Dataset Information

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations Over the Amhara Region, Ethiopia.

ABSTRACT: Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003-2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ?1-3 °C), and error (mean absolute error (MAE) ?1-3 °C), and the highest correlation with day-to-day variability of station temperature (COR ?0.7-0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ?-0.2-0.2 mm, MAE ?0.5-2 mm), and the best agreement (COR ?0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications.

SUBMITTER: Alemu WG

PROVIDER: S-EPMC7085700 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations Over the Amhara Region, Ethiopia.

Alemu Woubet G WG Wimberly Michael C MC

Sensors (Basel, Switzerland) 20200228 5

Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from ...[more]

PMID: 32121264

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Project description:BACKGROUND: Correlative modelling combines observations of species occurrence with environmental variables to capture the niche of organisms. It has been argued for the use of predictors that are ecologically relevant to the target species, instead of the automatic selection of variables. Without such biological background, the forced inclusion of numerous variables can produce models that are highly inflated and biologically irrelevant. The tendency in correlative modelling is to use environmental variables that are interpolated from climate stations, or monthly estimates of remotely sensed features. METHODS: We produced a global dataset of abiotic variables based on the transformation by harmonic regression (time series Fourier transform) of monthly data derived from the MODIS series of satellites at a nominal resolution of 0.1°. The dataset includes variables, such as day and night temperature or vegetation and water availability, which potentially could affect physiological processes and therefore are surrogates in tracking the abiotic niche. We tested the capacities of the dataset to describe the abiotic niche of parasitic organisms, applying it to discriminate five species of the globally distributed tick subgenus Boophilus and using more than 9,500 published records. RESULTS: With an average reliability of 82%, the Fourier-transformed dataset outperformed the raw MODIS-derived monthly data for temperature and vegetation stress (62% of reliability) and other popular interpolated climate datasets, which had variable reliability (56%-65%). The transformed abiotic variables always had a collinearity of less than 3 (as measured by the variance inflation factor), in contrast with interpolated datasets, which had values as high as 300. CONCLUSIONS: The new dataset of transformed covariates could address the tracking of abiotic niches without inflation of the models arising from internal issues with the descriptive variables, which appear when variance inflation is higher than 10. The coefficients of the harmonic regressions can also be used to reconstruct the complete original time series, being an adequate complement for ecological, epidemiological, or phylogenetic studies. We provide the dataset as a free download under the GNU general public license as well as the scripts necessary to integrate other time series of data into the calculations of the harmonic coefficients.

Dataset Information

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations Over the Amhara Region, Ethiopia.

Publications

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations Over the Amhara Region, Ethiopia.

Similar Datasets

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