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遥感技术与应用  2021, Vol. 36 Issue (5): 1178-1188    DOI: 10.11873/j.issn.1004-0323.2021.5.1178
遥感应用     
基于地物光谱季节曲线特征的毛竹林分布提取
魏雪馨1,2(),刘洋1(),闵庆文1,刘荣高1,张清洋3,叶晓星4,刘蓓蓓5
1.中国科学院地理科学与资源研究所,北京 100101
2.中国科学院大学,北京 100049
3.庆元县食用菌科研中心,浙江 庆元 323800
4.庆元县食用菌管理局,浙江 庆元 323800
5.应急管理部国家减灾中心,北京 100124
Extraction of Moso Bamboo Forest Distribution based on Characteristics of Vegetation Spectral Seasonal Curves
Xuexin Wei1,2(),Yang Liu1(),Qingwen Min1,Ronggao Liu1,Qingyang Zhang3,Xiaoxing Ye4,Beibei Liu5
1.Institute of Geographic Sciences and Natural Resources Research,Beijing 100101,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.Qingyuan Edible Fungi Research Center,Qingyuan 323800,China
4.Qingyuan County Edible Fungi Administration,Qingyuan 323800,China
5.National Disaster Reduction Center of China,Beijing 100124,China
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摘要:

毛竹是我国南方广泛分布的重要竹种,具有良好的生态效益和经济价值。毛竹林与其他森林区分难度大,现有提取方法多直接采用已有的晴空观测,未充分考虑分类时相的影响,限制了提取精度。以浙江省庆元县为例,从地物光谱的季节曲线特征入手,利用MODIS高时间分辨率观测充分挖掘各植被类型光谱季节曲线特征和差异,结合多时相Landsat OLI影像进行分类实验,优选毛竹林与其他植被区分度最大的季相,并采用随机森林方法实现了毛竹林分布的有效提取。结果表明:①初、中秋是区分研究区毛竹林与其他植被的最优时相,夏季次之,春季与冬季较差;②当初、中秋无晴空影像时,结合夏冬季影像的毛竹林提取精度最佳,用户和制图精度分别达到85.57%和78.06%;③10月影像提取毛竹林分布精度最高,用户和制图精度分别达到89.00%和86.91%,与当地森林资源调查数据相比精度优于89.23%。实验表明:在类似亚热带地区毛竹林提取中,应优先选择秋季初、中期影像;若此时期无晴空观测,应优先采用夏季与冬季影像共同分类。

关键词: 毛竹林季节曲线J-M距离遥感随机森林    
Abstract:

As an important bamboo species, moso bamboo forests are widely distributed in southern China and has great ecological and economic benefits. However, it is difficult to distinguish moso bamboo forests from other forests. Most of existing extraction methods directly use available clear sky observation, which do not fully consider the influence of classification time phase, limiting the extraction accuracy. Taking Qingyuan county, Zhejiang Province as an example, a method of moso bamboo forest extraction was established in this paper. The characteristics and differences of seasonal spectral curves were evaluated for typical local vegetation types using MODIS high resolution images, and 16 classification experiments were carried out on single and multi-temporal Landsat OLI images. Based on these analysis and experiments, the best seasonal phase to distinguish moso bamboo forest from other vegetation types was selected, and the distribution of moso bamboo forest was extracted effectively by using random forest classifier. The results showed that: (1) Early or middle autumn is the best period to distinguish moso bamboo forest from other vegetation in the study area, followed by summer and worst in winter and spring. (2) When there is no clear-sky observation in early and middle autumn, the extraction accuracy of moso bamboo forest is the best for combination of summer and winter images, with user and producer accuracy of 85.57% and 78.06%, respectively. (3) The extraction accuracy is the highest based on Landsat image in October, with user accuracy and producer accuracy up to 89.00% and 86.91%, and the extraction accuracy is better than 89.23% when compared with the local forestry resources census data. Experiments show that in extraction of moso bamboo forest in similar subtropical areas, the early or middle autumn image should be selected first; if there is no clear-sky observation in this period, the combination of summer and winter images should be chosen priority.

