地理学报 ›› 2012, Vol. 67 ›› Issue (3): 346-356.doi: 10.11821/xb201203006

• 遥感应用 • 上一篇    下一篇

农田景观空间异质性分析及遥感监测最优尺度选择——以三江平原为例

温兆飞1,2, 张树清1, 白静1,2, 丁长虹3, 张策4   

  1. 1. 中国科学院东北地理与农业生态研究所, 长春 130012;
    2. 中国科学院研究生院, 北京 100049;
    3. 空军航空大学, 长春130022;
    4. 哈尔滨师范大学, 哈尔滨 150025
  • 收稿日期:2011-07-19 修回日期:2011-10-17 出版日期:2012-03-20 发布日期:2012-05-14
  • 通讯作者: 张树清(1964-), 男, 吉林白山人, 博士, 研究员, 博士生导师, 主要从事3D/4D地理信息系统理论建模, 分析与计算以及遥感信息提取方面的研究。E-mail: zhangshuqing@neigae.ac.cn
  • 基金资助:
    国家科技支撑计划重大项目课题(2009BADB3B01-05); 中国科学院知识创新项目(KZCX2-YW-Q10-1-3)

Agricultural Landscape Spatial Heterogeneity Analysis and Optimal Scale Selection: An Example Applied to Sanjiang Plain

WEN Zhaofei1,2, ZHANG Shuqing1, BAI Jing1,2, DING Changhong3, ZHANG Ce4   

  1. 1. Northeast Institute of Geography and Agroecology, CAS, Changchun 130012, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Electrical and Electronic Teaching and Research Office, Aviation University of Air Force, Changchun 130022, China;
    4. Harbin Normal University, Harbin 150025, China
  • Received:2011-07-19 Revised:2011-10-17 Online:2012-03-20 Published:2012-05-14
  • Supported by:
    The National Key Technology R&D Program, No.2009BADB3B01-05; Knowledge Innovation Programs of the Chinese Academy of Sciences, No.KZCX2-YW-Q10-1-3

摘要: 农情遥感监测需要高时间分辨率的遥感数据,目前这些数据大都为中低空间分辨率影像。在这些尺度下,像元内部往往是异质的,从而影响农情参数反演精度。因此分析和表达农田景观空间异质性和最优尺度选择对遥感农情监测质量的提高具有重要的应用价值。选取建三江农垦区四种典型农田景观为研究点,Landsat/TM NDVI为实验数据,利用实验变异函数对四种景观类型的各向空间异质性进行了分析, 而后通过变异函数模型拟合,定量分析了各个研究点的整体空间异质性,并在此基础上进行了研究区遥感监测最优尺度选择。研究表明:(1) 基于实验变异函数的结构分析方法,可定性地认识空间异质性的大小和方向,进而挖掘出其背后的自然和人为驱动因素。(2) 对实验变异函数进行拟合分析,可定量地刻画不同景观格局各自的空间异质性特性。此外,基于变异函数对空间异质性的定量表达,讨论了利用积分变程A结合Nyquist-Shannon采样定理进行最优尺度选择的方法。

关键词: 建三江农垦区, 变异函数, 空间异质性, 尺度选择, 遥感

Abstract: Agricultural monitoring requires high temporal frequency data which are currently provided only by moderate spatial resolution sensors. At such moderate spatial resolutions, farmland that is heterogeneous within a pixel will be averaged and hence obscured. This would bias any non-linear estimation of crop growing processes (e.g., net primary productivity (NPP), leaf area index (LAI)). To modify this bias, a first approach is used to explicitly take into account the intra-pixel spatial heterogeneity in the retrieval algorithm. A second approach is to use the surface heterogeneity to disaggregate moderate spatial resolution estimates of land surface variable at a proper scale of spatial variation. Both approaches are required to quantify spatial heterogeneity,and a proper scale selection should be necessary for agricultural monitoring.To this ends, four typical landscape pattern sites in the Jiansanjiang Reclamation Area which is an important basin of commercial grain production in China, were selected and Landsat/TM NDVI image data were analyzed in this study. Based on the variogram analysis, some conclusions can be drawn. (1) Directional experiment variograms analysis can make clear how the human activates and natural factors affect the agricultural spatial heterogeneity qualitatively. For example, dry lands (including the landscape only with dry land and the landscape which is mosaic of dry land and paddy fields in this study) have the largest heterogeneity in North-South direction, while the landscape pattern which only have paddy fields have the largest heterogeneity in East-West direction. Based on this, we can demonstrate that spatial heterogeneity caused by human and natural factors can be examined deeply through variogram analysis. (2) The fitted variograms can present how different landscape patterns have their own spatial heterogeneity quantificationally. In this study, for example, the same type of land use can have lower heterogeneity as different types of land use landscape patterns have larger heterogeneity. (3) Through the variogram analysis of heterogeneity, a method used to select a proper scale (pixel size) for agricultural remote sensing monitoring is discussed.

Key words: Jiansanjiang Reclamation Area, variogram, spatial heterogeneity, scale selection, remote sensing