Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya
Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya
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DOI：
10.1186/1476-072x-6-21
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发表时间：
2007-06-05
影响因子：
4.9
通讯作者：
Novak, Robert J.
中科院分区：
文献类型：
--
作者：
Jacob, Benjamin G.;Muturi, Ephantus J.;Novak, Robert J.
背景资料：我们研究了疟疾测绘算法的影响，反射率校准的不确定性的准确性，三个植被指数（VI）的QuickBird数据在三个水稻农业村复合体Mwea，肯尼亚。我们还产生了推理统计，从实地采样的植被协变量，以确定水稻种植季节的阿拉伯按蚊。研究地点的所有水生生境进行分层的基础上水平的水稻阶段，淹没，整地，移栽后，分蘖，开花/成熟和收获后/休耕。设计了一套不确定性传播方程，模拟使用红色通道（波段3：0.63至0.69 μ m）和近红外通道（波段4：0.76至0.90 μ m）的校准不确定性传播，以生成归一化差异植被指数（NDVI）和土壤调整植被指数（SAVI）。抗大气植被指数（阿尔维）也进行了评估，纳入QuickBird蓝色波段（波段1：0.45至0.52 μ m），以规范大气影响。为了确定水稻生境的地方聚类Gi*（d）统计数据是从地面和遥感生态数据库中产生的。此外，所有的稻田栖息地进行了目视检查，使用光谱反射率的植被土地覆盖识别高产稻田按蚊产卵sites.Results：由此产生的VI的不确定性并没有从表面反射率或大气条件。Logistic回归分析所有现场采样的协变量显示，紧急植被与蚊子幼虫在三个研究地点呈负相关。此外，漂浮植被（-ve）与未成熟的蚊子在Rurumi和Kiuria（-ve）显着相关，而浊度也是重要的Kiuria。所有的空间模型都表现出正的自相关性;相似数量的对数计数往往在地理空间中聚集。水稻栖息地的光谱反射率，检查使用远程和现场分层，揭示后移栽和分蘖水稻阶段是最常见的高幼虫丰度和distribution.Conclusion：NDVI，SAVI和阿尔维产生的QuickBird数据和现场采样植被协变量建模不能识别高产水稻。arabiensis水生生境然而，结合QuickBird和实地采样数据的水稻栖息地的光谱反射率可以开发和实施基于幼虫生产力的综合病媒管理（IVM）计划。
Background: We examined algorithms for malaria mapping using the impact of reflectance calibration uncertainties on the accuracies of three vegetation indices (VI)'s derived from QuickBird data in three rice agro-village complexes Mwea, Kenya. We also generated inferential statistics from field sampled vegetation covariates for identifying riceland Anopheles arabiensis during the crop season. All aquatic habitats in the study sites were stratified based on levels of rice stages; flooded, land preparation, post-transplanting, tillering, flowering/maturation and post-harvest/fallow. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties using the red channel (band 3:0.63 to 0.69 mu m) and the near infra-red (NIR) channel (band 4:0.76 to 0.90 mu m) to generate the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The Atmospheric Resistant Vegetation Index (ARVI) was also evaluated incorporating the QuickBird blue band ( Band 1:0.45 to 0.52 mu m) to normalize atmospheric effects. In order to determine local clustering of riceland habitats Gi*(d) statistics were generated from the ground-based and remotely-sensed ecological databases. Additionally, all riceland habitats were visually examined using the spectral reflectance of vegetation land cover for identification of highly productive riceland Anopheles oviposition sites.Results: The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions. Logistic regression analyses of all field sampled covariates revealed emergent vegetation was negatively associated with mosquito larvae at the three study sites. In addition, floating vegetation (-ve) was significantly associated with immature mosquitoes in Rurumi and Kiuria (-ve); while, turbidity was also important in Kiuria. All spatial models exhibit positive autocorrelation; similar numbers of log-counts tend to cluster in geographic space. The spectral reflectance from riceland habitats, examined using the remote and field stratification, revealed post-transplanting and tillering rice stages were most frequently associated with high larval abundance and distribution.Conclusion: NDVI, SAVI and ARVI generated from QuickBird data and field sampled vegetation covariates modeled cannot identify highly productive riceland An. arabiensis aquatic habitats. However, combining spectral reflectance of riceland habitats from QuickBird and field sampled data can develop and implement an Integrated Vector Management (IVM) program based on larval productivity.