Estimating aboveground biomass of natural grassland based on multispectral images of Unmanned Aerial Vehicles

Shize SUN; Chuanjian WANG; Xiaojun YIN; Weiqiang WANG; Wei LIU; Ya ZHANG; Qingzhan ZHAO

doi:10.11834/jrs.20186388

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Estimating aboveground biomass of natural grassland based on multispectral images of Unmanned Aerial Vehicles
Vol. 22, Issue 5, Pages: 848-856(2018)
Received：03 March 2017，

Accepted：22 September 2017，

Published：2018-09
DOI： 10.11834/jrs.20186388
稿件说明：

移动端阅览

孙世泽, 汪传建, 尹小君, 王伟强, 刘伟, 张雅, 赵庆展. 2018. 无人机多光谱影像的天然草地生物量估算. 遥感学报, 22(5): 848–856 DOI： 10.11834/jrs.20186388.

Sun S Z, Wang C J, Yin X J, Wang W Q, Liu W, Zhang Y and Zhao Q Z. 2018. Estimating aboveground biomass of natural grassland based on multispectral images of Unmanned Aerial Vehicles. Journal of Remote Sensing, 22(5): 848–856 DOI： 10.11834/jrs.20186388.

摘要

地上草地生物量是衡量天然草地生态系统的重要指标，是草地资源合理利用和载畜平衡监测的重要依据。为了快速、准确、有效地估算天然草地地上生物量，掌握其变化规律，以天山北坡天然牧场为研究区，分析其地上生物量的时空分布特征。根据研究区阴坡与阳坡不同的草地类型和植被种类，利用多旋翼无人机获取的高分辨率多光谱影像(含近红外波段)，结合地面实测数据，在进行天然草地地上生物量与植被指数相关性分析的基础上，运用回归分析方法，建立生物量和多种植被指数的估算模型。结果表明：考虑地形因子(阴阳坡)之后，植被地上生物量与各植被指数的相关性系数显著提高；不同坡向，同一植被指数拟合精度差异较大；同一坡向，各个植被指数的敏感性也有所不同。总体上，比值植被指数(RVI)与阴阳坡草地生物量拟合效果最好，模型精度均达到75%以上。利用植被指数建立的生物量估算方法结果与实际相符，可为天然草地生态系统检测和草地资源合理利用提供方法和依据。

Abstract

The aboveground biomass of grasslands is an important measurement index for grassland ecosystems and an important basis for the optimal use of grassland resources. In addition

identification of aboveground biomass can be used in monitoring the balance between grassland forage supply and livestock demand. To estimate the aboveground biomass of natural grasslands and determine the variation trend rapidly

accurately

and effectively

we selected the natural rangeland in the northern hillside of Tianshan Mountain as a typical study area and analyzed the spatio-temporal change characteristic of its aboveground biomass. With their rapid development

Unmanned Aerial Vehicles (UAVs) have been extensively used in remote sensing because of their convenient operation

lower cost

and shorter revisit cycle compared with satellites. In addition

the lightweight sensors of UAV allow low-altitude remote sensing

which could capture high-spatial

high-spectral resolution images. We conducted a survey of the different grassland types and vegetation varieties in shady and sunny slopes of the rangeland. We used a multi-rotor UAV equipped with Micro-MCA12 Snap to obtain high-resolution multispectral images and collected field survey data. We established a relational model based on the correlation between the aboveground biomass and Vegetation Indexes (VIs) by regression analysis. Results showed poor correlations between the aboveground biomass and VIs

but these correlations improved remarkably after considering the terrain factors. The effectiveness of the VIs varied in different grassland types and vegetation fractions. Accuracy analysis showed large differences in the fitting accuracy of the different slope aspects and small differences in the effectiveness of the same slope aspect. In sum

the highest effectiveness between the Ratio Vegetation Index (RVI) and the aboveground biomass was obtained in the southern and northern slopes

with an estimation precision of more than 75%. The main conclusions are the following. (1) Different grassland types and vegetation fractions led to the poor correlations between the aboveground biomass in the entire area and VIs. (2) The RVI value in sunny slope was higher than that in shady slope

whereas the aboveground biomass in sunny slope was lower than that in shady slope. Grassland degradation resulted from sustained drought and high temperature. (3) This study proved indirectly the relative insensitivity of heavily vegetated areas. Therefore

the findings of this study coincided well with the actual situation. This research provided a reference for the monitoring of grassland ecosystems and reasonable utilization of grassland resources.

