基于GF-2影像的平原河网区规模化生猪养殖场提取方法研究

    Extracting Large-scale Pig Farms in Plain River Network Area From GF-2 Image

    • 摘要: 精准治理生猪养殖业污染要求相关部门精准快速地掌握规模化生猪养殖场空间分布信息,借助遥感技术可实现这一目标。以GF-2影像为遥感数据源,以地处长江中下游平原河网区的江苏省如皋市石庄镇、江安镇境内的规模化生猪养殖场分别作为样本与验证对象,基于样本对象先明确其组成要素为猪舍、粪污池和蓄水池3类地物,基于棋盘分割利用建筑物面积指数(building area index,BAI)去除工厂等成片建筑物对猪舍提取的干扰,再进行多尺度分割。最后,面向分割后对象选择上述3类地物的光谱、几何和纹理特征构建规模化生猪养殖场的提取规则集。基于验证对象先根据提取规则集提取出上述3类地物,再利用GIS空间叠加与距离分析方法提取规模化生猪养殖场空间分布,最后实地考察验证提取结果的空间一致性。结果表明:(1)就平原河网区而言,基于分割目标大小为516的棋盘分割,利用BAI>-0.150 923的阈值可较好地去除工厂等成片建筑物对猪舍提取的干扰;(2)去除工厂等成片建筑物后,运用面向对象的方法可有效提取规模化生猪养殖场空间分布,总体空间一致性达82.24%。利用高空间分辨率遥感影像,去除其中的工厂等成片建筑物后,采用面向对象提取的方法是提取平原河网区规模化生猪养殖场行之有效的策略。

       

      Abstract: Accurate control and treatment of pig breeding industry pollution requires relevant departments to accurately and quickly grasp the spatial distribution information of large-scale pig farms, which can be realized by remote sensing technology. Taking the GF-2 image as data source, the large-scale pig farms located in Shizhuang Township and Jiangan Township, Rugao City, Jiangsu Province, in the plain river network area of the middle and lower reaches of the Yangtze River were taken as sample and verification objects, respectively. Based on the sample objects, the compositional elements of them were firstly defined as pigsties, fecal sewage pools and impounding reservoirs. Then, based on the chessboard segmentation, the building area index (BAI) was used to remove the interference of factories and other buildings in cluster on pigsty extraction and the multiresolution segmentation was then implemented. Afterwards, the spectral, geometric and textural features of the above-mentioned ground objects were selected to construct the extraction rule set of large-scale pig farms. Following the same extraction rule set in conjunction with GIS spatial overlay and distance analyses, the above-mentioned three kinds of components of the verification objects were extracted. Ultimately, the spatial consistency of the extracted results was verified by using field investigations. The results show that: (1) for the plain river network area, based on the chessboard segmentation with the segmentation object size of 516, BAI >-0.150 923 could be used to remove the interference of factories and other buildings in cluster on pigsty extraction; (2) after removing the interference, the proposed object-oriented extraction method could effectively extract the spatial distribution of large-scale pig farms, with an overall spatial consistency reaching to 82.24%. In conclusion, using very high spatial resolution remote sensing imagery and removing factories and other buildings in cluster, the method of object-oriented extraction is an effective strategy to extract large-scale pig farms in the plain river network area.

       

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