多时相Sentinel-2影像在浙西北茶园信息提取中的应用
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摘要
利用Sentinel-2遥感影像研究一种快速、准确提取茶园空间分布的新方法,可为茶园经济林资源及其动态变化的快速检测提供新的手段。以浙江省西北部为研究区,根据实地调查选取6类典型植被,基于4个季节的Sentinel多光谱影像分析不同植被物候及光谱特征。茶园在5月经历修剪后与其他植被区别较大,根据红边与短波红外波段构建归一化茶园指数(NDTI)。基于新指数建立决策树模型提取茶园,通过谷歌地球对结果进行验证。结果显示:归一化茶园指数可以最大限度扩大茶园与其他植被之间的差距。基于该指数提取茶园的总精度达93.83%,Kappa系数为0.917,成功实现了浙西北茶园信息的提取,证明了使用红边波段提取茶园的潜力。图6表1参17
To develop a new method for accurately mapping the spatial distribution of tea gardens using Sentinel-2 remote sensing imagery, a new approach to the mapping of tea garden resources in Anji of northwestern Zhejiang Province was produced. First, six types of typical vegetation were selected according to a field survey,and their phenological and spectral characteristics were analyzed based on multi-temporal Sentinel imagery.Second, because tea gardens differed from other vegetation types after being pruned in May, a Normalized Tea garden Index(NDTI) was constructed based on the red edge and short-wave infrared bands. Third, a decision tree model based on the new index was used to identify the tea gardens, a total 600 validation points were obtained by field survey, the overall accuracy(OA) and Kappa coefficient were used to evaluate classification accuracy of tea gardens. The accuracy assessment result indicated an overall accuracy of 93.83% and a Kappa coefficient of 0.917. Spatial distribution of the tea gardens was accurately extracted demonstrating the potential to extract tea gardens using the red edge band. The tea gardens was extracted by constructing a normalized tea gardens index, which was easy to understand and realize, and it was easy to operate. [ Ch, 6 fig. 1 tab. 17 ref.]
To develop a new method for accurately mapping the spatial distribution of tea gardens using Sentinel-2 remote sensing imagery, a new approach to the mapping of tea garden resources in Anji of northwestern Zhejiang Province was produced. First, six types of typical vegetation were selected according to a field survey,and their phenological and spectral characteristics were analyzed based on multi-temporal Sentinel imagery.Second, because tea gardens differed from other vegetation types after being pruned in May, a Normalized Tea garden Index(NDTI) was constructed based on the red edge and short-wave infrared bands. Third, a decision tree model based on the new index was used to identify the tea gardens, a total 600 validation points were obtained by field survey, the overall accuracy(OA) and Kappa coefficient were used to evaluate classification accuracy of tea gardens. The accuracy assessment result indicated an overall accuracy of 93.83% and a Kappa coefficient of 0.917. Spatial distribution of the tea gardens was accurately extracted demonstrating the potential to extract tea gardens using the red edge band. The tea gardens was extracted by constructing a normalized tea gardens index, which was easy to understand and realize, and it was easy to operate. [ Ch, 6 fig. 1 tab. 17 ref.]
引文
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[2] LU Dengsheng, WENG Qihao. A survey of image classification methods and techniques for improving classification performance[J]. Int J Remote Sensing, 2007, 28(5):823-870.
[3] LIU Shanshan, WEI Xinliang, LI Dengqiu, et al. Examining forest disturbance and recovery in the subtropical forest region of Zhejiang Province using Landsat time-series data[J]. Remote Sensing, 2017, 9(5):479.
[4] LI Nan, LU Dengsheng, WU Ming, et al. Coastal wetland classification with multiseasonal high-spatial resolution satellite imagery[J]. Int J Remote Sensing, 2018, 39(23):8963-8983.
[5]梁守真，陈劲松，吴炳方，等.应用面向对象的决策树模型提取橡胶林信息[J].遥感学报，2015, 19(3):485-494.LIANG Shouzhen, CHEN Jinsong, WU Bingfang, et al. Extracting rubber plantation with decision tree model based on object-oriented method[J]. J Remote Sensing, 2015, 19(3):485-494.
[6] XI Zhenyuan, LU Dengsheng, LIU Lijuan, et al. Detection of drought-induced hickory disturbances in western Lin’an county, China, using multi-temporal Landsat imagery[J]. Remote Sensing, 2016, 8(4):345.
[7]周静平，李存军，胡海棠，等.森林植被遥感监测影像最佳分辨率选择[J].浙江农林大学学报，2018, 35(4):716-723.ZHOU Jingping, LI Cunjun, HU Haitang, et al. Optimal resolution selection for monitoring forest vegetation using remote sensing images[J]. J Zhejiang A&F Univ, 2018, 35(4):716-723.
[8] WANG Yajie, LU Dengsheng. Mapping Torreya grandis spatial distribution using high spatial resolution satellite imagery with the expert rules-based approach[J]. Remote Sensing, 2017, 9(6):564.
[9]梁文海，刘吉凯，张伟，等.基于面向对象方法的GF-2影像桉树林信息提取[J].浙江农林大学学报，2017, 34(4):721-729.LIANG Wenhai, LIU Jikai, ZHANG Wei, et al. Extracting Eucalyptus information using GF-2 images based on an object-oriented method[J]. J Zhejiang A&F Univ, 2017, 34(4):721-729.
[10] IMMITZER M, VUOLO F, ATZBERGER C. First experience with Sentinel-2 data for crop and tree species classifications in central Europe[J]. Remote Sensing, 2016, 8(3):166.
[11]许光明.基于面向对象和多源数据融合的茶园遥感提取研究[D].西安：陕西师范大学，2016.XU Guangming. Research on Remote Sensing Extraction of Tea Garden Based on Object-oriented and Multi-source Data Fusion[D]. Xi’an:Shaanxi Normal University, 2016.
[12]刁亚芹，韩莹，李兆富. 2000年以来天目湖流域茶园分布变化及趋势模拟[J].湖泊科学，2013, 25(6):799-808.DIAO Yaqin, HAN Ying, LI Zhaofu. Spatio-temporal change of tea plantation since 2000 and model-based prediction in the Tianmuhu Reservoir watershed[J]. J Lake Sci, 2013, 25(6):799-808.
[13]朱泽润.基于高分辨率遥感影像的茶园场景提取方法[D].武汉：武汉大学，2018.ZHU Zerun. Tea Garden Scene Detection from High-Resolution Remote Sensing Image[D]. Wuhan:Wuhan University, 2018.
[14]陆文渊，钱文春，赖建红，等.安吉白茶品质的气候成因初探[J].茶叶科学技术，2012(3):37-39.LU Wenyuan, QIAN Wenchun, LAI Jianhong, et al. Preliminary studies for the climate causes on quality characteristics of Anji-baicha[J]. Tea Sci Technol, 2012(3):37-39.
[15] TOMING K, KUTSER T, LAAS A, et al. First experiences in mapping lake water quality parameters with Sentinel-2MSI imagery[J]. Remote Sensing, 2016, 8(8):640.
[16] RICHTER R, KELLENBERGER T, KAUFMANN H. Comparison of topographic correction methods[J]. Remote Sensing, 2009, 1(3):184-196.
[17] TUCKER C J, PINZON J E, BROWN M E, et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data[J]. Int J Remote Sensing, 2005, 26(20):4485-4498.
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