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Remote Sensing Based Forest Inventories from Landscape to Global Scale...
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Remote Sensing Based Forest Inventories from Landscape to Global Scale II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 761

Special Issue Editors

Prof. Dr. Liyong Fu

E-Mail Website
Guest Editor
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: forestry statistics; forest modeling; forest management; remote sensing in forestry; informatization in forestry
Dr. Ram P. Sharma

E-Mail Website
Guest Editor
Institute of Forestry, Tribhuwan University, Kathmandu 44600, Nepal
Interests: forest ecology; forest management; silviculture; forestry modeling; biostatistics; LiDAR; UAV
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangxing Wang

E-Mail Website
Guest Editor
Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL, USA
Interests: remote sensing; GIS; spatial statistics and their applications to geography; natural and environmental resources with the specific areas; land use and land cover change detection; sampling design; forest inventory and forest growth modelling; forest carbon sequestration modeling and mapping; environmental dynamic modeling and quality assessment; quality assessment and spatial uncertainty analysis of remote sensing and GIS products
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaodi Zhao

E-Mail Website
Guest Editor Assistant
1. Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, China
2. Faculty of Forestry, The University of British Columbia, Vancouver, BC V6T1Z4, Canada
Interests: forest ecosystems; ecological services function; value assessment; forest carbon sink

Special Issue Information

Dear Colleagues,

Forest resources can play a significant role in addressing the problems posed by global climate change and maintaining global ecological security. Scientifically accurate forest inventory data are essential if we are to observe changes in forests’ ecological functions, particularly those changes related to the spatio-temporal evolution of carbon storage and its impact on carbon sink functions. In the early 1980s, remote sensing technology was initially applied in forest inventory management; when combined with GIS, this method enabled the real-time monitoring of dynamic changes in forest resources and environments. Compared to traditional methods, a remote-sensing-based approach offers numerous advantages, including its macroscopic, dynamic, convenient, and cost-effective nature. Remote sensing has become an indispensable tool in the execution of forestry surveys worldwide, receiving widespread application and acclaim.

This Special Issue aims to explore the application of advanced technologies such as remote sensing, GIS, UAV, and LiDAR in order to obtain forest inventory data in different countries and regions, as well as at various scales and scopes. It will also examine the current status and future trends related to the practical application of these data. Research topics will encompass various aspects of natural and social sciences, including the optimization of remote sensing techniques in forest inventory, the relationship between forests’ ecological functions and human activities, forest management, and the evolution of regional forest carbon storage.

We encourage the submission of research papers that analyze the acquisition or application of forest inventory data from different countries or subregions at the regional or global level. Papers that focus on changes in forests’ ecological functions and key ecological functional areas are particularly welcomed.

Prof. Dr. Liyong Fu
Dr. Ram P. Sharma
Prof. Dr. Guangxing Wang
Dr. Xiaodi Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • spatial statistics
  • forest ecosystem
  • ecological services function
  • value assessment
  • forest inventory
  • human activities
  • forest management
  • forest carbon sink
  • unmanned aerial vehicle

Related Special Issue

Published Papers (1 paper)

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Research

23 pages, 4848 KiB  
Article
Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna
by Yong Wu, Guanglong Ou, Tengfei Lu, Tianbao Huang, Xiaoli Zhang, Zihao Liu, Zhibo Yu, Binbing Guo, Er Wang, Zihang Feng, Hongbin Luo, Chi Lu, Leiguang Wang and Weiheng Xu
Remote Sens. 2024, 16(7), 1276; https://doi.org/10.3390/rs16071276 - 04 Apr 2024
Cited by 1 | Viewed by 444
Abstract
Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various [...] Read more.
Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies. Full article
(This article belongs to the Special Issue Remote Sensing Based Forest Inventories from Landscape to Global Scale II)
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