已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland

草原 遥感 植被(病理学) 环境科学 卫星图像 图像分辨率 草地生态系统 地理 生态学 计算机科学 人工智能 医学 病理 生物
作者
Bing Lu,Yuhong He
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing 卷期号:128: 73-85 被引量:197
标识
DOI:10.1016/j.isprsjprs.2017.03.011
摘要

Investigating spatio-temporal variations of species composition in grassland is an essential step in evaluating grassland health conditions, understanding the evolutionary processes of the local ecosystem, and developing grassland management strategies. Space-borne remote sensing images (e.g., MODIS, Landsat, and Quickbird) with spatial resolutions varying from less than 1 m to 500 m have been widely applied for vegetation species classification at spatial scales from community to regional levels. However, the spatial resolutions of these images are not fine enough to investigate grassland species composition, since grass species are generally small in size and highly mixed, and vegetation cover is greatly heterogeneous. Unmanned Aerial Vehicle (UAV) as an emerging remote sensing platform offers a unique ability to acquire imagery at very high spatial resolution (centimetres). Compared to satellites or airplanes, UAVs can be deployed quickly and repeatedly, and are less limited by weather conditions, facilitating advantageous temporal studies. In this study, we utilize an octocopter, on which we mounted a modified digital camera (with near-infrared (NIR), green, and blue bands), to investigate species composition in a tall grassland in Ontario, Canada. Seven flight missions were conducted during the growing season (April to December) in 2015 to detect seasonal variations, and four of them were selected in this study to investigate the spatio-temporal variations of species composition. To quantitatively compare images acquired at different times, we establish a processing flow of UAV-acquired imagery, focusing on imagery quality evaluation and radiometric correction. The corrected imagery is then applied to an object-based species classification. Maps of species distribution are subsequently used for a spatio-temporal change analysis. Results indicate that UAV-acquired imagery is an incomparable data source for studying fine-scale grassland species composition, owing to its high spatial resolution. The overall accuracy is around 85% for images acquired at different times. Species composition is spatially attributed by topographical features and soil moisture conditions. Spatio-temporal variation of species composition implies the growing process and succession of different species, which is critical for understanding the evolutionary features of grassland ecosystems. Strengths and challenges of applying UAV-acquired imagery for vegetation studies are summarized at the end.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Karol发布了新的文献求助10
2秒前
Grandir发布了新的文献求助10
3秒前
4秒前
糖配坤完成签到 ,获得积分10
4秒前
5秒前
Karol完成签到,获得积分10
9秒前
小小完成签到 ,获得积分10
11秒前
简单山水完成签到,获得积分10
15秒前
小二郎应助刺猬采纳,获得10
15秒前
22秒前
22秒前
22秒前
刺猬完成签到,获得积分10
23秒前
烟花应助简单山水采纳,获得10
25秒前
烟花应助认真的三问采纳,获得10
28秒前
30秒前
andrele发布了新的文献求助10
30秒前
权小夏完成签到 ,获得积分10
31秒前
吴嘉俊完成签到 ,获得积分10
34秒前
37秒前
轻松的惜芹应助aliu采纳,获得10
39秒前
41秒前
CC发布了新的文献求助10
42秒前
boymin2015完成签到 ,获得积分10
42秒前
生物科研小白完成签到 ,获得积分10
49秒前
LMX完成签到 ,获得积分10
49秒前
chenlc971125完成签到 ,获得积分10
49秒前
52秒前
52秒前
IfItheonlyone完成签到 ,获得积分10
1分钟前
SHD完成签到 ,获得积分10
1分钟前
yu完成签到 ,获得积分10
1分钟前
1分钟前
Mr兔仙森完成签到,获得积分10
1分钟前
悠悠我心完成签到,获得积分10
1分钟前
英姑应助霜鸣采纳,获得10
1分钟前
1分钟前
凶狠的寄风完成签到 ,获得积分10
1分钟前
1分钟前
水若琳发布了新的文献求助10
1分钟前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
Aktuelle Entwicklungen in der linguistischen Forschung 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989972
求助须知:如何正确求助?哪些是违规求助? 3532034
关于积分的说明 11256042
捐赠科研通 3270884
什么是DOI,文献DOI怎么找? 1805093
邀请新用户注册赠送积分活动 882256
科研通“疑难数据库(出版商)”最低求助积分说明 809216