Carbon stocks in peri-urban areas: a case study of remote sensing capabilities

Riccardo Scalenghe, Paolo Villa, Francesco Malucelli

Risultato della ricerca: Articlepeer review

7 Citazioni (Scopus)

Abstract

Peri-urban areas are the extension of cities into contiguous areas, where households and farms coexist. Carbon stocks (CSs) assessment, a concept here extended to urban features, has not yet been studied in depth over peri-urban areas due to uncertainties in such CSs quantification, level of detail required about construction materials, and the high spatial variability of those stocks. Remote sensing (RS)-based techniques have been successfully utilized in urban areas for assessing phenomena such as soil sealing, sprawl patterns, and dynamics of surface imperviousness, especially focusing on land cover classification at high to medium spatial scales. Over the floodplain study area of Emilia-Romagna region (Italy), we compared mapping products derived from Landsat multiseasonal data with different CSs, in soils and impervious surfaces, such as buildings and roads. A multiscale correlation analysis and regression assessment between CSs layers and satellite products were run at different grid cell sizes (100, 250, 500, and 1000 m). Results show that RS products from processing of mid-resolution satellite data can effectively perform well enough to estimate CSs in peri-urban areas, especially at 500–1000 m scale. Urban Fraction Cover method, derived through weighting urban land cover classes (including dense, sparse, and industrial urban features) can represent a good proxy of the ratio of anthropogenic over natural CSs (R2 up to 0.75). Imperviousness Index (II) product scored high positive correlation with CSs over built-up areas (R2 up to 0.77), and strong negative correlation with organic carbon density in soil (R2 up to 0.73)
Lingua originaleEnglish
Numero di pagine10
RivistaIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume7
Stato di pubblicazionePublished - 2014

All Science Journal Classification (ASJC) codes

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