基于GF-1/WFV数据的三江源草地月度NPP反演研究

doi:10.12082/dqxxkx.2018.180140

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (12): 1799-1809.doi: 10.12082/dqxxkx.2018.180140

基于GF-1/WFV数据的三江源草地月度NPP反演研究

袁烨城¹(), 李宝林^1,^*(), 王双², 孙庆龄^1,³, 张涛^1,³, 张志军⁴

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 中国标准化研究院,北京 100191
3. 中国科学院大学,北京 100049
3. 青海省生态环境遥感监测中心,西宁 810007

收稿日期:2018-03-19 出版日期:2018-12-25 发布日期:2018-12-20
通讯作者: 李宝林 E-mail:yuanyc@lreis.ac.cn;libl@lreis.ac.cn
作者简介:
作者简介：袁烨城（1983-）,男,浙江嵊州人,博士,主要从事生态环境质量评估和地理信息系统前沿技术的研究。E-mail: yuanyc@lreis.ac.cn
基金资助:
国家自然科学基金青年基金项目（41701475）;国家重点研发计划项目（2016YFC0500205）;国家自然科学基金创新群体项目（41421001）

Monthly Net Primary Production Estimation of Grassland in the Three-River Headwater Region Using GF-1/WFV Data

YUAN Yecheng¹(), LI Baolin^1,^*(), WANG Shuang², SUN Qingling^1,³, ZHANG Tao^1,³, ZHANG Zhijun⁴

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. China National Institute of Standardization, Beijing 100191, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Remote Sensing Monitoring Center of Qinghai Ecology and Environment, Xining 810007, China

Received:2018-03-19 Online:2018-12-25 Published:2018-12-20
Contact: LI Baolin E-mail:yuanyc@lreis.ac.cn;libl@lreis.ac.cn
Supported by:
National Natural Science Foundation of China, No.41701475;National Key Research and Development Program of China, No.2016YFC0500205;National Natural Science Foundation of China, No.41421001.

摘要/Abstract

摘要：

准确认识三江源植被生产力月度尺度的时空格局变化,对三江源畜牧业生产以及生态保护政策制定具有重要意义,可稳定获取的重访周期为4 d的16 m分辨率GF-1/WFV数据使中等空间分辨率的月度NPP产品生产成为可能。本文建立了一套以GF-1/WFV为基本数据源的中等空间分辨率草地月度NPP估算技术方法,并评估了其在三江源地区应用的可行性。在黄河源区玛多县的实验表明以GF-1/WFV为基础,以MODIS13Q1数据为补充,可以获得覆盖全区的中等空间分辨率月度NDVI数据,据其反演得到的草地NPP,地面验证精度在70%以上,优于MODIS NPP产品精度,且能更为详细地反映草地生产力变化的空间差异,在青海三江源地区利用GF-1/WFV数据生产中等空间分辨率的草地月度NPP产品是可行的。

关键词: GF-1/WFV, 三江源, 草地, NPP, CASA模型

Abstract:

This paper presented a method of monthly net primary production (NPP) estimation of grassland in the Three-River Headwater Region (TRH) based on GF-1/WFV data. First, a preprocessing of radiometric calibration and atmospheric correction is applied on GF-1/WFV 1A data by ENVI software. Secondly, geocoding is processed by Rational Function Model (RFM) with GF-1/WFV RPC (Rational Polynomial Coefficient) and the orthophoto images with high georeferenced accuracy are conducted after block adjustment. The processed GF-1/WFV data is comparable in space and time. Then, cloud and cloud shadow per scene are detected using Multi-feature Combined method; NDVI is retrieved based on GF-1/WFV image and monthly NDVI is generated by Maximum Value Composite (MVC) method. The values of pixels still affected by cloud or cloud shadow cover in monthly NDVI mosaic are extrapolated using linear regression using least square method based on MODIS 13Q1 NDVI. Finally monthly NPP of grassland is calculated based on Carnegie-Ames-Stanford Approach (CASA) with monthly NDVI and other variables including monthly total precipitation, monthly averaged temperature and monthly total solar radiation. A case study was conducted in Maduo country and results showed that: (1) reliable monthly NDVI data at medium spatial resolution can be obtained based on GF-1/WFV under the support of MODIS 13Q1 product; (2) The accuracy of estimated grassland NPP based on GF-1/WFV was over 70% based on field data validation, which is better than MODIS 17A3 NPP production and the former can occupied more detailed NPP spatial variation. Monthly NPP can be successfully estimated based on GF-1/WFV under the support of MODIS 13Q1 product in TRH. However, some details need to be improved for further study: (1) more area of cloud and cloud shadow in images, lower precision of the extrapolated NDVI and the error of simulated NPP may be greater; (2) in low temperature, NPP is 0 in CASA, which overestimates the grassland NPP because underground root of grassland is still alive in TRH in winter and NPP should be negative; (3) monthly NDVI generated by MVC represents the best growth situation of vegetation in the period, not the average one, which may overestimates NPP. Besides, mapping accuracy of vegetation type will also affect the simulated NPP result precision; (4) field data collection is difficulty due to the study area is in remote area of high altitude, so the current ground data is not enough to cover all months in growth season and the uncertainty of this method remains to be further tested.

