Modeling habitat suitability of migratory birds from remote sensing images using convolutional neural networks
文献类型：期刊论文
| 作者 | Su, Jin-He1,2; Piao, Ying-Chao1; Luo, Ze1; Yan, Bao-Ping1 |
| 刊名 | Animals
 |
| 出版日期 | 2018-05-01 |
| 卷号 | 8期号:5页码:15 |
| 关键词 | 1-d convolution Bar-head goose Convolutional neural network Dbic Habitat preference |
| ISSN号 | 2076-2615 |
| DOI | 10.3390/ani8050066 |
| 通讯作者 | Su, jin-he(sujinhe@cnic.cn) |
| 英文摘要 | 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 x 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. |
| WOS关键词 | SPECIES DISTRIBUTION ; CLASSIFICATION ; AREAS ; GIS |
| WOS研究方向 | Agriculture ; Veterinary Sciences |
| WOS类目 | Agriculture, Dairy & Animal Science ; Veterinary Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000435189700007 |
| 出版者 | MDPI |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2374229 |
| 专题 | 计算机网络信息中心 |
| 通讯作者 | Su, Jin-He |
| 作者单位 | 1.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Su, Jin-He,Piao, Ying-Chao,Luo, Ze,et al. Modeling habitat suitability of migratory birds from remote sensing images using convolutional neural networks[J]. Animals,2018,8(5):15. |
| APA | Su, Jin-He,Piao, Ying-Chao,Luo, Ze,&Yan, Bao-Ping.(2018).Modeling habitat suitability of migratory birds from remote sensing images using convolutional neural networks.Animals,8(5),15. |
| MLA | Su, Jin-He,et al."Modeling habitat suitability of migratory birds from remote sensing images using convolutional neural networks".Animals 8.5(2018):15. |
入库方式： iSwitch采集
来源：计算机网络信息中心
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