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引用本文:高燕,梁泽毓,王彪,吴艳兰,刘诗雨.基于无人机和卫星遥感影像的升金湖草滩植被地上生物量反演.湖泊科学,2019,31(2):517-528. DOI:10.18307/2019.0220
GAO Yan,LIANG Zeyu,WANG Biao,WU Yanlan,LIU Shiyu.UAV and satellite remote sensing images based aboveground biomass inversion in the meadows of Lake Shengjin. J. Lake Sci.2019,31(2):517-528. DOI:10.18307/2019.0220
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基于无人机和卫星遥感影像的升金湖草滩植被地上生物量反演
高燕1, 梁泽毓1, 王彪1,2, 吴艳兰1,2, 刘诗雨2
1.安徽大学资源与环境工程学院, 合肥 230601;2.安徽省高分辨率对地观测系统安徽数据与应用中心, 合肥 230001
摘要:
湿地植被地上生物量是衡量湿地生态系统健康状况的重要指标,对于珍稀水禽越冬繁殖、全球碳循环、生态净化具有重要意义,是生态学与遥感解译的研究热点之一.针对于地上生物量的测算,卫星遥感数据覆盖范围广但其空间分辨率较低,无人机遥感数据空间分辨率高但采集范围小,同时受湿地面积、观测系统及外界环境等条件的影响,使得遥感影像地上生物量反演更加复杂和困难.本研究基于无人机和高分一号数据对升金湖草滩植被地上生物量反演进行研究,结合升金湖保护区4个样区无人机可见光影像与相应样区实测样本数据,建立地上生物量与可见光波段、多种可见光植被指数的线性、幂函数、多项式、对数回归模型,并通过可决系数(R2)、平均绝对误差(MAE)和均方根误差(RMSE)对模型进行精度评价,选择最优模型对无人机影像进行地上生物量反演;通过可见光波段反演得到的生物量,与高分一号WFV归一化差分植被指数(Normalized Difference Vegetation Index,NDVI)影像相结合进行回归建模,获取整个升金湖草滩植被地上生物量分布.结果表明,利用无人机红光波段建立的多项式方程对地上生物量反演有着最高模拟精度,R2=0.86、预测精度MAE=111.33 g/m2RMSE=145.42 g/m2,且红光波段生物量反演方法得到的结果与实际生物量分布一致性较高,高分一号WFV NDVI与无人机反演生物量构建的多项式模型为最优模型,R2为0.91.本研究利用无人机和高分一号数据进行生物量反演研究,整合多源遥感数据优点,以获取更加丰富和准确的信息,进而提高地上生物量反演精度,为湿地监测和湿地恢复管理提供数据和技术支撑,具有重要研究意义和应用价值.
关键词:  湿地  地上生物量反演  无人机遥感  高分一号  植被指数  升金湖
DOI:10.18307/2019.0220
分类号:
基金项目:安徽省教育厅重点项目(KJ2018A0007)、安徽省人力厅留学人员创新项目择优资助计划和安徽省国土资源厅科技项目(2016KJ030002)联合资助.
UAV and satellite remote sensing images based aboveground biomass inversion in the meadows of Lake Shengjin
GAO Yan1, LIANG Zeyu1, WANG Biao1,2, WU Yanlan1,2, LIU Shiyu2
1.School of Resources and Environmental Engineering, Anhui University, Hefei 230601, P. R. China;2.Anhui Data and Application Center of High Resolution Earth Observation System in Anhui, Hefei 230001, P. R. China
Abstract:
The aboveground biomass of wetland vegetation, as an essential indicator of the wetland ecosystem health, is of great significance for the overwintering reproduction, global carbon cycle and ecological purification of rare waterfowl. It is one of the research hotspots in ecology and remote sensing interpretation. The advantage of satellite remote sensing data lies in its wide coverage, but its spatial resolution is low. UAV remote sensing data have high spatial resolution but small acquisition range. At the same time, because of the influence of wetland area, observation system and external environment, it is more complicated and difficult to retrieve the aboveground biomass from remote sensing images. This research studies a kind of inversion method of aboveground biomass based on UAV and GF-1 data. Firstly, UAV visible images of four sample areas and the ground measured sample data are used to establish linear, power function, polynomial, and logarithmic regression model of biomass, visible light band, and a variety of visible light vegetation index. The accuracy of this method was evaluated by the coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE). The optimal model was selected for biomass inversion of UAV images. Then the biomass data inverted from the visible light band and the GF-1 WFV normalized difference vegetation index (NDVI) image are used to establish a regression model to obtain the aboveground biomass distribution map of the vegetation in Lake Shengjin meadows. The results show that the polynomial equation was determined using the red band has higher simulation accuracy for biomass inversion, R2=0.86, MAE=111.33 g/m2, RMSE=145.42 g/m2,and the inversion results obtained by the red band biomass inversion method is highly consistent with the actual biomass distribution. The polynomial model, constructed by GF-1 WFV and biomass inversed by UAV, is the optimal model, and R2 reached 0.91. This study uses UAV and GF-1 data to conduct biomass inversion research. It integrates the advantages of each data and can obtain richer and more accurate information. It could improve inversion accuracy and provide data and technical support for wetland monitoring and wetland restoration management. Thus this work has important research significance and application value.
Key words:  Wetlands  aboveground biomass inversion  UAV  GF-1  vegetation index  Lake Shengjin
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