Modeling Anopheles mosquito density spatial and seasonal variations using remotely sensed imagery and statistical methods
© Geoinformatics International. Remotely sensed data and statistical model are integrated to develop the model for predicting Anopheles mosquitoes, which is called Anopheles Mosquito Density Predictive Model (AMDP model) It is found that NDVI values that are higher than 0.501, temperature values wit...
Saved in:
| Main Author: | |
|---|---|
| Format: | Journal |
| Published: |
2017
|
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018453581&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40925 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Summary: | © Geoinformatics International. Remotely sensed data and statistical model are integrated to develop the model for predicting Anopheles mosquitoes, which is called Anopheles Mosquito Density Predictive Model (AMDP model) It is found that NDVI values that are higher than 0.501, temperature values with the range of 25-29°C, relative humidity values with the range of 81-85%, and deciduous forest land cover are the best predictors of the Anopheles mosquito density classes in wet season, while NDVI values that are higher than 0.501, temperature values with the range of 25-29°C, deciduous forest land cover, and elevation 400-700 meters interval are the best predictors for the Anopheles mosquito density classes in dry season. AMDP model was able to predict correctly 79.7% and 73.8% in wet and dry seasons. This model has passed the model calibration and validation procedures. The results indicate that the model could be applied for prediction of the Anopheles mosquito density in other areas, malaria cases and a tool for decision-making system for malaria control planning. |
|---|