基于多种回归分析方法的西北干旱区植被NPP遥感反演研究

doi:10.18402/resci.2017.03.16

摘要/Abstract

摘要：

植被净初级生产力(Net Primary Productivity,NPP)是评价陆地生态系统的重要参数,植被NPP的反演为全球碳循环的研究提供了重要的参考依据。本文基于2000-2014年的MODIS及气象站点数据,引入逐步线性回归、主成分回归、偏最小二乘回归以及岭回归分析,构建了多元逐步回归模型、主成分回归模型、偏最小二乘回归模型和岭回归模型,并对这4种模型进行了评估。结果表明：①多元逐步回归模型被认为是一种可以简单、高效、准确地反演西北干旱区植被NPP的模型,且模拟结果与MOD17A3产品及实测值具有很强的相关性;②西北干旱区多年平均植被NPP的分布具有明显的区域差异,山区增加,平原区减少,呈现出北部及西北部高,而南部及东南部低的特征;③西北干旱区NPP在年际变化上呈波动增加趋势,线性增长率为0.40gC/(m·a)。自2000年以来,58.66%区域的NPP呈现增长趋势,13.64%的地区保持不变,27.7%的地区表现为退化趋势。

关键词: 净初级生产力, 逐步多元回归, 主成分回归, 偏最小二乘回归, 岭回归, 西北地区

Abstract:

Vegetation Net Primary Productivity(NPP)is an important parameter when evaluating terrestrial ecosystems and provides a significant reference for research into global carbon cycles. Based on MODIS data and meteorological station data from 2000 to 2014,we used multi-stepwise regression,principal components regression,partial least-squares regression and ridge regression and estimated vegetation NPP and temporal-spatial distribution patterns in the arid region of Northwest China. We found that the multi-stepwise regression model was superior and the model simulation results were coordinated with measured values in the spatial distribution. A multi-stepwise regression model can be used to retrieve NPP in arid and semi-arid areas and reflects vegetation growth and distribution in the study area. There were obvious regional differences in NPP distribution in the arid region of Northwest China; vegetation NPP in the mountains was increased and decreased in the plains. High values were found in the north,northwest and southeast of the arid region and low values in the south and southeast. At an annual scale,NPP in Northwest China slightly increased from 2000 to 2014 at a rate of 0.40 gC/(m²·a). Trends in annual vege-tation NPP are different among areas,vegetation NPP in 58.66% areas increased,13.64% of areas remained relatively stable,and 27.7% of areas slightly decreased from 2000.

Key words: net primary productivity, multi-stepwise regression, principal components regression, partial least-squares regression, ridge regression, Northwest China

焦伟, 陈亚宁, 李稚, 李玉朋, 黄晓然, 李海霞. 基于多种回归分析方法的西北干旱区植被NPP遥感反演研究[J]. 资源科学, 2017, 39(3): 545-556.

Wei JIAO, Yaning CHEN, Zhi LI, Yupeng LI, Xiaoran HUANG, Haixia LI. Inversion of net primary productivity in the arid region of Northwest China based on various regressions[J]. Resources Science, 2017, 39(3): 545-556.

图/表 9

图1

图2

图3

表1

图4

表2

岭回归模型描述"

