Special Issue "Advances in Remote Sensing of Forestry"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 October 2012)
Prof. Dr. David L. Skole
Department of Forestry, Michigan State University, 1405 S. Harrison Road, East Lansing, MI 48823, USA
Interests: global carbon cycle; biophysical remote sensing; land cover change; deforestation; tropical forests; REDD+; carbon remote sensing
In recent years there has been substantial progress by the research community developing ways to detect land cover change in tropical forests with remote sensing. What was initially a focus on measuring the conversion of tropical forests to non-forest, recent advances have made it possible to increase the variety of disturbances that can be detected for closed tropical forests to include logging and understory fires. Thus, there are now methods available to remotely detect a full range of disturbance intensities, from outright clearing to low levels of degradation, over large areas. Yet in spite of this progress two important next steps are needed. The first is to expand the measurement and monitoring capabilities to open forest systems, such as savanna woodlands, and to develop the means to measure trees outside of forests in agricultural landscapes. The second is to apply the technical means to the deployment of measurement, reporting and verification systems (MRV) to support carbon and climate change policy
Papers in the special issue will move on from the starting point of basic closed tropical forests monitoring and focus on the use of a variety of sensors and spatial resolutions to monitor the full suite of landscapes necessary to support the emerging REDD+ programs. This will include applications from a range of sensors and scales, including optical, microwave, and LiDAR. The purpose of this monitoring approach is to measure continuous fields of land cover and assign biomass and carbon attributes to these data sets. It will require monitoring deforestation, degradation, reforestation, agroforestry and trees outside of forests at the landscape level. Select papers will describe various technical approaches to forest carbon tracking, as well as information systems that can be developed to support a range of carbon monitoring needs.
Prof. David Skole
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly journal published by MDPI.
- tropical forests
- forest disturbance
- remote sensing
- open forests
Remote Sens. 2013, 5(2), 491-520; doi:10.3390/rs5020491
Received: 3 November 2012; in revised form: 24 December 2012 / Accepted: 15 January 2013 / Published: 25 January 2013| Download PDF Full-text (1578 KB) | Download XML Full-text
Article: Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation
Remote Sens. 2013, 5(2), 648-663; doi:10.3390/rs5020648
Received: 12 December 2012; in revised form: 29 January 2013 / Accepted: 30 January 2013 / Published: 4 February 2013| Download PDF Full-text (1795 KB) | Download XML Full-text
Article: Impacts of Spatial Variability on Aboveground Biomass Estimation from L-Band Radar in a Temperate Forest
Remote Sens. 2013, 5(3), 1001-1023; doi:10.3390/rs5031001
Received: 20 December 2012; in revised form: 17 February 2013 / Accepted: 18 February 2013 / Published: 26 February 2013| Download PDF Full-text (2988 KB) | Download XML Full-text
Remote Sens. 2013, 5(3), 1220-1234; doi:10.3390/rs5031220
Received: 20 December 2012; in revised form: 20 February 2013 / Accepted: 4 March 2013 / Published: 7 March 2013| Download PDF Full-text (247 KB) | Download XML Full-text
Article: A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005
Remote Sens. 2013, 5(4), 1842-1855; doi:10.3390/rs5041842
Received: 1 December 2012; in revised form: 1 April 2013 / Accepted: 9 April 2013 / Published: 15 April 2013| Download PDF Full-text (347 KB) | Download XML Full-text
Last update: 18 May 2012