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Remote Sensing Technology and Application  2020, Vol. 35 Issue (5): 1070-1078    DOI: 10.11873/j.issn.1004-0323.2020.5.1070
    
Topographic Correction of Leaf Area Index Product Derived from Remote Sensing Data
Yuetong Hu1,2(),Shuang Wu1,2,Xianfeng Feng1,2(), LiuYang1
1.State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy Sciences,Beijing 100101,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
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Abstract  

Terrain correction is an important approach to improve the accuracy of remote sensing quantification of surface parameters in complex terrain areas. The widely used remote sensing Leaf Area Index(LAI)productsalwayshave certain terrain error. It has a great importance to eliminate the influence of terrain and improve LAIproducts’ accuracy. Taking the Qianyanzhou area of Jiangxi Province as the research area, the paper aims to establish a terrain correction model which takes terrain error into account to promote the accuracy of GLOBMAP LAI product. Based on the measured LAI data, Landsat TM data, GLOBMAP LAI product and elevation data, the model achieved terrain correction by establishing the index relationship between elevation standard deviation and LAI product values. The terrain correction model of GLOBMAP LAI product was established , and then used to correct the product in the study area. The results indicated that the corrected leaf area index was closer to the ground measured data, and the RMSE between the LAI product and the ground measurement decreases from 2.11 to 2.04. The standard deviation of the corrected LAI dataset was reduced from 2.08 to 1.69, which meant the terrain error could be eliminated. The method in this paper had well completed the terrain correction of LAIproduct. The model is meaningful to improve the accuracy of LAI product.

Key words:  Qianyanzhou area      Leaf Area Index (LAI)      Topographic correction      Standard deviation of elevation      LAI product     
Received:  21 May 2019      Published:  26 November 2020
ZTFLH:  TP79  
Corresponding Authors:  Xianfeng Feng     E-mail:  huyuetong15@mails.ucas.ac.cn;fengxf@lreis.ac.cn
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Yuetong Hu
Shuang Wu
Xianfeng Feng
LiuYang

Cite this article: 

Yuetong Hu,Shuang Wu,Xianfeng Feng, LiuYang. Topographic Correction of Leaf Area Index Product Derived from Remote Sensing Data. Remote Sensing Technology and Application, 2020, 35(5): 1070-1078.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.5.1070     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I5/1070

Fig.1  The study area
数据项数据格式年份空间尺度时间尺度来源
高程数据栅格200930 m地理空间数据云
GlobeLand30土地覆盖数据栅格201030 m国家基础地理信息中心
中国植被图矢量20011:100万中国科学院中国植被图编辑委员会
GLOBMAP LAI产品栅格2008500 m8 d中科院地理所
LAI实测数据文本2008//南京大学
LandsatTM数据栅格200830 m
Table 1  Data list
Fig.2  Theworkflowofthisstudy
Fig.3  DEM and standard deyiation of elevation
植被类型GlobeLand30中国植被图
1 针叶林201
2 阔叶林203
3 针阔混交林202
4 灌丛404
5 草和农作物10,307,8,11
0 非植被809,0
Table 2  Conversion relationship of classification codes used in different land cover datasets
Fig.4  TM LAI of Qianyanzhou
Fig.5  Correlation between terrain characteristics and LAI
Fig.6  Correlation between σ and TM LAI
  
