Special Issue "Monitoring, Reporting and Verification of Forest Environment for Climate Change Mitigation and Adaptation"

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

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editors

Prof. Dr. Woo-Kyun Lee
Website SciProfiles
Guest Editor
Prof. Dr. Seongwoo Jeon
Website
Guest Editor
Division of Environmental Science and Ecological Engineering Colleage of Life Science, Korea University, South Korea
Interests: LULUCF; Environmental remote sening on Climate change and Biodiversity
Dr. Florian Kraxner
Website
Guest Editor
Center for Landscape Resilience & Management (CLR), Ecosystems Services and Management (ESM), International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Interests: Forest and land use modeling, sustainable biomass production, forest-based bioenergy, renewable energy systems, forest ecosystems services, forest certification, SDGs and the forest, land-based negative emission technologies (NETs) including afforestation, reforestation, restoration, and BECCS
Special Issues and Collections in MDPI journals
Prof. Dr. Joowon Park

Guest Editor
School of Forest Sciences and Landscape Architecture, Kyungpook National University, Daehak-Ro80 Buk-Gu, Daegu, Republic of Korea
Interests: forest management; Geographic Information System (GIS); Remote Sensing(RS); forest inventory, geospatial statistics; big data analysis; woody biomass energy management system
Dr. Chul-Hee Lim
Website
Guest Editor
Institute of Life Science and Natural Resource, Korea University
Interests: environmental geoinformatics; climate change impact and adaptation; spatial modeling of agriculture–water–ecosystem–disaster and their interactions; deforestation and forest degradation; satellite-based air quality monitoring
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In accordance with the Paris Agreement under UNFCCC, Nationally Determined Contributions (NDC) were announced, and carbon absorption from forest resources were included in NDC in many countries, including Republic of Korea (ROK). Securing a determined amount of carbon storage and absorption in forests can play an important role in ensuring NDC’s soundness. Forest biomass and carbon should be estimated through the analysis of forest activity data and the usage of a carbon accounting system. In addition, these two factors should be integrated into forest growth models that can predict future carbon stock under a forest management regime. A National Forest Inventory (NFI) system should be well operated to maintain a confidential national forest carbon database. Land cover/use change monitoring is also essential for monitoring the national carbon budget in relation to the LULUCF (Land Use, Land-Use Change, and Forestry) mechanism and the MRV (Measurement, Reporting, and Verification) system. Remote sensing has been utilized for national forest inventories and the MRV system for many decades, using mainly spaceborne, airborne, and recently unmanned aerial vehicle remote sensing.

In this Special Issue of Remote Sensing, we welcome original and innovative research papers focusing on forest monitoring, reporting, and verification associated with climate change mitigation and adaptation using earth observation data. We expect that this Special Issue will contribute to the improvement of monitoring, reporting, and the verification of forests using remote sensing technique, and to an enhancement in climate change responses by advancing our knowledge and understanding of mitigation and adaptation in the forest sector.

Topics can include but are not limited to the following:

  • Earth observation-based monitoring, reporting, and verification (MRV) of the forest environment;
  • The estimation of greenhouse gas (including carbon and nitrogen) using earth observation data;
  • Forest carbon stocks for a national greenhouse gas inventory;
  • Land Use, Land-Use Change and Forestry (LULUCF);
  • Climate change impact and vulnerability assessments of forests;
  • Adaptation to climate change in the forestry sector using spatial data;

Prof. Dr. Woo-Kyun Lee
Prof. Dr. Seong Woo Jeon
Dr. Florian Kraxner
Prof. Dr. Joowon Park
Dr. Chul-Hee Lim
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 2200 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

• Monitoring;
• Reporting;
• Verification;
• Impact and vulnerability assessment;
• Adaptation;
• Greenhouse gas (including carbon and nitrogen, etc.);
• Forest growth;
• Land use and land cover change;
• Earth observation data.

Published Papers (1 paper)

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Research

Open AccessArticle
Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling
Remote Sens. 2020, 12(1), 28; https://doi.org/10.3390/rs12010028 - 19 Dec 2019
Cited by 7Correction
Abstract
Leaf pigment contents, such as chlorophylls a and b content (Cab) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on [...] Read more.
Leaf pigment contents, such as chlorophylls a and b content (C a b ) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI < 1 m 2 /m 2 ), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C a b and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C a b , and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m 2 /m 2 , R 2 = 0.81); the band ratio ρ 0.750 μ m ρ 0.550 μ m retrieved C a b more accurately than other chlorophylls indices (RMSE = 5.21 μ g/cm 2 , R 2 = 0.73); RMSE over the 0.5–0.55 μ m interval showed encouraging results for Car estimations. Full article
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