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

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

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

The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping

One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data

Development of a High-Resolution Land Cover Dataset to Support Integrated Water Resources Planning and Management in Northern Utah

Integrated planning and management approaches, including bioregional planning and integrated water resources planning, are comprehensive strategies that strive to balance the sustainability of natural resources and the integrity of ecosystem processes with human development and activities. Implementation of integrated plans and programs remains complicated. However, geospatial technologies, such as geographic information systems and remote sensing, can significantly enhance planning and management processes. Through a United States Environmental Protection Agency Region 8 Wetland Program Development Grant, a high-resolution land cover dataset, with a primary emphasis on mapping and quantifying impervious surfaces, was developed for three watershed sub-basins in northern Utah - Lower Bear-Malad, Lower Weber, and Jordan - to support integrated water resources planning and management. This high-resolution land cover dataset can serve as an indicator of cumulative stress from urbanization; it can support the development of ecologically relevant metrics that can be integrated into watershed health and wetland condition assessments; it can provide general assessments of watershed condition; and it can support the identification of sites in need of restoration and protection

Impact of land-use land-cover change on stream water quality in the Reedy Fork- Buffalo Creek watershed, North Carolina: a spatio temporal analysis

The quality of rivers and streams are affected by the land-use-land-cover (LULC) compositions that are present within their watersheds and riparian buffers. Hence, understanding how these LULC compositions, present within watersheds, influences water quality of these water bodies is very important for river management and restoration. This dissertation research was undertaken with the goal of examining the effects changing LULC on stream system. The research was conducted in the Reedy Fork Buffalo Creek watershed in Guilford County, North Carolina to provide a study area of streams within a nested watershed assemblage with a variety of sub-watersheds and varying LULC proportions for comparison. Toward this end, LULC spatial fragmentation of the Reedy Fork Buffalo Creek watershed was quantified for the 2002 through 2013 study period based on remote sensing data. This watershed is located at the headwaters of the Cape Fear River basin, the largest river basin in North Carolina. Analysis of how river flow and several water quality variables were related to landscape attributes at three scales: 100 m, 150 m, and watershed was then performed. The Soil and Water Assessment Tool (SWAT) was used to examine the contribution of LULC to water yield and nitrate loadings in the year 2030 relative to future LULC change scenarios. Results show that the water quality of the Reedy Fork Buffalo Creek changed significantly during the recent decades. These changes in space and time indicate a trend of accelerating deterioration in water quality. Also, LULC pattern had major impacts on the flow and water quality of the Reedy Fork Creek at multiple spatial scales. In particular, impervious LULC, although small in percent cover, exerted a disproportionately large influence both locally and over distance. Results also show that most water quality variables (Conductivity, hardness, nitrate, TKN, and Turbidity) were correlated with landscape pattern on all three spatial scales although the correlation was stronger at the watershed scale than at the buffer scales. Additionally, results from the scenario analysis shows that, compared to the current situation (2010), a 13.5% increase in surface runoff, 9.26% increase in water yield, and 31.85% in increase in nitrate yield was recorded for 2030. These increases were due to the conversion of forest and grass into impervious surfaces. The research highlighted the probable role of the interactions between LULC spatial distribution and water quality. This scale multiplicity suggests that, while water-monitoring and river restoration need to adopt a multi-scale perspective, particular attention should be paid to the watershed scale. In the context of population growth and increasing urban development continuing into the 21st century, preservation and restoration of vegetative LULC and the elimination of impervious surfaces within the watershed should be a primary concern for the general public, the scientific community, and public policy decision makers

Putting Food on the Map: Automated Mapping of Community Gardens with High Resolution Aerial Imagery using an Object Based Approach in Google Earth Engine

Urban agriculture (UA), or growing and producing food within urban areas, is rising in popularity across the United States. There are social and environmental benefits from growing food within urban neighborhoods. UA presents the opportunity for food security in neighborhoods that do not have access to safe and healthy foods, which is disproportionately present in low-income communities. Growing food together also creates food literacy, community cohesion, and shared focus on achieving food security. Unfortunately, the benefits of UA are not accessible to everyone. The prices of food from UA at markets is often not affordable to low- income residents. Furthermore, as a form of greenspace, gardens raise surrounding rent prices, and have been shown to be correlated with gentrification. The socioeconomic dynamics associated with UA are complex and not studied well in part because UA is not well mapped in most U.S. cities. The goal of this study is to accurately map UA by exploiting the unique spatial pattern of UA in addition to spectral, structural, and temporal characteristics of UA vegetation. I used very high resolution aerial NAIP imagery from 2016, Sentinel-2 satellite imagery (2016-2020), and GEDI space lidar data (2019-2021) collected over the case study city of Portland, Oregon. I adopted a Geographic Object-Based Image Analysis (GEOBIA) approach by segmenting and classifying imagery using a Random Forest classifier. In an effort to capture the UA pattern, I applied morphological operators to the classified image and compared my results to an open database on community gardens from the Portland Bureau of Parks and Recreation. The object-based image classification achieved a 79.6% accuracy and the morphological operations captured 66.9% of the area of Portland Bureau of Parks and Recreation Community Gardens. The detection rate at individual community gardens averaged 65.4% and ranged from 3.8% to 98.2%. Higher detection rates were found in gardens that had strong vegetation signals in garden beds intermixed with bare paths between them. Lower detection rates resulted from tree canopy covering or casting shade over the community gardens. In achieving a fully automated and accurate UA detection using open remote sensing data, this approach can be applied to studying the spatial distribution and dynamics of urban agriculture across the U.S

Using the urban landscape mosaic to develop and validate methods for assessing the spatial distribution of urban ecosystem service potential

The benefits that humans receive from nature are not fully understood. The ecosystem service framework has been developed to improve understanding of the benefits, or ecosystem services, that humans receive from the natural environment. Although the ecosystem service framework is designed to provide insights into the state of ecosystem services, it has been criticised for its neglect of spatial analysis. This thesis contains a critical discussion on the spatial relationships between ecosystem services and the urban landscape in Salford, Greater Manchester. An innovative approach has been devised for creating a landscape mosaic, which uses remotely-sensed spectral indices and land cover measurements. Five ecosystem services are considered: carbon storage, water flow mitigation, climate stress mitigation, aesthetics, and recreation. Analysis of ecosystem service generation uses the landscape mosaic, hotspot identification and measurements of spatial association. Ecosystem service consumption is evaluated via original perspectives of physical accessibility through a transport network, and greenspace visibility over a 3D surface. Results suggest that the landscape mosaic accuracy compares favourably to a map created using traditional classification methods. Ecosystem service patterns are unevenly distributed across Salford. The regulating services draw from similar natural resource locations, while cultural services have more diverse sources. The accessibility and visibility analysis provides evidence for the importance of urban trees as mitigators of ‘grey’ views, and urban parks as accessible producers of multiple services. Comprehensive ecosystem service analysis requires integration of quantitative and qualitative approaches. Evaluation of spatial relationships between ecosystem services and the physical landscapes in this thesis provides a practical method for improved measurement and management of the natural environment in urban areas. These findings can be used by urban planners and decision makers to integrate ecological considerations into proposed development schemes