| 作成者 |
|
|
|
| 本文言語 |
|
| 出版者 |
|
| 発行日 |
|
| 収録物名 |
|
| 巻 |
|
| 開始ページ |
|
| 終了ページ |
|
| 会議情報 |
|
| 出版タイプ |
|
| アクセス権 |
|
| Crossref DOI |
|
| 権利関係 |
|
| 権利関係 |
|
| 概要 |
This study uses LiDAR derivatives and Landsat 8 OLI/TIRS for yield estimation of major crops in Butuan City, Agusan del Norte, Philippines. Yield estimation is crucial for crop management, food securi...ty, and economic impact. Remote sensing techniques and GIS were used for crop yield estimation. eCognition software classified photos, while LiDAR derivatives like Canopy Height Model (CHM), Digital Surface Model (DSM), and Normalized Digital Surface Model (nDSM) were produced. Vegetation objects were classified into High, Medium, and Low Elevation Groups based on LiDAR nDSM heights. Regression analysis developed the allometric equation for yield estimation. The study achieved 94.36% overall accuracy in map classification and yield estimations of 84.5% for banana, 87.9% for coconut, 72.5% for corn, and 86.4% for mango. Average production was 58.414 tons/ha for bananas, 1,001.15 tons/ha for coconut, 8.48 tons/ha for corn, and 1.256 tons/ha for mango.続きを見る
|
Mendeley出力