植被类型p1p2p3p4R2
针叶林1.02×10-5-1.60×10-4-6.92×10-20.500.91
阔叶林-3.61×10-54.21×10-31.63×10-20.860.98
灌丛-4.00×10-8-6.11×10-4-3.32×10-30.160.98
草和农作物1.43×10-51.35×10-3-1.26×10-20.570.90
不区分类型2.09×10-51.83×10-3-6.81×10-30.440.88
Table 3  Parameters of the topographic correction model
Fig.8  GLOBMAP LAI and corrected LAI
Fig.9  Comparisons of originalLAI、corrected LAIandtrue LAI(field measurement)
统计参数地形校正后地形校正前
最大值10.2310.07
最小值0.000.04
均值2.102.80
标准差1.692.08
Table 4  Statistical analysis between original LAI and corrected LAI
1 Liu Yang, Liu Ronggao, Chen Jingming, et al. Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data[J]. Geo-information Science, 2013, 15(5): 734-743.
1 刘洋, 刘荣高, 陈镜明, 等. 叶面积指数遥感反演研究进展与展望[J]. 地球信息科学学报, 2013, 15(5): 734-743.
2 Chen J M,Cihlar J. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images[J]. Remote Sensing of Environment, 1996(55): 153-162.
3 Chen J M, Black T. Defining Leaf Area Index for Non‐flat Leaves[J]. Plant, Cell & Environment, 1992, 15(4): 421-429.
4 Fang Xiuqin, Zhang Wanchang. The Application of Remotely Sensed Data to the Estimation of the Leaf Area Index[J]. Remote Sensing for Land and Resources, 2003,15(3): 58-62.
4 方秀琴, 张万昌. 叶面积指数(LAI)的遥感定量方法综述[J]. 国土资源遥感, 2003,15(3): 58-62.
5 Yan K, Park T, Yan G, et al. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements[J]. Remote Sensing, 2016, 8(5): 359.doi: .
doi: 10.3390/rs8050359
6 Verger A, Baret F, Weiss M. Near Real-time Vegetation Monitoring at Global Scale[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3473-3481.
7 Ganguly S, Schull M A, Samanta A, et al. Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 1: Theory[J]. Remote Sensing of Environment, 2008, 112(12): 4333-4343.
8 Xiao Z, Liang S, Wang J, et al. Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-Series MODIS Surface Reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 209-223.
9 Liu Yang, Liu Ronggao. Retrieval of Global Long-term Leaf Area Index from LTDR AVHRR and MODIS Observations. Journal of Geo-information Science, 2015, 17(11): 1304-1312.[刘洋, 刘荣高. 基于LTDRAVHRR和MODIS观测的全球长时间序列叶面积指数遥感反演[J].地球信息科学学报, 2015, 17(11): 1304-1312.]
10 Holben B, Justice C. An Examination of Spectral Band Ratioing to Reduce the Topographic Effect on Remotely Sensed Data[J]. International Journal of Remote Sensing, 1981, 2(2): 115-133.
11 Civco D L. Topographic Normalization of Landsat Thematic Mapper Digital Imagery[J]. Photogrammetric Engineering and Remote Sensing, 1989, 55(9): 1303-1309.
12 Jing Jincheng, Jin Hua’an, Tang Bin, et al. Intercomparison and Evaluation of Influencing Factors among Different LAI Products over Mountainous Areas[J]. Journal of Natural Resources, 2019, 34(2): 400-411.
12 景金城, 靳华安, 唐斌,等. 山区LAI遥感产品对比分析及影响因子评价[J]. 自然资源学报, 2019, 34(2): 400-411.
13 Xie X, Li A, Jin H, et al. Assessment of Five Satellite-derived LAI Datasets for GPP Estimations Through Ecosystem Models[J]. Science of The Total Environment, 2019, 690: 1120-1130.
14 Yu W, Li J, Liu Q, Topographic Effects on Leaf Area Index Retrieval by Remote Sensing Approach[C]∥ IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: IEEE, 2019.
15 Yu W, Li J, Liu Q, et al.A Simulation-based Analysis of Topographic Effects on LAI Inversion over Sloped Terrain[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 794-806.
16 Yang Yongshuai, Li Ainong, Jin Hua'an, et al. Intercomparison Among GEOV1, GLASS and MODIS LAI Products over Mountainous Area in Southwestern China[J]. Remote Sensing Technology and Application, 2016, 31(3):438-450.
16 杨勇帅, 李爱农, 靳华安,等. 中国西南山区GEOV1、GLASS和MODIS LAI产品的对比分析[J]. 遥感技术与应用, 2016, 31(3): 438-450.
17 Jin H, Li A, Bian J, et al. Intercomparison and Validation of Modis and Glass Leaf Area Index (LAI) Products over Mountain Areas: A Case Study in Southwestern China[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 55: 52-67.