Key words: Moso bamboo forests    Seasonal curves    J-M distance    Remote sensing    Random Forest
收稿日期: 2020-06-26 出版日期: 2021-12-08
ZTFLH:  TP79  
基金资助: 国家重点研发计划课题(2018YFC1508806);中国科学院战略性先导科技专项子项目(XDA19080303);中国科学院青年创新促进会项目(2019056)
通讯作者: 刘洋     E-mail: weixx.18s@igsnrr.ac.cn;liuyang@igsnrr.ac.cn
作者简介: 魏雪馨(1996-),女,河南新乡人,硕士研究生,主要从事遥感地学分析研究。E?mail: weixx.18s@igsnrr.ac.cn
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引用本文:

魏雪馨,刘洋,闵庆文,刘荣高,张清洋,叶晓星,刘蓓蓓. 基于地物光谱季节曲线特征的毛竹林分布提取[J]. 遥感技术与应用, 2021, 36(5): 1178-1188.

Xuexin Wei,Yang Liu,Qingwen Min,Ronggao Liu,Qingyang Zhang,Xiaoxing Ye,Beibei Liu. Extraction of Moso Bamboo Forest Distribution based on Characteristics of Vegetation Spectral Seasonal Curves. Remote Sensing Technology and Application, 2021, 36(5): 1178-1188.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.5.1178        http://www.rsta.ac.cn/CN/Y2021/V36/I5/1178

图1  庆元县地理位置审图号:浙S(2021)41
传感器行编号条带号获取时间
Landsat OLI411192017/11/3
Landsat OLI411192018/2/23
Landsat OLI411192018/3/11
Landsat OLI411192018/6/15
Landsat OLI411192018/10/5
MODIS2862013/01/01~2017/12/27
ASTER27118、119\
表1  本实验采用的遥感数据信息
地物类型训练样本ROIs像元数验证样本ROIs像元数
毛竹林662 12314291
常绿针叶林584 13823468
常绿阔叶/针阔混交林514 08320571
耕地312 58915125
建设用地91 51614123
水体92768182
总计22414 725941 760
表2  典型地物地面样本数量统计
图2  典型地物训练样本与验证样本分布及参考高分辨率影像 审图号:浙S(2021)41
光谱指数计算公式
NDVI(NIR - Red) / (NIR + Red)
DVINIR – Red
RVINIR / Red
RSISWIR2 / NIR
NDWI(Green - NIR) / (Green + NIR)
LSWI(NIR - SWIR1) / (NIR + SWIR1)
TCB0.204 3×Blue+0.415 8×Green+0.552 4×Red+0.574 1×NIR+0.312 4×SWIR1+0.230 3×SWIR2
TCG-0.160 3×Blue-0.2819×Green-0.493 4×Red+0.794 0×NIR-0.000 2×SWIR1-0.144 6×SWIR2
TCW0.031 5×Blue+0.202 1×Green+0.310 2×Red+0.159 4×NIR-0.680 6×SWIR1-0.610 9×SWIR2
表3  不同光谱指数计算公式
图3  不同植被类型典型站点的MODIS光谱反射率及光谱指数季节曲线及各季相区分度
分类特征较强较弱较强较弱较强较弱较强较弱
SWIR2BlueRedGreenNIRBlueRedGreenSWIR1BlueDVIGreenSWIR1DVIBlueNIR
TCWSWIR1NIRDVISWIR1RVISWIR2NDVISWIR2RedRSITCGSWIR2RSIGreen
NDVITCGDVIRVIRSINDVINIRNDVIRed
RVITCBTCGNDWIRVINDWIRVI
RSILSWINDWILSWITCG
NDWITCWLSWITCWTCB
LSWITCBTCW
TCB
数量统计2724724283226261
表4  各季节区分度强弱的分类特征数量统计

