欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
以中国东北区为例，基于GIS和I玛技术，探讨应用NOAA／AVHRR数据编制景观类型图过程中如何提高制图精度。采用多源信息复合NOAA／AVHRR影像分类方法，复合影像分为：1）数字地学综合影像，由年均降水、≥10℃活动积温和数字高程模型（DEM）叠加而成，主要考虑了植被受气候以及地形的影响；2）NoAA／AVHRR四季累加影像的第一主成分影像，在一定程度上去除了多波段NOAA数据冗余；3）NOAA／AVHRR四季NDVI累加值影像，突出了植被信息。将研究区景观（土地）类型进行二级划分，其中一级划分为6大类，二级划分为16类，结果与研究区实际情况基本相符，分类精度达70％。
英文摘要：
NOAA/AVHRR data is applied more and more often in landscape mapping in large scale recently for its low cost, high temporal resolution and pantoscopic scan. But the relative low spatial resolution （1.1 kin） and distortion of images limited the precision of landscape mapping. Based on prior work, this paper carried out studies on approaches to improve landscape mapping precision using NOAA/AVHRR data by GIS and RS techniques. Northeastern China was selected as a case in this paper. Classification based on integrated NOAA/AVHRR image of multi - source data was applied. The integrated image was composed of three bands. One is integrated digital geographical image,which is stacked by mean precipitation per year, active accumulated temperature more than 10 ℃ and DEM of study area regarding with the impact of climate and topography on vegetation. One is image of the first principle component of cumulated NOAA/AVHRR image of band 4,2,1 of four seasons,which can wipe off redundancy in multi- band NOAA/AVHRR data.The third band is cumulated NDVI image of NOAA/AVHRR of four seasons,which is to make information of vegetation prominent. The study area was divided into 6 landscape types in the first level and 16 types in the second level. The classification is coincide with the fact in general. Integrated by multi - source data, classification information of NOAA image was strengthened and the precision of classification was also improved to 70 % from original 50 %.
同期刊论文项目
同项目期刊论文
期刊信息
位置：