GRASS GIS の画像解析のための再分類スキーム: グラデント増強アルゴリズムを使用するジブチ,東アフリカのケース
1Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum-Università di Bologna, Via Irnerio 42, Emilia-Romagna, 40126 Bologna, Italy.
Journal of imaging
|August 27, 2025
まとめ
この研究では 機械学習と衛星画像の分析を用いて ジブチの土地覆いの変化をマッピングしました 気候の影響で砂漠化が増加しており,土地管理戦略の必要性を強調しています.
科学分野:
- 環境科学
- リモートセンシング
- 地理情報システム
背景:
- 衛星データの画像分析は環境動態と土地管理の監視に不可欠です.
- 景観の変化を理解することは,生態系プロセスと地球システムの機能を予測する鍵です.
研究 の 目的:
- 多スペクトル遠隔検知データと地理資源分析支援システム (GRASS) の地理情報システム (GIS) ソフトウェアを使用して,地表の種類をマッピングするための再分類スキームを開発し,適用する.
- バブ・エル・マンデブ海峡の沿岸地域の地表の動態を分析し,アサル湖周辺の変化に焦点を当てた.
- ジブチにおける砂漠化プロセスに対する気候の影響の評価
主な方法:
- タイムシリーズ分析のために,2015年,2019年,2021年,2023年のLandsat 8-9衛星画像を使用した.
- 画像分類と再分類のために機械学習 (ML) アルゴリズムとGRASS GISモジュール (例えば"r.reclass"",r.learn.predict"",i.cluster") を採用した.
- ランドサット画像の処理と土地カバー変数の特定のための様々なGRASS GISモジュールとスクリプト技術を統合しました.
主要な成果:
- この研究では,アサール湖周辺の風景の年間変動とジブチの半乾燥地帯と砂漠地帯の変化が明らかになりました.
- 半砂漠地帯の増加と自然植生の減少が観察され,砂漠化を示しています.
- 開発された地表の地図は,地域の時空の変化に関する貴重な洞察を提供しました.
結論:
- GRASS GISを用いたMLベースの方法論は,地表の動態と変化を分析するのに有効です.
- 気候の影響によるジブチでの砂漠化の進行を強調しています.
- このアプローチは,アフリカの他の乾燥地域での土地管理戦略にも適用できます.
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