Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices
Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices
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基于无人机超高地分辨率图像纹理和植被指数的冬小麦地上生物量估算
DOI：
10.1016/j.isprsjprs.2019.02.022
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发表时间：
2019-04-01
影响因子：
12.7
通讯作者：
Zhou, Chengquan
中科院分区：
文献类型：
--
作者：
Yue, Jibo;Yang, Guijun;Zhou, Chengquan
在处理多个生长阶段时，基于光学植被指数（VI）估计地上生物量（AGB）很困难，原因有两个：（i）光学VI在中等到高冠层覆盖时饱和，（ii）垂直生长的器官（例如，生殖器官和茎的生物量）很难通过冠层光谱VI检测。尽管通过使用窄带高光谱 VI、合成孔径雷达、激光强度方向和测距、作物表面模型技术及其组合在 AGB 估计方面取得了一些重大改进，但这些新技术的应用受到成本、可用性、数据处理困难和高维度的限制。因此，本研究评估了超高地面分辨率图像纹理、VI 及其组合的使用，以进行覆盖三个冬小麦生长阶段的 AGB 的多个时间估计和地图。所选择的基于灰度色调空间依赖矩阵的图像纹理（例如，方差、熵、数据范围、同质性、二阶矩、相异性、对比度、相关性）是通过使用安装在无人机（UAV）上的廉价 RGB 传感器获取的 1、2、5、10、15、20、25 和 30 厘米地面分辨率图像来计算的。光学 VI 数据是通过使用地面光谱仪分析无人机获取的 RGB 图像来获得的。基于光学 VI 的 AGB 估算的准确性各不相同，验证 R-2：0.59-0.78，均方根误差 (RMSE)：1.22-1.59 吨/公顷，平均绝对误差 (MAE)：1.03-1.27 吨/公顷。最准确的 AGB 估计是通过结合图像纹理和 VI 获得的，得出 R-2：0.89、MAE：0.67 吨/公顷和 RMSE：0.82 吨/公顷。结果表明，（i）从超高地面分辨率图像中选择的八个纹理与AGB显着相关，（ii）与仅使用光学VI或图像纹理相比，结合使用1至30厘米地面分辨率图像的图像纹理和VI可以提高AGB估计的准确性； (iii) 使用该方法可以准确估计冬小麦生殖生长阶段的高 AGB 值； (iv) 使用所提出的组合方法（使用 MSR 的 DIS1、SE30、B460、B560、B670、EVI2）对冬小麦 AGB（8-14 吨/公顷）的高估计显示，与仅使用光谱 VI（LCI、使用 MSR 的 NDVI）相比，提高了 22.63% (nRMSE)；与仅使用图像纹理（COR1、使用 MSR 的 DIS1、SE30、EN30）。因此，图像纹理和 VI 的结合使用有助于改进高冠层覆盖条件下 AGB 的估计。
When dealing with multiple growth stages, estimates of above-ground biomass (AGB) based on optical vegetation indices (VIs) are difficult for two reasons: (i) optical VIs saturate at medium-to-high canopy cover, and (ii) organs that grow vertically (e.g., biomass of reproductive organs and stems) are difficult to detect by canopy spectral VIs. Although several significant improvements have been made for estimating AGB by using narrow band hyperspectral VIs, synthetic aperture radar, laser intensity direction and ranging, the crop surface model technique, and combinations thereof, applications of these new techniques have been limited by cost, availability, data-processing difficulties, and high dimensionality. The present study thus evaluates the use of ultrahigh-ground-resolution image textures, VIs, and combinations thereof to make multiple temporal estimates and maps of AGB covering three winter-wheat growth stages. The selected gray-tone spatial-dependence matrix based image textures (e.g., variance, entropy, data range, homogeneity, second moment, dissimilarity, contrast, correlation) are calculated from 1-, 2-, 5-, 10-, 15-, 20-, 25-, and 30-cm-ground-resolution images acquired by using an inexpensive RGB sensor mounted on an unmanned aerial vehicle (UAV). Optical-VI data were obtained by using a ground spectrometer to analyze UAV-acquired RGB images. The accuracy of AGB estimates based on optical VIs varies, with validation R-2: 0.59-0.78, root mean square error (RMSE): 1.22-1.59 t/ha, and mean absolute error (MAE): 1.03-1.27 t/ha. The most accurate AGB estimate was obtained by combining image textures and VIs, which gave R-2: 0.89, MAE: 0.67 t/ha, and RMSE: 0.82 t/ha. The results show that (i) the eight selected textures from ultrahigh-ground-resolution images were significantly related to AGB, (ii) the combined use of image textures from 1- to 30-cm-ground-resolution images and VIs can improve the accuracy of AGB estimates as compared with using only optical VIs or image textures alone; and (iii) high AGB values from winter-wheat reproductive growth stages can be accurately estimated by using this method; (iv) high estimates of winter-wheat AGB (8-14 t/ha) using the proposed combined method (DIS1, SE30, B460, B560, B670, EVI2 using MSR) show a 22.63% (nRMSE) improvement compared with using only spectral VIs (LCI, NDVI using MSR), and a 21.24% (nRMSE) improvement compared with using only image textures (COR1, DIS1, SE30, EN30 using MSR). Thus, the combined use of image textures and VIs can help improve estimates of AGB under conditions of high canopy coverage.