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遥感技术与应用  2022, Vol. 37 Issue (4): 982-992    DOI: 10.11873/j.issn.1004-0323.2022.4.0982
遥感应用     
基于GEE和Sentinel-2时序数据的呼伦贝尔沙地及其周边植被类型识别研究
杨仙保1,2(),张王菲1,孙斌2,3(),高志海2,3,李毅夫2,3,王晗2,3
1.西南林业大学 地理与生态旅游学院,云南 昆明 650224
2.中国林业科学研究院资源信息研究所,北京 100091
3.国家林业和草原局林业遥感与信息技术重点实验室,北京 100091
Recognition of Vegetation Types in Hulunbuir Sandy Land and Its Surrounding Areas based on GEE Cloud Platform and Sentinel-2 Time Series Data
Xianbao Yang1,2(),Wangfei Zhang1,Bin Sun2,3(),Zhihai Gao2,3,Yifu Li2,3,Han Wang2,3
1.College of Geography and Ecotourism,Southwest Forestry University,Kunming 650224,China
2.Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China
3.Key Laboratory of Forestry Remote Sensing and Information System,Beijing 100091,China
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摘要:

沙地及其周边植被对固定沙丘、防止水土流失和环境治理等方面具有重要作用,开展沙地及其周边植被类型识别研究对于客观地反映沙地及其周边的生态环境,进而为沙地恢复治理政策制定具有重要意义。GEE云平台丰富的长时间序列遥感数据和强大的云计算能力,为开展大区域植被类型识别提供了便捷。本研究基于GEE云平台存储的2019年Sentinel-2时序数据,采用RF算法开展呼伦贝尔沙地及其周边主要植被类型的空间判识研究,探索了GEE平台下顾及植被物候信息的植被类型识别效果。研究发现:①Sentinel-2影像的光谱信息和近红外波段的纹理信息对研究区的主要植被类型识别能力有限,而物候特征有效地弥补了原始光谱特征等对研究区不同植被类型区分能力的不足;②基于RF算法顾及物候特征的植被类型识别精度达到84.37%,Kappa系数为0.8,比单一时相数据的识别精度提高了10.01%;③呼伦贝尔沙地及其周边主要植被类型的物候特征差异明显,有助于不同类型植被的空间识别,特别是提高了灌草丛和草原的识别精度。研究表明利用Sentinel-2数据和GEE云平台对沙地等大区域植被类型的识别具有较大的潜力和广阔的应用前景。

关键词: GEESentinel?2时序数据呼伦贝尔沙地植被类型识别    
Abstract:

Sand and its surrounding vegetation types play an important role in fixing dunes, preventing soil erosion and environmental management for sandy land. Identification of Sand and its surrounding vegetation types can objectively reflect the vegetation growth environment of sandy land and its surrounding areas, so as to provide a valuable reference for ecological restoration and the control policies formulating of sandy land. With huge amount of long-term earth observation data and powerful cloud computing capabilities, Google Earth Engine (GEE) cloud platform provides a convenient way for the identification of vegetation types in a large areas. In this study, based on the Sentinel-2 time series data of 2019 stored in the GEE cloud platform, the applied potentialities of GEE cloud platform in vegetation types identification was explored by combining the RF algorithm and vegetation phenology information in Hulunbuir sandy land and its surroundings. Results showed that: ① The spectral information of Sentinel-2 image and the texture information obtained from the near-infrared band have limited ability to identify the main vegetation types in the study area, but the phenological characteristics effectively make up for this shortcoming; ② Accuracy of the vegetation types identification method achieved by the RF algorithm and considering the phenological characteristics extracted from the long time series remote sensing data is 84.37% (with the Kappa coefficient of 0.8), which is 10.01% higher than that identification result acquired based on single-phase data; ③Phenological characteristics of the main vegetation types in the Hulunbuir sandy land and its surroundings show significant differences, which is helpful for the identification of the vegetation types, especially to improve the recognition accuracy of shrubs and grassland.The research shows that the use of Sentinel-2 data and GEE cloud platform to identify vegetation types in large areas such as sandy land has great potential and broad application prospects.

