| Creators: |
Midekisa, Alemayehu and Holl, Felix and Savory, David J. and Andrade-Pacheco, Ricardo and Gething, Peter and Bennett, Adam and Sturrock, Hugh J.W. |
| Title: |
Land cover mapping for Continental Africa |
| Item Type: |
Conference or Workshop Item |
| Event Title: |
ASTMH 65th Annual Meeting |
| Event Location: |
Georgia, Atlanta, USA |
| Event Dates: |
November 13-17, 2016 |
| Date: |
November 2016 |
| Divisions: |
Gesundheitsmanagement |
| Abstract: |
Land cover type influences transmission of a number of diseases, including
vector-borne diseases such as malaria. However, high spatial resolution
land cover data through time are lacking for continental Africa, hindering
the ability to model and test hypotheses. The objective of this study was
to develop a high spatial resolution (30 meter) land cover dataset for
continental Africa for the years 2000 and 2015. To generate gold standard
model data, high resolution satellite imagery was visually inspected and
used to identify (7212 sample points) Landsat pixels that were entirely
made up of 1 of 7 classes (water, impervious surface, high biomass, low
biomass, rock, sand and bare soil). For model validation purposes, 80% of
points from each class were used as training data, with 20% withheld as a
validation dataset. Cloud free Landsat 7 and 8 annual composites for 2000
and 2015 were generated. Spectral bands from the Landsat image were
then extracted for each of the training and validation points and a random
forest model using the full dataset was used to classify the 2000 and
2015 Landsat images into each of the 7 classes. In addition to the Landsat
spectral bands, spectral indices such as normalized difference vegetation
index (NDVI) and normalized difference water index (NDWI) were used as
covariates in the model. Additionally, calibrated night time light imageries
from the National Oceanic and Atmospheric Administration (NOAA)
were included as a covariate. Using the validation dataset, classification
accuracy including omission error and commission error were computed
for each land cover class. Model results showed that overall accuracy of
classification was over 90 percent. This high resolution land cover product
developed for the continental Africa will be available for public use and
can potentially enhance the ability to test models and hypotheses. |
| Forthcoming: |
No |
| Citation: |
Midekisa, Alemayehu and Holl, Felix and Savory, David J. and Andrade-Pacheco, Ricardo and Gething, Peter and Bennett, Adam and Sturrock, Hugh J.W.
(2016)
Land cover mapping for Continental Africa.
In: ASTMH 65th Annual Meeting, November 13-17, 2016, Georgia, Atlanta, USA.
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