Remote Sensing (RS) technologies over the last decade have made substantial progresses in terms of data accuracy, spatial coverage and temporal frequency of acquisitions. A valuable amount of information to investigate river systems have begun to emerge, which was not available in the past (Marcus and Fonstad 2010). This novel context opens new opportunities for river science and management whose uptake is just starting to occur amongst scientists and managers (Carbonneau and Piegay 2012). Orthophotos and high-resolution multi-spectral images have been utilised by fluvial geomorphologists and hydrologists for a long time to manually draw the recent (i.e. last 50-70 years) historical evolution of key channel geomorphic features (e.g. active channel width) and to map riparian corridors and ecological habitats over large scale using multi-spectral information (Surian and Rinaldi 2003; Clerici et al 2013). More recently LiDAR information has begun to provide accurate information on river topography opening exciting opportunity to map river morphology (Passalacqua et al 2012). However, large-scale analysis of river systems exploiting the availability of already acquired datasets of multi-spectral high-resolution images and LiDAR is surprising limited. Bizzi et al (2016) in a recent review points out the large availability of these datasets at European scale and discussed the fact that these datasets are rarely acquired for river characterizations purposes although they are valuable information to this aim. They also note that these days the bottleneck for river science and management is not data generation but data processing. RS data poses a number of data analysis challenges (Alber and Piégay 2011; Schmitt et al 2014), which cannot be handled solely by river geomorphologists and engineers, and call for multi-disciplinary working groups where river experts work side by side with remote sensing experts and data analysts. In this work, we show how a regional available dataset in the Piedmont region (north east of Italy) acquired in 2008 for landscape monitoring and urban planning purposes, including high-resolution multi-spectral images and LiDAR data, can be used to extract valuable information about geomorphic character of river systems. We provide one of the first applications at regional level where extent and topographic river geomorphic features have been classified through semi-automated procedures and tools creating a regional DataBase (DB) suitable for river geomorphic investigation. The developed tools can be easily applied in the future in other contexts with similar data availability. A regional classification of the main river typologies in the region has been developed using the DB. Moreover, statistical analyses have been used to exploit the information embedded in the spatial heterogeneity of the regional DB in order to enhance our understanding of how river systems shape their channels under various contexts of geology, hydrology and human pressures. We have been able to detect meaningful relationships between geomorphic drivers (e.g. channel gradient, basin area, geology, geographic area), human pressures and channel features providing robust basis to generate a firsts assessments of river system alterations induced by human activities at regional level, and generating valuable evidences to discuss the design of more effective river management plans and rehabilitation measures.
Assessing river geomorphic alteration at regional scale: the case of piedmont region, Convegno Nazionale di Idraulica e Costruzioni Idrauliche Bologna. Atti del XXXV Convegno Nazionale di Idraulica e Costruzioni Idrauliche
BIZZI, SIMONE;
2016-01-01
Abstract
Remote Sensing (RS) technologies over the last decade have made substantial progresses in terms of data accuracy, spatial coverage and temporal frequency of acquisitions. A valuable amount of information to investigate river systems have begun to emerge, which was not available in the past (Marcus and Fonstad 2010). This novel context opens new opportunities for river science and management whose uptake is just starting to occur amongst scientists and managers (Carbonneau and Piegay 2012). Orthophotos and high-resolution multi-spectral images have been utilised by fluvial geomorphologists and hydrologists for a long time to manually draw the recent (i.e. last 50-70 years) historical evolution of key channel geomorphic features (e.g. active channel width) and to map riparian corridors and ecological habitats over large scale using multi-spectral information (Surian and Rinaldi 2003; Clerici et al 2013). More recently LiDAR information has begun to provide accurate information on river topography opening exciting opportunity to map river morphology (Passalacqua et al 2012). However, large-scale analysis of river systems exploiting the availability of already acquired datasets of multi-spectral high-resolution images and LiDAR is surprising limited. Bizzi et al (2016) in a recent review points out the large availability of these datasets at European scale and discussed the fact that these datasets are rarely acquired for river characterizations purposes although they are valuable information to this aim. They also note that these days the bottleneck for river science and management is not data generation but data processing. RS data poses a number of data analysis challenges (Alber and Piégay 2011; Schmitt et al 2014), which cannot be handled solely by river geomorphologists and engineers, and call for multi-disciplinary working groups where river experts work side by side with remote sensing experts and data analysts. In this work, we show how a regional available dataset in the Piedmont region (north east of Italy) acquired in 2008 for landscape monitoring and urban planning purposes, including high-resolution multi-spectral images and LiDAR data, can be used to extract valuable information about geomorphic character of river systems. We provide one of the first applications at regional level where extent and topographic river geomorphic features have been classified through semi-automated procedures and tools creating a regional DataBase (DB) suitable for river geomorphic investigation. The developed tools can be easily applied in the future in other contexts with similar data availability. A regional classification of the main river typologies in the region has been developed using the DB. Moreover, statistical analyses have been used to exploit the information embedded in the spatial heterogeneity of the regional DB in order to enhance our understanding of how river systems shape their channels under various contexts of geology, hydrology and human pressures. We have been able to detect meaningful relationships between geomorphic drivers (e.g. channel gradient, basin area, geology, geographic area), human pressures and channel features providing robust basis to generate a firsts assessments of river system alterations induced by human activities at regional level, and generating valuable evidences to discuss the design of more effective river management plans and rehabilitation measures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