Key words: GF-1/WFV, Three- River Headwater Region, grassland, NPP, CASA

袁烨城, 李宝林, 王双, 孙庆龄, 张涛, 张志军. 基于GF-1/WFV数据的三江源草地月度NPP反演研究[J]. 地球信息科学学报, 2018, 20(12): 1799-1809.DOI:10.12082/dqxxkx.2018.180140

YUAN Yecheng,LI Baolin,WANG Shuang,SUN Qingling,ZHANG Tao,ZHANG Zhijun. Monthly Net Primary Production Estimation of Grassland in the Three-River Headwater Region Using GF-1/WFV Data[J]. Journal of Geo-information Science, 2018, 20(12): 1799-1809.DOI:10.12082/dqxxkx.2018.180140

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参考文献 39

[39]	[ Zhou C P, Ou Yang H, Wang Q X, et al.Estimation of net primary productivity in Tibetan Plateau[J]. Acta Geographica Sinica, 2004,59(1):74-79. ] doi: 10.3321/j.issn:0375-5444.2004.01.009
[40]	陈卓奇,邵全琴,刘纪远,等.基于MODIS的青藏高原植被净初级生产力研究[J].中国科学:地球科学,2012,42(3):402-410.
	[ Chen Z Q, Shao Q Q, Liu J Y, et al.Analysis of net primary productivity of terrestrial vegetation on the Qinghai-Tibetan Plateau, based on MODIS remote sensing data[J]. Science China Earth Science, 2012,42(3):402-410. ]
[41]	郭晓寅,何勇,沈永平,等.基于MODIS资料的2000-2004年江河源区陆地植被净初级生产力分析[J].冰川冻土,2006,28(4):512-518.
	[ Guo X Y, He Y, Shen Y P, et al.Analysis of the terrestrial NPP based on the MODIS in the source regions of Yangtze and Yellow Rivers from 2000 to 2004[J]. Journal of Glaciology and Geocryology, 2006,28(4):512-518. ]
[42]	孙庆龄,李宝林,李飞,等.三江源植被净初级生产力估算研究进展[J].地理学报,2016,71(9):1596-1612. doi: 10.11821/dlxb201609011
[1]	高清竹,万运帆,李玉娥,等.基于CASA模型的藏北地区草地植被净第一性生产力及其时空格局[J].应用生态学报,2007,18(11):2526-2532.
	[ Gao Q Z, Wan Y F, Li Y E, et al.Grassland net primary productivity and its spatiotemporal distribution in Northern Tibet: A study with CASA model[J]. Chinese Journal of Applied Ecology, 2007,18(11):2526-2532. ]
[2]	Running S W.Ecology. A measurable planetary boundary for the biosphere[J]. Science, 2012,337(6101):1458-9. doi: 10.1126/science.1227620 pmid: 22997311
[3]	朴世龙,方精云. 1982-1999年青藏高原植被净第一性生产力及其时空变化[J].自然资源学报,2002,17(3):373-380. doi: 10.3321/j.issn:1000-3037.2002.03.020
	[ Pu S L, Fang J Y.Terrestrial net primary production and its spatio-temporal patterns in Qinghai-Xizang Plateau, China during 1982-1999[J]. Journal of Natural Resources, 2002,17(3):373-380. ] doi: 10.3321/j.issn:1000-3037.2002.03.020
[4]	秦大河. 三江源区生态保护与可持续发展[M].北京:科学出版社,2014.
	[ Qin D H.Ecological Protection and Sustainable Development in the Three-River Headwater Region[M]. Beijing: Science Press, 2014. ]
[5]	邵全琴,樊江文.三江源区生态系统综合监测与评估[M].科学出版社,2012.
	[ Shao Q Q, Fan J W, et al.Integrated monitoring and evaluation of ecosystems in the Three-River Headwater Region[M]. Beijing: Science Press, 2012. ]
[6]	张颖,陈怀艳,李建龙.三江源生态系统近10年净初级生产力估测[J].天津农业科学,2014,20(10):25-28. doi: 10.3969/j.issn.1006-6500.2014.10.006
	[ Zhang Y, Chen H Y, Li J L.Quantitative estimation for net primary productivity of Three-rivers source ecosystem in recently 10 years[J]. Tianjin Agricultural Sciences, 2014,20(10):25-28. ] doi: 10.3969/j.issn.1006-6500.2014.10.006
[7]	沃笑,吴良才,张继平,等.基于CASA模型的三江源地区植被净初级生产力遥感估算研究[J].干旱区资源与环境,2014,28(9):45-50.
