陕北风沙过渡带植被净初级生产力变化特征及原因

doi:10.7522/j.issn.1000-694X.2017.00053

摘要/Abstract

摘要： 基于光能利用率原理，采用CASA（Carnegie-Ames-Stanford-Approach）模型，实现了2000—2014年陕北风沙过渡带地区植被净初级生产力（NPP）估算，对该地区NPP时空变化以及驱动机制进行了定量化分析。结果表明：（1） 2000—2014年陕北风沙过渡带NPP为6.71×10¹²gC·a^-1，单位面积值为202.57gC·m^-2·a^-1，受地貌和气候特征影响，植被空间异质性强，黄土区植被明显优于风沙区；（2）近十几年来该区域植被得到明显改善，NPP总体增速为10.98gC·m^-2·a^-1（R=0.85，P<0.01），植被增速存在空间差异，东南部的黄土区植被增长较快，西北风沙区植被增长较慢；（3）2000—2014年，降水、气温和辐射与NPP的相关系数分别为0.54（P<0.05）、-0.25、0.35，三者对植被增长的贡献量分别为3.95、0.71、2.75gC·m^-2·a^-1。这说明降水是气候因素中影响陕北风沙过渡带植被变化的主要因素；（4）近15年的植被恢复过程中，气候和人类活动都是重要的驱动因素，气候因子对植被增长的贡献更大，相对作用达到67.49%。区域内部，不同地区植被的主要驱动源存在差异，东部地区植被受气候因子主导，西部地区植被受人类活动主导。

关键词: NPP, CASA模型, 时空变化, 驱动因素

Abstract: Based on the principles of energy efficiency and the use of CASA model,this study estimated terrestrial net primary productivity (NPP) of the wind drift sand region in northern Shaanxi,to analyze the spatial and temporal patterns of NPP and its driving mechanisms during 2000-2014. The main conclusions are as follows:(1) The annual NPP of wind drift sand region in northern Shaanxi from 2000 to 2014 was 6.71×10¹²gC·yr^-1,the average unit area of NPP was 202.57 gC·m^-2·yr^-1. Spatial heterogeneity of vegetation was caused by differences of landform and climate. The vegetation of loess area had higher NPP than that of sandy area. (2) NPP in study area showed a signal upward trend during 2000-2014 (10.98 gC·m^-2·yr^-1,R=0.85,P<0.01). And NPP trend had spatial heterogeneity in wind drift sand region in northern Shaanxi. Vegetation in loess area showed a higher upward trend than that in sandy area. (3) During 2000-2014,the correlation coefficient of precipitation,temperature and solar radiation with NPP over the entire region were 0.54 (P<0.05),-0.25,0.35. And the contribution of precipitation,temperature and solar radiation to NPP trend were 3.95,0.71 and 2.75 gC·m^-2·yr^-1. Precipitation was the climate factor with the greatest influence on NPP variation. (4) Climate and human activity were both important driving factors of vegetation change in wind drift sand region in northern Shaanxi. The relative importance of climate factors and human activity were 67.49% and 32.51%,In eastern area,vegetation was mostly influenced by climate variations. And in west,the human activity was the leading factor.

Key words: NPP, CASA model, temporal process, driving factor

中图分类号:

Q948.15

倪向南, 郭伟, 乔凯. 陕北风沙过渡带植被净初级生产力变化特征及原因[J]. 中国沙漠, 2018, 38(4): 889-898.

Ni Xiangnan, Guo Wei, Qiao Kai. Spatial and Temporal Patterns of Net Primary Productivity and Their Attribution in Wind Drift Sand Region in Northern Shaanxi[J]. Journal of Desert Research, 2018, 38(4): 889-898.

