Special Issue "Forest Biomass and Carbon Observation with Remote Sensing"

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

Deadline for manuscript submissions: 30 July 2020.

Special Issue Editors

Dr. Dmitry Schepaschenko
E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), laxenburg, Austria
Interests: sample plots; visual interpretation of very high-resolution imagery; biomass structure; boreal forest; validation
Special Issues and Collections in MDPI journals
Dr. Martin Thurner
E-Mail Website
Guest Editor
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
Interests: vegetation carbon cycle; remote sensing of forest biomass; model–data integration; biomass allometry; boreal and temperate forests
Dr. Maurizio Santoro
E-Mail Website
Guest Editor
Gamma Remote Sensing, Bern, Switzerland
Interests: radar remote sensing; thematic mapping; estimation of forest biomass
Prof. Heiko Balzter
E-Mail Website
Guest Editor
School of Geography, Geology and Environment, University of Leicester, University Road, Leicester, LE1 7RH, UK
Fax: +44 116 252 3854
Interests: synthetic aperture radar (SAR); SAR interferometry; SAR polarimetry; ground-based; airborne and spaceborne light detection and ranging (LIDAR); digital elevation model generation; full carbon accounting; forest structure and biomass mapping; vegetation phenology; fire and burned area mapping from optical and SAR sensors
Special Issues and Collections in MDPI journals
Dr. Neha Joshi
E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), laxenburg, Austria
Interests: Synthetic Aperture Radar (SAR); mapping forest disturbances; time-series analysis and change detection; tropical forests

Special Issue Information

Dear Colleagues,

Forest biomass and carbon monitoring is high on the agenda of environmental research and policy due to the importance of forest carbon dynamics with regard to climate change mitigation, biodiversity preservation, and timber and bioenergy production. Multidisciplinary and multisensor remote sensing approaches are clearly needed to obtain a synoptic view of forest biomass, given the complexity of forest ecosystems, diversity of ecological and socioeconomic conditions, high dynamics of land use, and the limited accessibility of field information and reference data.

This Special Issue aims at gathering contributions exploring remote sensing approaches to quantify woody biomass and carbon stocks in forests and woodlands. We encourage applications tackling issues of integrating ground and satellite data for calibration and validation of remote sensing-based biomass observations. Contributions dealing with various types of sensors (active and passive) and carriers (terrestrial, airborne, unmanned aerial vehicles, space-borne) or combinations are welcome. Applications of data collected by new instruments (GEDI, ICESat-2) or within the framework of recent biomass mapping approaches (e.g., GlobBiomass, Biomass CCI) and their integration with state-of-the-art research to track biomass variation in space and time are very welcome. The key role of biomass remote sensing in forest and vegetation modeling, biodiversity, and forest management assessment is going to be the focus of this issue as well.

Dr. Dmitry Schepaschenko
Dr. Martin Thurner
Dr. Maurizio Santoro
Prof. Heiko Balzter
Dr. Neha Joshi
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. 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 2000 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

  • Above-ground biomass (AGB)
  • Synergy of remote sensing and in-situ data
  • Calibration and validation (cal/val)
  • Monitoring, reporting, and verification (MRV) systems
  • Model–data integration
  • Vegetation carbon cycle

Published Papers (4 papers)

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Research

Open AccessArticle
A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest
Remote Sens. 2019, 11(21), 2579; https://doi.org/10.3390/rs11212579 - 03 Nov 2019
Abstract
Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used [...] Read more.
Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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Open AccessArticle
Forest Canopy Height and Gaps from Multiangular BRDF, Assessed with Airborne LiDAR Data (Short Title: Vegetation Structure from LiDAR and Multiangular Data)
Remote Sens. 2019, 11(21), 2566; https://doi.org/10.3390/rs11212566 - 01 Nov 2019
Abstract
Both vegetation multi-angular and LiDAR (light detection and ranging) remote sensing data are indirectly and directly linked with 3D vegetation structure parameters, such as the tree height and vegetation gap fraction, which are critical elements in above-ground biomass and light profiles for photosynthesis [...] Read more.
Both vegetation multi-angular and LiDAR (light detection and ranging) remote sensing data are indirectly and directly linked with 3D vegetation structure parameters, such as the tree height and vegetation gap fraction, which are critical elements in above-ground biomass and light profiles for photosynthesis estimation. LiDAR, particularly LiDAR using waveform data, provides accurate estimates of these structural parameters but suffers from not enough spatial samplings. Structural parameters retrieved from multiangular imaging data through the inversion of physical models have larger uncertainties. This study searches for an analytical approach to fuse LiDAR and multiangular data. We explore the relationships between vegetation structure parameters derived from airborne vegetation LiDAR data and multiangular data and present a new potential angle vegetation index to retrieve the tree height and gap fraction using multi-angular data in Howland Forest, Maine. The BRDF (bidirectional reflectance distribution factor) index named NDMM (normalized difference between the maximum and minimum reflectance) linearly increases with the tree height and decreases with the gap fraction. In addition, these relationships are also dependent on the wavelength, tree species, and stand density. The NDMM index performs better in conifer (R = 0.451 for tree height and R = 0.472 for the gap fraction using the near infrared band) than in deciduous and mixed forests. It is superior in sparse (R = 0.569 for tree height and R = 0.604 for the gap fraction using the near infrared band) compared to dense forest. Moreover, the NDMM index is more strongly related to tree height and the gap fraction at the near infrared band than at the three visible bands. This study sheds light on the possibility of using multiangular data to map vegetation’s structural parameters in larger regions for carbon cycle studies through the fusion of LiDAR and multiangular remote sensing data. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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Open AccessArticle
Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
Remote Sens. 2019, 11(19), 2270; https://doi.org/10.3390/rs11192270 - 28 Sep 2019
Abstract
Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over [...] Read more.
Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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Open AccessArticle
Evaluating k-Nearest Neighbor (kNN) Imputation Models for Species-Level Aboveground Forest Biomass Mapping in Northeast China
Remote Sens. 2019, 11(17), 2005; https://doi.org/10.3390/rs11172005 - 25 Aug 2019
Abstract
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level [...] Read more.
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale. Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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