农业工程学报
農業工程學報
농업공정학보
2014年
2期
146-152
,共7页
邬明权%杨良闯%于博%王玉%赵昕%牛铮%王长耀
鄔明權%楊良闖%于博%王玉%趙昕%牛錚%王長耀
오명권%양량틈%우박%왕옥%조흔%우쟁%왕장요
遥感%农作物%测量%MPPS抽样%面向对象分类%种植面积
遙感%農作物%測量%MPPS抽樣%麵嚮對象分類%種植麵積
요감%농작물%측량%MPPS추양%면향대상분류%충식면적
remote sensing%crops%measurements%MPPS%object-oriented classification method%crop acreage
针对传统抽样调查工作中调查基础资料时效性不高和野外调查工作量较大等问题,该文提出了一种遥感与MPPS(multivariate probability proportional to size)抽样调查相结合的农作物种植面积测量方法。利用第2次农业普查数据进行抽样框的编制;利用时序中分辨率遥感数据进行农作物种植面积的分类;在中分辨率遥感分类图的基础上进行MPPS 抽样;采用高空间分辨率遥感数据对抽选样本进行面向对象的分类;根据MPPS 抽样方法进行总体农作物种植面积的推断;计算CV 值,评价抽样精度,以国家统计局公布数据为标准进行总体面积精度评价。以辽宁省北镇市为研究区对该方法进行了测试。结果显示,该方法能够有效的提取县级农作物种植面积,农作物种植面积提取精度优于92%。
針對傳統抽樣調查工作中調查基礎資料時效性不高和野外調查工作量較大等問題,該文提齣瞭一種遙感與MPPS(multivariate probability proportional to size)抽樣調查相結閤的農作物種植麵積測量方法。利用第2次農業普查數據進行抽樣框的編製;利用時序中分辨率遙感數據進行農作物種植麵積的分類;在中分辨率遙感分類圖的基礎上進行MPPS 抽樣;採用高空間分辨率遙感數據對抽選樣本進行麵嚮對象的分類;根據MPPS 抽樣方法進行總體農作物種植麵積的推斷;計算CV 值,評價抽樣精度,以國傢統計跼公佈數據為標準進行總體麵積精度評價。以遼寧省北鎮市為研究區對該方法進行瞭測試。結果顯示,該方法能夠有效的提取縣級農作物種植麵積,農作物種植麵積提取精度優于92%。
침대전통추양조사공작중조사기출자료시효성불고화야외조사공작량교대등문제,해문제출료일충요감여MPPS(multivariate probability proportional to size)추양조사상결합적농작물충식면적측량방법。이용제2차농업보사수거진행추양광적편제;이용시서중분변솔요감수거진행농작물충식면적적분류;재중분변솔요감분류도적기출상진행MPPS 추양;채용고공간분변솔요감수거대추선양본진행면향대상적분류;근거MPPS 추양방법진행총체농작물충식면적적추단;계산CV 치,평개추양정도,이국가통계국공포수거위표준진행총체면적정도평개。이요녕성북진시위연구구대해방법진행료측시。결과현시,해방법능구유효적제취현급농작물충식면적,농작물충식면적제취정도우우92%。
MPPS is a method widely used in crop area statistics in the Chinese crop area statistical investigation business. However, this method has two drawbacks. One is the outdated basic data. The other is the large workload of a field survey. The second land use survey data used as the basic data in the Chinese crop area statistical investigation is only updated every 10 years. The longer update cycle makes it difficult to react to the inter-annual change of crop areas. The artificial field survey is used in the Chinese crop area statistical investigations to survey the area of crops of every sampling village. Because of the large number of sample villages, the workload of field investigation is huge, and time-consuming and laborious. In order to solve those problems in a conditional sampling survey, a novel crop area extraction method was proposed in this paper using remote sensing and MPPS sampling technology. The sampling frame was prepared using the village-level administrative unit data of the second land use survey data. Crops were extracted using multi-temporal HJ-1 satellite data with a Spectral Angle Mapper method. Three HJ-1 satellite data sets acquired in April, May, and August were selected according to the Phonological data. In April, rice and winter wheat were in the seedling stage, and corn was not planted. In May, rice was in irrigation period. In August, winter wheat had been harvested, while rice and corn were in their maturity periods. So using images in those months, it was easy to differentiate rice from winter wheat and corn since the paddy land contains water, while the wheat and corn land were dry in May. It was also easy to differentiate winter wheat from corn because the growing period of winter wheat was 20 days earlier than the growing period of corn. Then the crop areas of each village were updated by the moderate resolution crop classification map. Combining the updated sampling frame data and MPPS sampling method, sampling villages were selected. Crops in the sampling villages were mapped using ZY-1 02c satellite data with an object-oriented classification method. The ZY-1 02C satellite is a new Chinese civil remote sensing satellite launched on December 22, 2011. It was the highest resolution civil remote sensing satellite in China which carried a panchromatic/multispectral sensor and a high-resolution sensor. The spatial resolution of the HR sensor was 2.36 m, and the spatial resolution of the panchromatic/multispectral sensor was 5m in a panchromatic band and 10m in three multispectral bands. Finally, according to the MPPS method, the total area of each crop in the study area and CV were calculated. The algorithm had been tested over a study area in Beizhen Country, Liaoning Province, China. The results showed that this method could effectively determine the rice and corn areas. A high mapping precision of 92%was obtained.