利用先进遥感技术开发水稻生长图谱

N. Ya, Loong Shern Lee, M. Ismail, S. M. Razali, Nor Athirah Roslin, M. H. Omar
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引用次数: 5

摘要

水稻监测是影响水稻产量的主要问题之一。由于气候变化、土壤条件、农民年龄和监测整个地区所花费的时间,农民在监测稻田方面面临困难。遥感技术是稻田监测的替代技术之一。无人驾驶飞行器(UAV)技术发展迅速,在农业工业中被频繁地用于监测作物状况。本研究的目标是利用航空影像和基于目标的影像分析(OBIA)技术创建作物生长图,并利用土壤植物分析开发(SPAD)和GreenSeeker数据验证稻田图中的归一化植被指数(NDVI)值。利用OBIA对多光谱图像进行处理,生成作物生长图。生成的作物生长图嵌入了能够使用NDVI指示水稻作物健康状况的信息。本研究在吉兰丹Ketereh的Ladang Merdeka(0.79公顷)的一个水田上进行,使用了PadiU Putra品种。研究结果表明,OBIA方法可以对植被和非植被进行分类,生成作物生长图。NDVI图与Greenseeker数据有很强的相关性(0.893),与SPAD表呈正相关(0.05)。作物生长图使农民能够利用遥感技术更有效地改善他们的水稻种植监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Rice Growth Map Using the Advanced Remote Sensing Techniques
Rice monitoring is one of the main issues in rice productivity. Farmers face difficulties in monitoring their rice fields due to climate change, soil conditions, age of the farmers and time consumed to monitor the whole area. Remote sensing technology is one of the alternatives to monitor rice field. The advancement of unmanned aerial vehicle (UAV) technology has been rapidly growing and frequently used in the agriculture industries to monitor crop condition. The objectives of this research are creating crop growth map using aerial imagery and object-based image analysis (OBIA) technique, and validating the normalized difference vegetative index (NDVI) value in rice field map using soil plant analysis development (SPAD) and GreenSeeker data. The multispectral image is processed using OBIA to produce crop growth map. The crop growth map produced is embedded with information that is able to indicate the health status of the rice crop using NDVI. This research was carried out at a paddy field planted using PadiU Putra variety in Ladang Merdeka, Ketereh, Kelantan (0.79 ha). The results from this research show that OBIA method can classify vegetation and non-vegetation to produce crop growth map. NDVI map has a strong correlation with Greenseeker data at 0.893 with positive correlation at 0.05 compared to SPAD meter. The crop growth map allows farmers to improve their rice farm monitoring more effectively using remote sensing technique.
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