Estimating the spatial distribution of green herbage biomass and quality by geostatistical analysis with field hyperspectral measurements
Estimating the spatial distribution of green herbage biomass and quality by geostatistical analysis with field hyperspectral measurements
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DOI：
10.1111/j.1744-697x.2011.00221.x
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发表时间：
2011-09
影响因子：
1.3
通讯作者：
H. Lee;K. Kawamura;N. Watanabe;S. Sakanoue;Y. Sakuno;S. Itano;N. Nakagoshi
中科院分区：
文献类型：
--
作者：
H. Lee;K. Kawamura;N. Watanabe;S. Sakanoue;Y. Sakuno;S. Itano;N. Nakagoshi
本研究旨在利用地面高光谱测量和地统计学分析，估算日本北海道混播草地的绿色牧草生物量（GBM）和粗蛋白（CP）浓度。混播牧场包括一个相对平坦的部分，通过过度播种草（子单元1，2.6公顷）和一个丘陵老化部分（子单元2，5.0公顷）进行翻新。高光谱反射率和植物数据收集了22天，在2009年8月从88个小区内的两个亚基。为了制图，从牧场永久横断面上的总共347块地块中获得了单独的光谱读数，没有进行植物采样。沿着永久横断面。基于遗传算法的波段选择与偏最小二乘（GA-PLS）回归分析进行预测GBM和CP浓度使用反射率和一阶导数反射率（FDR）数据集。然后，利用半变异函数进行地统计学分析，以确定亚基1和亚基2中GBM和CP浓度的采样间隔。在GA-PLS分析中，通过使用FDR校准GBM（交叉验证的决定系数，R 2 ¼ 0：55;交叉验证的均方根误差，RMSECV = 42）和使用原始反射率校准CP浓度（R 2 ¼ 0：42，RMSECV = 2.02）获得最准确的结果。半变异函数的地统计学分析表明，在景观尺度上，GBM斑块大小在亚基1和2分别为31和67米，和CP浓度分别为37和54米。这些值表明，这些牧场参数的空间分布格局在亚单元1比亚单元2更异质。分析结果还表明，GBM和CP浓度的采样间隔应小于15 m。这项工作表明，有能力估计GBM和CP浓度的空间分布，实施特定地点的放牧管理。
This study aimed to estimate green herbage biomass (GBM) and crude protein (CP) concentrations of a mixed-sown pasture in Hokkaido, Japan using ground based hyperspectral measurements and geostatistical analysis. The mixed-sown pasture consisted of a relatively flat section renovated by over-seeding a grass (Subunit 1, 2.6 ha) and a hilly aged section (Subunit 2, 5.0 ha). Hyperspectral reflectance and plant data were collected for 22 days in August 2009 from 88 plots within the two subunits. For mapping, separate spectral readings, without plant sampling, were obtained from a total of 347 plots along permanent transects in the pasture. Genetic algorithm-based wavebands selection with partial least squares (GA-PLS) regression analyses was performed to predict GBM and CP concentrations using both reflectance and first derivative reflectance (FDR) datasets. Then, geostatistical analysis with semivariograms was conducted to determine sampling interval of GBM and CP concentration in Subunits 1 and 2. In the GA-PLS analysis, the most accurate results were obtained by calibration of GBM using FDR (cross-validated coefficient of determination, R 2 ¼ 0:55; cross-validated root mean square error, RMSECV = 42) and of the CP concentration using raw reflectance (R 2 ¼ 0:42, RMSECV = 2.02). Geostatistical analysis with semivariograms showed that, at the landscape scale, the GBM patch sizes in Subunits 1 and 2 were 31 and 67 m, respectively, and those of the CP concentration were 37 and 54 m. These values indicate that the spatial distribution patterns of these pasture parameters were more heterogeneous in Subunit 1 than in Subunit 2. The analysis result also indicates that the sampling interval for GBM and CP concentration should be <15 m. This work shows the ability to estimate the spatial distribution of GBM and CP concentration for the implementation of site-specific grazing management.