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Special Issue "Remote Sensing Technology Applications in Forestry and REDD+"

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

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Dr. Kim Calders

Computational & Applied Vegetation Ecology, Ghent University, Belgium
Website | E-Mail
Interests: LiDAR, 3D measurements, remote sensing, forestry, ecosystem monitoring and modelling, fusion of ground-based and airborne data
Guest Editor
Dr. Inge Jonckheere

FAO of the United Nations, viale delle Terme di Caracalla, 00153 Roma, Italy
Website | E-Mail
Interests: Image processing: for applications in natural resources, forestry, land use and land use change in forestry (LULUCF), biodiversity, humanitarian and cultural heritage applications; In situ vegetation structure and biomass assessment: hemispherical photography and LiDAR; Carbon studies in the REDD+ context: UNFCCC processes, IPCC Guidelines and GPG for GHG-I
Guest Editor
Dr. Mikko Vastaranta

University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, P.O.Box 111, FI-80101, Joensuu, Finland
Website | E-Mail
Interests: detailed remote sensing of forests using point clouds; forest health monitoring and tree quality measurements; digitalization and improved use of forest resource information
Guest Editor
Dr. Joanne Nightingale

Earth Observation, Climate and Optical Group, National Physical Laboratory, Teddington TW11 0LW, UK
Website | E-Mail
Interests: assessing the quality of information about forests derived from in situ measurement devices and Earth Observation satellites; improving global satellite-derived biophysical product validation strategies; and contributing to good practice guidance for the evaluation of ECV data records

Special Issue Information

Dear Colleagues,

Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and an increased spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems and community-based monitoring of forests. The UNFCCC REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change.

Therefore, we invite scientists working on remote sensing technologies, close-range sensing and field data to contribute to this Special Issue. Topics of interest include (a) novel remote sensing applications that can advance the needs on forest resource information and REDD+ MRV; (b) case studies of applying remote sensing data for REDD+ MRV; (c) timeseries’ algorithms and methodologies for forest resource assessment at the different spatial scales varying from tree to national level; (d) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV; We would particularly welcome submissions on data fusion.

Dr. Kim Calders
Dr. Inge Jonckheere
Assoc. Prof. Mikko Vastaranta
Dr. Joanne Nightingale
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 papers will be 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 1800 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

  • Forestry
  • LULUCF
  • REDD+
  • MRV
  • Timeseries
  • Remote sensing
  • Close-range sensing

Published Papers (6 papers)

