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Forests
Special Issues
Forest Inventory Monitoring Based on Remote Sensing
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Forest Inventory Monitoring Based on Remote Sensing

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 286

Special Issue Editors

Dr. Xukai Zhang

E-Mail Website
Guest Editor
Department of Agriculture and Environmental Sciences, Lincon University-Missouri, Jefferson City, MO 65101, USA
Interests: remote sensing; LiDAR; UAV; forest inventory
Dr. Bradley D. Graham

E-Mail Website
Guest Editor
Missouri Department of Conservation, West Plains, MO 65775, USA
Interests: forest ecology; disturbance ecology; carbon sequestration; forest management
Dr. Xuelian Meng

E-Mail Website
Guest Editor
Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: remote sensing; GIS; LiDAR; UAV

Special Issue Information

Dear Colleagues,

Integrating remote sensing technology with forest inventory monitoring represents a cutting-edge and effective approach to understanding and managing forest ecosystems. With leveraging tools like satellites, drones, and LiDAR (Light Detection and Ranging) systems, remote sensing enables the acquisition of invaluable data on diverse forest attributes, including tree species, density, height, and health. Remote sensing not only streamlines the traditionally labor-intensive forest inventory process but also provides invaluable real-time insights into dynamic changes in forest cover, biodiversity, and carbon sequestration levels. It further empowers us to monitor and respond to disturbances such as wildfires, insect infestations, and deforestation promptly.

This Special Issue aims to encompass a wide range of remote sensing applications in forest inventory monitoring, showcase how this technology can substantially enhance our understanding and management of forest ecosystems, and foster more effective conservation efforts and sustainable practices.

Dr. Xukai Zhang
Dr. Bradley D. Graham
Dr. Xuelian Meng
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. Forests is an international peer-reviewed open access monthly 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 2600 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
  • forest inventory
  • LiDAR

Published Papers (1 paper)

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Research

23 pages, 6863 KiB  
Article
Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models
by Mi Luo, Shoaib Ahmad Anees, Qiuyan Huang, Xin Qin, Zhihao Qin, Jianlong Fan, Guangping Han, Liguo Zhang and Helmi Zulhaidi Mohd Shafri
Forests 2024, 15(6), 975; https://doi.org/10.3390/f15060975 (registering DOI) - 1 Jun 2024
Abstract
The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies [...] Read more.
The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process. Full article
(This article belongs to the Special Issue Forest Inventory Monitoring Based on Remote Sensing)
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