地理学报 ›› 2015, Vol. 70 ›› Issue (7): 1080-1090.doi: 10.11821/dlxb201507005

• 土地利用 • 上一篇    下一篇

1999-2013年中国耕地复种指数的时空演变格局

丁明军1(), 陈倩1, 辛良杰2, 李兰晖1, 李秀彬2()   

  1. 1. 江西师范大学 鄱阳湖湿地与流域教育部重点实验室/地理与环境学院,南昌 330022
    2. 中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室,北京 100101
  • 收稿日期:2014-10-23 修回日期:2015-03-27 出版日期:2015-07-20 发布日期:2015-08-11
  • 作者简介:

    作者简介:丁明军(1979-), 男, 湖北谷城人, 副教授, 主要从事土地利用/覆被变化及其与气候变化之间的关系研究。E-mail: dingmingjun1128@163.com

  • 基金资助:
    国家自然科学基金国际(地区)合作与交流项目(41161140352);江西省重大生态安全问题监控协同创新中心资助项目(JXS-EW-00);国家自然科学基金(41440004, 41101085)

Spatial and temporal variations of multiple cropping index in China based on SPOT-NDVI during 1999-2013

Mingjun DING1(), Qian CHEN1, Liangjie XIN2, Lanhui LI1, Xiubin LI2()   

  1. 1. Key Lab of Poyang Lake Wetland and Watershed Research of Ministry of Education and School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
    2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), CAS, Beijing 100101, China;
  • Received:2014-10-23 Revised:2015-03-27 Online:2015-07-20 Published:2015-08-11
  • Supported by:
    Projects of International Cooperation and Exchanges NSFC, No.41161140352;Collaborative Innovation Center for Major Ecological Security Issues of Jiangxi Province and Monitoring Implementation, No.JXS-EW-00;National Natural Science Foundation of China, No.41440004, No.41101085

摘要:

耕地复种是中国普遍的农业种植制度,对保障国家粮食安全和促进农村经济发展十分必要。本文基于1999-2013年1 km×1 km旬SPOT-NDVI数据和3期耕地数据,利用S-G (Savitzky-Golay)滤波方法,重建了农作物生长NDVI曲线;基于二次差分算法及相关阈值限定,提取了各时相复种指数;分析了1999-2013年中国耕地复种指数的时空变化过程。结果表明:① 中国耕地复种指数从北到南逐渐增加,其中种植制度上43.48%的耕地实行一年一熟,56.39%的耕地实行一年两熟,仅有0.13%的耕地实行一年三熟。② 1999-2013年间,中国耕地复种指数整体上呈现显著上升趋势,年均增加约为1.29%(P < 0.001);但空间差异明显,复种指数显著降低(P < 0.1)的耕地仅占全国耕地的2.12%,主要分布在河北、北京、天津交界地区,安徽中部,四川的成都平原,江西的鄱阳湖平原,湖南的北部和南部以及广西的中部;16.40%的耕地复种指数显著上升(P < 0.1),主要分布在甘肃的东部,陕西的渭河平原,山西的西部,河北、山东和天津交界处,山东的山东半岛和湖北的江汉平原。③ 耕地复种指数年际变化率与地形起伏和经济发展水平具有较好的相关关系:起伏度增强,复种指数上升;经济发展水平提高,复种指数降低。

关键词: NDVI, 复种指数, 时空演变, 格局, 中国

Abstract:

In this paper, the smooth crops growth NDVI curves from 1999 to 2013 were rebuilt by the S-G techniques, with the combination of 10-day SPOT time-series NDVI data from 1998 to 2013 with the spatial resolution of 1 km and land use data in 2000, 2005 and 2010. Spatial and temporal changes of multiple cropping index (MCI) from 1999 to 2013 in China were extracted by a difference algorithm. The results are as follows: (1) The total precision of sample validation based on visual identification was 91.95%, and the slope of linear regression of the MCI between remotely sensed data and statistical data was 0.73 (R=0.775, P<0.001), suggesting that this method is an effective way to extracting spatial information of the MCI for agricultural and land management. (2) From the north to the south of China, the MCI gradually becomes more and more complex. The percentages of the single, double and triple cropping system occupying the total cropland were 43.48%, 56.39% and 0.13%, respectively in China. (3) From 1999 to 2013, the overall cropping index increased with an annual rate of 1.29% (P<0.001) in China, while it exhibited significant differentiation in different zones. The area with a significant decreasing trend occupied 2.12% (P<0.1) of the total cropland and was found at the borders of Hebei, Beijing and Tianjin, central Anhui, the Chengdu Plain, the Poyang Lake Plain, northern and southern Hunan, and central Guangxi. The area with a significant increasing trend occupied 16.40% (P<0.1) of the total cropland and was distributed in eastern Gansu, the Weihe Plain of Shaanxi, western Shanxi, the borders of Hebei, Shandong and Tianjin, the Shandong Peninsula, and the Jianghan Plain. (4) Terrain and economic development level played an important role in the regional differentiation of MCI change. There is a positive correlation between terrain and the inter-annual changes of MCI, and a negative correlation between economic development level and the inter-annual changes of MCI.

Key words: NDVI, multiple cropping index, spatial and temporal variations, pattern, China