A Novel Multimodal Species Distribution Model Fusing Remote Sensing Images and Environmental Features

Xiaojuan Zhang, Yongxiu Zhou, Peihao Peng () and Guoyan Wang
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Xiaojuan Zhang: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Yongxiu Zhou: College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
Peihao Peng: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Guoyan Wang: Institute of Ecological Resources and Landscape Architecture, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2022, vol. 14, issue 21, 1-12

Abstract: Species distribution models (SDMs) are critical in conservation decision-making and ecological or biogeographical inference. Accurately predicting species distribution can facilitate resource monitoring and management for sustainable regional development. Currently, species distribution models usually use a single source of information as input for the model. To determine a solution to the lack of accuracy of the species distribution model with a single information source, we propose a multimodal species distribution model that can input multiple information sources simultaneously. We used ResNet50 and Transformer network structures as the backbone for multimodal data modeling. The model’s accuracy was tested using the GEOLIFE2020 dataset, and our model’s accuracy is state-of-the-art (SOTA). We found that the prediction accuracy of the multimodal species distribution model with multiple data sources of remote sensing images, environmental variables, and latitude and longitude information as inputs (29.56%) was higher than that of the model with only remote sensing images or environmental variables as inputs (25.72% and 21.68%, respectively). We also found that using a Transformer network structure to fuse data from multiple sources can significantly improve the accuracy of multimodal models. We present a novel multimodal model that fuses multiple sources of information as input for species distribution prediction to advance the research progress of multimodal models in the field of ecology.

Keywords: multimodal; deep learning; feature fusion; Transformer network; species distribution models; high-resolution remote sensing images (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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