Document Type
Conference Proceeding
Publication Date
9-30-2020
Abstract
Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R2> 0.95) in the testing and (R2> 0.93) in the evaluation process.
Recommended Citation
S. Perera, W. Li, E. Linstead, and H. El-Askary, “Forecasting vegetation health in the MENA region by predicting vegetation indicators with machine learning models,” 2020 IEEE International Geoscience and Remote Sensing Symposium, Sep. 2020.
Copyright
IEEE
Included in
Environmental Health and Protection Commons, Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Other Computer Engineering Commons, Other Computer Sciences Commons, Other Electrical and Computer Engineering Commons, Other Environmental Sciences Commons
Comments
This paper was presented at the virtual 2020 IEEE International Geoscience and Remote Sensing Symposium in September 2020.