Integrating Remote Sensing and Machine Learning for Regional-Scale Habitat Mapping: Advances and future challenges for desert locust monitoring

Increased access to reliable data and computationally efficient systems has created more collaborative potential between remote sensing and machine learning for species habitat prediction. Exploiting the integrative opportunities will require a deeper understanding of methods remote sensing instruments use to capture biophysical variables and the extent that data science can model ecological relationships. In this article, we provide the first systemic review of the integration of remote sensing and machine learning to predict habitat for the highly destructive desert locust and explore deep learning as a new method for increased classification. We evaluated the performance of six machine learning algorithms in two study regions (Niger and Sudan), using locust observations, multiple pseudoabsence data sets, and remotely sensed habitat data. In both regions, the
k
-nearest neighbor (
k
NN) and a deep neural network (DNN) were the best-preforming models. In Niger, the
k
NN average accuracy score was 88%, and the F-1 score was 89% for the Present
(1)
class. The DNN average accuracy score was 88%, and the F-1 score was 89%. In Sudan, the
k
NN average accuracy score was 88%, and the F-1 score was 88% for the Present
(1)
class. The DNN average accuracy score was 88%, and the F-1 score was 89%. Additionally, we outline a process for robustly modeling habitat through remote sensing data and highlight important limitations. We propose that the DNN model has the best potential for constructing a transferable representation and discuss novel methods to meet future challenges in desert locust management in a sustainable way.