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Estimation of Vegetation Fraction in Arid Areas Using ALOS Imagery

机译:利用ALOS影像估算干旱区植被比例。

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Fraction of vegetation (Fv) plays an important role in ecosystems. Estimation of Fv is essential for drought monitoring, natural resources studies, estimation of soil erosion volume etc. The aim of this study is to estimate Fv in an arid area in Iran using ALOS Imagery (June 2008). In order to find the best index for estimation of Fv, Seventeen vegetation indices (ARVI, DVI, EVI, GEMI, IPVI, MSAVI1, MSAVI2, NDVI, PVI, SAVI, SARVI, SARVI2, SR, TSAVI, WDVI) were used. The canopy cover percentage of 52 sample plots (50m by 50m) was measured in the field in June 2009. Regression models were used to assess the relationships between the field data and the calculated Fv. The 52 sample plots were randomly divided two times to 30 calibrations and 22 validations, and to 35 and 17 samples. Results revealed that selecting the calibration and validation data randomly leads to different results. Therefore, cross-validation method was used to reduce random division effect. Results indicated that, among all indices, vegetation indices such as MSAVI1, PVI, WDVI and TSAVI which are based on soil line have higher R~2 and lower RMSE (R~2 > 0.63, RMSE ≈ 3%). The results confirm the dominant effect of soil reflectance in arid areas.
机译:植被分数(Fv)在生态系统中起重要作用。 Fv的估算对于干旱监测,自然资源研究,土壤侵蚀量的估算等至关重要。本研究的目的是使用ALOS影像(2008年6月)估算伊朗干旱地区的Fv。为了找到评估Fv的最佳指标,使用了17种植被指数(ARVI,DVI,EVI,GEMI,IPVI,MSAVI1,MSAVI2,NDVI,PVI,SAVI,SARVI,SARVI2,SR,TSAVI,WDVI)。 2009年6月在野外测量了52个样地(50m×50m)的冠层覆盖率。使用回归模型评估野外数据与计算出的Fv之间的关系。将52个样地随机分为两次,分别进行30次校准和22次验证,再分别进行35次和17次采样。结果表明,选择校准和验证数据随机会导致不同的结果。因此,使用交叉验证方法来减少随机分割效果。结果表明,在所有指标中,基于土壤线的MSAVI1,PVI,WDVI和TSAVI等植被指数具有较高的R〜2和较低的RMSE(R〜2> 0.63,RMSE≈3%)。结果证实了干旱地区土壤反射率的主导作用。

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