	[ Wo X, Wu L C, Zhang J P, et al.Estimation of net primary production in the Three-river headwater region using CASA model[J]. Journal of Arid Land Resources and Environment, 2014,28(9):45-50. ]
[8]	蔡雨恋,郑有飞,王云龙,等.利用改进的CASA模型分析三江源区净植被生产力[J].南京信息工程大学学报,2013,5(1):34-42.
	[ Cai Y L, Zheng Y F, Wang Y L, et al.Analysis of terrestrial net primary productivity by improved CASA model in Three-river headwaters region[J]. Journal of Nanjing University of Information Science and Technology, 2013,5(1):34-42. ]
[9]	樊江文,邵全琴,刘纪远,等. 1988-2005年三江源草地产草量变化动态分析[J].草地学报,2010,18(1):5-10. doi: 10.11733/j.issn.1007-0435.2010.01.002
	[ Fan J W, Shao Q Q, Liu J Y, et al.Dynamic changes of grassland yield in Three-river headwater region from 1988 to 2005[J]. Acta Agrestia Sinica, 2010,18(1):5-10. ] doi: 10.11733/j.issn.1007-0435.2010.01.002
[10]	Fan J W, Shao Q Q, Liu J Y, et al.Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai-Tibet Plateau, China[J]. Environmental Monitoring and Assessment, 2010,170(1-4):571-584. doi: 10.1007/s10661-009-1258-1 pmid: 20041346
[11]	陈晓玲,曾永年.亚热带山地丘陵区植被NPP时空变化及其与气候因子的关系——以湖南省为例[J].地理学报,2016,71(1):35-48. doi: 10.11821/dlxb201601003
	[ Chen X L, Zeng Y N.Spatial and temporal variability of the net primary production (NPP) and its relationship with climate factors in subtropical mountainous and hilly regions of China: A case study in Hunan province[J]. Acta Geographica Sinica, 2016,71(1):35-48. ] doi: 10.11821/dlxb201601003
[12]	Li J, Cui Y, Liu J, et al.Estimation and analysis of net primary productivity by integrating MODIS remote sensing data with a light use efficiency model[J]. Ecological Modelling, 2013,252(1):3-10. doi: 10.1016/j.ecolmodel.2012.11.026
[13]	赵志平,吴晓莆,李果,等. 2009-2011年我国西南地区旱灾程度及其对植被净初级生产力的影响[J].生态学报,2015,35(2):350-360. doi: 10.5846/stxb201304040604
	[ Zhao Z P, Wu X P, Li G, Li J S.Drought in southwestern China and its impact on the net primary productivity of vegetation from 2009 to 2011[J]. Acta Ecologica Sinica, 2015,35(2):350-360. ] doi: 10.5846/stxb201304040604
[14]	徐剑波,宋立生,赵之重,等.近15 a来黄河源地区玛多县草地植被退化的遥感动态监测[J].干旱区地理,2012,35(4):615-622.
	[ Xu J B, Song L S, Zhao Z Z, et al.Monitoring grassland degradation dynamically at Maduo County in source region of Yellow River in past 15 years based on remote sensing[J]. Arid Land Geography, 2012,35(4):615-622. ]
[15]	封建民,王涛,齐善忠,等.黄河源区土地沙漠化的动态变化及成因分析——以玛多县为例[J].水土保持学报,2004,18(3):141-145. doi: 10.3321/j.issn:1009-2242.2004.03.036
	[ Feng J M, Wang T, Qi S Z, et al.Estimation of net primary productivity in Tibetan Plateau study on dynamic changes of land desertification and causal analysis in source region of Yellow River: A case study of Maduo County[J]. Journal of Soil and Water Conservation, 2004,18(3):141-145. ] doi: 10.3321/j.issn:1009-2242.2004.03.036
[16]	张镱锂,刘林山,摆万奇,等.黄河源地区草地退化空间特征[J].地理学报,2006,61(1):3-14. doi: 10.3321/j.issn:0375-5444.2006.01.001
	[ Zhang Y L, Liu L S, Bai W Q, et al.Grassland degradation in the source region of the Yellow River[J]. Acta Geographica Sinica, 2006,61(1):3-14. ] doi: 10.3321/j.issn:0375-5444.2006.01.001
[17]	伏洋,张国胜,李凤霞,等.青海省草地生态环境变化态势及驱动力分析[J].草业科学,2007,24(5):1-8. doi: 10.3969/j.issn.1001-0629.2007.05.001
