NDVI TECHNOLOGY FOR FOOD SECURITY AND BETTER HEALTH IN KENYA

Student(s):MANAL G. AHMED HADYAH MWIDAU MOHAMMED HAJI
Grade Level:Middle School (grades 6-8, ages 11-14)
GLOBE Teacher(s):Kennedy Otieno
Contributors:Mr. Kennedy Otieno
Report Type(s):Standard Research Report, International Virtual Science Symposium Report, Mission Mosquito Report
Protocols:Air Temperature, Precipitation, Relative Humidity, Water Vapor, Land Cover Classification, Earth As a System, Mosquitoes, Soil Fertility, Soil Moisture - Gravimetric, Soil pH
Presentation Poster: View Document
Optional Badges: Be a Collaborator, Be a Data Scientist, Make An Impact
Language(s):English
Date Submitted:02/17/2020

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ABSTRACT
Normalized Difference Vegetation Index (NDVI) is a remote sensing technology that is used to determine the amount of vegetation cover on the earth’s surface. It is important to determine vegetation cover on the earth’s surface because any changes on vegetation cover affects our health, economy and environment. In an effort to monitor major fluctuations in vegetation and understand how they affect the environment, 20 years ago Earth scientists began using satellite remote sensors to measure and map the density of green vegetation over the Earth. Using NOAA’s Advanced Very High Resolution Radiometer (AVHRR), scientists have been collecting images of our planet’s surface.
NDVI is useful for farmers as it helps them monitor and manage their farms remotely, and can predict climate changes such as drought. Since NDVI determines vegetation cover, it can also be correlated with incidences of vector borne diseases such as malaria, which is transmitted by the mosquito.
In this study, normalized data from Homa Bay County was used to determine whether there was a correlation between NDVI and malaria occurrence with weather conditions (precipitation, humidity and temperature) in Homa Bay County during the period between January, 2017 and February, 2018.
From the results, it was clear that both NDVI and malaria occurrence were highest during the month of May, 2017, which is the peak of the long rainy season in Kenya. The month of May, 2017 recorded the highest precipitation, humidity, malaria occurrence and the moderately high temperature. This month coincides with the growth of the maize crop, one of the most common food crops grown in Homa Bay. Hence, there is high vegetation cover during this month, which also encourages mosquito breeding. The levels of NDVI, precipitation and humidity steadily dropped between June and July 2017. This coincides with the harvesting season, whereby the maize stalks turn yellow in color, causing the NDVI values to drop. The NDVI value peaks again during the short rains, i.e. November, 2019, which also recorded relatively high precipitation and humidity. This coincides with the second growth season for the maize plant.
Malaria occurrence was found to be very high during the month of May and November, 2017, when there was high precipitation, high humidity and low temperature. During these months, the NDVI value was at its highest, which means the vegetation cover was very high. High vegetation and warm temperature encourages mosquito breeding, which results in high malaria transmission. Notably the adult female mosquitoes look for vegetation to rest on after laying their eggs- and thus the vegetation is an important variable.

NDVI is therefore a promising technology for monitoring vegetation cover, which in turn can provide information on vector borne diseases such as malaria. This information is useful to farmers as it will help to improve productivity.



Comments

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Greetings!

This is a fascinating research report,andI will have a few questions for you as I dig deeper into the content. I did note that you discuss using NOAA's AVHRR data, and wanted to make you aware of some other NASAdata products that are also measuring NDVI.

There is an instrument onboard both the Aqua and Terra satellites called MODIS. You can learn more here: https://modis.gsfc.nasa.gov/data/.

You can also use NASA's LANCE data (Land, Atmosphere Near real-time Capability for EOS). These data are used to monitor vegetation and crop condition. There are several global/regional scale systems in place that report on drought, food shortages and forecasting crop yields including the USGS Famine Early Warning Systems Network (FEWS NET) and Group on Earth Observations (GEO) Global Agricultural Monitoring (GEOGLAM) crop monitor.. You can learn more here: https://earthdata.nasa.gov/earth-observation-data/near-real-time/hazards-and-dis­asters/vegetation

Posted on 4/5/20 3:41 PM.

How difficult was it to get the data about the incidence of malaria for the Homa Bay region? Which resources did you use to gather that data? I ask as this is such a great research report, and often other educators are interested in doing something similar. I know sometimes it can be hard to access good data on the incidence of disease, and figured knowing how you are able to do this will help me help others!

Posted on 4/5/20 4:07 PM.