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Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

SIMPLE SUMMARY: The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have...

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Autores principales: Su, Jin-He, Piao, Ying-Chao, Luo, Ze, Yan, Bao-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981277/
https://www.ncbi.nlm.nih.gov/pubmed/29701686
http://dx.doi.org/10.3390/ani8050066
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author Su, Jin-He
Piao, Ying-Chao
Luo, Ze
Yan, Bao-Ping
author_facet Su, Jin-He
Piao, Ying-Chao
Luo, Ze
Yan, Bao-Ping
author_sort Su, Jin-He
collection PubMed
description SIMPLE SUMMARY: The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. ABSTRACT: With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.
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spelling pubmed-59812772018-06-01 Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks Su, Jin-He Piao, Ying-Chao Luo, Ze Yan, Bao-Ping Animals (Basel) Article SIMPLE SUMMARY: The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. ABSTRACT: With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction. MDPI 2018-04-26 /pmc/articles/PMC5981277/ /pubmed/29701686 http://dx.doi.org/10.3390/ani8050066 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Su, Jin-He
Piao, Ying-Chao
Luo, Ze
Yan, Bao-Ping
Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks
title Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks
title_full Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks
title_fullStr Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks
title_full_unstemmed Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks
title_short Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks
title_sort modeling habitat suitability of migratory birds from remote sensing images using convolutional neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981277/
https://www.ncbi.nlm.nih.gov/pubmed/29701686
http://dx.doi.org/10.3390/ani8050066
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