Sustainable Approaches for Highway Runoff Management During Construction and Operation

publishedVersio

Sustainable Approaches for Highway Runoff Management During Construction and Operation

Paper V and paper VI have not been published yet.Environmentally friendly approaches for highway runoff management during construction and operation are considered in this project. First, the state of the art in runoff management in terms of characterization, treatment, and modeling approaches were surveyed, and knowledge gaps were identified. Then, the characterization and treatment of tunneling wastewater (by natural and chemical coagulants) was investigated. In the next stage, the vulnerability of water quality to road construction activities was investigated by analyzing field monitoring data. In addition, two different approaches, involving information theory and gamma test theory, were suggested to optimize the water quality monitoring network during road construction. Lastly, the application of satellite data (i.e., Sentinel-2 Multi-Spectral Imager satellite imagery products) for water quality monitoring was examined. Based on the results, it can be shown that site-specific parameters (e.g., climate, traffic load) cause spatiotemporal variation in the characterization of highway runoff and performance of best management practices (BMP) to protect water quality. There is a knowledge gap regarding the characterization of highway runoff under different climatic scenarios, as well as the continuous monitoring and assessment of roadside water bodies. Analysis of the field monitoring data indicates that blasting, area cleaning, and construction of water management measures have the highest impact on surface water quality during road construction. Additionally, the application of information theory and gamma test theory indicate that the primary monitoring network assessed here is not optimally designed. The number and spatial distribution of monitoring stations could be modified and reduced, as the construction activities vary over time. Additionally, the suggested remote sensing techniques applied in this project are able to estimate water quality parameters (i.e., turbidity and chlorophyll-a) in roadside water bodies with a reliability consistent with field observations, reflecting the spatiotemporal effects of road construction and operations on water quality. Finally, an efficient two-step treatment strategy (15 min sedimentation followed by chemical coagulation and 45 min sedimentation) is suggested for the treatment of tunneling wastewater. The optimum coagulant dosages in the jar test exhibit high treatment efficiency (92-99%) for both turbidity and suspended solids (SS), especially for particle removal in the range of 10-100 μm, which is hard to remove by sedimentation ponds and may pose serious threats to the aquatic ecosystem. It is hoped the knowledge generated by this project will help decision-makers with management strategies and support UN Sustainable Development Goals (SDGs). The proposed approaches directly contribute to managing highway runoff and achieving SDG 6 (clean water and sanitation) and especially target 6.3 (water quality).publishedVersio

Mapping invasive plants using RPAS and remote sensing

The ability to accurately detect invasive plant species is integral in their management, treatment, and removal. This study focused on developing and evaluating RPAS-based methods for detecting invasive plant species using image analysis and machine learning and was conducted in two stages. First, supervised classification to identify the invasive yellow flag iris (Iris pseudacorus) was performed in a wetland environment using high-resolution raw imagery captured with an uncalibrated visible-light camera. Colour-thresholding, template matching, and de-speckling prior to training a random forest classifier are explored in terms of their benefits towards improving the resulting classification of YFI plants within each image. The impacts of feature selection prior to training are also explored. Results from this work demonstrate the importance of performing image processing and it was found that the application of colour thresholding and de-speckling prior to classification by a random forest classifier trained to identify patches of YFI using spectral and textural features provided the best results. Second, orthomosaicks generated from multispectral imagery were used to detect and predict the relative abundance of spotted knapweed (Centaurea maculosa) in a heterogeneous grassland ecosystem. Relative abundance was categorized in qualitative classes and validated through field-based plant species inventories. The method developed for this work, termed metapixel-based image analysis, segments orthomosaicks into a grid of metapixels for which grey-level co-occurrence matrix (GLCM)-based statistics can be computed as descriptive features. Using RPAS-acquired multispectral imagery and plant species inventories performed on 1m2 quadrats, a random forest classifier was trained to predict the qualitative degree of spotted knapweed ground-cover within each metapixel. Analysis of the performance of metapixel-based image analysis in this study suggests that feature optimization and the use of GLCM-based texture features are of critical importance for achieving an accurate classification. Additional work to further test the generalizability of the detection methods developed is recommended prior to deployment across multiple sites.remote sensingremotely piloted aircraft systemsRPASinvasive plant speciesmachine learnin

Assessing the current landscape of AI and sustainability literature:Identifying key trends, addressing gaps and challenges

The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.</p

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Multiseasonal Remote Sensing of Vegetation with One-Class Classification – Possibilities and Limitations in Detecting Habitats of Nature Conservation Value

