Proceedings Article | 14 February 2020
KEYWORDS: Calibration, Remote sensing, Earth observing sensors, Agriculture, Landsat, Vegetation, Image processing, Image fusion, Image classification, Data acquisition
[Objective] Based on the PIE SDK, Landsat-8 was used as the data source to realize the plug-in corn planting area extraction tool, which provided technical support for the rapid and objective acquisition of county corn planting area information, and assisted agricultural remote sensing application and agricultural development. [Methods] The PIE SDK was used for plug-in secondary development to realize the radiometric calibration, fusion and cropping functions of remote sensing images. The vegetation distribution of the experimental area was obtained by normalized vegetation index (NDVI), and the K-means classification method was used to realize Corn planting area extraction. [Results] Choosing Weishi County as experimental area，according to the Landsat-8/OLI data of September 4, 2014, the corn planting area was 29,800 hectares, accounting for 23.5% of the total area of the experimental area, mainly distributed in the central and eastern parts of the experimental area. (1) The corn planting area extracted by the development plug-in was 29,800 hectares. In 2014, the corn planting area in Weishi County was about 27,800 hectares. The total area error of the two compared with the experimental area was 2.25%. The high quality provides an effective tool for the survey of corn planting area in Weishi County. (2) The distribution map of corn plantation in Weishi County was obtained by processing the classification results, which is basically consistent with the distribution of corn planting in the county over the years. The method of extracting corn planting area in Weishi County by using NDVI and K-means method It is feasible, and the experimental results show that the maximum number of iterations is set to 30. This method can provide reference for the threshold setting of corn planting area information in the county. [Conclusions] Based on the PIE SDK for secondary development, to achieve the extraction of corn planting area in the county. The results basically meet the statistical data quality requirements, and also provide a reference for the plug-in development of similar crop extraction tools based on the PIE SDK, and can provide objective data to support fast and accurate information on the cultivation statistics, subsidies and insurance business as corn.
