http://cfs.nrcan.gc.ca/series/read/8?page=1&format=citation&lang=en_CAPublications by series - Information report (Petawawa)en-ca2013-05-23 09:43:422000-08-26 00:00:00 MSTwebmaster@nofc.cfs.nrcan.gc.cahttp://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4361Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4361http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=25698Tue, 20 Sep 2005http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=25698http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4362The present report describes Canada's forests for Rowe's (90) Forest Sections and the larger (10) Forest Regions. The information is summarized from the 1986 version of Canada's Forest Inventory. Resource data are presented for each section and region in several series of 12 figures. The extensive use of histograms makes it easier to compare sections and regions at a glance. Maps by section facilitate examination of regional trends. Forest area, timber yield, growth, and access statistics are reported. The data provided in this report is designed to assist ecologists and foresters in their evaluation and understanding of Canada's forest resources. Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4362http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4363The present report describes Canada's forests for Rowe's (90) Forest Sections and the larger (10) Forest Regions. The information is summarized from the 1986 version of Canada's Forest Inventory. Resource data are presented for each section and region in several series of 12 figures. The extensive use of histograms makes it easier to compare sections and regions at a glance. Maps by section facilitate examination of regional trends. Forest area, timber yield, growth, and access statistics are reported. The data provided in this report is designed to assist ecologists and foresters in their evaluation and understanding of Canada's forest resources. Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4363http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=6887Fri, 21 Jul 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=6887http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=21302Fri, 14 Mar 2003http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=21302http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=6886Fri, 21 Jul 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=6886http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=25697Tue, 20 Sep 2005http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=25697http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4105The Acid Rain National Early Warning System (ARNEWS) plot network was initiated in 1984 when the then Canadian Forestry Service decided to establish a national program to detect early signs of air pollution damage to Canada's forests. Since that time, more than 150 ARNEWS plots have been established across Canada to monitor changes in forest vegetation and soils caused by air pollution and environmental change. More than 10 000 trees are currently monitored in the ARNEWS plot network. All components of ARNEWS methods were revised with three objectives in mind: practicality, meaningfulness, and repeatability. Components such as plot establishment guidelines, protocols for collecting general mensuration data, soil analysis methods, analysis of tree condition, and assessments of ground vegetation and regeneration were all examined closely and revised where necessary. Numerous new figures are appended to describe parameters and to provide examples of procedures to follow for abnormal situations. Appendices have been attached listing tree species codes and summarizing other code values used. Codes were synchronized with other studies conducted by the Forest Insect and Disease Survey.Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4105http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10847The Acid Rain National Early Warning System (ARNEWS) plot network was initiated in 1984 when the then Canadian Forestry Service decided to establish a national program to detect early signs of air pollution damage to Canada's forests. Since that time, more than 150 ARNEWS plots have been established across Canada to monitor changes in forest vegetation and soils caused by air pollution and environmental change. More than 10 000 trees are currently monitored in the ARNEWS plot network . All components of ARNEWS methods were revised with three objectives in mind: practicality, meaningfulness, and repeatability. Components such as plot establishment guidelines, protocols for collecting general mensuration data, soil analysis methods, analysis of tree condition, and assessments of ground vegetation and regeneration were all examined closely and revised where necessary Numerous new figures are appended to describe parameters and to provide examples of procedures to follow for abnormal situations. Appendices have been attached listing tree species codes and summarizing other code values used. Codes were synchronized with other studies conducted by the Forest Insect and Disease Survey. Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10847http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10205Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10205http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10826 This bibliography is the fourth in a series published by the Petawawa National Forestry Institute (PNFI). It contains all the scientific and technical publications authored by PNFI staff for the years 1991 and 1992. The main body of the text consists of an annotated list of publications listed alphabetically by author. This list is numbered sequentially and all other index lists refer to the numbers in this section instead of page numbers. Indexes of authors, titles, and keywords complete this bibliography. Most of the publications listed here are indexed by Forestry Abstracts. Title abbreviations follow the format of Serial Sources for the BIOSIS Previews Database, 1992 edition (BioSciences Information Service, Philadelphia, Pa., USA). The species/key word index is derived from the CAB Thesaurus, C.A.B. International, 1990.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10826http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4367Canada's Forest Inventory 1991 (CanFI91) replaces the 1986 version Can FI86: The inventory is an aggregation from provincial and other sources of the best information available in 1991. The database is spatially referenced in 47 000 cells. The report presents some standard statistical and map products to describe the distribution and structure of the forest resource. The inventory now covers all major blocks of forest. In addition to the core attributes some auxiliary information has been added concerning forest region, stocking, growth, access, policy constraint, the wood production forest, and the importance of the forest for other uses. Successive versions of CanFI (1981, 1986, 1991) are the best available information at those times, but differences between them can not be used as estimates of real change. Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4367http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4366Canada's Forest Inventory 1991 (CanFI91) replaces the 1986 version Can FI86: The inventory is an aggregation from provincial and other sources of the best information available in 1991. The database is spatially referenced in 47 000 cells. The report presents some standard statistical and map products to describe the distribution and structure of the forest resource. The inventory now covers all major blocks of forest. In addition to the core attributes some auxiliary information has been added concerning forest region, stocking, growth, access, policy constraint, the wood production forest, and the importance of the forest for other uses. Successive versions of CanFI (1981, 1986, 1991) are the best available information at those times, but differences between them can not be used as estimates of real change. Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4366http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4365Provincial management inventories across Canada use similar approaches and have many common elements, but each has unique features resulting from the nature of the forest, requirements, historical developments, the personnel involved, and budgetary considerations. This report details, for each province, the procedures used, organizational structure, and schedule followed in the production of inventory maps. In addition a summary of the volume sampling component is given. A description of stand attributes and examples of the forest inventory map and legend for each province are provided.Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4365http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10774Provincial management inventories across Canada use similar approaches and have many common elements, but each has unique features resulting from the nature of the forest, requirements, historical developments, the personnel involved, and budgetary considerations. This report details, for each province, the procedures used, organizational structure, and schedule followed in the production of inventory maps. In addition a summary of the volume sampling component is given. A description of stand attributes and examples of the forest inventory map and legend for each province are provided.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10774http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10792This publication consists of 19 papers that encompass the latest developments in the field. The express purpose of the conference was to promote links between the forestry community and remote sensing specialists. Summaries of discussions at the work sessions are given, as well as descriptions of related programs.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10792http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10706 Lightning causes one third of the 9000 wildfires that occur in Canada. Annually, these lightning-caused fires account for 90% of the area burned and cost Canadians at least 150 million dollars in suppression costs and values destroyed. Unlike the fires caused by human negligence, lightning-caused fires often occur in multiple numbers in remote locations. A modern fire control organization can suppress all of these fires while they are still small only if it has time to position sufficient suppression forces before the fires occur. Therefore, predicting the occurrence of lightning fires hours in advance is an essential component of a successful suppression strategy. This paper describes the method currently used to predict the daily number and location of lightning-caused fires. A network of automated lightning sensors provides the locations and numbers of cloud-to-ground lightning flashes. For each flash the appropriate weather, fuel type, and moisture data are combined with models of the ignition, smouldering, and detectability processes. The ignition model predicts the chance of a flash causing ignition. The detectability model forecasts the probability of a fire being visually detectable during the burning period. The smouldering model tells us the chances of a fire surviving overnight (usually in a smouldering state). Because fires can remain in a dormant state for long periods, each flash that occurred during the previous 10 days is considered a potential ignition point for the current day. Fires predicted to have been ignited up to 10 days earlier are given the opportunity to smoulder; they are removed from consideration after detection. Remaining fires combined with likely new fires and the expected number of detectable fires during the next burning period gives the number of fires predicted for that day. Evaluation results are presented and discussed. In general, the prediction program produces fair to good results for small to medium morning storms and medium to large overnight storms. As well, for the previous day, the smouldering/survival model seems to work well. Poor predictions are generated, however, from afternoon storms, from occasions when rainfall data is not available, and from the smouldering model for periods longer then two days. The prediction program is perhaps best thought of as being an expert system where specific knowledge of lightning physics, rainfall patterns, and fire behavior are combined with expert opinions of the various lightning fire occurrence processes. There is still much to learn about lightning physics, how fires are ignited, the conditions necessary for ignition, the smouldering process, and the conditions needed for smoke production.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10706http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10707 Lightning causes one third of the 9000 wildfires that occur in Canada. Annually, these lightning-caused fires account for 90% of the area burned and cost Canadians at least 150 million dollars in suppression costs and values destroyed. Unlike the fires caused by human negligence, lightning-caused fires often occur in multiple numbers in remote locations. A modern fire control organization can suppress all of these fires while they are still small only if it has time to position sufficient suppression forces before the fires occur. Therefore, predicting the occurrence of lightning fires hours in advance is an essential component of a successful suppression strategy. This paper describes the method currently used to predict the daily number and location of lightning-caused fires. A network of automated lightning sensors provides the locations and numbers of cloud-to-ground lightning flashes. For each flash the appropriate weather, fuel type, and moisture data are combined with models of the ignition, smouldering, and detectability processes. The ignition model predicts the chance of a flash causing ignition. The detectability model forecasts the probability of a fire being visually detectable during the burning period. The smouldering model tells us the chances of a fire surviving overnight (usually in a smouldering state). Because fires can remain in a dormant state for long periods, each flash that occurred during the previous 10 days is considered a potential ignition point for the current day. Fires predicted to have been ignited up to 10 days earlier are given the opportunity to smoulder; they are removed from consideration after detection. Remaining fires combined with likely new fires and the expected number of detectable fires during the next burning period gives the number of fires predicted for that day. Evaluation results are presented and discussed. In general, the prediction program produces fair to good results for small to medium morning storms and medium to large overnight storms. As well, for the previous day, the smouldering/survival model seems to work well. Poor predictions are generated, however, from afternoon storms, from occasions when rainfall data is not available, and from the smouldering model for periods longer then two days. The prediction program is perhaps best thought of as being an expert system where specific knowledge of lightning physics, rainfall patterns, and fire behavior are combined with expert opinions of the various lightning fire occurrence processes. There is still much to learn about lightning physics, how fires are ignited, the conditions necessary for ignition, the smouldering process, and the conditions needed for smoke production.