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Article

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

1
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada
2
Alberta Agriculture and Forestry, Edmonton, AB T5K 2M4, Canada
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Author to whom correspondence should be addressed.
This paper is modified and shortened version of the first author’s MSc thesis titled “Predicting forest productivity using wet areas mapping and other remote sensed environmental data”.
Forests 2018, 9(3), 113; https://doi.org/10.3390/f9030113
Received: 23 January 2018 / Revised: 24 February 2018 / Accepted: 28 February 2018 / Published: 2 March 2018
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
The purpose of this study was to evaluate the relationships between environmental factors and the site index (SI) of trembling aspen, lodgepole pine, and white spruce based on the sampling of temporary sample plots. LiDAR generated digital elevation models (DEM) and wet areas mapping (WAM) provided data at a 1 m resolution for the study area in Alberta. Six different catchment areas (CA), ranging from 0.5 ha to 10 ha, were tested to reveal optimal CA for calculation of the depth-to-water (DTW) index from WAM. Using different modeling methods, species-specific SI models were developed for three datasets: (1) topographic and wet area variables derived from DEM and WAM, (2) only WAM variables, and (3) field measurements of soil and topography. DTW was selected by each statistical method for each species and, in most cases, DTW was the strongest predictor in the model. In addition, differences in strength of relationships were found between species. Models based on remotely-sensed information predicted SI with a root mean squared error (RMSE) of 1.6 m for aspen and lodgepole pine, and 2 m for white spruce. This approach appears to adequately portray the variation in productivity at a fine scale and is potentially applicable to forest growth and yield modeling and silviculture planning. View Full-Text
Keywords: site index; depth-to-water index; forest productivity; boreal forests site index; depth-to-water index; forest productivity; boreal forests
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MDPI and ACS Style

Bjelanovic, I.; Comeau, P.G.; White, B. High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes. Forests 2018, 9, 113. https://doi.org/10.3390/f9030113

AMA Style

Bjelanovic I, Comeau PG, White B. High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes. Forests. 2018; 9(3):113. https://doi.org/10.3390/f9030113

Chicago/Turabian Style

Bjelanovic, Ivan, Philip G. Comeau, and Barry White. 2018. "High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes" Forests 9, no. 3: 113. https://doi.org/10.3390/f9030113

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