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Estimating Bioenergy and Carbon Stocks within Forests Using Remote Sensing Data

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

Deadline for manuscript submissions: closed (25 December 2022) | Viewed by 3295

Special Issue Editors


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Guest Editor
1. Department of Ecology and Sustainable Agriculture, Agricultural High School, Polytechnic Institute of Viseu, 3500-606 Viseu, Portugal
2. Centre for the Research and Technology of Agro-Environmental and Biological Sciences, CITAB, University of Trás-os-Montes and Alto Douro, UTAD, Quinta de Prados, 5000-801 Vila Real, Portugal
Interests: biomass and carbon mapping; spatial modeling, GIS and remote sensing; forest inventory and management; silviculture and biodiversity

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Co-Guest Editor

Special Issue Information

Dear Colleagues,

Forests are the world’s largest source of above-ground living biomass (AGB) used for energy production, as part of international energy and climate change policies, and contribute a large fraction to the global terrestrial CO2 sink. Measurements of biomass and carbon stocks are essential for quantifying potential bioenergy, for monitoring forests’ growth/dynamics, and for studying the role of forest ecosystems in the biosphere–atmosphere interactions, including the ones in the global carbon cycle.

Remote sensing has been one of the technological tools with the greatest growth in forestry research in the last decades, being used for mapping, cartography, and incorporated in forest inventories by the indirect measurement of biophysical variables and modelling (e.g., Dbh, height, canopy cover, density, LAI, vegetation indices). The use of optical, radar and lidar remote sensing data, acquired by different platforms (terrestrial, space-borne, airborne, unmanned aerial vehicles), combined with different fusion techniques, as well as geostatistical and modelling methods, makes it possible to obtain increasingly rigorous estimates of biomass and carbon stocks, with high spatial and temporal resolution.

Abundant imagery data from satellites (e.g., Landsat, Sentinel, MODIS) with global coverage is available free of charge to the public, and the equipment to obtain high-resolution data at local/regional level (e.g., UAVs and cameras) are increasingly diverse and affordable on the market, making possible their use by researchers for a variety of studies.

This Special Issue aims to gather contributions exploring the most recent remote sensing approaches integrating data collected by different sensors/platforms, ground observations, laboratory analysis, and combining the latest processing techniques to quantify woody biomass for energy purposes, carbon stocks, and to evaluate the ecological aspects of biomass exploitation in forests and woodlands.

We would like to invite all research teams dealing with biomass and carbon stocks mapping and assessment to share their most recent results in this Special Issue: Estimating Bioenergy and Carbon stocks within Forests Using Remote Sensing Data.

Dr. Helder Viana
Dr. Krzysztof Stereńczak
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

  • Woody vegetation above-ground biomass
  • Forest inventory, allometry and data modeling
  • Biomass and carbon stocks mapping
  • Forest growth and monitoring
  • Ecology and biomass for bioenergy
  • Satellite data analysis and photogrammetry
  • Digital image processing
  • Multispectral and hyperspectral
  • Lidar/radar processing
  • Unmanned aerial vehicles (UAVs)
  • Multi-source data fusion and integration for forest modelling

Published Papers (1 paper)

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Research

18 pages, 4800 KiB  
Article
Forest Above-Ground Biomass Inversion Using Optical and SAR Images Based on a Multi-Step Feature Optimized Inversion Model
by Wangfei Zhang, Lixian Zhao, Yun Li, Jianmin Shi, Min Yan and Yongjie Ji
Remote Sens. 2022, 14(7), 1608; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071608 - 27 Mar 2022
Cited by 11 | Viewed by 2396
Abstract
Forest biomass change monitoring is essential for climate change. Synthetic aperture radar (SAR) and optimal remote sensing (RS) data are two very helpful data sources for forest biomass monitoring and estimation. During the procedure of biomass estimation using RS technique, optimal features selection [...] Read more.
Forest biomass change monitoring is essential for climate change. Synthetic aperture radar (SAR) and optimal remote sensing (RS) data are two very helpful data sources for forest biomass monitoring and estimation. During the procedure of biomass estimation using RS technique, optimal features selection and estimation models used are two critical steps. This paper therefore focuses on building an operational and robust method of biomass retrieval using optical and SAR RS data. First, random forest (RF) algorithms are used for reducing time-consuming and decreasing computational burden; then, an iterative procedure was embedded in K-nearest neighbor (KNN) algorithms for the best optimal feature selection and combination; last, the best feature combinations and KNN models were applied for forest biomass estimation. Moreover, forest type effects and RS feature source effects were considered. The results showed that feature combination of two optical images and the SAR image showed highest estimation accuracy by using the proposed algorithm (R2 = 0.70 for Forest-1, R2 = 0.72 for Forest-2, and R2 = 0.71 for Forest-3; RMSE = 16.18 Mg/ha for Forest-1, RMSE =17.66 Mg/ha for Forest-2, and RMSE = 18.67 Mg/ha for Forest-3, where Forest-1 is natural pure forests of Yunnan Pines, Forest-2 is natural mixed coniferous forests, and Forest-3 is the combination of Forest-1 and Forest-2). With the comparative analysis of proposed algorithm and different non-parametric algorithms, traditional nonparametric algorithms performed better in Forest-1, but worse in Forest-2 and Forest-3, while the proposed algorithm performed no obvious difference in three forest types and using five feature groups. The results revealed that the proposed algorithm was robust in biomass estimation, with almost no feature source and forest structure dependent for biomass estimation. Full article
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