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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: 31 May 2025 | Viewed by 4267

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

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Published Papers (3 papers)

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Research

19 pages, 5194 KiB  
Article
Continuous Monitoring of Forests in Wetland Ecosystems with Remote Sensing and Probability Sampling
by Aoyun Zhao, Xinjie Cheng, Rong Cao, Liuyuan Huang and Zhengyang Hou
Remote Sens. 2024, 16(18), 3508; https://doi.org/10.3390/rs16183508 - 21 Sep 2024
Viewed by 1192
Abstract
With the drastic reduction in wetland areas, it is essential to conduct an annual monitoring of the biomass or carbon content of wetland ecosystems to support international initiatives and agreements focused on sustainable development, climate change, and carbon equity. Forests in wetland ecosystems [...] Read more.
With the drastic reduction in wetland areas, it is essential to conduct an annual monitoring of the biomass or carbon content of wetland ecosystems to support international initiatives and agreements focused on sustainable development, climate change, and carbon equity. Forests in wetland ecosystems play a crucial role in carbon sequestration; however, the monitoring of small, fragmented forest components in wetlands remains insufficient, leading to an underestimation of their ecological and carbon sequestration functions. This study utilizes a model-assisted (MA) estimator, a monitoring procedure that is asymptotically design-unbiased and incorporates remote sensing, to assess the status and trends in the above-ground biomass (AGB) of forest components in wetlands, while also proposing a method of optimizing the sample size to enable continuous monitoring. Based on the population of the forest component of Baiyangdian wetland, major findings indicate that: (1) neglecting the forest component of Baiyangdian wetland will lead to an underestimation of the total aboveground biomass by 224.34 t/ha and 243.64 t/ha in the years 2022 and 2023, respectively; (2) in either year-specific monitoring or interannual change monitoring, the MA estimator is more cost-effective than the expansion estimator, a comparable procedure that relies solely on field observations; (3) the method used to optimize sample size can effectively tackle the cost-related concerns of subsequent continuous monitoring. Overall, the neglect of forest components is inevitably bound to give rise to an underestimation of wetlands, and use of an MA estimator and optimizing the sample size could effectively address the cost issue in continuous monitoring. This holds significant importance when developing management strategies to prevent the further degradation of wetland ecological functions and carbon sink capabilities. Full article
(This article belongs to the Special Issue Remote Sensing-Assisted Forest Inventory Planning)
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20 pages, 1502 KiB  
Article
Using Multi-Source National Forest Inventory Data for the Prediction of Tree Lists of Individual Stands for Long-Term Simulation
by Jouni Siipilehto, Helena M. Henttonen, Matti Katila and Harri Mäkinen
Remote Sens. 2024, 16(14), 2513; https://doi.org/10.3390/rs16142513 - 9 Jul 2024
Viewed by 947
Abstract
Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting [...] Read more.
Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area’s strategic information, the accuracy is considered adequate. Full article
(This article belongs to the Special Issue Remote Sensing-Assisted Forest Inventory Planning)
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24 pages, 6954 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 - 15 Jun 2024
Cited by 1 | Viewed by 1256
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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