	[ Fu Y, Zhang G S, Li F X, et al.Analysis on rangeland eco-environment change situation and driving force in Qinghai Province[J]. Pratacultural Science, 2007,24(5):1-8. ] doi: 10.3969/j.issn.1001-0629.2007.05.001
[18]	宋春桥,柯灵红,游松财,等.基于TIMESAT的3种时序NDVI拟合方法比较研究——以藏北草地为例[J].遥感技术与应用,2011,26(2):147-155.
	[ Song C Q, Ke L H, You S C, et al.Comparison of three NDVI Time-series fitting methods based on Timesat-taking the grassland in Northern Tibet as case[J]. Remote Sensing Technology and Application, 2011,26(2):147-155. ]
[19]	Hutchinson M F.Interpolating mean rainfall using thin plate smoothing splines[J]. International Journal of Geographical Information Systems, 1995,9(4):385-403. doi: 10.1080/02693799508902045
[20]	Hutchinson M F.A locally adaptive approach to the interpolation of digital elevation models[J]. Molecular Microbiology, 1996,47(5):1395-1406.
[21]	胡自治,孙吉雄,张映生,等.高山线叶嵩草草地的第一性生产和光能转化率[J].生态学报,1988,8(2):183-190.
	[ Hu Z Z, Sun J X, Zhang Y S, et al.Studies on production and energy Efficiency in Tianzhu Alpine Kobreisa Capillifolia Meadow[J]. Acta Ecologica Sinica, 1988,8(2):183-190. ]
[22]	Gill R A, Kelly R H, Parton W J, et al.Using simple environmental variables to estimate below-ground productivity in grasslands[J]. Global Ecology & Biogeography, 2002,11(1):79-86. doi: 10.1046/j.1466-822X.2001.00267.x
[23]	吴伊波,车荣晓,马双,等.高寒草甸植被细根生产和周转的比较研究[J].生态学报,2014,34(13):3529-3537. doi: 10.5846/stxb201307031831
	[ Wu Y B, Che X R, Ma S, et al.Estimation of root production and turnover in an alpine meadow: Comparison of three measurement methods[J]. Acta Ecologica Sinica, 2014,34(13):3529-3537. ] doi: 10.5846/stxb201307031831
[24]	Yang Y, Fang J, Ji C, et al.Above- and belowground biomass allocation in Tibetan grasslands[J]. Journal of Vegetation Science, 2009,20(1):177-184. doi: 10.1111/j.1654-1103.2009.05566.x
[25]	王密,杨博,李德仁,等.资源三号全国无控制整体区域网平差关键技术及应用[J].武汉大学学报·信息科学版,2017,42(4):427-433. doi: 10.13203/j.whugis20160534
	[ Wang M, Yang B, Li D R, et al.Technologies and applications of block adjustment without control for ZY-3 images covering China[J]. Geomatics and Information Science of Wuhan University, 2017,42(4):427-433. ] doi: 10.13203/j.whugis20160534
[26]	Zhang Y, Wan Y, Wang B, et al.Automatic processing of chinese GF-1 wide field of view IMAGES, Schreier G, Skrovseth P E, Staudenrausch H, editor, 36th International Symposium on Remote Sensing of Environment, 2015:729-734.
[27]	Li Z W, Shen H F, Li H F, et al.Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery[J]. Remote Sensing of Environment, 2017,191(3):342-358. doi: 10.1016/j.rse.2017.01.026
[28]	Zuritamilla R, Kaiser G, Clevers J G P W, et al. Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics[J]. Remote Sensing of Environment, 2009,113(9):1874-1885. doi: 10.1016/j.rse.2009.04.011
[29]	Zheng Y, Wu B F, Zhang M, et al.Crop phenology detection using high spatio-temporal resolution data fused from SPOT5 and MODIS products[J]. Sensors, 2016,16(12):2099-2120. doi: 10.3390/s16122099 pmid: 5191079
[30]	Potter C S, Randerson J T, Field C B, et al.Terrestrial ecosystem production: A process model based on global satellite and surface data[J]. Global Biogeochemical Cycles, 1993,7(4):811-841. doi: 10.1029/93GB02725
