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自然资源遥感  2023, Vol. 35 Issue (3): 274-283    DOI: 10.6046/zrzyyg.2022221
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
GF-6与Landsat8混合像元分解的石漠化信息提取差异研究——以普定县为例
张士博1,2(), 胡文敏2,3,4(), 韩祯颖1, 李果2,3, 王忠诚1, 高志海4
1.中南林业科技大学林学院,长沙 410004
2.湖南省自然保护地风景资源大数据工程技术研究中心,长沙 410004
3.中南林业科技大学风景园林学院,长沙 410004
4.中国林业科学研究院资源信息研究所,北京 100001
Differences in rocky desertification information extracted from GF-6 and Landsat8 using the pixel unmixing method: A case study of Puding County
ZHANG Shibo1,2(), HU Wenmin2,3,4(), HAN Zhenying1, LI Guo2,3, WANG Zhongcheng1, GAO Zhihai4
1. College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
2. Engineering Technology Research Center of Big Data for Landscape Resources in Natural Protected Areas of Hunan Province, Changsha 410004, China
3. Department of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
4. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100001, China
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摘要 

目前不同中空间分辨率遥感卫星在利用混合像元分解方法提取石漠化信息上存在效果差异,比较不同遥感卫星在提取石漠化信息上的差异,有助于进一步提高石漠化信息提取精度。本研究以贵州省普定县为例,采用GF-6与Landsat8卫星数据,利用顶点成分分析(vertex component analysis,VCA)和完全约束最小二乘法(fully constrained least squares,FCLS)相结合的混合像元分解方法进行石漠化信息提取,探究GF-6与Landsat8在石漠化信息提取的端元特征和等级差异,以此探索GF-6在提取石漠化信息的可行性与有效性。研究结果表明: ①红边波段范围上,GF-6植被端元波谱曲线明显区别于基岩与土壤端元,更易识别出植被端元; ②石漠化信息端元提取精度上,GF-6和Landsat8提取植被端元OA分别为0.63和0.45,Kappa系数分别为0.50和0.29,RMSE分别为1.19和1.71,GF-6和Landsat8提取基岩端元OA分别为0.79和0.61,Kappa系数分别为0.63和0.42,RMSE分别为0.54和0.88; ③石漠化等级评价上,GF-6和Landsat8提取石漠化等级OA分别为0.76和0.59,Kappa系数分别为0.56和0.38,RMSE分别为0.64和1.27。因此,GF-6在混合像元分解提取石漠化信息精度要优于Landsat8,且GF-6的红边波段能更好地识别石漠化区域植被信息,基于GF-6的混合像元分解方法可作为一种石漠化监测手段应用于实际工作中。
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张士博
胡文敏
韩祯颖
李果
王忠诚
高志海
关键词 ： 石漠化,  GF-6,  顶点成分分析法,  完全约束最小二乘法,  混合像元分解    
Abstract：

