1. 农业部 农业环境重点实验室,北京,100081
2. 黄石市国土资源局,湖北,黄石,435000
3. 西北农林科技大学 资源环境学院, 陕西 杨凌,712100
4. 中国科学院 寒区旱区环境与工程研究所,甘肃,兰州,730000
纸质出版:2016
移动端阅览
石志华, 刘梦云, 吴健利, 等. 基于CASA模型的陕西省植被净初级生产力时空分析[J]. 水土保持通报, 2016,36(1):206-211.
SHI Zhihua, LIU Mengyun, WU Jianli, et al. Spatial-temporal Analysis of Vegetation Net Primary Productivity in Shaanxi Province Based on CASA Model[J]. Bulletin of Soiland Water Conservation, 2016, 36(1): 206-211.
石志华, 刘梦云, 吴健利, 等. 基于CASA模型的陕西省植被净初级生产力时空分析[J]. 水土保持通报, 2016,36(1):206-211. DOI: 10.13961/j.cnki.stbctb.2016.01.037.
SHI Zhihua, LIU Mengyun, WU Jianli, et al. Spatial-temporal Analysis of Vegetation Net Primary Productivity in Shaanxi Province Based on CASA Model[J]. Bulletin of Soiland Water Conservation, 2016, 36(1): 206-211. DOI: 10.13961/j.cnki.stbctb.2016.01.037.
[目的
]
探明陕西省近年来植被净初级生产力(net primary productivity
NPP)的变化及评估植被的生长条件. [方法
]
基于CASA(Carnegie Ames Stanford Approach)模型估算陕西省2003—2012年逐月NPP
并分析其年际、年内的时空变化特征及趋势. [结果
]
(1) 陕西省2003—2012年植被NPP总体呈增长趋势(p
<
0.01)
年均增量为3.940 6 g/(m
2
·a)(以C含量计);年总NPP从2003年的84.44 Tg(1 Tg=1012 g)增加到2012年的91.98 Tg.(2) NPP年内变化明显
夏季NPP占年总量的比例最高
达到52.43%
7
8两月占比最高
分别为18.52%和18.41%.只有3和8月NPP增长趋势显著或极显著
其他月份NPP变化不显著.(3) 不同植被NPP的年际变化比较平稳
除永久湿地外
其他植被类型的NPP呈增长趋势
耕地的NPP增长最快(p
<
0.01)
年均增量为5.89 g/(m
2
·a).(4) NPP整体呈南高北低、高低相间的分布特征
全省78.53%的区域NPP呈增长趋势
24.47%的区域增长显著或极显著;NPP降低显著/极显著的面积仅占2.27%
主要分布在陕西中部和西安周边地区. [结论
]
陕西植被生长条件总体在改善
但局部在恶化.
[Objective] This study aims to verify variation of vegetation net primary productivity(NPP) in Shaanxi Province in recent years in order to evaluate vegetation growing conditions. [Methods] Based on the Carnegie Ames Stanford approach(CASA) model
this study estimated the monthly NPP from 2003 to 2012 in Shaanxi Province. The annual and inter-annual variation of NPP were analyzed at both spatial and temporal scale. [Results] (1) The NPP in Shaanxi Province showed an increasing trend and increased (p<0.01) at a rate of 3.940 6 g/(m2·a). The total annual NPP increased from 84.44 Tg (1 Tg=1 012 g) in 2003 to 91.98 Tg in 2012(in terms of carbon content). (2) The NPP varied greatly in different seasons. The highest NPP occurred in summer
which accounted for 52.43% of the total NPP
and NPP in July
August accounted for 18.52%
18.41% respectively. NPP in March or August increased significantly or extremely significantly
while no significant change of NPP was found in the other months. (3) Annual variation of NPP for different vegetation types was comparatively stable
and showed an increasing tendency except the permanent wetlands. The fastest increase of NPP was crop land (p<0.01)
with an average annual increment rate of 5.89 g/(m2·a). (4) The NPP in Southern Shaanxi was higher than the northern
78.53% of the area showed a growing trend in NPP
and 24.47% of the area increased significant or extremely significant. Only 2.27% of the area showed a significantly or extremely significantly decreasing trend in NPP
those areas were mainly distributed in the central Shaanxi Province and surrounding area of Xi'an City. [Conclusion] The vegetation growing conditions in Shaanxi Province was generally improved
but the local region were deteriorating.
