利用卫星图像进行野生动物探测、计数和调查:我们做到了吗?

IF 6 2区 地球科学 Q1 GEOGRAPHY, PHYSICAL GIScience & Remote Sensing Pub Date : 2024-05-09 DOI:10.1080/15481603.2024.2348863
Alexandre Delplanque, Jérôme Théau, Samuel Foucher, Ghazaleh Serati, Simon Durand, Philippe Lejeune
{"title":"利用卫星图像进行野生动物探测、计数和调查:我们做到了吗?","authors":"Alexandre Delplanque, Jérôme Théau, Samuel Foucher, Ghazaleh Serati, Simon Durand, Philippe Lejeune","doi":"10.1080/15481603.2024.2348863","DOIUrl":null,"url":null,"abstract":"Wildlife surveys are key to assessing the health of global biodiversity. Traditional field and aerial methods however have significant limitations, including high costs, substantial time investment...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wildlife detection, counting and survey using satellite imagery: are we there yet?\",\"authors\":\"Alexandre Delplanque, Jérôme Théau, Samuel Foucher, Ghazaleh Serati, Simon Durand, Philippe Lejeune\",\"doi\":\"10.1080/15481603.2024.2348863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wildlife surveys are key to assessing the health of global biodiversity. Traditional field and aerial methods however have significant limitations, including high costs, substantial time investment...\",\"PeriodicalId\":55091,\"journal\":{\"name\":\"GIScience & Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GIScience & Remote Sensing\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/15481603.2024.2348863\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GIScience & Remote Sensing","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/15481603.2024.2348863","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

野生动物调查是评估全球生物多样性健康状况的关键。然而,传统的野外和空中方法有很大的局限性,包括成本高、时间投入大...
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Wildlife detection, counting and survey using satellite imagery: are we there yet?
Wildlife surveys are key to assessing the health of global biodiversity. Traditional field and aerial methods however have significant limitations, including high costs, substantial time investment...
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
11.20
自引率
9.00%
发文量
84
审稿时长
6 months
期刊介绍: GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
期刊最新文献
Synthesizing Landsat images using time series model-fitting methods for China’s coastal areas against sparse and irregular observations Methods to compare sites concerning a category’s change during various time intervals LSL-SS-Net: level set loss-guided semantic segmentation networks for landslide extraction Monitoring the Amazon River plume from satellite observations Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1