当前位置: X-MOL 学术Int. J. Appl. Earth Obs. Geoinf. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Designing a field sampling plan for landscape-pest ecological studies using VHR optical imagery
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2018-06-05 , DOI: 10.1016/j.jag.2018.05.016
V. Soti , C. Lelong , F-R. Goebel , T. Brévault

The objective of this study was to develop an easily replicable sampling methodology using very high spatial resolution (VHSR) optical imagery to study the effect of landscape composition on crop pest incidence and biological control. The methodology was developed for the millet head miner (MHM), Heliocheilus albipunctella (de Joannis) (Lepidoptera: Noctuidae), a key pest of millet in Senegal (West Africa). The sampling plan was developed according to two main hypotheses: (i) pest incidence increases with millet abundance in the landscape, and (ii) biological control increases with the abundance of semi-natural habitats in the landscape. VHSR satellite imagery (<1 m) provided from a Pléiades sensor was used to map and to quantify the landscape elements. Covering a square region of 20 × 20 km, a hierarchical, broad-scale land cover map focusing on crop (millet and peanut crops) and tree (tree vegetation) categories was produced and validated with ground truth data. Then, the landscape variables (tree density index and millet crop density index) were calculated based on a regular grid of 100 ha for each cell size covering the study area; the variables were then split into three density classes (low-medium-high) representative of the full landscape heterogeneity and combined into nine landscape patterns. Finally, according to sampling capacity, track accessibility, and statistical constraints, 45 field sites, including five replicates for each landscape pattern, were validated and selected for pest monitoring.



中文翻译:

使用VHR光学图像设计景观虫生态研究的野外采样计划

这项研究的目的是使用非常高的空间分辨率(VHSR)光学图像开发一种易于复制的采样方法,以研究景观成分对农作物有害生物发生率和生物防治的影响。该方法是针对小米头部采矿者(MHM)的Heliocheilus albipunctella(de Joannis)(鳞翅目:夜蛾科)开发的,这是塞内加尔(西非)的一种小米害虫。抽样计划是根据两个主要假设制定的:(i)害虫的发生率随景观中小米的丰度增加而增加,(ii)生物防治随景观中半自然生境的丰富度而增加。从所提供的VHSR卫星图像(<1米)传感器用于绘制和量化景观元素。绘制了一个覆盖20×20 km的正方形区域的分层,大规模的土地覆盖图,该地图着重于作物(粟和花生作物)和树木(树木植被)类别,并使用地面真实数据进行了验证。然后,基于覆盖研究区域的每个单元格的面积为100公顷的规则网格,计算景观变量(树木密度指数和谷类作物密度指数);然后将变量分为代表整个景观异质性的三个密度类别(低-中-高),并组合成九种景观模式。最后,根据抽样能力,轨道可及性和统计约束条件,对45个田地进行了验证,并选择了每种景观模式的五个重复地点进行虫害监测。

更新日期:2018-06-05
down
wechat
bug