Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

L'abstract è presente nell'allegato / the abstract is in the attachmen

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 In Black Hawk County, wetland maps are outdated, and ground surveys have proved to be too time-consuming and expensive.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

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented

Remote Sensing of Riparian Areas and Invasive Species

Riparian areas are critical landscape features situated between terrestrial and aquatic environments, which provide a host of ecosystem functions and services. Although important to the environmental health of an ecosystem, riparian areas have been degraded by anthropogenic disturbances. These routine disturbances have decreased the resiliency of riparian areas and increased their vulnerability to invasive plant species. Invasive plant species are non-native species which cause harm to the ecosystem and thrive in riparian areas due to the access to optimal growing conditions.Remote sensing provides an opportunity to manage riparian habitats at a regional and local level with imagery collected by satellites and unmanned aerial systems (UAS). The aim of this study was two-fold: firstly, to investigate riparian delineation methods using moderate resolution satellite imagery; and secondly, the feasibility of UAS to detect the invasive plant Fallopia japonica (Japanese Knotweed) within the defined areas. I gathered imagery from the Landsat 8 OLI and Sentinel-2 satellites to complete the regional level study and collected UAS imagery at a study site in northern New Hampshire for the local level portion. I obtained a modest overall accuracy from the regional riparian classification of 59% using the Sentinel-2 imagery. The local invasive species classification yielded thematic maps with overall accuracies of up to 70%, which is comparable to other studies with the same focus species. Remote sensing is a valuable tool in the management of riparian habitat and invasive plant species