关键词

Keywords

references

Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp M L and Bareth G. 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39: 79–87

曹卫彬, 杨邦杰, 裴志远, 宋金鹏. 2003. 新疆棉花遥感监测系统建立的对策. 石河子大学学报(自然科学版), 7(2): 165–168

Cao W B, Yang B J, Pei Z Y and Song J P. 2003. Countermeasure against the establishment of Xinjiang cotton remote sensing monitoring system. Journal of Shihezi University (Natural Science), 7(2): 165–168 (

除多, 德吉央宗, 普布次仁, 姬秋梅, 唐红. 2013. 藏北草地地上生物量及遥感监测模型研究. 自然资源学报, 28(11): 2000–2011

Chu D, Deji Y Z, Pubu C R, Ji Q M and Tang H. 2013. Aboveground biomass in the North Tibet and estimate model using remote sensing data. Journal of Natural Resources, 28(11): 2000–2011 (

黄敬峰, 王秀珍, 王人潮, 胡新博. 2001. 天然草地牧草产量遥感综合监测预测模型研究. 遥感学报, 5(1): 69–74

Huang J F, Wang X Z, Wang R C and Hu X B. 2001. A study on monitoring and predicting models of grass yield in natural grassland using remote sensing data and meteorological data. Journal of Remote Sensing, 5(1): 69–74 (

金云翔, 徐斌, 杨秀春, 李金亚, 王道龙, 马海龙. 2011. 内蒙古锡林郭勒盟草原产草量动态遥感估算. 中国科学: 生命科学, 41(12): 1185–1195

Jin Y X, Xu B, Yang X C, Li J Y, Wang D L and Ma H L. 2011. Remote sensing dynamic estimation of grass production in Xilinguole, Inner Mongolia. Science Sinica Vitae, 41(12): 1185–1195 (

雷添杰, 李长春, 何孝莹. 2011. 无人机航空遥感系统在灾害应急救援中的应用. 自然灾害学报, 20(1): 178–183

Lei T J, Li C C and He X Y. 2011. Application of aerial remote sensing of pilotless aircraft to disaster emergency rescue. Journal of Natural Disasters, 20(1): 178–183 (

李建龙, 蒋平. 2003. 利用3S技术动态监测天山草地农业产量及其成因分析. 安全与环境学报, 3(2): 8–12

Li J L and Jiang P. 2003. New approach to grass-yield estimation and eco-environment formation analysis by using 3-S methods in Tianshan area, Xinjiang. Journal of Safety and Environment, 3(2): 8–12 (

李园园, 宋于洋, 姜小露. 2015. 利用植被指数(VI)提取遥感影像荒漠林覆盖度信息的差异性研究. 石河子大学学报(自然科学版), 33(1): 72–77

Li Y Y, Song Y Y and Jiang X L. 2015. Study on the differences among 3 kinds of vegetation indexes (VI) in extracting coverage of desert forest with remote sensing image. Journal of Shihezi University (Natural Science), 33(1): 72–77 (

李宗南, 陈仲新, 王利民, 刘佳, 周清波. 2014. 基于小型无人机遥感的玉米倒伏面积提取. 农业工程学报, 30(19): 207–213

Li Z N, Chen Z X, Wang L M, Liu J and Zhou Q B. 2014. Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle. Transactions of the Chinese Society of Agricultural Engineering, 30(19): 207–213 (

刘姣娣, 曹卫彬, 李华, 唐湘玲, 欧阳异能. 2011. 基于植被指数的新疆棉花遥感估产模型研究. 石河子大学学报(自然科学版), 29(2): 153–157

Liu J D, Cao W B, Li H, Tang X L and Oyang Y N. 2011. The research of cotton yield estimation based on vegetation index using remote sensing in Xinjiang. Journal of Shihezi University (Natural Science), 29(2): 153–157 (

鲁恒, 李永树, 林先成. 2011. 无人机高空间分辨率影像分类研究. 测绘科学, 36(6): 106–108

Lu H, Li Y S and Lin X C. 2011. Classification of high resolution imagery by unmanned aerial vehicle. Science of Surveying and Mapping, 36(6): 106–108 (

牛志春, 倪绍祥. 2003. 青海湖环湖地区草地植被生物量遥感监测模型. 地理学报, 58(5): 695–702

Niu Z C and Ni S X. 2003. Study on models for monitoring of grassland bilmass around Qinghai Lake assisted by remote sensing. Acta geographica sinica, 58(5): 695–702 (

潘琛, 杜培军, 罗艳, 袁林山. 2009. 一种基于植被指数的遥感影像决策树分类方法. 计算机应用, 29(3): 777–780

Pan C, Du P J, Luo Y and Yuan L S. 2009. Decision tree classification of remote sensing images based on vegetation indices. Journal of Computer Application, 29(3): 777–780 (

Rango A, Laliberte A, Herrick J E, Winters C, Havstad K, Steele C and Browning D. 2009. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3(1): 033542

田振坤, 傅莺莺, 刘素红, 刘峰. 2013. 基于无人机低空遥感的农作物快速分类方法. 农业工程学报, 29(7): 109–116

Tian Z K, Fu Y Y, Liu S H and Liu F. 2013. Rapid crops classification based on UAV low-altitude remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 29(7): 109–116 (

王玲, 姚亚楠, 奚秀梅, 苏建安, 任远景. 2011. 基于遥感的石河子垦区植被生态需水量的研究. 石河子大学学报(自然科学版), 29(2): 218–223

