Special Issue "Modeling Saturation of Spectral Reflectance and Radar Data and Developing Corresponding Methods for Biomass Estimation of Various Forests"

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

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Prof. Dr. Guangxing Wang Website E-Mail
Southern Illinois University, Department of Geography and Environmental Resources
Phone: 618-453-6017
Interests: remote sensing, GIS, spatial statistics, natural and environmental resources, sampling design; environmental quality assessment; forest and city vegetation carbon sequestration, desertification trend monitoring, quality assessment and spatial uncertainty analysis
Guest Editor
Prof. Dengsheng Lu Website E-Mail
School of Geographic Sciences, Fujian Normal University, No.8 Shangsan Road, Cangshan District, Fuzhou, Fujian, China 350007
Interests: Remote sensing, urban and environment interaction, forest carbon modeling
Guest Editor
Prof. Dr. Qi Chen Website E-Mail
Department of Geography, University of Hawaiˈi at Mānoa, 2424 Maile Way, Honolulu, HI 96822, USA
Interests: LiDAR remote sensing of vegetation; statistical learning; mathematical models; geospatial analysis
Guest Editor
Prof. Dr. Markus Holopainen E-Mail
Department of Forest Resource Management, University of Helsinki, Helsinki, Finland
Interests: forest biomass estimation using remotely sensed data including optical images and LiDAR data
Guest Editor
Dr. Liyong Fu E-Mail
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: forest growth and yield modeling, forest biomass estimation

Special Issue Information

Dear Colleagues,

Forests play a critical role in reducing carbon concentrations in the atmosphere and in the mitigation of global warming. Thus, accurately estimating and mapping forest biomass/carbon at regional, national and global scales is very important. Various remotely sensed data including optical images, LiDAR and radar data have been used for the purpose. However, saturation of spectral reflectance and radar data impedes the increase of estimation accuracy of forest biomass/carbon, and currently there have been only few reports that deal with examining the saturation and searching for corresponding solutions. LiDAR data provide a solution for the purpose, but, their applications are limited because of high cost for estimating and mapping forest biomass/carbon for large areas. Therefore, there is strong need of modeling the saturation of spectral reflectance and radar data for biomass/carbon estimation of various forests and developing corresponding methods.

This Special Issue, "Modeling Saturation of Spectral Reflectance and Radar Data and Developing Corresponding Methods for Biomass Estimation of Various Forests”, will call for papers that demonstrate the original research that can overcome current significant gaps in examining the saturation of spectral reflectance and radar data and develop corresponding solutions. Review articles are also welcome. The topics will include: 1) examining the saturation of spectral reflectance of optical images for estimating and mapping biomass/carbon of various forest ecosystems; 2) examining the saturation of radar data for estimating and mapping biomass/carbon of various forest ecosystems; and 3) developing new methods and algorithms for overcoming the saturations.

Prof. Dr. Guangxing Wang
Prof. Dr. Dengsheng Lu
Prof. Dr. Qi Chen
Prof. Dr. Markus Holopainen
Dr. Liyong Fu
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 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.

Published Papers (3 papers)

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Open AccessArticle
Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images
Remote Sens. 2019, 11(16), 1872; https://doi.org/10.3390/rs11161872 - 10 Aug 2019
Abstract
For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) [...] Read more.
For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images and ground measurements were employed to estimate growing stem volume (GSV) of Chinese fir plantations located in a hilly area of southern China. To investigate the relationships between forest GSV and polarization characteristics, single and fused variables were derived by the Yamaguchi decomposition and the saturation value of GSV was estimated using a semi-exponential empirical model as a base model. Based on the estimated saturation values and relative root mean square error (RRMSE), the single and fused characteristics and corresponding models were selected and integrated, which led to a novel saturation-based multivariate method used to improve the GSV estimation and mapping of Chinese fir plantations. The new findings included: (1) All the original polarimetric characteristics, statistically, were not significantly correlated with the forest GSV, and their logarithm and ratio transformation fused variables greatly improved the correlations, thus the estimation accuracy of the forest GSV. (2) The logarithm transformation of surface scattering resulted in the greatest saturation, value but the logarithm transformation of double-bounce scattering resulted in the smallest RRMSE of the GSV estimates. (3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of RRMSE. (4) The saturation-based multivariate method led to more accurate estimates of the forest GSV than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables. This implied that the novel saturation-based multivariate method provided greater potential to improve the estimation and mapping of the forest GSV. Full article
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Open AccessArticle
Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison
Remote Sens. 2019, 11(7), 738; https://doi.org/10.3390/rs11070738 - 27 Mar 2019
Abstract
Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and [...] Read more.
Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and by the effect of spectral reflectance from underlayer shrub, grass, and bare soil for young stands. This usually leads to overestimations and underestimations for smaller and larger values, respectively, and makes it very challenging to improve the estimation accuracy of forest AGB. In this study, a novel methodology was proposed by incorporating stand age as a dummy variable into four models to improve the estimation accuracy of the Pinus densata forest AGB in Yunnan of Southwestern China. A total of eight models, including two parametric models (LM: linear regression model and LMC: LM with combined variables), two nonparametric models (RF: random forest and ANN: artificial neural network) without the age dummy variable, and four corresponding models with the age dummy variable (DLM, DLMC, DRF, and DANN), were compared to estimate AGB. Landsat 8 Operational Land Imager (OLI) images and 147 sample plots were acquired and utilized. The results showed that (1) compared with the two parametric models, the two nonparametric algorithms resulted in significantly greater estimation accuracies of Pinus densata forest AGB, and the increases of accuracy varied from 8% to 32% for 100 modeling plots and from 12% to 35% for 47 test plots based on root mean square error (RMSE); (2) compared with the models without the age dummy variable, the models with the age dummy variable greatly reduced the overestimations for the plots with AGB values smaller than 70 Mg/ha and the underestimations for the plots with AGB values larger than 180 Mg/ha and, thus, significantly improved the overall estimation accuracy by 14% to 42% for the modeling plots and by 32% to 44% for the test plots based on RMSE; and (3) the texture measures derived from the Landsat 8 OLI images contributed more to improving the estimation accuracy than the original spectral bands and other transformations. This implied that two nonparametric models, coupled with the use of the age dummy variable and texture measures, offered a great potential for improving the estimation accuracy of Pinus densata forest AGB. Full article
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Open AccessTechnical Note
Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach
Remote Sens. 2019, 11(9), 1050; https://doi.org/10.3390/rs11091050 - 03 May 2019
Abstract
Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate [...] Read more.
Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements. Full article
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