$R 2$	$R adj 2$	SRMSE	sig
0.795	0.791	0.44	0.000

表2

表3

图5

图6

参考文献 44

[1]	Pan S F,Tian H Q,Dangal S,et al.Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century[J]. Journal of Geographical Sciences,2015,25(9):1027-1044.
[2]	Li X,Zhu Z,Zeng H,et al.Estimation of gross primary production in China(1982-2010)with multiple ecosystem models[J]. Ecological Modelling,2016,324:33-44.
[3]	Li Z,Chen Y N,Wang Y,et al.Dynamic changes in terrestrial net primary production and their effects on evapotranspiration[J]. Hydrology and Earth System Sciences,2016,20(6):2169-2178.
[4]	Guo Q,Hu Z,Li S,et al. Contrasting responses of gross primary productivity to precipitation events in a water-limited and a temperature-limited grassland ecosystem[J]. Agricultural and Forest Meteorology,2015,214-215(3):169-177.
[5]	Flanagan L B,Sharp E J,Gamon J A.Application of the photosynthetic light-use efficiency model in a Northern Great Plains grassland[J]. Remote Sensing of Environment,2015,168(6):239-251.
[6]	Beck P S A,Judayg P,Alix C,et al. Changes in forest productivity across Alaska consistent with biome shift[J]. Ecology Letters,2011,14(4):373-379.
[7]	Van B R,Haneca K,Hoogesteger J,et al.A century of tree line changes in sub-Arctic Sweden shows local and regional varia-bility and only a minor influence of 20th century climate warming[J]. Journal of Biogeography,2011,38(5):907-921.
[8]	Mekonnen Z A,Grant R F,Schwalm C. Contrasting changes in gross primary productivity of different regions of North America as affected by warming in recent decades[J]. Agricultural and Forest Meteorology,2016,218-219(1-2):50-64.
[9]	Williams A P,Allen C D,Millar C I,et al.Forest responses to increasing aridity and warmth in the southwestern United States[J]. Proceedings of the National Academy of Sciences,2010,107(50):21289-21294.
[10]	陈亚宁,李稚,范煜婷,等. 西北干旱区气候变化对水文水资源影响研究进展[J]. 地理学报,2014,69(9):1295-1304.
	[Chen Y N,Li Z,Fan Y T,et al.Research progress on the impact of climate change on water resources in the arid region of North-west China[J]. Journal of Geographical Sciences,2014,69(9):1295-1304.]
[11]	陈亚宁,杨青,罗毅,等. 西北干旱区水资源问题研究思考[J]. 干旱区地理,2012,35(1):1-9.
	[Chen Y N,Yang Q,Luo Y,et al.Ponder on the issues of water resources in arid region of northwest China[J]. Arid Land Geography,2012,35(1):1-9.]
[12]	罗天祥,李文华,冷允法,等. 青藏高原自然植被总生物量的反演与净初级生产量的潜在分布[J]. 地理研究,1998,17(4):337-344.
	[Luo T X,Li W H,Leng Y F,et al.Estimation of total biomass and potential distribution of net primary productivity in the Tibetan plateau[J]. Geographical Research,1998,17(4):337-344.]
[13]	方精云. 全球生态学[M]. 北京:高等教育出版社,2000.
	[Fang J Y.Global Ecology[M]. Beijing:Higher Education Press,2000.]
[14]	Lieth H.Modeling the primary productivity of the world[J]. Indian Forester,1972,98(6):327-331.
[15]	高志强,刘纪远. 中国植被净生产力的比较研究[J]. 科学通报,2008,53(3):317-326.
	[Gao Z Q,Liu J Y.A comparative study of the net primary productivity in China[J]. Chinese Science Bulletin,2008,53(3):317-326.]
[16]	朱文泉,潘耀忠,龙中华,等. 基于GIS和RS的区域陆地植被NPP反演-以中国内蒙古为例[J]. 遥感学报,2005,9(3):300-307.
	[Zhu W Q,Pan Y Z,Long Z H,et al.Estimating net primary productivity of terrestrial vegetation based on GIS and RS:A case study in Inner Mongolia,China[J]. Journal of Remote Sensing,2005,9(3):300-307.]
