Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem

Galidaki, Georgia; Zianis, Dimitris; Gitas, Ioannis Z.; Radoglou, K.; Karathanassi, Vassilia; Tsakiri–Strati, Maria; Woodhouse, Iain; Mallinis, Giorgos

doi:10.1080/01431161.2016.1266113

Cited by 101 publications

(79 citation statements)

References 124 publications

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“…By fitting separate models on conifers and broadleaves, and using a non-parametric, adaptive learning algorithm, capable of handling non-linearity and threshold relationships, we were able to estimate forest volume with a mean validation error of -23% and -27% respectively across a large region (before updating). While in local studies the relationships between remotely sensed imagery and volume or biomass may attain a goodness-of-fit larger than 0.70, and up to 0.98 (Galidaki et al 2016), such accuracy in a variable that is not directly "seen" from satellite sensors is uncommon for estimates of volume spanning large areas and forests with different species composition (Zhang et al 2014) and management type (high forests and coppices). Data fusion between model-assisted estimates and national forest inventory statistics at the provincial level helped increase the accuracy of volume estimates and made them consistent with official forest statistics.…”

Section: Discussionmentioning

confidence: 99%

“…Even at the local scale, where forest composition and structure are more homogenous, the accuracy of estimates is hindered by the lack of a direct relationship between optical signals and biomass (R-squared between 0.39 to 0.74 - Galidaki et al 2016), especially in broadleaved forests and for structures such as coppices, where the relationship between stem and foliage mass is inherently different than in high forests.…”

Section: Introductionmentioning

confidence: 99%

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

Vacchiano¹,

Berretti²,

Motta³

et al. 2018

iForest

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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. . This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Vacchiano¹,

Berretti²,

Motta³

et al. 2018

iForest

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“…[1][2][3][4][5][6][7] Different methods have been developed to estimate AGB with remote sensing and ground data, based on passive and/or active instruments. [8][9][10] Active sensors, such as light detection and ranging (LIDAR) and synthetic aperture radar (SAR), have the advantage to penetrate the canopy; for this reason, they are considered the most useful tools for providing vertical structure or volumetric forest measures.…”

Section: Introductionmentioning

confidence: 99%

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Laurin

Balling

Corona³

et al. 2018

J. Appl. Rem. Sens.

121

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Abstract. The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R 2 ¼ 0.7) using integrated sensors when an upper bound of 400 Mg ha −1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…LiDAR is the most accurate method we have available to retrieve vegetation structure and this role is widely accepted. Passive remote sensing data have not been seen as providing satisfactory estimates of structural variables, such as biomass or tree height, because of the difficulty to accurately measure 3D vegetation structure from relatively large pixels and wide spectral bands [101]. IS data may be better suited than multispectral data because the narrow spectral bands better resolve absorption and scattering across the spectrum.…”

Section: Discussionmentioning

confidence: 99%

Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California

et al. 2019

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Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R2 for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping.

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Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem

Cited by 101 publications

References 124 publications

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

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

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California

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