Bhogendra Mishra, Rupesh Bhandari, K. P. Bhandari, Dinesh Mani Bhandari, Nirajan Luintel, Ashok Dahal, Shobha Poudel
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引用次数: 0
Abstract
Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.
可持续农业管理需要了解作物种植的地点和时间,它们是什么以及种植多长时间。然而,尼泊尔还没有这方面的资料。遥感加上农民的知识为填补这一空白提供了一个解决方案。在这项研究中,我们利用Sentinel-2 a /B时间序列图像和农民知识,通过半自动工作流程,在尼泊尔山区创建了高分辨率(10米)的季节性作物地图和种植模式。我们使用数字高程模型自动通过哨兵图像和地形信息的迭代自组织数据聚类来识别农业区域。对该农业地区进行了分析,以制定作物日历,并使用基于规则的方法跟踪季节性作物动态。最后,我们计算了一个像素级的作物强度图。最后,将我们的结果与实地收集的真实数据和已发表的作物日历进行比较,总体精度为88%,kappa系数为0.83。我们发现整个研究区域的作物种植强度和季节性作物推广存在差异,具有灌溉设施的平原地区的作物种植强度较高,干旱和丘陵地区的休耕周期较长。半自动工作流程在异质地形中成功实施,适用于整个国家的不同地形,为绘制和监测作物提供了重要信息,这对制定战略农业计划和尼泊尔的粮食安全非常有用。
期刊介绍:
Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences.
The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology.
Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements