{"title":"通过整合多时 GEDI 数据、卫星图像和卷积神经网络，绘制澳大利亚东南部火灾前后的林冠高度图","authors":"Tsung-Chi Chou, Xuan Zhu, Ruth Reef","doi":"10.1080/01431161.2024.2343429","DOIUrl":null,"url":null,"abstract":"This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1,...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"30 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network\",\"authors\":\"Tsung-Chi Chou, Xuan Zhu, Ruth Reef\",\"doi\":\"10.1080/01431161.2024.2343429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1,...\",\"PeriodicalId\":14369,\"journal\":{\"name\":\"International Journal of Remote Sensing\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/01431161.2024.2343429\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/01431161.2024.2343429","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network
This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1,...
期刊介绍：
The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include:
• Remotely sensed data collection, analysis, interpretation and display.
• Surveying from space, air, water and ground platforms.
• Imaging and related sensors.
• Image processing.
• Use of remotely sensed data.
• Economic surveys and cost-benefit analyses.
• Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).
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