Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data
Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data
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DOI：
10.1016/j.jag.2017.01.004
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发表时间：
2017-06
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通讯作者：
R. R. Pullanagari-R.;G. Kereszturi;I. Yule
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作者：
R. R. Pullanagari-R.;G. Kereszturi;I. Yule
新西兰农业严重依赖放牧的牧草来喂养牲畜;因此，为了保持有利可图和可持续的草地管理，提供高质量的美味牧草非常重要。非光合植被（NPV）的存在，如死植被在牧场严重限制了质量和生产力的牧场。即使使用遥感方法，量化混合牧场中死亡植被的比例也是一个巨大的挑战。在这项研究中，一个高的空间分辨率与像素分辨率为1米和光谱分辨率为3.5-5.6 nm的成像光谱数据从AisaFENIX（380-2500 nm）被用来评估死亡的植被成分在混合牧场在新西兰的丘陵农村农场的分数。我们使用了不同的方法来检索死亡植被分数的光谱，窄带植被指数，全光谱偏最小二乘（PLS）回归和特征选择PLS回归。在所有方法中，基于特征选择的PLS模型在预测精度方面表现出更好的性能（R2 CV = 0.73，RMSECV= 6.05，RPDCV= 2.25）。结果与验证数据一致，并且在外部测试数据上也表现良好（R2= 0.62，RMSE = 8.06，RPD = 2.06）。此外，进行了统计检验，以确定地形变量，如坡度和方面的死植被部分的积累的效果。陡坡（>25°）有显著（p <0.05）较高的死亡植被量。与此相反，方面表现出不显着的影响，死植被积累。研究结果表明，AisaFENIX成像光谱数据可能是一个有用的工具，准确地映射死亡植被分数。
New Zealand farming relies heavily on grazed pasture for feeding livestock; therefore it is important to provide high quality palatable grass in order to maintain profitable and sustainable grassland management. The presence of non-photosynthetic vegetation (NPV) such as dead vegetation in pastures severely limits the quality and productivity of pastures. Quantifying the fraction of dead vegetation in mixed pastures is a great challenge even with remote sensing approaches. In this study, a high spatial resolution with pixel resolution of 1 m and spectral resolution of 3.5–5.6 nm imaging spectroscopy data from AisaFENIX (380–2500 nm) was used to assess the fraction of dead vegetation component in mixed pastures on a hill country farm in New Zealand. We used different methods to retrieve dead vegetation fraction from the spectra; narrow band vegetation indices, full spectrum based partial least squares (PLS) regression and feature selection based PLS regression. Among all approaches, feature selection based PLS model exhibited better performance in terms of prediction accuracy (R2CV= 0.73, RMSECV= 6.05, RPDCV= 2.25). The results were consistent with validation data, and also performed well on the external test data (R2= 0.62, RMSE = 8.06, RPD = 2.06). In addition, statistical tests were conducted to ascertain the effect of topographical variables such as slope and aspect on the accumulation of the dead vegetation fraction. Steep slopes (>25°) had a significantly (p <0.05) higher amount of dead vegetation. In contrast, aspect showed non-significant impact on dead vegetation accumulation. The results from the study indicate that AisaFENIX imaging spectroscopy data could be a useful tool for mapping the dead vegetation fraction accurately.