Chapter 4 Description of the environmental covariates used

4.1 Introduction

The table below details the covariates that we extract.

code type short_name long_name unit temporality data_sources
TND Temperature Daily minimum temperature Average daily minimum land surface temperature celcius degrees 40 days before the HLC night MOD11A1.v006 MYD11A1.v006 MOD11A2.v006 MYD11A2.v006
TMD Temperature Daily maximum temperature Average daily maximum land surface temperature celcius degrees 40 days before the HLC night MOD11A1.v006 MYD11A1.v006 MOD11A2.v006 MYD11A2.v006
TNW Temperature Weekly minimum temperature Average 8-days minimum land surface temperature celcius degrees 40 days before the HLC night MOD11A2.v006 MYD11A2.v006
TMW Temperature Weekly maximum temperature Average 8-days maximum land surface temperature celcius degrees 40 days before the HLC night MOD11A2.v006 MYD11A2.v006
RTD Precipitation Daily rainfall (source: TAMSAT) Average daily total precipitation (source�: TAMSAT) mm 40 days before the HLC night TAMSAT
RGD Precipitation Daily rainfall (source: GPM) Average daily total precipitation (source�: GPM) mm 40 days before the HLC night GPM_3IMERGDF
EVT Evapotranspiration Evapotranspiration Average 8-days evapotranspiration kg/m�/8day 40 days before the HLC night MOD16A2.v006 MYD16A2.v006
VNI Vegetation Normalized Difference Vegetation Index Average 8-days Normalized Difference Vegetation Index unitless 40 days before the HLC night MOD13Q1.v006 MYD13Q1.v006
VEI Vegetation Enhanced Vegetation Index Average 8-days Enhanced Vegetation Index unitless 40 days before the HLC night MOD13Q1.v006 MYD13Q1.v006
RGH Precipitation Half-houly rainfall Proportion of half-hours with positive precipitation for the whole duration of the HLC % HLC night GPM_3IMERGHH
WDR Wind Wind direction Mean of wind direction during the HLC night degrees (0 to 360) HLC night ERA5
WSP Wind Wind speed Mean of wind speed during the HLC night m/s HLC night ERA5
LMN Light/Moon Apparent magnitude of the Moon Apparent magnitude of the Moon on the HLC night unitless HLC night MIRIADE
LNL Light/Settlements Nightly radiance Monthly average radiance on the HLC night NanoWatt/cm2/sr HLC night VIIRS DNB
TEL Topography Elevation Mean elevation meters above the see No temporality SRTMGL1_v003
TSL Topography Slope Mean slope % No temporality SRTMGL1_v003
TAS Topography Aspect Mean aspect No temporality SRTMGL1_v003
TCI Topography Terrain Classification Index Mean Terrain Classification Index unitless No temporality SRTMGL1_v003
TWI Topography Topographic Wetness Index Mean Topographic Wetness Index unitless No temporality SRTMGL1_v003
WAC Water Accumulation Mean accumulation ha No temporality SRTMGL1_v003
WAD Water Average distance to hydrographic network Average distance to hydrographic network m No temporality SRTMGL1_v003
WMD Water Distance to closest hydrographic network Distance to closest hydrographic network m No temporality SRTMGL1_v003
WLS Water Total length of the hydrographic network Total length of the hydrographic network m No temporality SRTMGL1_v003
WAL Water Accumulation * distance to sampling point Accumulation * distance to sampling point ha/m No temporality SRTMGL1_v003
POP Population Population (source : REACT) Population (census/ground data) person No temporality Own surveys
POH Population Population (source : HRSL) Population (modelled data) person No temporality HRSL
BDE Built-up Distance to the edge of the village Distance to the nearest edge of the village m No temporality Own surveys
BCH Built-up Degree of clustering of the households in the village Degree of clustering or ordering of the households NA No temporality Own surveys
HYS Pedology Proportion of hydromorphic soils Proportion of hydromorphic soils % No temporality IRD
LSM Land cover Landscape metrics Set of landscape metrics calculated over 6 different land cover layers various units depending on the metric No temporality Own very high resolution land cover maps Moderate dynamic land cover 100m S2 prototype Land Cover 20m map of Africa 2016

The R script to import, tidy and transform the data is located here : - raw R Markdown : https://github.com/ptaconet/malamodpkg/blob/master/vignettes/import_tidy_transform_envdata.Rmd - Web version : https://ptaconet.github.io/malamodpkg/articles/import_tidy_transform_envdata.html

4.2 Temperature

  • How does temperature influence the presence/abundance/resistance of mosquitoes ?

