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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 274-283     DOI: 10.6046/zrzyyg.2022221
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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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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.
Keywords rocky desertification      GF-6      vertex component analysis method      fully constrained least squares method      pixel unmixing     
ZTFLH:  TP79  
Issue Date: 19 September 2023
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Shibo ZHANG
Wenmin HU
Zhenying HAN
Guo LI
Zhongcheng WANG
Zhihai GAO
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Shibo ZHANG,Wenmin HU,Zhenying HAN, et al. Differences in rocky desertification information extracted from GF-6 and Landsat8 using the pixel unmixing method: A case study of Puding County[J]. Remote Sensing for Natural Resources, 2023, 35(3): 274-283.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022221     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/274
Fig.1  Location map of Puding County
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 and Landsat8 band correspondence information
Fig.2  Technology roadmap
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  Rocky desertification grade evaluation indicators and criteria
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  Remote sensing interpretation flags
Fig.3  Vegetation, bedrock and soil spectral curves
Fig.4  Distribution of vegetation endmembers in GF-6 and Landsat8
Fig.5  Distribution of bedrock endmembers in GF-6 and Landsat8
Fig.6  Accuracy verification of vegetation endmember and bedrock endmember
Fig.7  Distribution of grade and the percentage of rocky desertification area in GF-6 and Landsat8
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  Accuracy evaluation of rocky desertification at various levels in GF-6 and Landsat8
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