The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability

Mapping wetlands and potential wetland restoration areas in Black Hawk County, Iowa using object-oriented classification and a GIS-based model

Wetlands are transitional lands between terrestrial and aquatic systems that provide many benefits, including: floodwater retention, non-point pollution treatment, wildlife habitat, and soil-erosion control. Wetlands in Iowa have decreased over 95% in the last 200 years. Therefore, there is a need to map and monitor these resources, as well as to determine potential sites for wetland restoration. In Black Hawk County, wetland maps are outdated, and ground surveys have proved to be too time-consuming and expensive. Traditional pixel-based automated classifiers of remotely-sensed imagery have also proven to be inaccurate in classifying wetlands because of spectral confusion. This study tests multispectral data, hybrid data, hyperspectral data, a seasonal matrix, and a new object-oriented classifier. These are tested against traditional multispectral, pixelbased (ISODATA and Maximum-Likelihood) classifiers both to see if wetland classification accuracies from remotely-sensed imagery can be increased and to produce an updated wetlands map for Black Hawk County. A hyperspectral image of Eddyville, Iowa is tested to evaluate how well wetlands are classified when a hyperspectral image is used with an object-oriented classifier and a hyperspectral pixel-based (Spectral Angle Mapper or SAM) classifier. A GIS-based wetland restoration model is developed to identify potential wetland restoration sites in Black Hawk County. This study shows that the object-oriented classifier is more accurate in identifying wetlands and overall land-cover than pixel-based ones (ISODATA, Maximum-Likelihood, SAM) in both multispectral, hybrid-multispectral, and hyperspectral imagery. The summer/fall seasonal matrix produced unacceptable accuracies. Wetlands in Black Hawk County decreased by 1500 acres (plus or minus an error margin of 375 acres) from 1983 to 2003. The restoration model identified 2,971 acres in Black Hawk County as being highly suitable, 34,307 acres as being moderately suitable, and 121,271 acres as having low suitability for wetland restoration. The results are available at http://gisrl-9.geog.uni.edu/wetland. Limitations of the study include file size when using the object-oriented classifier, image availability for the seasonal matrix, and the number of variables employed in the GIS-based restoration model. The future direction of the study lies in obtaining hyperspectral data for Black Hawk County, more current Landsat multispectral imagery for the seasonal matrix, and testing of more non-parametric classifiers, such as the CART algorithm

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability.EThOS - Electronic Theses Online ServiceGBUnited Kingdo