Mapping of habitats relevant for nature conservation often involves the identification of patches of target habitats in a complex mosaic of vegetation types extraneous for conservation planning. In field surveys, this is often a time-consuming and work-intensive task. Limiting the necessary ground reference to a small sample of target habitats and combining it with area-wide remote sensing data could greatly reduce and therefore support the field mapping effort. Conventional supervised classification methods need to be trained with a representative set of samples covering an exhaustive set of all classes. Acquiring such data is work intensive and hence inefficient in cases where only one or few classes are of interest. The usage of one-class classifiers (OCC) seems to be more suitable for this task – but has up until now neither been tested nor applied for large scale mapping and monitoring in programs such as those requested for the Natura 2000 European Habitat Directive or the High Nature Value (HNV) farmland Indicator. It is important to uncover the possibilities and mark the obstacles of this new approach since the usage of remote sensing for conservation purposes is currently controversially discussed in the ecology community as well as in the remote sensing community. Thus, the focal and innovative point of this thesis is to explore possibilities and limitations in the application of one-class classifiers for detecting habitats of nature conservation value with the help of multi-seasonal remote sensing and limited field data. The first study ascertains the usage of an OCC is suitable for mapping Natura 2000 habitat types. Applying the Maxent algorithm in combination with a low number of ground reference points of four habitat types and easily available multi-seasonal satellite imagery resulted in a combined habitat map with reasonable accuracy. There is potential in one-class classification for detecting rare habitats – however, differentiating habitats with very similar species composition remains challenging. Motivated by these positive results, the topic of the second study of this thesis is whether low and HNV grasslands can be differentiated with remotely-sensed reflectance data, field data and one-class classification. This approach could supplement existing field survey-based monitoring approaches such as for the HNV farmland Indicator. Three one-class classifiers together with multi-seasonal, multispectral remote sensing data in combination with sparse field data were analysed for their usage A) to classify HNV grassland against other areas and B) to differentiate between three quality classes of HNV grassland according to the current German HNV monitoring approach. Results indicated reasonable performances of the OCC to identify HNV grassland areas, but clearly showed that a separation into several HNV quality classes is not possible. In the third study the robustness and weak spots of an OCC were tested considering the effect of landscape composition and sample size on accuracy measurements. For this purpose artificial landscapes were generated to avoid the common problem of case-studies which usually can only make locally valid statements on the suitability of a tested approach. Whereas results concerning target sample size and the amount of similar classes in the background confirm conclusions of earlier studies from the field of species distribution modelling, results for background sample size and prevalence of target class give new insights and a basis for further studies and discussions. In conclusion the utilisation of an OCC together with reflectance and sparse field data for addressing rare vegetation types of conservation interest proves to be useful and has to be recommended for further research

Analysis of urban green space in Chongqing and Nanjing using multi-resolution segmentation, object-oriented classification approach and landscape ecology metrics.

