基于Landsat TM遥感数据的山核桃产量预测——以浙江临安市为例
发布时间：2018-07-21 17:06
【摘要】：以浙江省临安市的山核桃为研究对象,基于2008—2011年连续4年的样地实测产量为基础,利用每年4个生长时期的Landsat TM遥感数据,系统地分析比较每个生长时期的植被指数与产量的关系。研究结果表明:NDVI在各个生长期均与产量的相关性最高;SAVI与产量的相关性居中,DVI最低。以每个时期的NDVI为因子,建立不同时期山核桃产量的预估模型。各时期模型的预估效果为果实膨大期花芽分化及授粉期采摘至落叶期休眠期。以不同时期的NDVI为因子,利用逐步回归,建立多因子的山核桃产量的预估模型。最优预估模型为y=126.51_(x_2)+26.61_(x_1)+12.56_(x_3)-67.42(R~2=0.642,SEE=12.17),为山核桃产量的预测提供可行,快速,有效的方法。
[Abstract]:Taking pecan in Linan City, Zhejiang Province as the research object, based on the measured yield of four consecutive years from 2008 to 2011, Landsat TM remote sensing data of 4 growing periods per year were used. The relationship between vegetation index and yield in each growing period was analyzed and compared systematically. The results showed that the correlation between the yield and the yield was the highest in each growth period, and the correlation between SAVI and yield was the lowest. Based on NDVI of each period, the prediction model of pecan yield in different periods was established. The predicted effects of the models were flower bud differentiation during fruit expansion and dormancy from picking to falling leaves at pollination stage. Based on NDVI of different periods, a multi-factor prediction model of pecan yield was established by stepwise regression. The optimal prediction model is YP126.51 _ (xS _ 2) 26.61 _ (xStu1) 12.56 _ (xSt3) -67.42 (RP2O0.642SEEEN 12.17), which provides a feasible, rapid and effective method for predicting the yield of pecan.
【作者单位】： 国家林业局调查规划设计院;安徽农业大学林学与园林学院;
【基金】：国家高技术发展计划(863计划)(2013AA102605) 国家自然科学基金(31170637)
【分类号】：S127;S664.1
本文编号：2136207
[Abstract]:Taking pecan in Linan City, Zhejiang Province as the research object, based on the measured yield of four consecutive years from 2008 to 2011, Landsat TM remote sensing data of 4 growing periods per year were used. The relationship between vegetation index and yield in each growing period was analyzed and compared systematically. The results showed that the correlation between the yield and the yield was the highest in each growth period, and the correlation between SAVI and yield was the lowest. Based on NDVI of each period, the prediction model of pecan yield in different periods was established. The predicted effects of the models were flower bud differentiation during fruit expansion and dormancy from picking to falling leaves at pollination stage. Based on NDVI of different periods, a multi-factor prediction model of pecan yield was established by stepwise regression. The optimal prediction model is YP126.51 _ (xS _ 2) 26.61 _ (xStu1) 12.56 _ (xSt3) -67.42 (RP2O0.642SEEEN 12.17), which provides a feasible, rapid and effective method for predicting the yield of pecan.
【作者单位】： 国家林业局调查规划设计院;安徽农业大学林学与园林学院;
【基金】：国家高技术发展计划(863计划)(2013AA102605) 国家自然科学基金(31170637)
【分类号】：S127;S664.1
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