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

  • role： First author第一作者
  • Email：the_answer_sunsz@163.com
  • Introduction：孙世泽，1991年生，男，硕士研究生，研究方向为信息采集与处理。E-mail：the_answer_sunsz@163.com
Shize SUN,  
  • role： Corresponding author通信作者
  • Email：wcj_inf@shzu.edu.cn
  • Introduction：汪传建，1977年生，男，副教授，研究方向为时空数据挖掘、时空数据安全。E-mail：wcj_inf@shzu.edu.cn
Chuanjian WANG*,  
  • No information about the author is available
Xiaojun YIN,  
  • No information about the author is available
Weiqiang WANG,  
  • No information about the author is available
Wei LIU,  
  • No information about the author is available
Ya ZHANG,  
  • No information about the author is available
Qingzhan ZHAO

Resümee

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.

Schlüsselwort

natural grassland;biomass;multispectral images;shady-slope and sunny-slope;estimation models;Unmanned Aerial Vehicles (UAV)

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