[TODO]

  • Covariates extracted:

    • TND : Average daily minimum land surface temperature ( celcius degrees )

    • TMD : Average daily maximum land surface temperature ( celcius degrees )

    • TNW : Average 8-days minimum land surface temperature ( celcius degrees )

    • TMW : Average 8-days maximum land surface temperature ( celcius degrees )

  • Where do the data come from ?

Temperature, as well as vegetation and evapotranspiration data (see related sections below), are all extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite’s instrument products. MODIS is an instrument aboard NASA’s Terra and Aqua Earth observation satellites, launched respectively in 1999 and 2002. The satellites are acquiring data of the entire Earth’s surface in 36 spectral bands every 1 to 2 days. Overall, MODIS enables to acquire very valuable information on the Earth’s global dynamics and processes.

MODIS’s raw observations are automatically processed by various algorithms to generate high-level products, directly usable by the various scientific communities (oceanography, biology, atmospheric science, etc.). MODIS high-level products include, for instance, surface reflectance, land and sea surface temperature, snow cover, ocean’s chlorophyll-a concentration, etc. The spatial and temporal resolutions depend on the product. All MODIS data are open and free of charge. For our study we use 3 MODIS products :

4.3 Vegetation

  • How does vegetation influence the presence/abundance/resistance of mosquitoes ?

[TODO]

  • Covariates extracted:

    • VNI : Average 8-days Normalized Difference Vegetation Index ( unitless )

    • VEI : Average 8-days Enhanced Vegetation Index ( unitless )

  • Where do the data come from ?

Vegetation data come from the MODIS collection. See section Temperature for additional information.

4.4 Evapotranspiration

  • How does evapotranspiration influence the presence/abundance/resistance of mosquitoes ?

[TODO]

  • Covariates extracted:

    • EVT : Average 8-days evapotranspiration ( kg/m�/8day )
  • Where do the data come from ?

Evapotranspiration data come from the MODIS collection. See section Temperature for additional information.

4.5 Precipitation

  • How do precipitation influence the presence/abundance/resistance of mosquitoes ?

[TODO]

  • Covariates extracted:

    • RTD : Average daily total precipitation (source�: TAMSAT) ( mm )

    • RGD : Average daily total precipitation (source�: GPM) ( mm )

    • RGH : Proportion of half-hours with positive precipitation for the whole duration of the HLC ( % )

  • Where do the data come from ?

GPM :

from the GPM official website :

The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow. Building upon the success of the Tropical Rainfall Measuring Mission (TRMM), the GPM concept centers on the deployment of a “Core” satellite carrying an advanced radar / radiometer system to measure precipitation from space and serve as a reference standard to unify precipitation measurements from a constellation of research and operational satellites.

Initiated by NASA and the JAXA, the GPM mission is an international project comprising a constellation of satellites belonging to a many international space agencies worldwide. At its finer spatio-temporal resolution, it provides rainfall estimates at a 0.1° / half-hourly resolutions for the whole globe in near real time (4 hours latency from satellite acquisition in “early run”). The estimates are further consolidated as more data arrive (“final run”), to create research-level products.

GPM data are generated at various temporal resolutions (half-hour, 1 day, 3 days, 7 days, 1 month). All the products are open. To get additional information on the GPM data, go to https://pmm.nasa.gov/data-access/downloads/gpm.

For our study, we use two GPM “final run” collections : the daily precipitations (GPM_3IMERGDF.06) 40 days before the catch, and the half-hourly precipitations (GPM_3IMERGHH.06) for the night of catch.

TAMSAT :

Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.name/knitr/.

———. 2019. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.