000 03919nam a22005055i 4500
001 978-1-4419-7762-5
003 DE-He213
005 20140220083725.0
007 cr nn 008mamaa
008 101104s2011 xxu| s |||| 0|eng d
020 _a9781441977625
_9978-1-4419-7762-5
024 7 _a10.1007/978-1-4419-7762-5
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMBNS
_2bicssc
072 7 _aMED090000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aRobinson, Andrew P.
_eauthor.
245 1 0 _aForest Analytics with R
_h[electronic resource] :
_bAn Introduction /
_cby Andrew P. Robinson, Jeff D. Hamann.
264 1 _aNew York, NY :
_bSpringer New York,
_c2011.
300 _aXIV, 354p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUse R
505 0 _aIntroduction -- Forest data management -- Data analysis for common inventory methods -- Imputation and Interpolation -- Fitting dimensional distributions -- Linear and non-linear models -- Fitting linear hierarchical models -- Simulations -- Forest estate planning and optimization.
520 _aForest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document. Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University. Both authors have presented numerous R workshops to forestry professionals and scientists, and others.
650 0 _aStatistics.
650 0 _aForests and forestry.
650 0 _aEnvironmental sciences.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
650 2 4 _aForestry.
650 2 4 _aForestry Management.
650 2 4 _aMath. Appl. in Environmental Science.
700 1 _aHamann, Jeff D.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441977618
830 0 _aUse R
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-7762-5
912 _aZDB-2-SMA
999 _c105850
_d105850