Author: Dan Loeffler
Publisher:
Published: 2010
Total Pages: 52
ISBN-13:
DOWNLOAD EBOOK →This annotated bibliography is a synthesis of information products available to land managers in the western United States regarding economic and financial aspects of forestry-based woody biomass removal, a component of fire hazard and/or fuel reduction treatments. This publication contains over 200 forestry-based biomass papers, financial models, sources of biomass and log price information, and biomass utilization facility locations.
Author: United States Department of Agriculture
Publisher: CreateSpace
Published: 2015-01-02
Total Pages: 56
ISBN-13: 9781505877519
DOWNLOAD EBOOK →This annotated bibliography is a synthesis of information products available to land managers in the western United States regarding economic and financial aspects of forestry-based woody biomass removal, a component of fire hazard and/or fuel reduction treatments. This publication contains over 200 forestry-based biomass papers, financial models, sources of biomass and log price information, and biomass utilization facility locations.
Author: Grant Humphries
Publisher: Springer
Published: 2018-11-05
Total Pages: 441
ISBN-13: 3319969781
DOWNLOAD EBOOK →Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.
Author: I. S. Alemdag
Publisher: Forestry Canada = Forêts Canada
Published: 1993
Total Pages: 308
ISBN-13:
DOWNLOAD EBOOK →This annotated bibliography presents a compilation of all published and unpublished reports produced under Forestry Canada's ENFOR program of research and development on the production of forest biomass for energy purposes. The citations and summaries for 480 reports are included, covering the period from 1979 to 1990. These are arranged according to several broad subject areas. The reports are further categorized in a reference section which provides indices by tree species, geographic area, author, title and added keywords.