Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimate...

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Main Authors: Vacchiano G, Berretti R, Motta R, Mondino Borgogno E
Format: Article
Language:English
Published: Italian Society of Silviculture and Forest Ecology (SISEF) 2018-08-01
Series:iForest - Biogeosciences and Forestry
Subjects:
Online Access:https://iforest.sisef.org/contents/?id=ifor2655-011
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spelling doaj-db4a464031d643fba2409232ad9c1dfa2020-11-24T21:25:53ZengItalian Society of Silviculture and Forest Ecology (SISEF)iForest - Biogeosciences and Forestry1971-74581971-74582018-08-0111145946810.3832/ifor2655-0112655Assessing the availability of forest biomass for bioenergy by publicly available satellite imageryVacchiano G0Berretti R1Motta R2Mondino Borgogno E3Università degli Studi di Milano, DISAA, v. Celoria 2, I-20123 Milano (Italy)Università degli Studi di Torino, DISAFA, l.go Braccini 2, I-10095 Grugliasco, TO (Italy)Università degli Studi di Torino, DISAFA, l.go Braccini 2, I-10095 Grugliasco, TO (Italy)Università degli Studi di Torino, DISAFA, l.go Braccini 2, I-10095 Grugliasco, TO (Italy)Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume. Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. Predicted aboveground forest volume ranged from 81 to 391 m3 ha-1. In forests available for wood supply (70% of the total), planned volume removals averaged 25.4 m3 ha-1, or 18.7% of the average standing stock across. Biomass available for bioenergy supply was 1.295.921 million Mg dry matter or 8.95 Mg ha-1. This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component.https://iforest.sisef.org/contents/?id=ifor2655-011Artificial Neural NetworksMixed ForestsLandsatMediterranean MountainsVegetation IndicesWood Energy Chain
collection DOAJ
language English
format Article
sources DOAJ
author Vacchiano G
Berretti R
Motta R
Mondino Borgogno E
spellingShingle Vacchiano G
Berretti R
Motta R
Mondino Borgogno E
Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery
iForest - Biogeosciences and Forestry
Artificial Neural Networks
Mixed Forests
Landsat
Mediterranean Mountains
Vegetation Indices
Wood Energy Chain
author_facet Vacchiano G
Berretti R
Motta R
Mondino Borgogno E
author_sort Vacchiano G
title Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery
title_short Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery
title_full Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery
title_fullStr Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery
title_full_unstemmed Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery
title_sort assessing the availability of forest biomass for bioenergy by publicly available satellite imagery
publisher Italian Society of Silviculture and Forest Ecology (SISEF)
series iForest - Biogeosciences and Forestry
issn 1971-7458
1971-7458
publishDate 2018-08-01
description Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume. Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. Predicted aboveground forest volume ranged from 81 to 391 m3 ha-1. In forests available for wood supply (70% of the total), planned volume removals averaged 25.4 m3 ha-1, or 18.7% of the average standing stock across. Biomass available for bioenergy supply was 1.295.921 million Mg dry matter or 8.95 Mg ha-1. This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component.
topic Artificial Neural Networks
Mixed Forests
Landsat
Mediterranean Mountains
Vegetation Indices
Wood Energy Chain
url https://iforest.sisef.org/contents/?id=ifor2655-011
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