scholarly journals Long‐term hydrologic recovery after wildfire and post‐fire forest management in the interior Pacific Northwest

2020 ◽  
Vol 34 (5) ◽  
pp. 1182-1197 ◽  
Author(s):  
Ryan J. Niemeyer ◽  
Kevin D. Bladon ◽  
Richard D. Woodsmith
1970 ◽  
Vol 16 (2) ◽  
pp. 3-11 ◽  
Author(s):  
H Meilby ◽  
L Puri ◽  
M Christensen ◽  
S Rayamajhi

To monitor the development of four community-managed forests, networks of permanent sample plots were established in 2005 at sites in Chitwan, Kaski and Mustang Districts, Nepal. This research note documents the procedures used when preparing for establishment of the plot networks, evaluates the applied stratification of the forest on the basis of data gathered in pilot surveys conducted in the early 2005, and provides a discussion on the implications of the choices made. Key words: Community-managed forests; permanent sample plots; stratification; allocation; estimates Banko Janakari Vol.16(2) 2006 pp.3-11


2021 ◽  
Vol 494 ◽  
pp. 119312
Author(s):  
C. Deval ◽  
E.S. Brooks ◽  
J.A. Gravelle ◽  
T.E. Link ◽  
M. Dobre ◽  
...  

1981 ◽  
Vol 57 (5) ◽  
pp. 233-238 ◽  
Author(s):  
T. H. Hall

This paper describes an approach to forest management decision-making. Acknowledging both objective and subjective elements, the approach offers a methodology to encourage more creative design in forest planning. It uses the descriptive capabilities of simulation modeling in tandem with the prescriptive capabilities of graphical evaluation techniques, to facilitate the use and interpretation of technical forestry information in decision-making problems. It emphasizes a need for an overview of long-term resource behavior as a prerequisite to, and a framework for, forest planning.


2014 ◽  
Vol 21 (2) ◽  
pp. 594-604 ◽  
Author(s):  
Bryan A. Black ◽  
Jason B. Dunham ◽  
Brett W. Blundon ◽  
Jayne Brim-Box ◽  
Alan J. Tepley

2000 ◽  
Vol 16 (6) ◽  
pp. 883-894 ◽  
Author(s):  
SIMON J. GROVE ◽  
STEPHEN M. TURTON ◽  
DANNY T. SIEGENTHALER

Tropical Cyclone ‘Rona’ crossed the coast of the Daintree lowlands of northeastern Australia in 1999. This study reports on its impact on forest canopy openness at six lowland rain forest sites with contrasting management histories (old-growth, selectively logged and regrowth). Percentage canopy openness was calculated from individual hemispherical photographs taken from marked points below the forest canopy at nine plots per site 3–4 mo before the cyclone, and at the same points a month afterwards. Before the cyclone, when nine sites were visited, canopy openness in old-growth and logged sites was similar, but significantly higher in regrowth forest. After the cyclone, all six revisited sites showed an increase in canopy openness, but the increase was very patchy amongst plots and sites and varied from insignificant to severe. The most severely impacted site was an old-growth one, the least impacted a logged one. Although proneness to impact was apparently related to forest management history (old-growth being the most impacted), underlying local topography may have had an equally strong influence in this case. It was concluded that the likelihood of severe impact may be determined at the landscape-scale by the interaction of anthropogenic with meteorological, physiographic and biotic factors. In the long term, such interactions may caution against pursuing forest management in cyclone-prone areas.


2017 ◽  
Vol 26 (5) ◽  
pp. 399 ◽  
Author(s):  
Tomaž Šturm ◽  
Tomaž Podobnikar

The aim of this study is to develop a long-term forest fire occurrence probability model in the Karst forest management area of Slovenia. The target area has the greatest forest fire occurrence rates and the largest burned areas in the country. To discover how the forest stand characteristics influence forest fire occurrence, we developed a long-term linear regression model. The geographically weighted regression method was applied to build the model, using forest management plans and land-based datasets as explanatory variables and a past forest fire activity dataset as a predicted variable. The land-based dataset was used to represent human activity as a key component in fire occurrence. Variables representing the natural and the anthropogenic environment used in the model explained 39% of past forest fire occurrences and predicted areas with the highest likelihood of forest fire occurrence. The results show that forest fire occurrence probability in a stand increases with lower wood stock, lower species diversity and lower thickness diversity, and in stands dominated by conifer trees under normal canopy closure. These forests stand characteristics are planned to be used in forest management and silviculture planning to reduce fire damage in Slovenian forests.


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