Assessing land take by urban development and its impact on carbon storage: Findings from two case studies in Italy

Sallustio, Lorenzo; Quatrini, Valerio; Geneletti, Davide; Corona, Piermaria; Marchetti, Marco

doi:10.1016/j.eiar.2015.05.006

Cited by 89 publications

(39 citation statements)

References 64 publications

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“…The vast majority of land conversion in the simplified developed classification occurred in areas previously classified from the NLCD as developed open space, or areas that contained less than 20% impervious surfaces. The development of energy infrastructure in such environments is likely similar to increased urbanization which has been shown to reduce carbon storage (Sallustio et al, 2015) and biodiversity (McKinney, 2002) and increase non-native species richness (Whitney, 1985). Development of energy infrastructure in aquatic or catchment areas has been shown to increase the salinity (Bern et al, 2015) and sediment load (Williams et al, 2008) of surface runoff and increase the risk of wastewater spills into adjacent aquatic resources (Preston and ChesleyPreston, 2015).…”

Section: Discussionmentioning

confidence: 97%

Land cover changes associated with recent energy development in the Williston Basin; Northern Great Plains, USA

Preston

Kim

2016

Science of The Total Environment

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Section: Discussionmentioning

confidence: 97%

Land cover changes associated with recent energy development in the Williston Basin; Northern Great Plains, USA

Preston

Kim

2016

Science of The Total Environment

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“…Although a great deal of effort has been made for monitoring, assessing, and reporting on forest ecosystems [1,2], several challenges remain because forest cover is increasing, and, in addition, due to the national and international commitments to biodiversity conservation and renewable energy, which require even more information on forests and on Sustainable Forest Management (SFM). The current trend of afforestation and natural succession of abandoned lands [3,4] have increased the EU's forest area by around 0.4% per year in recent decades [5]. As a result, on the one hand, there is an increase of forest cover, while, on the other hand, there is a strong interest in assessing the productivity of forests and the trade-off between the production of wood and non-wood forest products, and other ecosystem services, derived from forest resources [6].…”

Section: Introductionmentioning

confidence: 99%

Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest

Sačkov

Santopuoli

Bucha

et al. 2016

Forests

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Abstract:Airborne laser scanning is a promising technique for efficient and accurate, remote-based forest inventory, due to its capacity for direct measurement of the three-dimensional structure of vegetation. The main objective of this study was to test the usability and accuracy of an individual tree detection approach, using reFLex software, in the evaluation of forest variables. The accuracy assessment was conducted in a selected type of multilayered deciduous forest in southern Italy. Airborne laser scanning data were taken with a YellowScan Mapper scanner at an average height of 150 m. Point density reached 30 echoes per m 2 , but most points belonged to the first echo. The ground reference data contained the measured positions and dimensions of 445 trees. Individual tree-detection rates were 66% for dominant, 48% for codominant, 18% for intermediate, and 5% for suppressed trees. Relative root mean square error for tree height, diameter, and volume reached 8.2%, 21.8%, and 45.7%, respectively. All remote-based tree variables were strongly correlated with the ground data (R 2 = 0.71-0.79). At the stand-level, the results show that differences ranged between 4% and 17% for stand height and 22% and 40% for stand diameter. The total growing stock differed by −43% from the ground reference data, and the ratios were 64% for dominant, 58% for codominant, 36% for intermediate, and 16% for suppressed trees.

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“…The availability of up-to-date maps, with high resolution and thematic accuracy, and a homogeneous European classification system represents a substantial advance towards environmental monitoring efficiency. In addition to their simple use for the monitoring of land cover changes, these maps play a primary role as input data for several environmental modelling tools, such as those used in the mapping and assessment of ecosystem services (Sallustio, Quatrini, Geneletti, Corona, & Marchetti, 2015).…”

Section: Discussionmentioning

confidence: 99%

Copernicus high-resolution layers for land cover classification in Italy

et al. 2016

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The high-resolution layers (HRLs) are land cover maps produced for the entire Italian territory (approximately 30 million hectares) in 2012 by the European Environment Agency, aimed at monitoring soil imperviousness and natural cover, such as forest, grassland, wetland, and water surface, with a high spatial resolution of 20 m. This study presents the methodologies developed for the production, verification, and enhancement of the HRLs in Italy. The innovative approach is mainly based on (a) the use of available reference data for the enhancement process, (b) the reduction of the manual work of operators by using a semiautomatic approach, and (c) the overall increase in the cost-efficiency in relation to the production and updating of land cover maps. The results show the reliability of these methodologies in assessing and enhancing the quality of the HRLs. Finally, an integration of the individual layers, represented by the HRLs, was performed in order to produce a National High-Resolution Land Cover map. ARTICLE HISTORY

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Assessing land take by urban development and its impact on carbon storage: Findings from two case studies in Italy

Cited by 89 publications

References 64 publications

Land cover changes associated with recent energy development in the Williston Basin; Northern Great Plains, USA

Land cover changes associated with recent energy development in the Williston Basin; Northern Great Plains, USA

Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest

Copernicus high-resolution layers for land cover classification in Italy

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