Special Issue "Remote Sensing of Forest Health"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 July 2016).
Interests: Remote sensing; scaling approaches; linked open data; semantic web; data science approaches; spectral abiotic and biotic traits; spectral trait and trait variation concepts; spatial-temporal process-pattern interactions; vegetation; biodiversity ecosystem health; land-use intensity using RS approaches; essential biodiversity variables (EBV)
Special Issues and Collections in MDPI journals
Special Issue in Remote Sensing: Data Science in Remote Sensing
Special Issue in Remote Sensing: Teaching and Learning in Remote Sensing
Special Issue in Remote Sensing: Upscaling and Downscaling Modelling and/or Identification of Relevant Scales and Thresholds for Environmental Impacts in Ecology by Remote Sensing
Special Issue in Remote Sensing: Monitoring of Status and Disturbances of Bio- and Geodiversity, Their Traits and Interactions Using Remote Sensing
Forest National Park, Germany/ University of Freiburg, Germany
Interests: lidar applications in forest ecology and management; remote sensing in wildlife ecology; essential biodiversity variables
Special Issues and Collections in MDPI journals
The significance of forest ecosystems, their role in ecosystem processes and services, their functioning and impacts on humanity is partially well understood. Increasing anthropogenic pressure on ecosystems, the exploitation of natural resources as well as pressure from a constantly expanding population and economic growth continue to put a strain on and irretrievably threaten global forest ecosystems.
Traditional approaches to forest monitoring have not been able to deliver a comprehensive, global and comparable monitoring system of forest ecosystems, their state and changes to such systems on different spatial, temporal and scaling levels.
In our rapidly changing environment and landscape, there is an increased focus on measuring, quantifying and modeling the state of our forests in a high temporal resolution based on space and airborne remote-sensing techniques. The great interest in using EO for mapping forest health is also driven by the fact that novel EO sensors will soon be available, such as the hyperspectral satellite EnMAP (to be launched in 2018), the ESA satellite FLEX (Fluorescence Explorer, to be launched in 2018), the laser-based instrument GEDI – Global Ecosystem Dynamics Investigation or LiDAR from NASA (to be launched in 2019), that will enable large-scale, long-term, standardized and spatially complete, continuous as well as affordable information that can be used for mapping, modelling and forecasting forest health, which is currently covered by comparable airborne-based sensors such as HySpex, AISA, APEX, LiDAR or thermal infrared sensors in space. Simultaneously, as a result of the increasing openness of Landsat data and Spot data archives, there is also the immediate and freely available remote-sensing data from the Copernicus Mission (sentinel 1-5) or EnMAP data and remote-sensing data products.
In order to compile existing research using remote-sensing techniques in the field of forest mapping, we would like to invite you to submit articles about your recent research with respect to the following topics:
- Remote Sensing of forest health: Methods for assessment and monitoring of forest mortality
- Remote Sensing: Spectral indicators for assessing of forest health
- Remote Sensing of forest health and protection
- Remote Sensing of forest health: Utilitarian and ecosystem services perspective
- Remote Sensing of forest health: Modelling and prognosis of pest infestation
- Monitoring structural and functional forest biodiversity indicators in context of forest heath with Remote Sensing
- Remote Sensing of forest structure: Physically based modelling for better mapping and quantification forest heath
- Remote Sensing: Roles of climate, air pollution, anthropogen pressures and disturbances on forest heath by remote sensing
- Remote Sensing of forest health: Enhanced forest inventory by remote sensing
- Comparison and evaluation of different remote sensing sensors and methods for assessing forest health.
- Improvement and evaluation of input data needed for the retrieval of forest health by remote sensing
- Review articles covering one or more of these topics are also welcome.
- Experiments for mapping forest health by remote sensing
- Forest resilience and global change processes by remote sensing: Monitoring the effects of air pollution and soil acidification
Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.
Dr. Angela Lausch
Dr. Marco Heurich
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.