Steen Magnussen, René I. Alfaro, Paul Boudewyn. (2005). Survival-time analysis of white spruce during spruce budworm defoliation. Silva Fennica vol. 39 no. 2 article id 382. https://doi.org/10.14214/sf.382

Keywords: mortality; hazard rates; defoliation stress index; Cox proportional hazard regression; Choristoneura fumiferana

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Mortality and defoliation (DF%) in 987 white spruce (Picea glauca (Moench) Voss) trees were followed from 1992 to 2003 during an outbreak of the spruce budworm Choristoneura fumiferana (Clem.) in 15 white-spruce-dominated uneven-aged stands in the Fort Nelson Forest District near Prince George, British Columbia. Four stands were aerially sprayed with Bacillus thuringiensis (Bt). Defoliation and mortality levels were elevated in non-sprayed stands. The relationship between defoliation and survival-times was captured in a Cox proportional hazard model with a defoliation stress index (DSI), diameter (DBH), crown class (CCL), a random stand effect, Bt-treatment, and number of years of exposure to stand-level defoliation (DYEAR) as predictors. The DSI, optimized for discrimination between survivors and non-survivors, is the discounted sum of five lagged DF% values. Survival probabilities were predicted with a maximum error of 0.02. Hazard rates increased by 0.06 for every one point increase in DSI. CCL and random stand effects were highly significant. Bt-treatment effects were fully captured by DSI, CCL, and DYEAR.

Magnussen, Canadian Forest Service, Victoria, BC, Canada. V8Z 1M5 E-mail: smagnussen@pfc.forestry.ca
Alfaro, Canadian Forest Service, Victoria, BC, Canada. V8Z 1M5 E-mail: ria@nn.ca
Boudewyn, Canadian Forest Service, Victoria, BC, Canada. V8Z 1M5 E-mail: pb@nn.ca

article id 618, category Research article

Steen Magnussen, Paul Boudewyn, Mike Wulder, David Seemann. (2000). Predictions of forest inventory cover type proportions using Landsat TM. Silva Fennica vol. 34 no. 4 article id 618. https://doi.org/10.14214/sf.618

Keywords: neural net; maximum likelihood classification; agreement of predictions

Abstract | View details | Full text in PDF | Author Info

The feasibility of generating via Landsat TM data current estimates of cover type proportions for areas lacking this information in the national forest inventory was explored by a case study in New Brunswick. A recent forest management inventory covering 4196 km2 in south-eastern New Brunswick (the test area) and a coregistered Landsat TM scene was used to develop predictive models of 12 cover type proportions in an adjacent 4525 km2 region (the validation area). Four prediction models were considered, one using a maximum likelihood classifier (MLC), and three using the proportions of 30 TM clusters as predictors. The MLC was superior for non-vegetated cover types while a neural net or a prorating of cluster proportions was chosen for predicting vegetated cover types. Most predictions generated for national inventory photo-plots of 2 x 2 km were closer to the most recent inventory results than estimates extrapolated from the test area. Agreement between predictions and current inventory results varied considerably among cover types with model-based predictions outperforming, on average, the simple spatial extensions by about 14%. In this region, an 11-year-old forest inventory for the validation area provided estimates that in half the cases were closer to current inventory estimates than predictions using the optimal Landsat TM model. A strong temporal correlation of photo-plot-level cover type proportions made old-values more consistent than predictions using the optimal Landsat TM model in all but three cases. Prorating of cluster proportions holds promise for large-scale multi-sensor predictions of forest inventory cover types.

Magnussen, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: smagnussen@pfc.forestry.ca
Boudewyn, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: pb@nn.ca
Wulder, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: mw@nn.ca
Seemann, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: ds@nn.ca

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