地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (7): 1325-1337.doi: 10.12082/dqxxkx.2021.200583
收稿日期:
2020-10-08
修回日期:
2021-01-31
出版日期:
2021-07-25
发布日期:
2021-09-25
通讯作者:
周小成
作者简介:
熊皓丽(1996— ),女,安徽池州人,硕士生,研究方向为遥感信息处理与应用。E-mail: N185527029@fzu.edu.cn
基金资助:
XIONG Haoli(), ZHOU Xiaocheng*(
), WANG Xiaoqin, CUI Yajun
Received:
2020-10-08
Revised:
2021-01-31
Online:
2021-07-25
Published:
2021-09-25
Contact:
ZHOU Xiaocheng
Supported by:
摘要:
福建省作为中国的产茶大省,快速准确获取茶园的空间分布对福建省农业经济发展和生态环境保护具有重要的决策意义,然而,传统的方法难以保证大范围准确地获取茶园空间分布。本文基于GEE云平台,快速获取覆盖福建省的Sentinel-1雷达影像、Sentinel-2光学影像及地形数据,从中提取光谱特征、纹理特征、地形特征等98个特征,利用递归消除支持向量机算法(SVM_RFE)对特征变量进行筛选,通过支持向量机分类器(SVM)进行茶园提取,首次得到福建省2019年10 m分辨率茶园种植区空间分布图。结果表明:① 光谱特征在茶园信息提取中占据重要地位,纹理特征和地形特征次之;② 利用SVM_RFE可以有效筛选出最有利于茶园提取的特征子集,有效提高提取精度,总体精度为94.65%,Kappa系数为0.93,茶园的生产者精度为91.64%,用户精度为92.91%;③ 基于Sentinel-1及Sentinel-2影像获取的福建省2019年茶园种植面积为1913 km2,主要分布在安溪县、福鼎市、福安市、武夷山市和寿宁县,其茶园总面积达910 km2,约占据全省茶园面积的48%。利用云计算技术可以克服大尺度茶园监测运算能力不足的问题,结合Sentinel-1和Sentinel-2影像能够较准确地提取福建省茶园分布,对南方丘陵山区茶园及其他作物提取具有参考价值,并为政府及有关部门进行茶园管理提供支持。
熊皓丽, 周小成, 汪小钦, 崔雅君. 基于GEE云平台的福建省10 m分辨率茶园专题空间分布制图[J]. 地球信息科学学报, 2021, 23(7): 1325-1337.DOI:10.12082/dqxxkx.2021.200583
XIONG Haoli, ZHOU Xiaocheng, WANG Xiaoqin, CUI Yajun. Mapping the Spatial Distribution of Tea Plantations with 10 m Resolution in Fujian Province Using Google Earth Engine[J]. Journal of Geo-information Science, 2021, 23(7): 1325-1337.DOI:10.12082/dqxxkx.2021.200583
表1
本文所用的植被指数
植被指数简称 | 植被指数说明 | 计算公式 | 公式编号 | 参考文献 |
---|---|---|---|---|
NDVI | 归一化植被指数 | NDVI = (B8 - B4)/(B8 + B4) | (1) | Tucker[ |
NDWI | 归一化水体指数 | NDWI= (B3 - B8)/(B3 + B8) | (2) | Gao[ |
LSWI | 地表水分指数 | LSWI = (B8 - B11)/(B8 + B11) | (3) | Xiao等[ |
NDTI | 归一化差异耕作指数 | NDTI = (B11 - B12)/(B11 + B12) | (4) | Deventer等[ |
MNDVI | 修正型归一化植被指数 | MNDVI = (B4 - B3)/(B4 + B3) | (5) | Dihkan等[ |
IRECI | 新型倒红边叶绿素指数 | IRECI = (B7 - B4)/(B5/B6) | (6) | Frampton等[ |
MTCI | 地面叶绿素指数 | MTCI= (B6 - B5)/(B5 - B4) | (7) | Dash等[ |
NDVIre1 | 归一化植被指数红边1 | NDVIre1 = (B8A - B5)/(B8A + B5) | (8) | Gitelson等[ |
NDVIre2 | 归一化植被指数红边2 | NDVIre2 = (B8A - B6)/(B8A + B6) | (9) | 张磊等[ |
NDVIre3 | 归一化植被指数红边3 | NDVIre3 = (B8A - B7)/(B8A + B7) | (10) | 张磊等[ |
NDre1 | 归一化差异红边1 | NDre1 = (B6 - B5)/(B6 + B5) | (11) | Gitelson等[ |
NDre2 | 归一化差异红边2 | NDre2 = (B7 - B5)/(B7 + B5) | (12) | Merzlyak等[ |
CIre | 红边叶绿素指数 | CIre = B7/B5 - 1 | (13) | Gitelson等[ |
表3
本文涉及的特征信息汇总
数据源 | 特征名称 | 特征说明或介绍 | 特征数目 |
---|---|---|---|
S2 | 原始光谱特征 | B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12 | 13 |
植被指数特征 | NDVI, NDWI,LSWI, NDTI, MNDVI | 5 | |
红边指数特征 | IRECI, MTCI, NDVIre1, NDVIre2, NDVIre3, NDre1, NDre2, CIre | 8 | |
B8波段纹理特征 | NIR近红外波段的17个纹理特征 | 17 | |
NDTI指数纹理特征 | NDTI指数的17个纹理特征 | 17 | |
S1 | 雷达纹理特征 | VV和VH的17个纹理特征 | 34 |
SRTM | 地形特征 | Elevation, Slope, Aspect, Hillshade | 4 |
合计 | 98 |
表5
不同实验方案的分类精度统计
实验方案 | OA/% | Kappa系数 | 人工地表 | 耕地 | 林地 | 茶园 | 水体 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PA/% | UA/% | PA/% | UA/% | PA/% | UA/% | PA/% | UA/% | PA/% | UA/% | |||||||
1 | 89.42 | 0.85 | 93.70 | 96.07 | 83.28 | 80.38 | 92.28 | 91.74 | 83.45 | 84.31 | 95.33 | 96.62 | ||||
2 | 93.04 | 0.90 | 94.79 | 96.11 | 87.54 | 90.20 | 95.11 | 94.01 | 90.27 | 90.12 | 96.67 | 96.03 | ||||
3 | 93.87 | 0.92 | 94.79 | 96.11 | 91.15 | 91.45 | 96.09 | 94.70 | 90.27 | 91.52 | 96.00 | 96.64 | ||||
4 | 94.65 | 0.93 | 95.62 | 96.14 | 93.11 | 92.51 | 96.19 | 95.44 | 91.64 | 92.91 | 96.67 | 96.67 |
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