Remote Sensing and Visualization Methods

The aim of this reprint is to provide a comprehensive exploration of the latest advancements in forestry through the integration of emerging technologies. This publication delves into several key areas, including the application of remote sensing and visualization techniques, the use of digital twin...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Zhang, Huaiqing, Suárez-Minguez, Juan, Chen, Qi, Wang, Yunsheng, Tharib, Safa, Sun, Hua, Jing, Weipeng, Huang, Huaguo, Yun, Ting, Wang, Meili
التنسيق: Online
اللغة:الإنجليزية
منشور في: MDPI - Multidisciplinary Digital Publishing Institute 2026
الموضوعات:
الوصول للمادة أونلاين:https://directory.doabooks.org/handle/20.500.12854/170593
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author Zhang, Huaiqing
Suárez-Minguez, Juan
Chen, Qi
Wang, Yunsheng
Tharib, Safa
Sun, Hua
Jing, Weipeng
Huang, Huaguo
Yun, Ting
Wang, Meili
author_browse Chen, Qi
Huang, Huaguo
Jing, Weipeng
Sun, Hua
Suárez-Minguez, Juan
Tharib, Safa
Wang, Meili
Wang, Yunsheng
Yun, Ting
Zhang, Huaiqing
author_facet Zhang, Huaiqing
Suárez-Minguez, Juan
Chen, Qi
Wang, Yunsheng
Tharib, Safa
Sun, Hua
Jing, Weipeng
Huang, Huaguo
Yun, Ting
Wang, Meili
author_sort Zhang, Huaiqing
collection Directory of Open Access Books
description The aim of this reprint is to provide a comprehensive exploration of the latest advancements in forestry through the integration of emerging technologies. This publication delves into several key areas, including the application of remote sensing and visualization techniques, the use of digital twins and the metaverse, and the role of artificial intelligence in enhancing forest management. Each of these aspects plays a crucial role in shaping the future of smart forestry. First, remote sensing technologies have revolutionized our ability to create high-precision 3D models of forest ecosystems. These detailed models of terrain, trees, and entire environments provide unprecedented insights into forest structure and dynamics, which are essential for effective monitoring and management. Second, by leveraging the power of digital twin technology and the metaverse, this reprint explores how virtual and real-world environments can be seamlessly integrated. Third, artificial intelligence (AI) algorithms play a pivotal role in enhancing the efficiency and accessibility of forestry processes. AI-driven analytics can process vast amounts of data quickly, providing actionable insights that support decision-making in forest management. This reprint shows how AI is transforming forestry by improving the speed and accuracy of data analysis.
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spelling doab-20.500.12854ir-1705932026-01-02T16:14:21Z Remote Sensing and Visualization Methods Zhang, Huaiqing Suárez-Minguez, Juan Chen, Qi Wang, Yunsheng Tharib, Safa Sun, Hua Jing, Weipeng Huang, Huaguo Yun, Ting Wang, Meili Forest Monitoring Visualization Remote Sensing The aim of this reprint is to provide a comprehensive exploration of the latest advancements in forestry through the integration of emerging technologies. This publication delves into several key areas, including the application of remote sensing and visualization techniques, the use of digital twins and the metaverse, and the role of artificial intelligence in enhancing forest management. Each of these aspects plays a crucial role in shaping the future of smart forestry. First, remote sensing technologies have revolutionized our ability to create high-precision 3D models of forest ecosystems. These detailed models of terrain, trees, and entire environments provide unprecedented insights into forest structure and dynamics, which are essential for effective monitoring and management. Second, by leveraging the power of digital twin technology and the metaverse, this reprint explores how virtual and real-world environments can be seamlessly integrated. Third, artificial intelligence (AI) algorithms play a pivotal role in enhancing the efficiency and accessibility of forestry processes. AI-driven analytics can process vast amounts of data quickly, providing actionable insights that support decision-making in forest management. This reprint shows how AI is transforming forestry by improving the speed and accuracy of data analysis. 2026-01-02T16:14:17Z 2026-01-02T16:14:17Z 2025 book 978-3-7258-4721-1 https://directory.doabooks.org/handle/20.500.12854/170593 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/topic/11319 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4722-8 10.3390/books978-3-7258-4722-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 978-3-7258-4721-1 300 CH open access
spellingShingle Forest
Monitoring
Visualization
Remote Sensing
Zhang, Huaiqing
Suárez-Minguez, Juan
Chen, Qi
Wang, Yunsheng
Tharib, Safa
Sun, Hua
Jing, Weipeng
Huang, Huaguo
Yun, Ting
Wang, Meili
Remote Sensing and Visualization Methods
title Remote Sensing and Visualization Methods
title_full Remote Sensing and Visualization Methods
title_fullStr Remote Sensing and Visualization Methods
title_full_unstemmed Remote Sensing and Visualization Methods
title_short Remote Sensing and Visualization Methods
title_sort remote sensing and visualization methods
topic Forest
Monitoring
Visualization
Remote Sensing
topic_facet Forest
Monitoring
Visualization
Remote Sensing
url https://directory.doabooks.org/handle/20.500.12854/170593
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AT suarezminguezjuan remotesensingandvisualizationmethods
AT chenqi remotesensingandvisualizationmethods
AT wangyunsheng remotesensingandvisualizationmethods
AT tharibsafa remotesensingandvisualizationmethods
AT sunhua remotesensingandvisualizationmethods
AT jingweipeng remotesensingandvisualizationmethods
AT huanghuaguo remotesensingandvisualizationmethods
AT yunting remotesensingandvisualizationmethods
AT wangmeili remotesensingandvisualizationmethods