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纸质出版日期： 2018-9 ，
录用日期： 2017-9-22
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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
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
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。
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.
天然草地生物量多光谱影像阴阳坡估算模型无人机
natural grasslandbiomassmultispectral imagesshady-slope and sunny-slopeestimation modelsUnmanned Aerial Vehicles (UAV)
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