参考文献

[1] Behrenfeld M J,Field C B.Primary production of the biosphere:integrating terrestrial and oceanic components[J].Science,1998,281(5374):237.
[2] 孙媛媛,季宏兵,罗建美,等.气候驱动的中国陆地生态系统碳循环研究进展[J].首都师范大学学报:自然科学版,2006,27(5):90-95.
[3] Berry J A,Ehleringer J R,Field C B,et al.Commentary carbon metabolism of the terrestrial biosphere:a multitechnique approach for improved understanding[J].Ecosystems,2000,3(2):115-130.
[4] Lieth H,Whittaker R H.Primary productivity of the biosphere[M]//Lieth H,Whittaker R H.Ecological Studies Analysis & Synthesis.Berlin,Germany:Springer-Verlag,1975.
[5] 赵俊芳,延晓冬,朱玉洁.陆地植被净初级生产力研究进展[J].中国沙漠,2007,27(5):780-786.
[6] 陈利军,刘高焕,励惠国.中国植被净第一性生产力遥感动态监测[J].遥感学报,2002,6(2):129-135.
[7] Lieth H.Modeling the primary productivity of the world[J].Nature & Resources,1975,8(1):237-263.
[8] 刘敏.基于RS和GIS的陆地生态系统生产力估算及不确定性研究——以青藏高原草地样带为例[D].南京:南京师范大学,2008.
[9] 马良,朱再春,曾辉.NPP评估过程模型应用研究进展[J].中国沙漠,2017,37(6):1250-1260.
[10] 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.
[11] Sellers P J,Meeson B W,Hall F G,et al.Remote sensing of the land surface for studies of global change:models-algorithms-experiments[J].Remote Sensing of Environment,1995,51(1):3-26.
[12] Matsushita B,Tamura M.Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia[J].Remote Sensing of Environment,2002,81(1):58-66.
[13] 陈斌,王绍强,刘荣高,等.中国陆地生态系统NPP模拟及空间格局分析[J].资源科学,2007,29(6):45-53.
[14] 李银鹏,季劲钧.全球陆地生态系统与大气之间碳交换的模拟研究[J].地理学报,2001,56(4):379-389.
[15] 朱文泉,潘耀忠,龙中华,等.基于GIS和RS的区域陆地植被NPP估算——以中国内蒙古为例[J].遥感学报,2005,9(3):300-307.
[16] 郑中,祁元,潘小多,等.基于WRF模式数据和CASA模型的青海湖流域草地NPP估算研究[J].冰川冻土,2013,35(2):465-474.
[17] 董丹,倪健.利用CASA模型模拟西南喀斯特植被净第一性生产力[J].生态学报,2011,31(7):1855-1866.
[18] 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.
[19] 张美玲,蒋文兰,陈全功,等.基于CSCS改进CASA模型的中国草地净初级生产力模拟[J].中国沙漠,2014,34(4):1150-1160.
[20] 李晶,任志远,孙根年.GIS支持下陕西省植被生产有机物价值研究[J].植物生态学报,2002,26(6):684-688.
[21] 彭茹燕,张慧芝,哈斯,等.陕北风沙区景观格局分析[J].干旱区研究,2005,22(1):51-56.
[22] 尚爱军.陕北长城沿线风沙区农业自然资源利用研究[J].水土保持研究,2008,15(4):241-243.
[23] 李贵才.基于MODIS数据和光能利用率模型的中国陆地净初级生产力估算研究[D].北京:中国科学院遥感应用研究所,2004.
[24] 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.
[25] 包刚.基于MODIS数据的内蒙古陆地植被净第一性生产力遥感估算研究[D].呼和浩特:内蒙古师范大学,2009.
[26] 陈正华,麻清源,王建,等.利用CASA模型估算黑河流域净第一性生产力[J].自然资源学报,2008,23(2):263-273.
[27] 朴世龙,方精云,郭庆华.利用CASA模型估算我国植被净第一性生产力[J].植物生态学报,2001,25(5):603-608.
[28] 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.
