Combining GIS and forest modelling in estimating regional supply of harvest residues in Norway

Abstract

New and ambitious targets for renewable energy production put attention to increased supply of biomass. Harvest residues are only to a limited extent demanded by the traditional forest industries and represent an unutilized resource for increased production of renewable energy in Norway. The overall objective of this paper is to study how GIS and forest modelling can be combined to improve estimates of the supply of harvest residues, taking different environmental and economic constraints into consideration. The analyses are based on a case study of a forest area of more than 40 000 ha in Southern Norway divided into about 500 private forest properties. The study was carried out by computations of timber harvest using the forestry scenario model SGIS based on extensive forest inventory data at stand level. In the studied area energy utilization of harvest residues is not profitable below an energy price of about €3.2/GJ (NOK 0.10 /kWh) when the distance from roadside to industry is 20 km. Above this level supply increases rapidly over a rather narrow price range and is nearly inelastic above €4.1/GJ (NOK 0.12/kWh). We did not find significant negative shifts in the residues supply caused by changes in location of roundwood harvest over time. Exclusion of collection from stands with a site index (H 40) below 14 reduced the potential supply of residues by 16-27%. The optimisation method combined selection of exogenous variables in order to map observed harvesting level and is probably the best approach to map future harvest.

Impact of maximum opening area constraints on profitability and biomass availability in forestry – a large, real world case

“…harvest residues from roundwood harvests, are quantified using biomass expansion factors from Lehtonen et al (2004). Following the procedures of Rørstad et al (2010), we arrive at an estimate for retrievable amount of harvest residues. Cost of residue extraction (EUR ton -1 ) is estimated at stand level according to Nurmi (2007) and Rørstad et al (2010), where estimates of productivity (ton h -1 ) are combined with machine costs (EUR h -1 ).…”

“…Following the procedures of Rørstad et al (2010), we arrive at an estimate for retrievable amount of harvest residues. Cost of residue extraction (EUR ton -1 ) is estimated at stand level according to Nurmi (2007) and Rørstad et al (2010), where estimates of productivity (ton h -1 ) are combined with machine costs (EUR h -1 ). To convert biomass into energy, we assume a fixed lower heating value equal to 5320 kWh ton -1 dead matter (d.m.).…”

Estimating the bioenergy potential of forest chips from final fellings in Central Finland based on biomass maps and spatially explicit constraints

“…The next steps might be further studies utilising road map data to calculate the supply cost by optimising the biomass flow from the stands to the plants (see, for example, Ranta 2005;Kanzian et al 2009). More accurate and detailed stand inventory data would make it possible to simulate the development and operations for the stands and optimise forest management (Rørstad et al 2010). Stand inventory databases may not always, however, be available.…”

Regional balance of forest chip supply and demand in Finland in 2030

“…Indeed, several studies have utilized GIS in the estimation of potentials at national, regional, or plant level (see for example Nord-Larsen and Talbot 2004;Rørstad et al 2010;Yoshioka et al 2011;Bouchard et al 2013;Fernandez-Lacruz et al 2015;Castellano et al 2009;Muinonen et al 2013;Lundmark et al 2015;Noon and Daly 1996).…”