[31]	朱文泉,潘耀忠,何浩,等.中国典型植被最大光利用率模拟[J].科学通报,2006,51(6):700-706. doi: 10.3321/j.issn:0023-074X.2006.06.014
	[ Zhu W Q, Pan Y Z, He H, et al.Simulation of maximum light use efficiency of typical vegetation in China[J]. Chinese Science Bulletin, 2006,51(6):700-706. ] doi: 10.3321/j.issn:0023-074X.2006.06.014
[32]	朱文泉,陈云浩,潘耀忠,等.基于GIS和RS的中国植被光利用率估算[J].武汉大学学报·信息科学版,2004,29(8):694-698. doi: 10.3321/j.issn:1671-8860.2004.08.009
	[ Zhu W Q, Chen Y H, Pan Y Z, et al.Method for simulative calculation for design of GPS horigontal network and its application[J]. Geomatics and Information Science of Wuhan University, 2004,29(8):694-698. ] doi: 10.3321/j.issn:1671-8860.2004.08.009
[33]	Ruimy A, Saugier B, Dedieu G.Methodology for the estimation of terrestrial net primary production from remotely sensed data[J]. Journal of Geophysical Research-Atmospheres, 1994,99(D3):5263-5283. doi: 10.1029/93JD03221
[34]	Hatfield J L, Asrar G, Kanemasu E T.Intercepted photosynthetically active radiation estimated by spectral reflectance[J]. Remote Sensing of Environment, 1984,14(1):65-75. doi: 10.1016/0034-4257(84)90008-7
[35]	Sellers P J.Canopy reflectance, photosynthesis and transpiration[J]. International Journal of Remote Sensing, 1985,6(6):1335-1372. doi: 10.1080/01431168508948283
[36]	卫亚星,王莉雯.青海省植被光能利用率模拟研究[J].生态学报,2010,30(19):5209-5216.
	[ Wei Y X, Wang L W.The study on simulating light use efficiency of vegetation in Qinghai Province[J]. Acta Ecologica Sinica, 2010,30(19):5209-5216. ]
[37]	Field C B, Randerson J T, Malmström C M.Global net primary production: Combining ecology and remote sensing[J]. Remote Sensing of Environment, 1995,51(1):74-88. doi: 10.1016/0034-4257(94)00066-V
[38]	朴世龙,方精云,郭庆华.1982-1999年我国植被净第一性生产力及其时空变化[J].北京大学学报(自然科学版),2001,37(4):563-569. doi: 10.3321/j.issn:0479-8023.2001.04.019
	[ Pu S L, Fan J W, Guo Q H.Terrestrial net primary production and its spatio-temporal patterns in China during 1982-1999[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2001,37(4):563-569. ] doi: 10.3321/j.issn:0479-8023.2001.04.019
[42]	[ Sun Q L, Li B L, Li F, et al.Review on the estimation of net primary productivity of vegetation in the Three-River Headwater Region, China[J]. Acta Geographica Sinica, 2016,71(9):1596-1612. ] doi: 10.11821/dlxb201609011
[39]	周才平,欧阳华,王勤学,等.青藏高原主要生态系统净初级生产力的估算[J].地理学报,2004,59(1):74-79. doi: 10.3321/j.issn:0375-5444.2004.01.009

类型	R/S	根系周转率	地下部分含碳量	地上部分含碳量	干鲜比
高寒草甸	6.8	0.65	0.37	0.43	0.5
高寒草甸草原	6.0	0.58	0.37	0.43	0.5
高寒草原	5.2	0.50	0.37	0.43	0.5
高寒荒漠	4.4	0.50	0.37	0.43	0.5

类型	NDVI 最大值	NDVI 最小值	最大光能利用率/（gC/MJ）
高寒草甸	0.537	0.023	0.302
高寒草甸草原	0.469	0.023	0.273
高寒草原	0.382	0.023	0.229
高寒荒漠	0.356	0.023	0.150

	云和云影检测		缺失数据插补
	综合精度/%	Kappa系数	RMSE	MAPE/%
5月	91.5	0.43	-	-
6月	89.5	0.83	0.057	45.8
7月	91.5	0.78	0.081	27.4
8月	94.5	0.58	-	-
9月	94.0	0.79	-	-
均值	92.2	0.76	0.062	34.4

基于GF-1/WFV数据的三江源草地月度NPP反演研究

Monthly Net Primary Production Estimation of Grassland in the Three-River Headwater Region Using GF-1/WFV Data

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