基于HJ-1高光谱影像的黄河口芦苇和碱蓬生物量估测模型研究

doi:10.3969/j.issn.1001-909X.2014.04.004

海洋学研究 ›› 2014, Vol. 32 ›› Issue (4): 27-34.DOI: 10.3969/j.issn.1001-909X.2014.04.004

基于HJ-1高光谱影像的黄河口芦苇和碱蓬生物量估测模型研究

任广波¹, 张杰¹, 汪伟奇², 耿延杰², 陈妍君², 马毅¹

1.国家海洋局第一海洋研究所,山东青岛 266063;
2.中国海洋大学信息科学与工程学院,山东青岛 266063

收稿日期:2014-05-14 修回日期:2014-09-04 出版日期:2014-12-15 发布日期:2022-11-25
作者简介:任广波(1983-),男,山东济宁市人,主要从事海岛海岸带高分辨率遥感技术与应用方面的研究。E-mail:renguangbo@126.com
基金资助:
国家自然科学基金青年基金项目资助(41206172);国家海洋局第一海洋研究所基本科研业务费项目资助(GY02-2012G12)

Reeds and suaeda biomass estimation model based on HJ-1 hyperspectal image in the Yellow River Estuary

REN Guang-bo ¹, ZHANG Jie ¹, WANG Wei-qi ², GENG Yan-jie ², CHEN Yan-jun ², MA Yi ¹

1. First Institute of Oceanography, SOA, Qingdao 266063,China;
2. College of Information Science and Engineering, Ocean University of China, Qingdao 266063, China

Received:2014-05-14 Revised:2014-09-04 Online:2014-12-15 Published:2022-11-25

摘要/Abstract

摘要： 湿地植被的生物量是湿地生态评价、保护和利用的重要基础数据,遥感技术已经成为湿地生物量高效、准确监测的重要手段。基于2013年9月的HJ-1 高光谱遥感影像,应用准同步现场踏勘数据,通过单变量线性回归和多变量线性回归的方法,针对7种常用的窄波段植被指数和2种红边指数对黄河口芦苇和碱蓬生物量(地上干重)的估测能力进行了评价。结果表明:(1)单光谱指数变量情况下,对于芦苇,选择近红外827 nm波段和红635 nm波段简单植被指数(SRI)和线性插值红边指数(REP_ linear interpolation)取得了最佳的单变量回归结果,决定系数分别达到0.42和0.58;对于碱蓬,选择近红外807 nm波段和红692 nm波段的归一化差值植被指数(NDVI)、SRI和优化的土壤校正植被指数(OSAVI)取得了较好的回归结果,决定系数分别达到0.60,0.59和0.47;(2)多光谱指数变量情况下,以在单变量回归分析中取得较好结果的SRI和REP_ linear interpolation指数为变量,芦苇得到了与其生物量之间决定系数为0.71的高相关性;同时,以NDVI、SRI和OSAVI为变量,与碱蓬生物量的决定系数达到了0.66。

关键词: 生物量遥感, HJ-1 高光谱, 植被指数, 红边指数

Abstract: Wetland vegetation biomass is the basic information of wetland ecological assessment, protection and utilization. Remote sensing has become one of the most efficient technologies of wetland biomass monitoring. Utilizing the HJ-1 hyperspectral remote sensing image that acquired in September 2013 and the coinstantaneous field survey data, the biomass estimation capabilities of 7 kinds of narrow-band vegetation indices and 2 kinds of red edge position indices of reeds and suaeda in the Yellow River Estuary have been studied. The results reveal that (1) In single variable estimation model case, for reeds, the SRI index with 635 nm and 827 nm bands and REP_ linear interpolation index get the best R² measures, and for suaeda, the NDVI and SRI indices with 692 nm and 807 nm bands and OSAVI index get the best biomass estimation results. (2) In multiple variable case, for reeds and suaeda, the R² measure get 0.71 and 0.66 respectively.

Key words: biomass remote sensing, HJ-1 hyperspectral image, vegetation indices, red edge position indices

中图分类号:

TP75

任广波, 张杰, 汪伟奇, 耿延杰, 陈妍君, 马毅. 基于HJ-1高光谱影像的黄河口芦苇和碱蓬生物量估测模型研究[J]. 海洋学研究, 2014, 32(4): 27-34.

REN Guang-bo , ZHANG Jie , WANG Wei-qi , GENG Yan-jie , CHEN Yan-jun , MA Yi. Reeds and suaeda biomass estimation model based on HJ-1 hyperspectal image in the Yellow River Estuary[J]. Journal of Marine Sciences, 2014, 32(4): 27-34.

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基于HJ-1高光谱影像的黄河口芦苇和碱蓬生物量估测模型研究

Reeds and suaeda biomass estimation model based on HJ-1 hyperspectal image in the Yellow River Estuary

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