摘要
统计行政单元内粮食作物种植面积及其空间分布是粮食产量估算的基础,也是制定粮食政策和调整种植结构的重要依据。以3S为代表的空间信息技术,是实现农作物种植面积统计的关键技术,也是实现常规统计进入空间统计的重要因素。该研究以玉米种植面积遥感测量为目标,选取种植结构复杂的农业区河南省原阳县为试验区,通过高分辨率融合影像建立地块边界数据,以TM影像为核心数据源,对TM数据进行预处理,结合NDVI及特征波段信息采用决策树方法对试验区进行预分类,初步获取玉米种植范围;将玉米预分类结果与耕地地块数据空间叠加分析,以地块内玉米的预分类面积比例为分层标志,建立分层模型,结合交通数据,布设野外样方;采用遥感影像与车载GPS结合的方式,设计合理的野外调查路线,开展野外样方实测工作,获取样本地块内的玉米种植比例;然后以野外GPS调查点为依据,通过决策树方法对玉米预分类结果进行修正。最后通过野外测量获取的样本地块玉米百分比及统计数据对TM数据提取的玉米种植面积结果进行评价,求得位置精度为81.8%,总量精度为91.1%。说明借助耕地地块数据库,能够提高多时相TM分类的位置精度和总量精度。
Stating the cultivation area of cereal crops in administrative unit and the spatial distribution information of cereal crops are not only the basis to estimate the food production, but also the important basis to constitute the food policy and adjust the planting structures. The spatial information technology represented by RS, GIS and GPS is the key technical support to state the cultivation area of cereal crops, and it is also the important part to achieve from conventional statistics to spatial statistics step by step. In order to obtain the planting area of corn by remote sensing measurement, this study chose Yuanyang county of Henan province where was the agricultural region with complex planting structure as experimental area and established field background database by high-resolution image. After the data was pretreated, the pre-classification was carried by the normalized difference vegetation index （NDVI） and digital number （DN） according to the multi-temporal thematic mapper （TM） images, and the planting range was preliminarily obtained. Then we integrated the classification results and vector field boundary, taking the area proportion of corn in the field as the delamination symbol to establish the delamination model, then we went out to investigate the real area proportion of corn in the selected field by the combination of remoting sensing images and vehicular GPS. By the GPS points, the corn pre-classification results were corrected using decision tree. At last, we used the investigated results as standard to judge the classification results, the position precision was 81.8%, and the gross precision was 91.1%. It shows that the position precision and gross precision of multi-temporal TM images can be improved by the database of field boundary.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2009年第8期147-151,F0003,共6页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家863项目(2006AA120101
2006AA108A308)
农业部行业科技项目(200803037)