Wang L, Yao Y N, Xi X M, Su J A and Ren Y J. 2011. The ecological water requirement of vegetation in Shihezi based on remote sensing. Journal of Shihezi University (Natural Science), 29(2): 218–223 (

王利民, 刘佳, 杨玲波, 陈仲新, 王小龙, 欧阳斌. 2013. 基于无人机影像的农情遥感监测应用. 农业工程学报, 29(18): 136–145

Wang L M, Liu J, Yang L B, Chen Z X, Wang X L and Ouyang B. 2013. Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring. Transactions of the Chinese Society of Agricultural Engineering, 29(18): 136–145 (

汪小钦, 王苗苗, 王绍强, 吴云东. 2015. 基于可见光波段无人机遥感的植被信息提取. 农业工程学报, 31(5): 152–159

Wang X Q, Wang M M, Wang S Q and Wu Y D. 2015. Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the Chinese Society of Agricultural Engineering, 31(5): 152–159 (

行敏锋, 何彬彬. 2015. 主被动遥感数据协同估算干旱区草原植被生物量. 遥感技术与应用, 30(6): 1122–1128

Xing M F and He B B. 2015. Estimation of vegetation biomass in arid region with active and passive remote sensing data. Remote Sensing Technology and Application, 30(6): 1122–1128 (

延昊, 王长耀, 牛铮, 姜小光, 王汶. 2002. 遥感植被指数对多时相AVHRR数据主成分分析的影响. 遥感学报, 6(1): 30–34

Yan H, Wang C Y, Niu Z, Jiang X G and Wang W. 2002. Application of principal component analysis by using different vegetation index derived from multitemporal AVHRR data. Journal of Remote Sensing, 6(1): 30–34 (

杨红飞, 李建龙, 穆少杰, 杨齐, 胡潇潇, 金国平, 赵万羽. 2012. 新疆三种主要草地植被类型的高光谱反射特征研究. 草业学报, 21(6): 258–266

Yang H F, Li J L, Mu S J, Yang Q, Hu X X, Jin G P and Zhao W Y. 2012. Analysis of hyperspectral reflectance characteristics of three main grassland types in Xinjiang. Acta Prataculturae sinica, 21(6): 258–266 (

于磊, 鲁为华, 阎平, 罗良俊, 张前兵. 2014. 新疆昭苏县境沙尔套山天然草地牧草资源与评价. 中国草地学报, 36(6): 4–11

Yu L, Lu W H, Yan P, Luo L J and Zhang Q B. 2014. Resources and assessment of natural grassland in shaertao mountains, Zhaosu county in Xinjiang. Chinese Journal of Grassland, 36(6): 4–11 (

张园, 陶萍, 梁世祥, 梁万里. 2011. 无人机遥感在森林资源调查中的应用. 西南林业大学学报, 31(3): 49–53

Zhang Y, Tao P, Liang S X and Liang W L. 2011. Research on application of UAV RS techniques in forest inventories. Journal of Southwest Forestry University, 31(3): 49–53 (

张艳楠, 牛建明, 张庆, 杨艳, 董建军. 2012. 植被指数在典型草原生物量遥感估测: 应用中的问题探讨. 草业学报, 21(1): 229–238

Zhang Y N, Niu J M, Zhang Q, Yang Y and Dong J J. 2012. A discussion on applications of vegetation index for estimating aboveground biomass of typical steppe. Acta Prataculturae Sinica, 21(1): 229–238 (

张正健, 李爱农, 边金虎, 赵伟, 南希, 靳华安, 谭剑波, 雷光斌, 夏浩铭, 杨勇帅, 孙明江. 2016. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究. 遥感技术与应用, 31(1): 51–62

Zhang Z J, Li A N, Bian J H, Zhao W, Nan X, Jin H A, Tan J B, Lei G B, Xia H M, Yang Y S and Sun M J. 2016. Estimating aboveground biomass of grassland in Zoige by visible vegetation index derived from unmanned aerial vehicle image. Remote Sensing Technology and Application, 31(1): 51–62 (

赵庆展, 刘伟, 尹小君, 张天毅. 2016. 基于无人机多光谱影像特征的最佳波段组合研究. 农业机械学报, 47(3): 242–248, 291

Zhao Q Z, Liu W, Yin X J and Zhang T Y. 2016. Selection of optimum bands combination based on multispectral images of UAV. Transactions of the Chinese Society for Agricultural Machinery, 47(3): 242–248, 291 (

周洁萍, 龚建华, 王涛, 汪东川, 杨荔阳, 赵向军, 洪宇, 赵忠明. 2008. 汶川地震灾区无人机遥感影像获取与可视化管理系统研究. 遥感学报, 12(6): 877–884

Zhou J P, Gong J H, Wang T, Wang D C, Yang L Y, Zhao X J, Hong Y and Zhao Z M. 2008. Study on UAV remote sensing image acquiring and visualization management system for the area affected by 5•12 Wenchuan earthquake. Journal of Remote Sensing, 12(6): 877–884 (

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