091110001', 'https://openalex.org/W2087339179', 'https://openalex.org/W2051061607', 'https://openalex.org/W1835147332', 'https://openalex.org/W1805865405', 'https://openalex.org/W1524303412'], 'abstract_inverted_index': {'近年,生態学と地理学において空間パターンとスケールの関係を明らかにすることはますます中心的な課題となった.スケールとは粒度または空間分解能として定義づけられる.一方,景観評価においては誤ったスケールを選択すると結果の解釈にバイアスをもたらすことがある.特に,近年衛星リモートセンシングの普及に伴って,ローカル・スケールからグロバール・スケールにわたる景観動態のモニタリングと抽出が可能となり,研究対象のスケールに応じて適切な空間分解能を持つ衛星の選択が必要とされる.また,衛星リモートセンシングのさらなる効率化を図るために異なる衛星データ間の「スケーリング」を明らかにすることも重要である.本論では,異なる空間分解能の衛星データ(QuickBird(2.5m),ALOS': [0], 'AVNIR-2(10m),Terra': [1], 'ASTER(15m),Landsat': [2], 'ETM+(30m))を用いた景観解析の粒度のスケール効果およびスケーリングの関係を提示することを目的とする.解析対象地は,千葉県佐倉市にある典型的な谷津景観を代表する畔田地区に設定した.解析には,まず,最尤法を用いて各衛星データについて土地被覆分類を行なった.精確な土地被覆の分類図が得られた後,衛星データごとに多数法を用いて分類結果をリサンプリングし異なる粒度の土地被覆分類図を作成した.最後,FRAGSTATSにある複数の景観指数を用いて粒度の拡大とともに景観構造特性がどのように変化するか各衛星データのスケール効果とスケーリング関係を調べた.その結果,粒度の拡大に伴って多くの景観指数はいずれの衛星データもべき乗則と対数関数のスケーリング関係が顕著でそれに従って各指数の値が増加・減少することが明らかになった.': [3]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W2313709145', 'counts_by_year': [], 'updated_date': '2024-09-16T04:17:44.407683', 'created_date': '2016-06-24'}