以福建省厦门市同安区莲花镇为研究区，以Sentinel-2 遥感影像为数据源，分析多时相的Sentinel-2光谱在不同地物上的光谱特征差异，选择敏感光谱构建的植被指数和纹理特征作为茶园提取特征，使用随机森林分类方法提取研究区茶园的分布范围。结果表明，随机森林方法结合Sentinel-2光谱和纹理信息，在多个时期都能达到较好的提取效果。其中，在10月的总体精度和Kappa系数最高，分别为95.53%和0.9088；4月和12月的分类精度比较接近，总体精度分别为94.28%和94.41%，Kappa系数分别为0.8889和0.8881；7月的分类精度最低，总体精度为91.05%，Kappa系数为0.8254。分类结果满足茶园管理研究需要。
The reasonable development of tea plantations is of great significance for the local economy and ecological environment protection. Obtaining accurate spatial distribution of tea plantations is an important factor in tea plantations management and land use. This paper takes Lianhua Town, Tong'an District, Xiamen City, Fujian Province as the research area, and takes Sentinel-2 remote sensing image as the data source to analyze the difference of spectral characteristics of multi-temporal Sentinel-2 spectrum in different ground objects. The vegetation indices with sensitive spectrum and texture features were used as the extraction characteristics of tea plantations. The distribution range of tea plantations in the research area was extracted using the random forest classification method. The results showed that the random forest method combined with Sentinel-2 spectral and texture information can achieve good results in many periods. Among them, the overall accuracy and Kappa coefficient in October were the highest, reaching 95.53% and 0.9088, respectively; the classification accuracy in April and December was relatively close, with overall accuracies of 94.28% and 94.41%, and Kappa coefficients of 0.8889 and 0.8881, respectively; the classification accuracy in July was the lowest, with an overall accuracy of 91.05% and a Kappa coefficient of 0.8254. The classification results meet the research needs.
[1] 张敏, 陈永根, 于翠平, 潘志强, 范冬梅, 骆耀平, 王校常. 在茶园生产周期过程中茶树群落生物量和碳储量动态估算[J]. 浙江大学学报(农业与生命科学版), 2013, 39(6): 687–694.
[2] 贾玉洁, 刘云根, 杨思林, 王妍, 张超, 徐红枫, 郑淑君. 面向Sentinel-2A影像的大理市土地利用分类方法适用性研究[J]. 浙江农林大学学报, 2022, 39(6): 1350–1358.
[3] 黄邵东, 徐伟恒, 吴超, 戴杨, 寇卫利. 遥感在茶园监测中的应用研究进展[J]. 西部林业科学, 2020, 49(2): 1–9, 23.
[4] 甄佳宁, 廖静娟, 沈国状. 1987以来海南省清澜港红树林变化的遥感监测与分析[J]. 湿地科学, 2019, 17(1): 44–51.
[5] 王文静, 张霞, 赵银娣, 王树东. 综合多特征的Landsat 8时序遥感图像棉花分类方法[J]. 遥感学报, 2017, 21(1): 115–124.
[6] 马超, 杨飞, 王学成. 基于中尺度光谱和时序物候特征提取南方丘陵山区茶园[J]. 国土资源遥感, 2019, 31(1): 141–148.
[7] 赵晓晴, 王萍, 荆林海, 谭炳香, 赵鑫, 刘德军. 时序Sentinel-2A影像光谱特征的茶园提取应用[J]. 测绘科学, 2020, 45(6): 80–88.
[8] 李龙伟, 李楠, 陆灯盛. 多时相Sentinel-2影像在浙西北茶园信息提取中的应用[J]. 浙江农林大学学报, 2019, 36(5): 841–848.
[9] Dihkan M, Guneroglu N, Karsli F, Guneroglu A. Remote sensing of tea plantations using an SVM classifier and pattern-based accuracy assessment technique [J]. International Journal of Remote Sensing, 2013, 34(20): 8549–8565.
[10] 杨艳魁, 陈芸芝, 吴波, 汪小钦. 基于高分二号影像结合纹理信息的茶园提取[J]. 江苏农业科学, 2019, 47(2): 210–214.
[11] 柏佳, 孙睿, 张赫林, 王岩, 金志凤. 基于GF-1和Sentinel-2时序数据的茶园识别[J]. 农业工程学报, 2021, 37(14): 179–185.
[12] Dimosthenis T, Peter R. Mapping Mediterranean seagrasses with Sentinel-2 imagery [J]. Marine Pollution Bulletin, 2017, 134: 197–209.
[13] Zhu J, Pan Z W, Wang H, Huang P J, Liu Z Z. An improved multi-temporaland multi-feature tea plantation identification method usingSentinel-2 imagery [J]. Sensors, 2019, 19(9): 2087.
[14] Deering D W. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors [D]. College Station, TX: Texas A & M University, 1978.
[15] Gao B C. NDWI—A normalized difference water index for remote sensing of vegetation quid water from space [J]. Remote Sensing of Environment, 1996, 58(3): 257–266.
[16] 刘佳, 王利民, 滕飞, 杨玲波, 高建孟, 姚保民, 杨福刚. RapidEye卫星红边波段对农作物面积提取精度的影响[J]. 农业工程学报, 2016(13): 140–148.
[17] Haralick R M, Shanmugam K , Dinstein I. Textural features for image classification [J]. Studies in Media and Communication, 1973, SMC-3(6): 610–621.
[18] 孙文瑞, 姜慧芳, 左晓庆, 李潇雨, 刘怀鹏. Landsat8影像分类多分类器对比分析[J]. 地理空间信息, 2022, 20(1): 101–104.
[19] 王斌, 何丙辉, 林娜, 王伟, 李天阳. 基于随机森林特征选择的茶园遥感提取[J]. 吉林大学学报(工学版), 2022, 52(7): 1719–1732.
闽公网安备 35020602000789号