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
Abstract Background Remote sensing techniques for assessing fire severity using two-dimensional imagery, such as satellite data, are limited to a single severity value per pixel, typically at a 30-m resolution. This often leads to an underestimation of understory fire severity, as live tree crowns can obscure the extent of the burned area beneath. By leveraging the three-dimensional capabilities of drone imagery, a more comprehensive assessment of fire severity across different canopy height strata can be achieved. Methods We show how drone digital aerial photogrammetry (dDAP), also known as structure from motion, can be used to generate three-dimensional multispectral photogrammetric point clouds for quantifying fire effects at various canopy height strata as well as classify ground cover below normally occluding overstory trees. Conducted during prescribed fires at Fort Jackson, South Carolina, RGB and multispectral imagery were collected via drone both pre- and post-fire at five plots, with two additional unburned plots flown to serve as controls. Multispectral photogrammetric point clouds were generated and NDVI values were calculated for each point. Point clouds were segmented into 2-m height stratum layers, to compare NDVI values for different canopy height strata pre- and post-fire. Orthoimages of the understory, overstory, and traditional nadir views were generated. Conclusions Findings showed that prescribed fire had a substantial effect on NDVI values up to 6 m in height, with only minor effects observed above 6 m. Ground cover under the canopy, typically occluded from overhead imagery, was classified with 87% accuracy. This study demonstrated the ability to digitally remove occluding tall vegetation using dDAP and to derive a more precise assessment of fire effects on ground and understory vegetation compared to two-dimensional satellite imagery.
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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