Sunday, 28 October 2012

[ANFES] 2012-10-28 NRMI: This Issue - Remote Sensing for Species Classification (Part 1) and Some Other Good Things


This Issue - Remote Sensing for Species Classification (Part 1) and Some Other Good Things

REMOTE SENSING FOR SPECIES CLASSIFICATION (Part 1) – In September Mounir Louhaichi wrote, “I am looking for references that explain the limitation of spectral similarities between forest species hindering attainment of high classification accuracy.” Here are some sites that may be of interest. If you know of others, please contact Mounir at Mounir.Louhaichi@OREGONSTATE.EDU

Abd-Elrahman, Amr et al. 2011. Design and Development of a Multi-Purpose Low-Cost Hyperspectral Imaging System. Remote Sens. 2011, 3, 570-586.

Anon. 2012. Forest Type Precise Identification Based on Hyperion Data. Agricultural Science.

Baily, J.-S et al. 2003. Tree perception accuracy in high-resolution images: exploratory analysis of combined effects of image parameters and stand characteristics. Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International. 2532-2534. Abstract.

Boyd, D.S.; Danson, F.M. 2005. Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography 29(1):1–26.

Bubier, Jill L. et al. 1997. Spectral reflectance measurements of boreal wetland and forest mosses. J. Geoph. Res. 102(24): 483-494.

Carleer, A.; Wolff, E. 2004. Exploitation of Very High Resolution Satellite Data for Tree Species Identification. Photogrammetric Engineering & Remote Sensing 70(1): 135-140.

Castro, K.L. et al. 2003. Monitoring secondary tropical forests using space-borne data: implications for Central America. Int. J. Remote Sensing 24(9):1853-1894.

Clark, Matthew L.; Roberts, Dar A. 2012. Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based Classifier. Remote Sens. 4: 1820-1855.

Collin, Antoine et al. 2011. Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners.

de Paula, Pedro et al. 2010. Forest Species Recognition Using Color-Based Features. ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition: 4178-4181. Abstract.

Dian, Yuanyong et al. 2008? Discriminating tree species using hyper-spectral reflectance data. 7 p.

Díaz Varela, R. A. et al. 2007. Automatic habitat classification methods based on satellite images: A practical assessment in the NW Iberia coastal mountains. Environ Monit Assess.

Féret, Jean-Baptiste; Asner, Gregory P. 2012. Semi-Supervised Methods to Identify Individual Crowns of Lowland Tropical Canopy Species Using Imaging Spectroscopy and LiDAR. Remote Sens. 4: 2457-2476.

Forest Cover Mapping and


Safont, E., et al. 2012. Use of Environmental Impact Assessment (EIA) tools to set priorities and optimize strategies in biodiversity conservation. Biol. Conserv. 149(1):113-121. Abstract.

Sager-Fradkin, Kimberly A. et al. 2007. Fix Success and Accuracy of Global Positioning System Collars in Old-Growth Temperate Coniferous Forests. The Journal of Wildlife Management 71(4): 128- 13089

Sass, G.Z., et al. 2012. Defining protected area boundaries based on vascular-plant species richness using hydrological information derived from archived satellite imagery. Biol. Conserv. 147(1):143-152. Abstract.

Scabin, A.B., et al. 2012. The spatial distribution of illegal logging in the Anavilhanas archipelago (Central Amazonia) and logging impacts on species. Environ. Conserv. 39(2):111-121. Abstract.

Schulz, B.K.; Gray, A.N. 2012. The new flora of northeastern USA: quantifying introduced plant species occupancy in forest ecosystems. Environmental Monitoring and Assessment. DOI: 10.1007/s10661-012-2841-4. Abstract.

Sloan, S., et al. 2012. Does Indonesia's REDD+ moratorium on new concessions spare imminently threatened forests? Conserv. Lett. 5(3):222-231. Abstract.

Suárez, A., et al. 2012. Local knowledge helps select species for forest restoration in a tropical dry forest of central Veracruz, Mexico. Agroforestry Syst. 85(1):35-55. Abstract.

Thomsen, P.F., et al. 2012. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21(11):2565-2573.

Waldron, A., et al. 2012. Conservation through Chocolate: a win-win for biodiversity and farmers in Ecuador's lowland tropics. Conserv. Lett. 5(3):213-221.

Whiteley, A.R., et al. 2012. Sampling strategies for estimating brook trout effective population size. Conserv. Genet. 13(3):625-637. Abstract.

KEEPING UP-TO-DATE – PRODUCTS, NEWSLETTERS, EMAIL LISTS, JOURNALS. See also,, and Directory of Open Access Journals.

IUFRO-WFSE Newsletter 1/2012

The Forest Process Models Wiki - A bulletin board, wiki, and discussion forum about forest process and wood quality modelling has been started by IUFRO 4.01.05 at . Read access is public. Researchers interested in contributing are welcome to register through the web site or by contacting Oscar Garcia, Coordinator IUFRO 4.01.05, at


MOVING AHEAD – OPPORTUNITIES – See also: Scholarships-Positions - , Forestry, Arboriculture, Agriculture, Agronomy & Natural Resource Management Jobs at, Riley Guide to Agriculture, Forestry, & Farming Jobs, Finding Your Dream Job in Natural Resources, The Job Seekers Guide for International and Environmental Careers and Scholarship Listing

Supervisory IM Project Manager - The USDA Forest Service is preparing to fill a permanent, full-time (PFT) position at the Portland Forestry Sciences Laboratory located in Portland, Oregon, USA. The full performance level of this Supervisory Information Management (IM) Project Manager is GS-2210-13. This position is the leader of an Information Management team in the Forest Inventory and Analysis (FIA) work unit, and a member of the program’s management team. The incumbent will interact and collaborate with other IM team leaders and IM personnel in the national FIA program, working across functional areas. The position supervises a group of 12 employees performing work at the GS-7 through GS-12 levels that are based in Portland, Oregon and Anchorage, Alaska. The work involves the day-to-day leadership of IM staff to facilitate and manage the transfer, compilation, quality assurance, and delivery of forest inventory data, tools, analyses, geospatial products, and research assistance. Conducts strategic planning and project management activities for the IM team in relation to production inventory and IM research project operations. Facilitates and manages staff activities including conducting needs analyses, developing goals, objectives, and strategies, and initiating strategic and tactical business planning efforts. Develops and implements project management procedures, activities, and infrastructure, and designs tools and planning templates. Provides project management training and assistance to technical staff to ensure all projects are adequately managed. The IM team is responsible for all aspects of data development, working within a coordinated national infrastructure. The team develops and manages large comprehensive databases, generates compilation programs, creates focused applications and software, and implements widespread quality assurance procedures on all IM products. The team works closely with clients that include data collection and analysis groups. For more information contact Joseph Donnegan at by 16 November 2012. (I believe this position is open only to USA citizens – Gyde).

NEXT ISSUE – Remote Sensing for Species Classification (Part 2)

Pay It Forward – Cheers, Gyde          



--   H. Gyde Lund    Forest Information Services  6238 Settlers Trail Place  Gainesville, VA 20155-1374 USA  Tel: +1-703-743-1755  Email: gyde<at>    URL:  CV:   Publications:   Skype: forestgyde   


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