首页> 外文期刊>Precision Agriculture >Crop height variability detection in a single field by multi-temporal terrestrial laser scanning
【24h】

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

机译:多时相地面激光扫描在单场中进行作物高度变异性检测

获取原文
获取原文并翻译 | 示例
           

摘要

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK-GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK-GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R-2 = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R-2 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r 0.44).
机译:有关作物高度,作物生长和生物量分布的信息对于作物管理和环境建模很重要。为了确定这些参数,在单个领域中连续两年以多时间方式进行了结合实时运动GPS(RTK-GPS)测量的地面激光扫描。因此,将飞行时间激光扫描仪安装在三脚架上。对于点云的地理配准,通过RTK-GPS测量了激光扫描仪的所有八到九个位置以及几个反射目标。在2008年(甜菜)和2009年(主要是大麦)的生长期,进行了三到四次调查。在每个调查日期都建立了作物表面模型,其水平分辨率为1 m,可用于得出植物高度和植物生长的图。检测到的作物高度与全景图像和手动测量的观测值一致(R-2 = 0.53,RMSE = 0.1 m)。使用地形和土壤参数对检测到的作物高度变异性进行统计分析,并发现显着相关性。回归分析(R-2 <0.31)强调了所选参数与一个田间作物高度变异性之间基本关系的不确定性。同样,这些模式与从卫星图像获得的归一化差异植被指数(NDVI)相比,显示出较小的显着相关性(r <0.44)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号