Dynamics of Urban Density in China: Estimations Based on DMSP/OLS Nighttime Light Data

In China, rapid urbanization has increased the demand for urban land and intensified the conflict between limited land resources and urban development. In response, high urban density has been proposed to realize sustainable urban development. Achieving this goal requires an examination of the dynamics of urban density in China. Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) are a good indicator of human activity. We applied NTL data to measure urban density in 70 major cities in China during 1992–2010. Based on temporal changes in NTL, we identified seven classes of urban density and clustered the distributions of urban density in 70 cities into six types. The dynamics of urban density were then obtained from the GDP density as an index of city development. The curves of urban density distribution gradually changed from a concave increase to W-shaped and S-shaped to a concave decrease, indicating that the current urban land use in China is unsustainable and that the shortage of land resources must be addressed. An examination of the distribution of urban density in Hong Kong revealed a different pattern and a potential solution for cities in mainland China.published_or_final_versio

UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW

The process of identifying change in remote sensing images has been a focal point of research for decades now. Many classical algorithms exist, and many new modern ones are still being developed. These algorithms can be divided into supervised and unsupervised. In this work an unsupervised method is presented. This method relies on the scene alignment algorithm SIFT flow. It is shown that building upon simple principles an accurate change map can be obtained from the SIFT descriptor flow of the two input images. Furthermore, it is shown that this method despite its simplicity exceeds other unsupervised methods and comes close to supervised ones, even exceeding them in some metrics. Lastly, the advantages of SIFT flow in comparison to the supervised methods are highlighted alongside its own downsides

Mapping urban growth and investigating its potential impact on surface water quality in Chattanooga, Tennessee using GIS and remote sensing

Urban development involves the conversion of land cover from pervious to impervious surfaces. Impervious surfaces (IS) can have ramifications for urban stormwater and facilitate the movement of pollutants and other substances to nearby water bodies. This study investigated the changes in IS in and around the city of Chattanooga, Tennessee using GIS and remote sensing technologies based on Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) acquired in 1986 and 2016, respectively. A model was developed utilizing the Normalized Difference Vegetation Index (NDVI) and a supervised image classification algorithm to detect IS growth. The changes in IS were quantified at watershed level scale including stream riparian areas. The obtained results show a net growth of 45.12 km2 of IS, 9.96 km2 being within 90 m of streams, a conversion of 6% of the study site’s land cover. A stream risk assessment study was conducted using the riparian zone percent imperviousness to assess the potential of stream impairment due to IS growth. This assessment shows a significant increase in the number of streams that are potentially at risk to be impaired due to current urban growth

Neural network-based urban change monitoring with deep-temporal multispectral and SAR remote sensing data

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991-2011 and Sentinel 1 and 2 for 2017-2021. For every era, we use three different urban sites-Limassol, Rotterdam, and Liege-with at least 500 km(2) each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.Web of Science1315art. no. 300

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Sustainable managment of stormwater in a changing environment under Mediterranean climate conditions

2016 - 2017The problem of the increase in the magnitude and frequency of flooding events in urban areas can be approached by means of techniques of sustainable urban stormwater management. In this PhD dissertation, the effectiveness of one of these technologies namely the green roof (GR), has been investigated. For this purpose, a daily scale hydrological model for GRs, mainly based on meteorological data and with three levels of complexity has been proposed. Since, the evapotranspiration (ET) fluxes impact the GR retention performances, a study of the dynamics involved in ET process has been carried out. The use of green roofs technology in Mediterranean climate is very limited so two GR experimental benches has been placed in the campus of University of Salerno and preliminary results about the hydrological performances depending on the climate and constructions characteristics have been illustrated. Subsequently, the effectiveness of the proposed technology for the sustainable urban drainage management have been tested at a large scale and Sarno peri-urban basin has been presented as case study since it represents a hydrogeological hazard prone system. The analysis focused on the potential hydrological benefits in terms of peak runoff, peak delay and volume runoff in respect of several hypothetical scenarios of rainfall and GR retrofitting percentage. In high urbanized areas, the implementation of GRs at basin scale, allows a reduction of runoff rainwater from roofs close to 100% for some rainfall and greening scenarios. Where the GR retrofit potential is very low, satisfactory performances in terms of water management can be reached coupling this green technology to other sustainable techniques. [abstract editeb by Author]XXX cicl

An integrated framework to assess compound flood risks for interdependent critical infrastructure in a coastal environment

Compound flooding refers to flood events caused by multiple factors, including marine processes (e.g. storm tides and waves), hydrometeorological signals (e.g. rainfall and river flows) among others. Saint Lucia is a tropical island in eastern Caribbean Sea, which is frequently affected by weather-related extreme events such as tropical storms and the associated risks are exacerbated due to its mountainous topography and high concentrations of infrastructure and human communities close to the coast. At the southern coast of Saint Lucia, significant infrastructures such as Hewanorra International Airport and Vieux Fort Seaport, and human settlements such as towns of Vieux Fort and La Tourney are located at low-lying areas and are at risk of compound flooding. A hydrologic model (i.e. HYdrological MODel) and a two-dimensional hydrodynamic model (i.e. LISFLOOD-FP) are set up and calibrated to investigate the combined effects of storm tides, wave run-up, rainfall, and river flows on flood risks in Saint Lucia. Results indicate the necessity to consider multiple contributing factors as well as to characterize the effects of uncertain boundary conditions. In flood-prone areas, there are infrastructures supporting major services in the study area, and by extension, the economy of the Island. A network-based model, which considers direct and indirect connections between infrastructures, is set up to explore risks of assets in conditions of non-flooding and flooding. Modelling results reveal the fundamental importance of various components including electricity distribution, flood control, information and communication services, transportation, housing and human settlements, tourism, and particularly the normal operations of Hewanorra International Airport. Prioritization of risks is critical for developing effective mitigation methods for infrastructure networks

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months