Different moderate-resolution remote sensing satellites exhibit various effects in extracting rocky desertification information using the pixel unmixing method. Comparing these various effects can help further improve the extraction accuracy of rocky desertification information. By extracting information on rocky desertification in Puding County, Guizhou Province from GF-6 and Landsat8 using the pixel unmixing method, this study investigated the end member characteristics and desertification grade differences between GF-6 and Landsat8. Furthermore, this study explored the feasibility and effectiveness of GF-6 in extracting rocky desertification information. The results are as follows: ① Within the red-edge band of GF-6 data, the vegetation end member exhibited significantly different spectrum curves from bedrock and soil end members, making it easier to identify the vegetation end member. ② In terms of end member extraction accuracy of rocky desertification information, GF-6 and Landsat8 yielded overall accuracy (OA) of 0.63 and 0.45 in extracting the vegetation end member, respectively, corresponding to Kappa coefficients of 0.50 and 0.29 and RMSEs of 1.19 and 1.71, respectively. Moreover, GF-6 and Landsat8 yielded OA of 0.79 and 0.61 in extracting the bedrock end member, respectively, corresponding to Kappa coefficients of 0.63 and 0.42 and RMSEs of 0.54 and 0.88, respectively. ③ In the evaluation of rocky desertification grades, GF-6 and Landsat8 yielded OA of 0.76 and 0.59 in extracting rocky desertification grades, respectively, corresponding to Kappa coefficients of 0.56 and 0.38 and RMSEs of 0.64 and 1.27. Therefore, GF-6 outperforms Landsat8 in the accuracy of extracting rocky desertification information using the pixel unmixing method. In addition, the red-edge band of GF-6 data can effectively identify the vegetation information in areas with rocky desertification. In summary, the pixel unmixing method based on GF-6 data can be practically applied to rocky desertification monitoring.
Key words： rocky desertification    GF-6    vertex component analysis method    fully constrained least squares method    pixel unmixing
收稿日期: 2022-05-26      出版日期: 2023-09-19
ZTFLH:  TP79  
基金资助:国家科技重大专项“高分辨率对地观测系统重大专项: 高分林业遥感应用示范系统(二期)”(21-Y30B02-9001-19/22);湖南省教育厅科学研究重点项目“亚热带丘岗区森林生态系统碳储功能评估及其技术优化”(21A0153);湖南省自然资源厅标准项目“国土空间资源环境开发利用生态风险本底调查与评价标准”(202259)
通讯作者: 胡文敏(1985-),男,博士,研究方向为环境遥感技术应用。Email: wenmin115@163.com。
作者简介: 张士博(1999-),男,硕士研究生,研究方向为水土保持与荒漠化防治。Email: 747883829@qq.com。
引用本文:   
张士博, 胡文敏, 韩祯颖, 李果, 王忠诚, 高志海. GF-6与Landsat8混合像元分解的石漠化信息提取差异研究——以普定县为例[J]. 自然资源遥感, 2023, 35(3): 274-283.
ZHANG Shibo, HU Wenmin, HAN Zhenying, LI Guo, WANG Zhongcheng, GAO Zhihai. Differences in rocky desertification information extracted from GF-6 and Landsat8 using the pixel unmixing method: A case study of Puding County. Remote Sensing for Natural Resources, 2023, 35(3): 274-283.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022221      或      https://www.gtzyyg.com/CN/Y2023/V35/I3/274
Fig.1  普定县位置示意图
波段编号 GF-6 WFV Landsat8 OLI
波长范围/μm 空间分
辨率/ m		 波长范围/μm 空间分
辨率/ m
Band1 0.45~0.52 16 0.433~0.453 30
Band2 0.52~0.60 0.450~0.515
Band3 0.63~0.69 0.525~0.600
Band4 0.76~0.90 0.630~0.680
Band5 0.69~0.73
(红边波段Ⅰ)
0.845~0.885



1.560~1.660
Band6 0.73~0.77
(红边波段Ⅱ)




2.100~2.300





—
Band7 0.40~0.45
Band8 0.59~0.63
Tab.1  GF-6与Landsat8波段对应信息
Fig.2  技术路线图
编号 石漠化程度 植被覆盖率/% 基岩裸露率/% 等级
1 无石漠化 (70,100] (0,20] 1
2 潜在石漠化 (50,70] (20,30] 2
3 轻度石漠化 (30,50] (30,50] 3
4 中度石漠化 (20,30] (50,70] 4
5 重度石漠化 (10,20] (70,90] 5
6 极重度石漠化 (0,10] (90,100] 6
Tab.2  石漠化等级评价指标与标准
石漠化等级 遥感影像 植被覆盖度/% 基岩裸露率/% 说明 实际调查照片 样点个数
无石漠化 (70,100] (0,20] 影像为饱和度很高的绿色,有明显的颗粒状地物且分布密集 4
潜在石漠化 (50,70] (20,30] 影像为饱和度较高的浅绿色,有颗粒状地物但分布稀疏 14
轻度石漠化 (30,50] (30,50] 影像为中度饱和度的浅绿色,几乎无颗状地物分布 29
中度石漠化 (20,30] (50,70] 影像为饱和度较低的浅绿色,无颗粒状地物分布,有明显的人类活动迹象 109
重度石漠化 (10,20] (70,90] 影像为棕色,无颗粒状地物分布,有人类活动的迹象但不明显 20
极重度石漠化 (0,10] (90,100] 影像颜色偏银色,无明显地物分布,无人类活动迹象 2
Tab.3  遥感解译标志
Fig.3  植被、基岩与土壤波谱曲线
Fig.4  GF-6和与Landsat8植被端元分布
Fig.5  GF-6和Landsat8基岩端元分布
Fig.6  植被端元和基岩端元精度验证
Fig.7  GF-6和Landsat8各等级石漠化分布与石漠化面积占比
石漠化等级 GF-6 Landsat8
UA PA OA Kappa
系数		 RMSE UA PA OA Kappa
系数		 RMSE
无石漠化 0.75 1.00 0.76 0.56 0.64 0.25 1.00 0.59 0.38 1.27
潜在石漠化 0.79 0.61 0.36 0.71
轻度石漠化 0.59 0.94 0.48 0.70
中度石漠化 0.87 0.84 0.65 0.86
重度石漠化 0.45 0.38 0.64 0.26
Tab.4  GF-6与Landsat8各等级石漠化精度评价
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