[29] 王莺,夏文韬,梁天刚.基于CASA模型的甘南地区草地净初级生产力时空动态遥感模拟[J].草业学报,2011,20(4):316-324.
[30] 张峰,周广胜,王玉辉.基于CASA模型的内蒙古典型草原植被净初级生产力动态模拟[J].植物生态学报,2008,32(4):786-797.
[31] 李军玲,邹春辉,刘忠阳,等.河南省陆地植被净第一性生产力估算及其时空分布[J].草业科学,2011,28(10):1839-1844.
[32] Mu Q,Heinsch F A,Zhao M,et al.Development of a global evapotranspiration algorithm based on MODIS and global meteorology data[J].Remote Sensing of Environment,2007,111(4):519-536.
[33] 王保林,王晶杰,杨勇,等.植被光合有效辐射吸收分量及最大光能利用率算法的改进[J].草业学报,2013,22(5):220-228.
[34] 辛明月,殷红,张涛,等.基于高光谱遥感的水稻冠层吸收光合有效辐射的估算研究[J].中国水稻科学,2011,25(4):443-446.
[35] 张美玲,陈全功,蒋文兰,等.基于草地综合顺序分类法的CASA模型改进[J].中国草地学报,2011,33(4):5-11.
[36] 张美玲,蒋文兰,陈全功,等.基于CSCS改进CASA模型的中国草地净初级生产力模拟[J].中国沙漠,2014,34(4):1150-1160.
[37] 张新时.研究全球变化的植被-气候分类系统[J].第四纪研究,1993,13(2):157-169.
[38] 周广胜,张新时.自然植被净第一性生产力模型初探[J].植物生态学报,1995,19(3):193-200.
[39] 周广胜,张新时.全球气候变化的中国自然植被的净第一性生产力研究[J].植物生态学报,1996,20(1):11-19.
[40] 朱文泉,潘耀忠,张锦水.中国陆地植被净初级生产力遥感估算[J].植物生态学报,2007,31(3):413-424.
[41] Los S O,Tucker C O J,A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data[J].International Journal of Remote Sensing,1994,15(17):3493-3518.
[42] Running S W,Thornton P E,Nemani R,et al.Global terrestrial gross and net primary productivity from the Earth Observing System[M]//Sala O E,Jackson R B,Mooney H A,et al.Methods in Ecosystem Science.New York,USA:Springer,2000.
[43] Zhang Y,Zhang C,Wang Z,et al.Vegetation dynamics and its driving forces from climate change and human activities in the Three-River Source Region,China from 1982 to 2012[J].Science of the Total Environment,2016,563/564:210-220.
[44] 陈雅敏,张韦倩,杨天翔,等.中国不同植被类型净初级生产力变化特征[J].复旦学报:自然科学版,2012,51(3):377-381.
[45] 程杰.黄土高原草地植被分布与气候响应特征[D].陕西杨凌:西北农林科技大学,2011.
[46] 史晓亮,杨志勇,王馨爽,等.黄土高原植被净初级生产力的时空变化及其与气候因子的关系[J].中国农业气象,2016,37(4):445-453.
[47] 宋富强,康慕谊,陈雅如,等.陕北黄土高原植被净初级生产力的估算[J].生态学杂志,2009,28(11):2311-2318.
[48] 王连喜,李萌萌,李琪,等.基于CASA模型的陕西省NPP遥感估算[J].西部林业科学,2015,44(2):74-79.
[49] 赵景波,侯甬坚,黄春长.陕北黄土高原人工林下土壤干化原因与防治[J].中国沙漠,2003,23(6):9-12.
[50] 信忠保,许炯心,郑伟.气候变化和人类活动对黄土高原植被覆盖变化的影响[J].中国科学:D辑,2007,37(11):1504-1514.
[51] 杨延征,赵鹏祥,郝红科,等.基于SPOT-VGT NDVI的陕北植被覆盖时空变化[J].应用生态学报,2012,23(7):1897-1903.
[52] 何福红,蒋卫国,黄明斌.黄土高原沟壑区苹果基地退果还耕的生态水分效应[J].地理研究,2010,29(10):1863-1869.
[53] Wang Y Q,Shao M,Liu Z P.Large-scale spatial variability of dried soil layers and related factors across the entire Loess Plateau of China[J].Geoderma,2010,159(1):99-108.