[17]	Liang W,Yang Y T,Fan D M,et al.Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010[J]. Agricultural and Forest Meteorology,2015,204:22-36.
[18]	杨存建,刘纪远,张增祥. 热带森林植被生物量遥感反演探讨[J]. 地理与地理信息科学,2004,20(6):22-25.
	[Yang C J,Liu J Y,Zhang Z X.Study on the estimation of the tropical forest vegetation biomass by using remote sensing data,meteorological data and topographical data[J]. Geography and Geo-Information Science,2004,20(6):22-25.]
[19]	Fan W Y,Zhang H Y,Yu Y,et al.Comparison of three models of forest biomass estimation[J]. Chinese Journal of Plant Ecology,2011,35(4):402-410.
[20]	张旭琛,牛华忠,钟华平,等. 新疆伊犁地区草地植被地上生物量遥感反演[J]. 草业学报,2015,24(6):25-34.
	[Zhang X S,Niu H Z,Zhong H P,et al.Assessment of above-ground Biomass of grassland using remote sensing,Ili,Xinjiang[J]. Act Prataculturae Sinica,2015,24(6):25-34.]
[21]	Lu Y M,Yue T X,Chen C F,et al.Solar radiation modeling based on stepwise regression analysis in China[J]. Journal of Remote Sensing,2010,14(5):852-864.
[22]	胡馨月,高明希,任玉,等. 基于主成分回归的温度分布反演研究[J]. 光谱学与光谱分析,2012,32(10):2789-2793.
	[Hu X Y,Gao M X,Ren Y,et al.Study on inversion of temperature distribution based on principal components analysis[J]. Spectroscopy and Spectral Analysis,2012,32(10):2789-2793.]
[23]	Bin J,Ai F F,Liu N,et al.Supervised principal components:A new method for multivariate spectral analysis[J]. Journal of Chemometrics,2013,27(12):457-465.
[24]	高思远,崔晨风,范玉平. 基于岭估计的青海省东部农业区 ET0遥感反演研究[J]. 自然资源学报,2016,31(4):693-702.
	[Gao S Y,Cui C F,Fan Y P.Remote sensing inversion of ET0 in eastern agricultural area of Qinghai Province based on ridge estimation[J]. Journal of Natural Resource,2016,31(4):693-702.]
[25]	张杰,潘晓玲,高志强,等. 干旱生态系统净初级生产力反演及变化探测[J]. 地理学报,2006,61(1):15-25.
	[ Zhan J,Pan X L,Gao Z Q,et al.Satellite estimates and change detection of net primary productivity of oasis-desert based on ecosystem process with remotely sensed forcing in Arid Western China[J]. Journal of Geographical Sciences,2006,61(1):15-25.]
[26]	崔林丽,杜华强,史军,等.中国东南部植被NPP的时空格局变化及其与气候的关系研究[J]. 地理科学,2016,36(5):787-793.
	[Cui L L,Du H Q,Shi J,et al.Spatial and temporal pattern of vegetation NPP and its relationship with climate in the Southeastern China[J]. Journal of Geographical Sciences,2016,36(5):787-793.]
[27]	吴珊珊,姚治君,姜丽光,等. 基于MODIS的长江源植被NPP时空变化特征及其水文效应[J]. 自然资源学报,2016,31(1):39-50.
	[Wu S S,Yao Z J,Jiang L G,et al.The spatial-temporal variations and hydrological effects of vegetation NPP based on MODIS in the source region of the Yangtze River[J]. Journal of Natural Resources,2016,31(1):39-50.]
[28]	San B T,Suzen M L.Digital elevation model(DEM)generation and accuracy assessment from ASTER stereo data[J]. Inter-national Journal of Remote Sensing,2005,26(22):5013-5027.
[29]	Gu Y,Hunt E,Wardlow B,et al.Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data[J]. Geophysical Research Letters,2008,35(22):1092-1104.
[30]	Harris I,Jones P D,Osborn T J,et al.Updated high-resolution grids of monthly climatic observations-the CRU TS3.10 Dataset[J]. International Journal of Climatology,2014,34(3):623-642.
[31]	中国气象数据网[EB/OL].(2015-05)[2016-08-22].
	[China Meteorological Data Service Centre[EB/OL].(2015-05)[ 2016-08-22]. .]
