引入地形因子的无人机遥感精准管理分区研究

UAV Remote Sensing Accurate Management Zoning with Terrain Factor

根据作物长势的空间差异对耕地进行精准管理分区,可以指导田间变量管理,漫川漫岗黑土区地形复杂,分区时应考虑微地形对作物的影响。以典型黑土区玉米田块为研究区,利用1.1 m空间分辨率的无人机多光谱影像提取玉米大喇叭口期(播种后约45 d)归一化植被指数(Normalized difference vegetation index,NDVI),分别结合4种地形因子(高程、坡度、地形起伏度、地表粗糙度),通过面向对象分割方法进行分区,并利用产量数据对分区结果进行评价,对比无人机影像结合不同地形因子分区的精度。结果表明:研究地块产量、土壤养分及植株生理参数均存在显著变异性,产量与地形存在相关性;相较使用单期NDVI分区,结合地形因子能够显著提高分区精度;结合不同地形因子后,无人机分区精度变化存在差异,NDVI同时结合4种地形因子的分区精度最高,其次分别为结合高程、地形起伏度、坡度、地表粗糙度。研究结果证明了NDVI与地形因子结合作为输入量提高分区精度的可行性,为精准施肥及其他田间变量管理提供了理论基础,为智慧农业的发展提供新思路。

According to the spatial differences of crop growth, the precise management and zoning of cultivated land can guide the management of field variables. The black soil area with overflowing rivers and hills is complex, so the influence of micro-topography on crops should be considered in the zoning. Using the corn field of the typical black soil area as the research area, we extracted the Normalized Difference Vegetation Index(NDVI) of corn flare opening stage(45 days after sowing) by means of the unmanned aerial vehicle(UAV) multispectral image with 1.1 m spatial resolution. Combined with four kinds of terrain factors(elevation, slope, terrain relief, surface roughness), the object-oriented segmentation method was used for partition, and the production data were used to evaluate the partition results, and the accuracy of UAV images combined with different terrain factors was compared. The results are as follows:(1) Yield, soil nutrients and plant physiological parameters of the study plots all showed significant variability, and yield was correlated with topography;(2) Compared with single-stage NDVI partitioning, the combination of terrain factors can significantly improve the accuracy of partitioning;(3) There are differences in the variation of UAV zoning accuracy after combining different terrain factors, NDVI combined with four terrain factors simultaneously has the highest zoning accuracy, followed by the combination of elevation, relief of terrain, slope and surface roughness.The results prove that the combination of NDVI and terrain factors can be used as input to improve zoning accuracy, which provides a theoretical basis for precise fertilization and other field variable management, and a new idea for the development of smart agriculture.