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首页> 外文期刊>International journal of remote sensing >Integrating remote sensing and spatial statistics to model herbaceous biomass distribution in a tropical savanna
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Integrating remote sensing and spatial statistics to model herbaceous biomass distribution in a tropical savanna

机译：整合遥感和空间统计数据以模拟热带稀树草原中草本生物量的分布

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摘要

Modelling herbaceous biomass is critical for an improved understanding of wildlife feeding patterns and distribution as well as for the development of early warning systems for fire management. Most savannas in South Africa are characterized by complex stand structure and abundant vegetation species. This has prohibited accurate estimation of biomass in such environments. We investigated the possibility of improving biomass predictions in tropical savannas using cokriging. Individual bands and ratios computed from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery were correlated with field measurements of biomass covering the Kruger National Park, South Africa. The band that yielded the highest correlation with biomass was then used for further analysis using cokriging. Three variogram models were developed: one for the herbaceous biomass, one for the best MODIS band and a cross variogram between all pairs of variables involved in the estimation. The variogram models were then used in cokriging to predict biomass distribution over the whole study area. Results indicate that a combination of remotely sensed data with field biomass measurements through cokriging improves the estimation accuracy compared to ordinary kriging and stepwise linear regression. Given the high temporal resolution of the freely available MODIS imagery, the result is critical for the improved monitoring and management of wildlife habitats.
机译：对草本生物量进行建模对于提高对野生动物摄食模式和分布的了解以及对火灾管理预警系统的开发至关重要。南非大多数稀​​树草原的特征是林分结构复杂，植被种类丰富。这已经禁止了在这样的环境中精确估计生物量。我们调查了使用cokriging改善热带稀树草原生物量预测的可能性。根据中分辨率成像光谱仪（MODIS）图像计算得出的各个波段和比率与覆盖南非克鲁格国家公园的生物量的野外测量相关。然后，使用cokriging将与生物质产生最高相关性的谱带用于进一步分析。开发了三个变量图模型：一个用于草本生物量，一个用于最佳MODIS波段，以及涉及估计的所有变量对之间的交叉变异函数。然后将变异函数模型用于协同克里格法预测整个研究区域的生物量分布。结果表明，与普通克里金法和逐步线性回归法相比，将遥感数据与通过协同克里金法进行的田间生物量测量相结合可提高估计精度。鉴于免费提供的MODIS图像具有高时间分辨率，因此该结果对于改善野生动植物栖息地的监视和管理至关重要。

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  • 来源
    《International journal of remote sensing》 |2006年第16期|p.3499-3514|共16页
  • 作者

    O. MUTANGA; D. RUGEGE;

  • 作者单位

    University of Kwazulu-Natal, Discipline of Geography, School of Applied Environmental Sciences, PO Box X01, Scottsville 3209, Pietermaritzburg, South Africa;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 遥感技术;
  • 关键词

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