Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine

Abstract

In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats.

Mapping the Urban Environments of Aedes aegypti Using Drone Technology

“…Vegetation indexes are an indirect measure of environmental elements that affect mosquito populations [41], and these present high correlations with mosquito behavior and biological cycles. One of the most commonly used is NDVI [42].…”

“…Remote sensors and geographic information systems can analyze factors associated with malaria transmission and Anopheles species development in rural and semi-urban environments [41,[51][52][53][54][55]. Recently, some studies have recommended using a high spatial resolution to better identify and characterize Anopheles mosquitoes' breeding sites [16,41].…”

A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin

“…As a matter of fact, GEE is indeed a web-based open-source processing platform that combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. It uses Java scripts for downloading satellite data, detecting landscape changes, classifying land cover types, mapping trends, and quantifying differences on the Earth’s surface, and has been recently used for various remote sensing applications [ 36 , 37 ]. The dataset was collected from LANDSAT/LT05/C01/T1_SR and LANDSAT/LC08/C01/T1_SR for Landsat-5 and Landsat-8, respectively.…”

Research on the Assessment Method of Sugarcane Cultivation Suitability in Guangxi Province, China, Based on Multi-Source Data

“…The elevation data were obtained directly from the DEM data, while the slope and slope direction data were derived using surface analysis techniques in GIS based on the DEM data [51][52][53]. In addition, the remaining eight indicators were calculated using the daily average temperature data, daily minimum temperature data, and daily precipitation data based on the GEE platform [54][55][56][57][58]. The second step was to optimize the indicator data to obtain assessment data.…”