So Lek Hang Lake.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 196-203).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.viiTable of Content --- p.ixList of Figures --- p.xiiiList of Tables --- p.xviChapter CHAPTER 1. --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.1Chapter 1.2 --- Research Objectives --- p.5Chapter 1.3 --- Research Significance --- p.6Chapter 1.4 --- Organization of the thesis --- p.7Chapter 1.5 --- Definition of Urban Green Space --- p.9Chapter CHAPTER 2. --- Literatu re Re view --- p.10Chapter 2.1 --- Introduction --- p.10Chapter 2.2 --- Urban Green Space --- p.10Chapter 2.2.1 --- Classification of Urban Green Space --- p.11Chapter 2.2.2 --- Configuration of Urban Green Space System --- p.12Chapter 2.2.3 --- Different Approaches to Urban Green Space Study --- p.14Chapter 2.3 --- Urban Green Space in China --- p.15Chapter 2.3.1 --- General Problems --- p.16Chapter 2.3.2 --- Increasing Awareness of Environment --- p.16Chapter 2.3.3 --- Chinese Definition of Urban Green Space --- p.18Chapter 2.4 --- Remote Sensing Techniques --- p.21Chapter 2.4.1 --- Review of Image Classification Techniques --- p.21Chapter 2.4.1.1 --- Conventional Classification Methods --- p.22Chapter 2.4.1.2 --- Mixed Pixels Problem --- p.23Chapter 2.4.1.3 --- Mixed Pixels，Effects on Conventional Classifiers --- p.25Chapter 2.4.1.4 --- Alternative Solutions to Mixed Pixels Problems (Fuzzy Sets) --- p.26Chapter 2.4.1.5 --- Problems Fuzzy Classifications are unable to solve --- p.28Chapter 2.4.2 --- Object-oriented Classification Concept --- p.30Chapter 2.4.2.1 --- Multiresolution Segmentation --- p.30Chapter 2.4.2.2 --- Fuzzy Classification Procedure --- p.31Chapter 2.4.2.3 --- Object-orien ted Approach to Image Processing --- p.32Chapter 2.4.2.4 --- E cognition --- p.33Chapter 2.4.2.5 --- Research about ecognition --- p.34Chapter 2.5 --- Landscape Ecology --- p.35Chapter 2.5.1 --- Basic Principles --- p.35Chapter 2.5.2 --- Landscape Metrics --- p.36Chapter 2.5.3 --- Application of Landscape Ecology in Landscape Analysis --- p.38Chapter 2.6 --- Conclusion --- p.39Chapter CHAPTER 3. --- Study Sites and Methodology --- p.41Chapter 3.1 --- lntroduction --- p.41Chapter 3.2 --- Study Area --- p.41Chapter 3.2.1 --- Chongqing --- p.41Chapter 3.2.1.1 --- Geography and geomorphology --- p.42Chapter 3.2.1.2 --- Administration and governance --- p.42Chapter 3.2.1.3 --- Environmental Quality --- p.43Chapter 3.2.1.4 --- Governm ent Attempt to Improvement --- p.43Chapter 3.2.2 --- Nanjing --- p.46Chapter 3.2.2.1 --- Geography and Geomorphology --- p.46Chapter 3.2.2.2 --- Administration and Governance --- p.46Chapter 3.2.2.3 --- Landscape Planning of Nanjing --- p.47Chapter 3.2.3 --- Comparison between Chongqing and Nanjing --- p.47Chapter 3.2.3.1 --- Geographical setting --- p.49Chapter 3.2.3.2 --- Population --- p.49Chapter 3.2.3.3 --- Urbanization and Industrialization Levels --- p.51Chapter 3.2.3.4 --- Variation in Landscape Quantity --- p.51Chapter 3.2.3.5 --- Comparison from Satellite Images --- p.52Chapter 3.3 --- Working procedures --- p.56Chapter 3.3.1 --- Data --- p.56Chapter 3.3.1.1 --- VNIR chann els --- p.58Chapter 3.3.1.2 --- SWIR channels --- p.59Chapter 3.3.1.3 --- Data Fusion --- p.59Chapter 3.3.2 --- Designing Hierarchical Classification System --- p.60Chapter 3.3.2.1 --- Chongqing --- p.60Chapter 3.3.2.2 --- Nanjing --- p.61Chapter 3.3.3 --- Object-oriented Classification --- p.62Chapter 3.3.3.1 --- Introdu ction --- p.63Chapter 3.3.3.2 --- Procedure of Object-oriented Classification --- p.65Chapter 3.3.3.2.1 --- Analysis of Image Objects --- p.65Chapter 3.3.3.2.2 --- Image Segmentation --- p.67Chapter 3.3.3.2.3 --- Selection of Features and Data Conversion --- p.67Chapter 3.3.3.2.4 --- Class-based Objects Sampling --- p.68Chapter 3.3.3.2.5 --- Class-based Objects Analysis --- p.68Chapter 3.3.3.2.6 --- Designing Object Level Hierarchy --- p.69Chapter 3.3.3.2.7 --- Designing Class Hierarchy --- p.69Chapter 3.3.3.2.8 --- Decision Tree Classification Structure --- p.69Chapter 3.3.4 --- Comparison with other classification algorithms --- p.70Chapter 3.4 --- Landscape Analyses --- p.71Chapter 3.4.1 --- Selection of Landscape Metrics --- p.72Chapter 3.4.2 --- Landscape Analysis for entire cities --- p.74Chapter 3.4.3 --- Buffer Analysis --- p.74Chapter 3.5 --- Conclusion --- p.77Chapter CHAPTER 4. --- Results and Discussion I Variations of Image Object Signatures for Sampled Land Covers --- p.78Chapter 