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Remote Sensing
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Remote Sensing-Assisted Forest Inventory Planning
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Remote Sensing-Assisted Forest Inventory Planning

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 554

Special Issue Editors

Prof. Dr. Zhengyang Hou

E-Mail Website
Guest Editor
The Key Laboratory for Silviculture and Conservation (Ministry of Education), Beijing Forestry University, Beijing 100083, China
Interests: remote sensing; forest inventory; statistics; survey sampling
Dr. Qing Xu

E-Mail Website
Guest Editor
International Centre for Bamboo and Rattan, Beijing, China
Interests: remote sensing; forest inventory; statistics; survey sampling
Prof. Dr. Timo Tokola

E-Mail Website
Guest Editor
School of Forest Science, University of Eastern Finland, 80101 Joensuu, Finland
Interests: forest management; forest IT; remote sensing; GIS applications; terrain mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the challenges brought on by climate change, energy consumption and economic growth, the sustainable utilisation of forest resources and environmental protection is extremely essential to achieve the sustainable development of human societies. Remote sensing plays a critical role in improving understanding of forest structure, ecosystem functions, as well as their interactions with human societies and climate drivers. In recent years, a large amount of remotely sensed data (e.g., multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar) and a large variety of platforms (e.g., satellite, airborne, unmanned aerial vehicles, and ground-based) have emerged to provide us with a powerful tool to precisely estimate and monitor forest resources. Remote sensing not only streamlines the traditional forest inventory procedure, but also provides invaluable real-time insights into dynamic changes in forest cover, carbon sequestration and biodiversity.

This Special Issue on “Remote Sensing-assisted Forest Ecosystem Inventory and Management” centres on leveraging remote sensing for promoting forest ecosystem management with cutting-edge theories and techniques. Topics include but are not limited to:

  • Forest inventory and monitoring
  • Forest management planning
  • Design-based inference
  • Model-based inference
  • Uncertainty analysis
  • Survey sampling
  • Optimization
  • Simulation
  • Modelling

Prof. Dr. Zhengyang Hou
Dr. Qing Xu
Prof. Dr. Timo Tokola
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

  • forest ecosystems
  • forest management
  • forest inventory
  • forest planning
  • biostatistics

Published Papers (1 paper)

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Research

24 pages, 6955 KiB  
Article
Spatial Prediction of Diameter Distributions for the Alpine Protection Forests in Ebensee, Austria, Using ALS/PLS and Spatial Distributional Regression Models
by Arne Nothdurft, Andreas Tockner, Sarah Witzmann, Christoph Gollob, Tim Ritter, Ralf Kraßnitzer, Karl Stampfer and Andrew O. Finley
Remote Sens. 2024, 16(12), 2181; https://doi.org/10.3390/rs16122181 (registering DOI) - 15 Jun 2024
Viewed by 231
Abstract
A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties [...] Read more.
A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data were used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. In the distributional regression approach, covariates can enter the model in a flexible form, such as via nonlinear smooth curves, cyclic smooths, or spatial effects. Supported by Bayesian diagnostics DIC and WAIC, nonlinear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. With BAMLSS, spatial interval predictions of the DBH distribution at any new geo-locations were enabled via straightforward access to the posterior predictive distributions of the model terms and by offering simple plug-in solutions for new covariate values. A cross-validation analysis validated the robustness of the proposed method’s parameter estimation and out-of-sample prediction. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape. Therefore, the intensity of final felling needs to be increased to reduce shading from the dense, overmature shelter trees and to promote sunlight for the young regeneration trees. Full article
(This article belongs to the Special Issue Remote Sensing-Assisted Forest Inventory Planning)
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