인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The study presents an analysis of using the Geoscience Laser Altimeter System of Ice, Cloud, and land Elevation Satellite (GLAS/ICESat) for the detection and characterization of canopy gaps in the Bolo-Est classified forest. This classified forest is located in southwest Côte d’Ivoire, a region facing increasing deforestation due to agriculture, in particular cocoa cultivation. The main objective of the study was to demonstrate the effectiveness of this Light Detection and Ranging (LiDAR) sensor for mapping canopy gaps, crucial for understanding the dynamics of tropical forest degradation. The methodology used integrates key steps: acquisition and pre-processing of GLAS/ICESat data, normalization of elevations to correspond to the World Geodetic System (WGS 84), calculation of canopy heights, detection of gaps via a thresholding process, and validation of results by field observations. The analysis revealed that 52% of the points analyzed corresponded to gaps, covering 19% of the study area, mainly caused by human activities such as agriculture and logging. The gaps identified vary in size from 50 m2 to 100 m2, indicating large canopy gap sizes in this forest. The results confirm that LiDAR GLAS/ICESat offers interesting accuracy and efficiency for detecting canopy gaps as a complement to traditional optical and radar remote sensing methods. The LiDAR approach thus provides a good understanding of degradation processes, offering useful data for environmental monitoring and sustainable forest management. This study demonstrates the potential of LiDAR remote sensing to support conservation initiatives such as the reducing emissions from deforestation and forest degradation (REDD+), contributing to the accurate assessment of anthropogenic canopy gaps, and proves a promising tool for the sustainable management of tropical forests on a national scale.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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