4.1 --- Introduction --- p.78Chapter 4.2 --- Chongqing --- p.79Chapter 4.2.1 --- Spectral-shape ratio --- p.79Chapter 4.2.1.1 --- Selection Criteria --- p.80Chapter 4.2.1.2 --- Observations --- p.80Chapter 4.2.2 --- Segmentation levels --- p.85Chapter 4.2.2.1 --- Selection Criteria --- p.85Chapter 4.2.2.2 --- Observations --- p.86Chapter 4.2.3 --- Classifying Rules --- p.93Chapter 4.2.3.1 --- Selection Criteria --- p.93Chapter 4.2.3.2 --- Level 9 --- p.94Chapter 4.2.3.3 --- Level 5 --- p.101Chapter 4.2.3.4 --- Level 1 --- p.103Chapter 4.3 --- Nanjing --- p.104Chapter 4.3.1 --- Spectral-shape ratio --- p.104Chapter 4.3.1.1 --- Selection Criteria --- p.105Chapter 4.3.1.2 --- Observations --- p.105Chapter 4.3.2 --- Segmentation Levels --- p.111Chapter 4.3.2.1 --- Selection Criteria --- p.111Chapter 4.3.2.2 --- Observations --- p.111Chapter 4.3.3 --- Classifying Rules --- p.119Chapter 4.3.3.1 --- Selection Criteria --- p.119Chapter 4.3.3.2 --- Level 8 --- p.119Chapter 4.3.3.3 --- Level 4 --- p.126Chapter 4.3.3.4 --- Level 1 --- p.129Chapter 4.4 --- Discussion --- p.131Chapter CHAPTER 5. --- Results and Discussion II Image Classification --- p.134Chapter 5.1 --- lntroduction --- p.134Chapter 5.2 --- Chongqing --- p.135Chapter 5.2.1 --- Class hierarchy --- p.135Chapter 5.2.2 --- Description of the site --- p.136Chapter 5.2.3 --- Classification of “lake´ح --- p.138Chapter 5.2.4 --- "Classification of ""crops and grassland""" --- p.139Chapter 5.2.5 --- Classification of “low density urban´ح --- p.140Chapter 6.3.3 --- Classification Result --- p.142Chapter 5.2.7 --- Error matrix --- p.144Chapter 5.2.8 --- Class Proportion --- p.144Chapter 5.2.9 --- Post-classification Aggregation --- p.147Chapter 5.3 --- Nanjing --- p.149Chapter 5.3.1 --- Class Hierarchy --- p.149Chapter 5.3.2 --- Description of the site --- p.151Chapter 5.3.3 --- Classification of lake --- p.151Chapter 5.3.4 --- "Classification of ""crops and grassland II´ح" --- p.153Chapter 5.3.5 --- "Classification of ""low density urban""" --- p.154Chapter 5.3.6 --- Classification Result --- p.155Chapter 5.3.7 --- Error Matrix --- p.156Chapter 5.3.8 --- Class Proportion --- p.161Chapter 5.3.9 --- Post-classification Aggregation --- p.161Chapter 5.4 --- Discussion --- p.163Chapter 5.4.1 --- Problems of object-oriented classification --- p.163Chapter 5.4.2 --- Strengths of object-oriented classification --- p.165Chapter 5.4.3 --- Transferability of classifying rules --- p.166Chapter CHAPTER 6. --- "Results and Discussion HI Landscape Structure of ""Urban Green Space"", Chongqing and Nanjing" --- p.167Chapter 6.1 --- Introduction --- p.167Chapter 6.2 --- Chongqing --- p.167Chapter 6.2.1 --- Landscape composition --- p.167Chapter 6.2.2 --- Fragmentation --- p.169Chapter 6.2.3 --- Contagion --- p.171Chapter 6.2.4 --- Patch Shape Complexity --- p.171Chapter 6.3 --- Nanjing --- p.173Chapter 6.3.1 --- Landscape composition --- p.173Chapter 6.3.2 --- Fragmentation --- p.175Chapter 6.3.3 --- Contagion --- p.177Chapter 6.3.4 --- Patch Shape Complexity --- p.178Chapter 6.4 --- Discussion --- p.179Chapter 6.4.1 --- Similarities --- p.179Chapter 6.4.2 --- Differences --- p.182Chapter CHAPTER 7. --- Conclusion --- p.186Chapter 7.1 --- Summary on findings --- p.186Chapter 7.1.1 --- Summary on image object analyses --- p.186Chapter 7.1.2 --- Summary on object-oriented classification --- p.187Chapter 7.1.3 --- Summary on landscape studies of ´ب´بurban green space´ح --- p.189Chapter 7.2 --- Limitations of the research --- p.190Chapter 7.2.1 --- Data preparation --- p.190Chapter 7.2.2 --- Image classification --- p.191Chapter 7.2.3 --- Landscape Analysis --- p.193Chapter 7.3 --- Suggestions for further research --- p.194Bibliography --- p.196Appendix 1´ؤEquations of object features --- p.204Appendix 2´ؤEquations for Landscape Metrics --- p.208Appendix 3´ؤVariations of Object Features along Segmentation Levels in Chongqing --- p.216Appendix 4´ؤVariations of Object Features along Segmentation Levels in Nanjing --- p.244Appendix 5´ؤClassifying Rules --- p.277Appendix 6´ؤVariations in Landscape Metrics along Buffers from City Center in Chongqing --- p.282Appendix 7´ؤVariations in Landscape Metrics along Buffers from City Center in Nanjing --- p.29

Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X

Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent. A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150m² can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery