利用无人机激光雷达和多光谱图像估算冠层容重和冠层底高

遥感学报 Pub Date : 2023-01-01 DOI:10.11834/jrs.20233094
Hao SUN, Xiaoyi GUO, Hongyan ZHANG, Jianjun ZHAO
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引用次数: 0

摘要

树冠体积密度(CBD):树冠底座重量(CBH)。我是 "CBDåCBH "的创始人之一。LiDAR的""""""""""""""等字样,是我的最爱。CBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBD5142;RMSE:0.0773 kg/m3;RMSE:40.73%。17%,LiDar的评分为3分。6个月大,10个月大52. "我是一个很有才华的人,我想成为一个很有才华的人。炖汤:炖汤的时候要注意炖的时间,炖的时间越长,汤的味道就越浓,炖的时间越长,汤的味道就越淡,炖的时间越长,汤的味道就越浓。
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Estimating Canopy Bulk Density and Canopy Base Height using UAV LiDAR and Multispectral Images
æ£®æž—å† å±‚ä½“å¯†åº¦ï¼ˆCanopy Bulk Density,CBDï¼‰å’Œå† å±‚åŸºé«˜ï¼ˆCanopy Base Height,CBHï¼‰æ˜¯è®¸å¤šç«è¡Œä¸ºæ¨¡åž‹çš„å ³é”®è¾“å ¥å‚æ•°ã€‚ç„¶è€Œï¼Œåœ¨ä¸­å›½å¾ˆå°‘æœ‰ç ”ç©¶å ³æ³¨è¿™äº›å‚æ•°çš„ä¼°ç®—ä»¥åŠåœ¨åŒºåŸŸçš„ç©ºé—´åˆ†å¸ƒæƒ å†µã€‚æ— äººæœºæŠ€æœ¯çš„å‘å±•ä¸ºç²¾ç»†å°ºåº¦ä¼°ç®—CBD和CBHçš„ç©ºé—´åˆ†å¸ƒæä¾›äº†æœºé‡ã€‚æœ¬ç ”ç©¶é¦–å ˆåˆ©ç”¨é‡Žå¤–è°ƒæŸ¥æ•°æ®è®¡ç®—æ ·åœ°çš„CBD和CBHï¼›è€ŒåŽï¼Œåˆ©ç”¨æ— äººæœºLiDARç‚¹äº‘å’Œå¤šå ‰è°±å½±åƒï¼Œæž„å»ºåŸºäºŽé¢çŠ¶åŒºåŸŸçš„æœ€ä¼˜å­é›†å’Œéšæœºæ£®æž—ä¼°ç®—æ¨¡åž‹ï¼Œå¹¶å¯¹ä¼°ç®—ç»“æžœè¿›è¡Œè¯„ä»·ï¼›æœ€åŽï¼Œç»˜åˆ¶ç ”ç©¶åŒºçš„CBD和CBHç©ºé—´åˆ†å¸ƒå›¾ã€‚ç ”ç©¶ç»“æžœè¡¨æ˜Žï¼šé‡‡ç”¨ç›¸åŒæ•°æ®æºå’Œæ¨¡åž‹æ—¶ï¼Œä¼°ç®—CBH的R2总是高于CBD。CBD最优估算方法为融合LiDARå’Œå¤šå ‰è°±æ•°æ®çš„éšæœºæ£®æž—æ¨¡åž‹ï¼ŒR2为0.5142,RMSE为0.0773 kg/m3,rRMSE为40.73%。CBHçš„æœ€ä¼˜ä¼°ç®—æ–¹æ³•ä¸ºä» ä½¿ç”¨LiDAR数据的随机森林模型,R2为0.6477,RMSE为1.6245 m,rRMSE为31.17%。使用单一数据源时,LiDARä¼°ç®—ç²¾åº¦æ˜Žæ˜¾é«˜äºŽå¤šå ‰è°±æ•°æ®ã€‚èžåˆä¸¤ç§æ•°æ®æºä¸ä¸€å®šæå‡CBD和CBHçš„ä¼°ç®—ç²¾åº¦ã€‚æœ¬ç ”ç©¶ä¸­æž„å»ºçš„æœ€ä¼˜å­é›†æ¨¡åž‹éœ€è¦3-6ä¸ªç‰¹å¾å˜é‡ï¼Œéšæœºæ£®æž—æ¨¡åž‹åˆ™éœ€è¦è¾“å ¥10-52ä¸ªç‰¹å¾å˜é‡ã€‚ä» ä½¿ç”¨å¤šå ‰è°±å½±åƒä¼°ç®—CBD和CBHæ—¶ï¼Œæœ€ä¼˜å­é›†å›žå½’ä¼°ç®—ç²¾åº¦æ›´å¥½ï¼Œä½†æ˜¯ç©ºé—´é¢„æµ‹ç»“æžœæ˜“å—åœ°è¡¨è¦†ç›–ç±»åž‹çš„å½±å“ã€‚æœ¬ç ”ç©¶èƒ½å¤Ÿä¸ºæ£®æž—å† å±‚å¯ç‡ƒç‰©å‚æ•°ä¼°ç®—æä¾›æ–¹æ³•å‚è€ƒï¼ŒåŒæ—¶ä¹Ÿå¯ä»¥ä¸ºæž—ç«è¡Œä¸ºé¢„æµ‹æ¨¡åž‹æä¾›ç²¾ç»†å°ºåº¦çš„è¾“å ¥æ•°æ®ã€‚
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来源期刊
遥感学报
遥感学报 Social Sciences-Geography, Planning and Development
CiteScore
3.60
自引率
0.00%
发文量
3200
期刊介绍: The predecessor of Journal of Remote Sensing is Remote Sensing of Environment, which was founded in 1986. It was born in the beginning of China's remote sensing career and is the first remote sensing journal that has grown up with the development of China's remote sensing career. Since its inception, the Journal of Remote Sensing has published a large number of the latest scientific research results in China and the results of nationally-supported research projects in the light of the priorities and needs of China's remote sensing endeavours at different times, playing a great role in the development of remote sensing science and technology and the cultivation of talents in China, and becoming the most influential academic journal in the field of remote sensing and geographic information science in China. As the only national comprehensive academic journal in the field of remote sensing in China, Journal of Remote Sensing is dedicated to reporting the research reports, stage-by-stage research briefs and high-level reviews in the field of remote sensing and its related disciplines with international and domestic advanced level. It focuses on new concepts, results and progress in this field. It covers the basic theories of remote sensing, the development of remote sensing technology and the application of remote sensing in the fields of agriculture, forestry, hydrology, geology, mining, oceanography, mapping and other resource and environmental fields as well as in disaster monitoring, research on geographic information systems (GIS), and the integration of remote sensing with GIS and the Global Navigation Satellite System (GNSS) and its applications.
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