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Research

Open AccessArticle Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians
Forests 2019, 10(2), 127; https://doi.org/10.3390/f10020127
Received: 4 January 2019 / Revised: 30 January 2019 / Accepted: 31 January 2019 / Published: 5 February 2019
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Abstract
Old-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the [...] Read more.
Old-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the Ukrainian Carpathians to test whether Sentinel-2 satellite images could be used to correctly identify these forests. We used summer and autumn 2017 Sentinel-2 satellite images comprising 10 and 20 m resolution bands to create 6 vegetation indices and 9 textural features. We used a Random Forest classification model to discriminate between dominant tree species within old-growth forests and between old-growth and other forest types. Beech and Norway spruce were identified with an overall accuracy of around 90%, with a lower performance for mountain pine (70%) and mixed forest (40%). Old-growth forests were identified with an overall classification accuracy of 85%. Adding textural features, band standard deviations and elevation data improved accuracies by 3.3%, 2.1% and 1.8% respectively, while using combined summer and autumn images increased accuracy by 1.2%. We conclude that Random Forest classification combined with Sentinel-2 images can provide an effective option for identifying old-growth forests in Europe. Full article
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
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Open AccessArticle Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China
Forests 2019, 10(2), 104; https://doi.org/10.3390/f10020104
Received: 17 December 2018 / Revised: 25 January 2019 / Accepted: 25 January 2019 / Published: 29 January 2019
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Abstract
Forest aboveground biomass (AGB) estimation modeling based on remote sensing is an important method for large-scale biomass estimation; the accuracy of the estimation models has been a topic of broad and current interest. In this study, we used permanent sample plot data and [...] Read more.
Forest aboveground biomass (AGB) estimation modeling based on remote sensing is an important method for large-scale biomass estimation; the accuracy of the estimation models has been a topic of broad and current interest. In this study, we used permanent sample plot data and Landsat 8 Operational Land Imager (OLI) images of western Hunan. Remote-sensing-based models were developed for different vegetation types, and different crown density classes were incorporated. The linear model, linear dummy variable model, and linear mixed-effects model were used to determine the most effective and accurate method for remote-sensing-based AGB estimation. The results show that the adjusted coefficient of determination (R2adj) and root mean square error (RMSE) of the linear dummy model and linear mixed-effects model were significantly better than those of the linear model; the R2adj increased more than 0.16 and the RMSE decreased more than 2.12 for each vegetation type, and the F-test also showed significant differences between the linear model and linear dummy variable model and between the linear model and linear mixed-effects model. The accuracies of the AGB estimations of the linear dummy variable model and the linear mixed-effects model were significantly better than those of linear model in the thin and dense crown density classes. There were no significant differences in the AGB estimation performance between the linear dummy variable model and linear mixed-effects model; these two models were more flexible and more suitable than the linear model for remote-sensing-based AGB estimation. The results of this study provide a new approach for solving the low-accuracy estimations of linear models. Full article
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
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Open AccessArticle Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions
Forests 2018, 9(12), 778; https://doi.org/10.3390/f9120778
Received: 30 October 2018 / Revised: 3 December 2018 / Accepted: 13 December 2018 / Published: 17 December 2018
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Abstract
Biomass is a key biophysical parameter used to estimate carbon storage and forest productivity. Spatially-explicit estimation of biomass provides invaluable information for carbon stock calculation and scientific forest management. Nevertheless, there still exists large uncertainty concerning the relationship between biomass and influential factors. [...] Read more.
Biomass is a key biophysical parameter used to estimate carbon storage and forest productivity. Spatially-explicit estimation of biomass provides invaluable information for carbon stock calculation and scientific forest management. Nevertheless, there still exists large uncertainty concerning the relationship between biomass and influential factors. In this study, aboveground biomass (AGB) was estimated using the random forest algorithm based on remote sensing imagery (Landsat) and field data for three regions with different topographic conditions in Zhejiang Province, China. AGB distribution and change combined with stratified terrain classifications were analyzed to investigate the relations between AGB and topography conditions. The results indicated that AGB in three regions increased from 2010 to 2015 and the magnitude of growth varied with elevation, slope, and aspect. In the basin region, slope had a greater influence on AGB, and we attributed this negative AGB-elevation relationship to ecological forest construction. In the mountain area, terrain features, especially elevation, showed significant relations with AGB. Moreover, AGB and its growth showed positive relations with elevation and slope. In the island region, slope also played a relatively more important role in explaining the relationship. These results demonstrate that AGB varies with terrain conditions and its change is a consequence of interactions between the natural environment and anthropogenic behavior, implying that biomass retrieval based on Landsat imagery could provide considerable important information related to regional heterogeneity investigations. Full article
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
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Open AccessArticle LiDAR-Based Regional Inventory of Tall Trees—Wellington, New Zealand
Forests 2018, 9(11), 702; https://doi.org/10.3390/f9110702
Received: 8 October 2018 / Revised: 9 November 2018 / Accepted: 10 November 2018 / Published: 13 November 2018
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Abstract
Indigenous forests cover 23.9% of New Zealand’s land area and provide highly valued ecosystem services, including climate regulation, habitat for native biota, regulation of soil erosion and recreation. Despite their importance, information on the number of tall trees and the tree height distribution [...] Read more.
Indigenous forests cover 23.9% of New Zealand’s land area and provide highly valued ecosystem services, including climate regulation, habitat for native biota, regulation of soil erosion and recreation. Despite their importance, information on the number of tall trees and the tree height distribution across different forest classes is scarce. We present the first region-wide spatial inventory of tall trees (>30 m) based on airborne LiDAR (Light Detection and Ranging) measurements in New Zealand—covering the Greater Wellington region. This region has 159,000 ha of indigenous forest, primarily on steep mountainous land. We implement a high-performance tree mapping algorithm that uses local maxima in a canopy height model (CHM) as initial tree locations and accurately identifies the tree top positions by combining a raster-based tree crown delineation approach with information from the digital surface and terrain models. Our algorithm includes a check and correction for over-estimated heights of trees on very steep terrain such as on cliff edges. The number of tall trees (>30 m) occurring in indigenous forest in the Wellington Region is estimated to be 286,041 (±1%) and the number of giant trees (>40 m tall) is estimated to be 7340 (±1%). Stereo-analysis of aerial photographs was used to determine the accuracy of the automated tree mapping. The giant trees are mainly in the beech-broadleaved-podocarp and broadleaved-podocarp forests, with density being 0.04 and 0.12 (trees per hectare) respectively. The inventory of tall trees in the Wellington Region established here improves the characterization of indigenous forests for management and provides a useful baseline for long-term monitoring of forest conditions. Our tree top detection scheme provides a simple and fast method to accurately map overstory trees in flat as well as mountainous areas and can be directly applied to improve existing and build new tree inventories in regions where LiDAR data is available. Full article
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
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Open AccessArticle Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest
Forests 2018, 9(10), 623; https://doi.org/10.3390/f9100623
Received: 10 September 2018 / Revised: 4 October 2018 / Accepted: 7 October 2018 / Published: 10 October 2018
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Abstract
The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated [...] Read more.
The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau. Full article
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
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Open AccessArticle Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery
Forests 2018, 9(10), 582; https://doi.org/10.3390/f9100582
Received: 31 August 2018 / Revised: 12 September 2018 / Accepted: 19 September 2018 / Published: 20 September 2018
Cited by 1 | PDF Full-text (3777 KB) | HTML Full-text | XML Full-text
Abstract
Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched [...] Read more.
Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10−3, 0.07, 0.08 Mg·ha−1, and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha−1, and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha−1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques. Full article
(This article belongs to the Special Issue Remote Sensing Technology Applications in Forestry and REDD+)
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