[32]	Sun J G.A correlation principal component regression analysis of NIR data[J]. Journal of Chemo metrics,1995,9(1):21-29.
[33]	Gusnanto A,Pawitan Y,Huang J,et al.Variable selection in random calibration of near-infrared instruments:Ridge regres-sion and partial least squares regression settings[J]. Journal of Chemometrics,2003,17(3):174-185.
[34]	Pan S F,Tian H Q,Dangal S,et al.Modeling and monitoring terrestrial primary production in a changing global environment:Toward a multiscale synthesis of observation and simulation[J]. Advances in Meteorology,2014,doi:10.1155/2014/965936.
[35]	徐建华. 计量地理学[M]. 北京:高等教育出版社,2005.
	[Xu J H.Quantity Geography [M]. Beijing:Higher Education Press,2005.]
[36]	曾九孙,刘祥官,罗世华. 主成分回归和偏最小二乘法在高炉冶炼中的应用[J]. 浙江大学学报(理学),2009,36(1):33-36.
	[Zeng J S,Liu X G,Luo S H.Application of principal com-ponent regression and partial least square in blast furnace iron-making[J]. Journal of Zhejiang University(Science Edition),2009,36(1):33-36.]
[37]	尹康. 常用统计软件关于岭回归计算原理的比较分析[J]. 统计研究,2013,30(2):109-112.
	[Yi K.Comparison and analysis of the principle of ridge regression in common statistical software[J]. Statistical Research,2013,30(2):109-112.]
[38]	Jobbagy E G,Sala O E,Paruelo J M.Patterns and controls of primary production in the Patagonian steppe:A remote sensing approach[J]. Ecology,2002,83(2):307-319.
[39]	Yang Y T,Long D,Guan H D,et al.GRACE satellite observed hydrological controls on interannual and seasonal variability in surface greenness over mainland Australia[J]. Journal of Geo-physical Research:Biogeosciences,2014,119(12):2245-2260.
[40]	李鹏飞,孙小明,赵昕奕. 近50年中国干旱半干旱地区降水量与潜在蒸散量分析[J]. 干旱区资源与环境,2012,26(7):57-63.
	[Li P F,Sun X M,Zhao X Y.Analysis of precipitation and potential evapotranspiration in arid and semiarid area of China in recent 50 years[J]. Journal of Arid Land Resources and Environment,2012,26(7):57-63.]
[41]	姚俊强,杨青,陈亚宁,等. 西北干旱区气候变化及其对生态环境影响[J]. 生态学杂志,2013,32(5):1283-1291.
	[Yao J Q,Yang Q,Chen Y N,et al.Climate change in arid areas of Northwest China in past 50 years and its effects on the local ecological environment[J]. Chinese Journal of Ecology,2013,32(5):1283-1291.]
[42]	Chen Y N,Li Z,Fan Y T,et al.Progress and prospects of climate change impacts on hydrology in the arid region of northwest China[J]. Environ Res,2015,139:11-19.
[43]	Arredondo T,garcìa-Moya E,Huber-Sannwald E,et al. Drought manipulation and its direct and legacy effects on productivity of a monodominant and mixed-species semi-arid grassland[J]. Agricultural and Forest Meteorology,2016,223:132-140.
[44]	李霞,李晓兵,陈云浩,等. 中国北方草原植被对气象因子的时滞响应[J]. 植物生态学报,2007,31(6):1054-1062.
	[Li X,Li X B,Chen Y H,et al.Temporal responses of vegetation to climate variables in temperate steppe of northern China[J]. Journal of Plant Ecology,2007,31(6):1054-1062.]

变量	非标准化系数	标准化系数	显著性	共线性
常数	71.073		0.011
NDVI	701.609	0.878	0.000	1.021
降水	0.088	0.114	0.000	1.085
PET	-0.055	-0.096	0.000	1.028
日照时数	-0.025	-0.070	0.000	1.083

	逐步回归模型		主成分回归		偏最小二乘回归		岭回归
	拟合	预测	拟合	预测	拟合	预测	拟合	预测
NS	0.83	0.85	0.40	0.40	0.80	0.81	0.79	0.81
Precision	79.94	84.43	55.06	60.32	77.83	82.35	78.11	82.63
PBIAS	-0.68	-0.77	9.42	14.02	0.23	-1.24	-0.35	-0.87
R²	0.82	0.85	0.45	0.50	0.80	0.81	0.80	0.81
SRMSE	0.42	0.39	0.78	0.77	0.45	0.44	0.45	0.44