于贵瑞,孙晓敏.陆地生态系统通量观测的原理与方法[M].北京:高等教育出版社,2006.
Yuan Quanzhi, Wu Shaohong, Zhao Dongsheng, et al. Modeling net primary productivity of the terrestrial ecosystem in China from 1961 to 2005[J]. Journal of Geographical Sciences, 2014,24(1):3-17.
Zhang Yulong, Song Conghe, Zhang Kerong, et al. Effects of land use/land cover and climate changes on terrestrial net primary productivity in the Yangtze River Basin, China, from 2001 to 2010[J]. Journal of Geophysical Research: Biogeosciences, 2014,119(6):1092-1109.
Pachavo G, Murwira A. Remote sensing net primary productivity(NPP)estimation with the aid of GIS modelled shortwave radiation(SWR)in a Southern African Savanna[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,30(8):217-226.
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 Cycle, 1993,7(4):811-841.
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.
张娜,毛飞跃,龚威.2009年武汉市植被净初级生产力估算[J].武汉大学学报:信息科学版,2011,36(12):1447-1450.
韩艳飞,柯长青,李晶.近30 a关天经济区植被净初级生产力对土地利用变化的动态响应[J].干旱区资源与环境,2014,28(6):68-74.
张禹舜,贾文雄,赵一飞,等.基于CASA模型研究祁连山地区植被净初级生产力的时空变化[J].西北植物学报,2014,34(10):2085-2091.
陈学兄,张小军,陈永贵,等.陕西省1998—2008年植被覆盖度的时空变化研究[J].武汉大学学报:信息科学版,2013,38(6):674-678.
谢宝妮,秦占飞,王洋,等.黄土高原植被净初级生产力时空变化及其影响因素[J].农业工程学报,2014,30(11):244-253.
李登科,范建忠,王娟.基于MOD17A3的陕西省植被NPP变化特征[J].生态学杂志,2011,30(12):2776-2782.
陕西省统计局,国家统计局陕西调查总队.陕西统计年鉴2014[M].北京:中国统计出版社,2014.
Pan Yi, Li Xin, Gong Peng, et al. An integrative classification of vegetation in China based on NOAA AVHRR and vegetation-climate indices of the Holdridge life zone[J]. International Journal of Remote Sensing, 2003,24(5):1009-1027.
冉有华,李新,卢玲.基于多源数据融合方法的中国1 km土地覆盖分类制图[J].地球科学进展,2009,24(2):192-203.
Ran Youhua, Li Xin, Lu Ling, et al. Large-scale land cover mapping with the integration of multi-source information based on the Dempster-Shafer theory[J]. International Journal of Geographical Information Science, 2012,26(1):169-191.
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.
朱文泉.中国陆地生态系统植被净初级生产力遥感估算及其与气候变化关系的研究[D].北京:北京师范大学资源学院,2005.
马明国,王建,王雪梅.基于遥感的植被年际变化及其与气候关系研究进展[J].遥感学报,2006,10(3):421-431.
罗玲,王宗明,毛德华,等.松嫩平原西部草地净初级生产力时空格局[J].中国草地学报,2012,34(1):5-11.
陶波,李克让,邵雪梅,等.中国陆地净初级生产力时空特征模拟[J].地理学报,2003,58(3):372-380.
刘宪锋,杨勇,任志远,等.2000—2009年黄土高原地区植被覆盖度时空变化[J].中国沙漠,2013,33(4):1244-1249.
0
浏览量
3829
下载量
22
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621