Key words: GEE    Sentinel-2    Time series data    Hulunbuir sandy land    Identification of vegetation types
收稿日期: 2021-02-11 出版日期: 2022-09-28
:  K901.4  
基金资助: “中央级公益性科研院所基本科研业务费专项”(CAFYBB2019ZB004);“国家高分辨率对地观测系统重大专项”(21?Y20A06?9001?17/18)
通讯作者: 孙斌     E-mail: 2296857887@qq.com;sunbin@ifrit.ac.cn
作者简介: 杨仙保(1995-),男,云南德宏人,硕士研究生,主要从事区域地理研究。E?mail:2296857887@qq.com
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引用本文:

杨仙保,张王菲,孙斌,高志海,李毅夫,王晗. 基于GEE和Sentinel-2时序数据的呼伦贝尔沙地及其周边植被类型识别研究[J]. 遥感技术与应用, 2022, 37(4): 982-992.

Xianbao Yang,Wangfei Zhang,Bin Sun,Zhihai Gao,Yifu Li,Han Wang. Recognition of Vegetation Types in Hulunbuir Sandy Land and Its Surrounding Areas based on GEE Cloud Platform and Sentinel-2 Time Series Data. Remote Sensing Technology and Application, 2022, 37(4): 982-992.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.4.0982        http://www.rsta.ac.cn/CN/Y2022/V37/I4/982

图1  研究区地理位置示意图 审图号:GS(2019)3333
图2  技术路线
代码植被类型特征
1针叶林森林郁闭度≥60%,以针叶林为主要类型,以樟子松、落叶松等针叶树为建群种组成的森林群落。
2阔叶林森林郁闭度≥60%,以阔叶林为主要类型,以白桦和山杨等为建群种组成的森林群落。
3针阔混交林森林郁闭度≥60%,针叶林和阔叶林相互混生且每种类型面积均不超过50%,是针叶林向阔叶林过渡的植被类型,是白桦、山杨和樟子松、落叶松相互混生的森林群落。
4灌草丛林木郁闭度在10%—60%,高度在2 m以下的灌丛,以中生或旱中生多年生草本植物为主要建群种,其中散生灌木的植物群落。主要以小叶锦鸡、冷蒿、差巴嘎蒿、黄柳、榆树等植被为主。
5草原林灌郁闭度<10%,且覆盖度大于5%,是一种以生长草本植物为主,由旱生或中旱生草本植物组成的草本植物群落,主要有线叶菊、贝加尔针茅、羊草、大针茅、克氏针茅、隐子草等。
6农作物农业上栽培的各种粮食和经济植物,包括大豆、玉米、小麦、油菜、蔬菜等植被。
7其他植被主要由天然草本植物为主的沼泽化低地草甸、沼泽植被和水生植被等。
8非植被植被覆盖度小于5%,表层主要由水体、人造地表、盐碱地、裸沙、裸地、矿坑等覆盖。
表1  分类体系
特征类型特征信息
光谱特征B1、B2、B3、B4、B5、B6、B7、B8、B8A、B9、B11、B12
植被指数NDVI、NDBI、MNDWI、EVI
纹理特征B8_asm、B8_contrast、B8_corr、B8_var、B8_idm、B8_savg、B8_svar、B8_sent、B8_ent、B8_dvar、B8_dent、B8_imcorr1、B8_imcorr2、B8_maxcorr、B8_diss、B8_inertia、B8_shade、B8_prom
植被覆盖度fv
物候特征1月—12月NDVI
地形特征坡向、海拔、山体阴影
表2  特征信息表
图3  不同植被类型的光谱曲线
图4  不同植被类型的NDVI变化曲线
图5  特征维数与识别精度的关系
数据

总体精度

/%

Kappa 系数类别生产精度 /%用户精度 /%
单一时相数据74.360.66针叶林60.6164.52
阔叶林71.7951.85
针阔混交林31.8250.00
灌草丛62.6862.07
草原76.9266.67
农作物72.8679.69
其他植被12.550.00
非植被87.2186.89
时序数据84.370.80针叶林76.0077.56
阔叶林85.1967.65
针阔混交林43.7577.78
灌草丛78.0772.92
草原93.7580.54
农作物84.7886.67
其他植被31.8287.50
非植被91.0494.31
表3  不同植被类型识别的精度评价表
图6  不同时相的识别结果局部放大图
图7  不同时相数据的植被类型识别结果
图8  不同植被类型的比例
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