Remote Sensing and GIS for Identifying and Monitoring the Environmental Factors Associated with Vector-borne Disease: An Overview

2006 ◽  
Author(s):  
W. Zeng ◽  
X. Cui ◽  
X. Liu ◽  
H. Cui ◽  
P. Wang
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Remote Sensing And Gis ◽  
Factors Associated ◽  
Vector Borne Disease ◽  
Vector Borne

scholarly journals Dispersion of tongka langit banana in Buru and Seram, Maluku Province, Indonesia, based on topographic and climate factors

2020 ◽  
Vol 21 (5) ◽  
Author(s):  
HALVINA GRASELA SAIYA ◽  
Adriana Hiariej ◽  
ANNEKE PESIK ◽  
ELIZABETH KAYA ◽  
MEITTY LOUISE HEHANUSSA ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Remote Sensing And Gis ◽  
Climate Factors ◽  
Climate Data ◽  
Climate Condition ◽  
Climate Conditions ◽  
Shuttle Radar Topographic Mission ◽  
Wet Climate ◽  
Gis Methods

Abstract. Saiya HG, Hiariej A, Pesik A, Kaya E, Hehanussa ML, Puturuhu F. 2020. Dispersion of tongka langit banana in Buru and Seram, Maluku Province, Indonesia, based on topographic and climate factors. Biodiversitas 21: 2035-2046. The aim of this research is to understand the dispersion of tongka langit banana as one of the important endemic species in Maluku and also to know the topographic and climate factors hypothetically influencing the dispersion of tongka langit banana. The associated environmental factors are an initial approach that can be used to assess why the species only exists in certain locations. The data of coordinates were collected from survey activity; meanwhile, the slope and contour data were from the Shuttle Radar Topographic Mission (SRTM); and the climate data were from Meteorology, Climatology, and Geophysical Agency through statistic data publication. Then, all data were analyzed using Remote Sensing and GIS methods. The results showed that in Buru Island, tongka langit bananas were found in four locations, with climate condition was rather wet and found on slope grade of 0-8% and 8-15%. Whereas in Seram Island, tongka langit bananas were found in fifteen locations, with wet climate conditions, and on the same condition of slope as those found in Buru island.

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

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1316
Author(s):  
Woubet G. Alemu ◽  
Michael C. Wimberly
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Absolute Error ◽  
Early Warning Systems ◽  
Amhara Region ◽  
Vector Borne Disease ◽  
Vector Borne ◽  
Moderate Resolution Imaging Spectroradiometer

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.

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