Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai–Tibetan Plateau
Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (...
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| Published in | International journal of remote sensing Vol. 37; no. 8; pp. 1922 - 1936 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Taylor & Francis
17.04.2016
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| Abstract | Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai–Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC UAV) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC gᵣₒᵤₙd), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R ²) between FVC UAV and vegetation indices (VIs) was significantly greater than that between FVC gᵣₒᵤₙd and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods. |
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| AbstractList | Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai-Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC
UAV
) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC
ground
), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R
2
) between FVC
UAV
and vegetation indices (VIs) was significantly greater than that between FVC
ground
and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods. Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai–Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC UAV) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC gᵣₒᵤₙd), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R ²) between FVC UAV and vegetation indices (VIs) was significantly greater than that between FVC gᵣₒᵤₙd and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods. Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai-Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC sub(UAV)) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC sub(ground)), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R super(2)) between FVC sub(UAV) and vegetation indices (VIs) was significantly greater than that between FVC sub(ground) and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods. Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai–Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC UAV) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC gᵣₒᵤₙd), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R ²) between FVC UAV and vegetation indices (VIs) was significantly greater than that between FVC gᵣₒᵤₙd and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods. |
| Author | Wang, Xiaoyun Chen, Jianjun Qin, Yu Yi, Shuhua |
| Author_xml | – sequence: 1 givenname: Jianjun surname: Chen fullname: Chen, Jianjun organization: University of Chinese Academy of Sciences – sequence: 2 givenname: Shuhua surname: Yi fullname: Yi, Shuhua email: yis@lzb.ac.cn organization: State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences – sequence: 3 givenname: Yu surname: Qin fullname: Qin, Yu organization: State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences – sequence: 4 givenname: Xiaoyun surname: Wang fullname: Wang, Xiaoyun organization: University of Chinese Academy of Sciences |
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| SubjectTerms | Aerial surveys China climate change ecosystems Estimates Grasslands Pixels Remote sensing Satellite imagery satellites soil erosion surveys Unmanned aerial vehicles Vegetation vegetation cover vegetation index |
| Title | Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai–Tibetan Plateau |
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