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10707http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10777Somatic embryogenesis in conifers is a breakthrough for micropropagation. From a single immature or mature embryo or from cotyledons, an embryogenic tissue can be obtained from which an unlimited number of propagules can be produced. It is a tissue culture process that mimics zygotic embryogenesis, resulting in the production of somatic embryos that mature, germinate, and form plantlets. This tissue culture method is now widely used for clonal plantlet production and for other types of studies, such as genetic transformation. Because of numerous requests for information on the protocols developed in the authors' laboratories, this guide has been assembled to assist those who want to use somatic embryogenesis in spruce and larch. It covers all the important steps in the production of embryogenic tissue and in plantlet regeneration. In each section, the literature published on the topic under consideration is reviewed so that the reader can refer to original published work. In addition, the guide covers special applications such as protoplast isolation from embryogenic cell lines and cryopreservation of tissues.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10777http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=28295Somatic embryogenesis in conifers is a breakthrough for micropropagation. From a single immature or mature embryo or from cotyledons, an embryogenic tissue can be obtained from which an unlimited number of propagules can be produced. It is a tissue culture process that mimics zygotic embryogenesis, resulting in the production of somatic embryos that mature, germinate, and form plantlets. This tissue culture method is now widely used for clonal plantlet production and for other types of studies, such as genetic transformation. Because of numerous requests for information on the protocols developed in the authors' laboratories, this guide has been assembled to assist those who want to use somatic embryogenesis in spruce and larch. It covers all the important steps in the production of embryogenic tissue and in plantlet regeneration. In each section, the literature published on the topic under consideration is reviewed so that the reader can refer to original published work. In addition, the guide covers special applications such as protoplast isolation from embryogenic cell lines and cryopreservation of tissues.Tue, 22 Apr 2008http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=28295http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=11714This publication describes detailed training guidelines of tree seed testing that are used at both national and international levels. An historical overview of tree seed testing in Canada is also given. The basic requirements of testing are included along with applications to numerous species. Several exercices complete the text to verify theoretical and practical knowledge of testing. Because the material contains all the usual procedures of tree seed testing and follows guidelines of the International Tree Seed Testing Association, it forms an useful document for all those working in seed testing.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=11714http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10697Information is given on the native and exotic forest tree and shrub seed available for research purposes from the Forestry Canada Seed Bank at the Petawawa National Forestry Institute. It supersedes Forestry Canada's Information ReportPI-X-58E/F. Reference is made to the procurement of seed and the maintenance of seed quality.Tue, 22 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=10697http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4368The detection of ongoing changes in forested areas monitored with satellite images is traditionally accomplished with multi-date band rationings, enhancements, and classifications. The Forestry Expert Package (FEP) uses a completely different method and is an example of a new philosophy in image analysis: an artificial intelligence approach. It uses single-date Landsat classifications as input data, compares them, and decides on the significance and cause of detected changes based on rules derived from forestry knowledge. For the present experiment, classifications of geometrically corrected images of different years and different seasons were obtained with an unsupervised classifier for a test area near Lake Traverse in Algonquin Park, Ontario. Several tests were run to determine if the FEP would detect logging activities and the subsequent regeneration known to have occurred in this area. A secondary objective was to see if any improvement in the classification of forest types of the non-changing areas could be achieved over conventional single data classifications. The FEP was found to be good at detecting changes in the forest due to logging, and moderately good at monitoring the regrowth of conifers. However, the FEP is not yet at a stage where it could be of use as a forest management decision-making tool. Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4368http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4369The detection of ongoing changes in forested areas monitored with satellite images is traditionally accomplished with multi-date band rationings, enhancements, and classifications. The Forestry Expert Package (FEP) uses a completely different method and is an example of a new philosophy in image analysis: an artificial intelligence approach. It uses single-date Landsat classifications as input data, compares them, and decides on the significance and cause of detected changes based on rules derived from forestry knowledge. For the present experiment, classifications of geometrically corrected images of different years and different seasons were obtained with an unsupervised classifier for a test area near Lake Traverse in Algonquin Park, Ontario. Several tests were run to determine if the FEP would detect logging activities and the subsequent regeneration known to have occurred in this area. A secondary objective was to see if any improvement in the classification of forest types of the non-changing areas could be achieved over conventional single data classifications. The FEP was found to be good at detecting changes in the forest due to logging, and moderately good at monitoring the regrowth of conifers. However, the FEP is not yet at a stage where it could be of use as a forest management decision-making tool. Sat, 26 Aug 2000http://cfs.nrcan.gc.ca/publications?page=1&format=citation&lang=en_CA&id=4369