分类

特征

毛竹林-常绿针叶林毛竹林-常绿阔叶/针阔混交林

毛竹林-

耕地

毛竹林-

建设用地

毛竹林-水体常绿针叶林-常绿阔叶/针阔混交林常绿针叶林-耕地常绿阔叶/针阔混交林-耕地
Blue1.996 3721.999 7581.999 7581.999 9181.999 6991.994 5031.998 9311.999 879
Green1.998 9791.999 9911.999 9861.999 9791.999 8351.998 5241.999 6881.999 996
Red1.996 5291.999 4931.999 8152.000 0001.999 3631.993 3421.998 9761.999 904
NIR1.999 9992.000 0002.000 0001.999 9971.999 9382.000 0002.000 0002.000 000
SWIR11.999 9511.999 9992.000 0002.000 0001.999 8691.999 9601.999 9882.000 000
SWIR21.999 3481.999 9841.999 9791.999 9991.999 3851.999 0681.999 7801.999 987
RVI1.999 9742.000 0001.997 0881.993 2641.982 9842.000 0001.999 5941.999 995
NDWI2.000 0002.000 0002.000 0001.999 9991.012 2552.000 0002.000 0002.000 000
DVI1.999 9972.000 0001.999 9991.999 9581.999 7982.000 0001.999 9992.000 000
NDVI2.000 0002.000 0002.000 0001.999 9961.319 1372.000 0002.000 0002.000 000
TCB1.999 9952.000 0002.000 0002.000 0001.999 9851.999 9992.000 0002.000 000
TCW1.999 8581.999 9951.999 9961.999 9991.999 3541.999 8511.999 9461.999 998
TCG1.999 9701.999 9981.999 9431.999 1661.998 9171.999 9981.999 9091.999 994
LSWI1.990 3501.997 8851.580 1451.043 0331.135 5431.999 9641.930 2851.978 565
RSI1.457 0291.978 3271.909 8841.993 2551.087 4971.129 0631.632 3381.926 334
DEM1.983 3441.999 9871.999 7961.999 7881.999 9381.984 9371.982 3601.999 946
坡度1.997 4751.998 7511.986 3981.976 1931.987 5741.997 7421.978 2641.987 028
坡向1.998 7511.997 2231.997 7301.997 3761.996 8031.998 9581.998 9851.998 330

分类

特征

常绿针叶林-建设用地常绿针叶林-水体常绿阔叶/针阔混交林-建设用地常绿阔叶/针阔混交林-水体

耕地-

建设用地

耕地-水体建设用地-水体
Blue1.999 6461.998 3101.999 9561.999 8851.999 9451.999 7781.999 992
Green1.999 7311.999 0271.999 9911.999 9091.999 9911.999 9831.999 921
Red1.999 2781.997 2391.999 9551.999 9971.999 9451.999 6771.999 825
NIR1.999 9971.999 9062.000 0001.999 7172.000 0002.000 0001.999 864
SWIR11.999 9771.998 7552.000 0001.999 9432.000 0001.999 9731.999 973
SWIR21.999 9421.996 5462.000 0001.999 6561.999 9981.999 6241.999 977
RVI1.999 2811.997 5992.000 0001.999 9881.913 7801.838 8751.720 148
NDWI1.999 9970.879 9702.000 0001.392 8531.999 5601.495 4981.030 671
DVI1.999 9411.999 6621.999 9971.999 9791.999 9851.999 8981.998 091
NDVI1.999 9131.144 2952.000 0001.645 3701.986 2861.575 4171.999 050
TCB1.999 9971.999 8572.000 0001.999 9992.000 0002.000 0001.999 995
TCW1.999 9551.997 5912.000 0001.999 4451.999 9991.999 7031.999 821
TCG1.998 7381.998 2851.999 7511.999 7531.998 5371.998 0061.997 182
LSWI1.171 6701.514 8761.552 6931.583 9071.133 5490.710 8161.162 951
RSI1.976 5950.811 8871.994 1231.276 3741.992 0741.079 9331.667 178
DEM1.985 3201.981 8051.999 9801.979 7861.999 2401.999 6931.999 905
坡度1.955 0041.979 7871.975 0401.988 0271.797 5261.929 0241.940 467
坡向1.998 7711.998 6281.997 9871.997 6831.997 6951.997 2201.996 693
表5  不同分类特征的J-M距离
序号实验一实验二实验三实验四实验五实验六实验七实验八
分类时相2018/02/232018/03/112018/06/152018/10/052017/11/03

2018/02/23

2018/10/05

2018/03/11

2018/10/05

2018/06/15

2018/10/05

序号实验9实验10实验11实验12实验13实验14实验15实验16
分类时相

2018/02/23

2018/03/11

2018/02/23

2018/06/15

2018/03/11

2018/06/15

2018/02/23

2018/03/11

2018/06/15

2018/02/23

2018/03/11

2018/10/05

2018/02/23

2018/06/15

2018/10/05

2018/03/11

2018/06/15

2018/10/05

2018/02/23

2018/03/11

2018/06/15

2018/10/05

分类特征Blue、Green、Red、NIR、SWIR1、SWIR2, NDVI、DVI、NDWI、RVI、TCB、TCW
表6  不同时相分类实验设计
图4  各组实验分类精度(用户精度和制图精度均指毛竹林)
图5  2018年庆元县土地利用和毛竹林分布图 审图号:浙S(2021)41
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