18 Chen W, Cao C. Topographic Correction-based Retrieval of Leaf Area Index in Mountain Areas[J]. Journal of Mountain Science, 2012, 9(2): 166-174.
19 Jin Hua’an, Li Ainong, Bian Jinhu, et al. Leaf Area Index (LAI) Estimation from Remotely Sensed Observations in Different Topographic Gradients over Southwestern China[J]. Remote Sensing Technology and Application, 2016, 31(1): 42-50.
19 靳华安, 李爱农, 边金虎, 等. 西南地区不同山地环境梯度叶面积指数遥感反演[J]. 遥感技术与应用, 2016, 31(1): 42-50.
20 Li Yingcheng. Analysis and Correction of Topographic Effect of Digital Remote Sensing Images [J]. Beijing Surveying and Mapping, 1994, (2):14-19.
20 李英成. 数字遥感影像地形效应分析及校正[J]. 北京测绘, 1994, (2): 14-19.
21 Gao Yong, Zhang Wanchang. Comparison Test and Research Progress of Topographic Correction on Remotely Sensed Data[J]. Geographic Reaseach, 2008, (2): 467-477,484.
21 高永, 张万昌. 遥感影像地形校正研究进展及其比较实验[J]. 地理研究, 2008, (2): 467-477,484.
22 Teillet P M, Guindon B, Goodenough D G. On the Slope-aspect Correction of Multispectral Scanner Data[J]. Canadian Journal of Remote Sensing, 1982, 8(2) : 1537-1540.
23 Civco D L. Topographic Normalization of Landsat Thematic Mapper Digital Imagery[J]. Photogramm Etric Engineering and Remote Sensing, 1989, 55(9): 1303-1309.
24 Gu D, Gillespie A. Topographic Normalization of Landsat TM Images of Forest based on Subpixel Sun-canopy-sensor Geometry[J]. Remote Sensing of Environment, 1998, 64: 166-175.
25 Soenen S A, Peddle D R, Coburn C A. SCS+C: A Modified Sun- canopy-sensor Topographic Correction in Forested Terrain[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(9) : 2148- 2159.
26 Liao Yubing, Chen Xinfang, Chen Xi, et al. Effect of Topographic Correction on the Estimation of Leaf Area Index based on Landsat TM[J]. Remote Sensing Information, 2011(5): 47-51,64.廖钰冰, 陈新芳, 陈喜, 等. 地形校正对叶面积指数遥感估算的影响[J]. 遥感信息, 2011(5): 47-51,64.
27 Xia Xueqi, Tian Jiuqing, Du Fenglan. Analysis of Topographical Effect on Retrieval of LAI from Remotely Sensed Data[J]. Remote Sensing Information, 2004(2): 16-19,37.
27 夏学齐, 田庆久, 杜凤兰. 遥感提取叶面积指数的地形影响分析[J]. 遥感信息, 2004(2): 16-19,37.
28 Cai Wenjie, Sha Jinming.Inversion of Leaf Area Index based on Geographical Environment Factors[J]. Journal of Subtropical Resources and Environment, 2019,14(2): 55-64.
28 蔡雯洁, 沙晋明. 寄语地理环境要素的叶面积指数遥感定量反演[J]. 亚热带资源与环境学报, 2019,14(2): 55-64.
29 Jin H, Li A, Xu W, et al. Evaluation of Topographic Effects on Multiscale Leaf Area Index Estimation Using Remotely Sensed Observations from Multiple Sensors[J].ISPRS Journal of Photogrammetry and Remote Sensing,2019,154: 176-188.
30 Xiao Z Q,Liang S L,Jiang B.Evaluation of Four Long Time-series Global Leaf Area Index Products[J]. Agricultural and Forest Meteorology, 2017,246:218-230.
31 Li Xianfeng, Ju Weimin, Chen Shu, et al. Influence of Land Cover Data on Regional Forest Leaf Area Index Inversion[J]. Journal of Remote Sensing, 2010, 14(5): 974-989.
31 李显风, 居为民, 陈姝, 等. 地表覆盖分类数据对区域森林叶面积指数反演的影响[J]. 遥感学报, 2010, 14(5): 974-989.
32 Liu Yibo, Ju Weimin, Zhu Gaolong, et al. Retrieval of Leaf Area Index for Different Grasslands in Inner Mongolia Prairie Using Remote Sensing Data[J].Acta Ecologica Sinica, 2011, 31(18): 5159-5170.
32 柳艺博, 居为民, 朱高龙, 等. 内蒙古不同类型草地叶面积指数遥感估算[J]. 生态学报, 2011, 31(18): 5159-5170.
33 Chen Jun, Liao Anping, Chen Jin, et al. 30-Meter Global Land Cover Data Product-GlobeLand30[J]. Geomatics world, 2017, 24(1): 1-8.
33 陈军, 廖安平, 陈晋, 等. 全球30m地表覆盖遥感数据产品-GlobeLand30[J]. 地理信息世界, 2017, 24(1): 1-8.
34 Pisek J, Chen J M, Lacaze R, et al. Expanding Global Mapping of the Foliage Clumping Index with Multi-angular Polder Three Measurements: Evaluation and Topographic Compensation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(4): 341-346.
35 Zhu G, Ju W, Chen J M, et al. Foliage Clumping Index over China's Landmass Retrieved from the Modis Brdf Parameters Product[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(6): 2122-2137.
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