Relationship between Urban Morphological Properties and Ventilation in the Intensely Developed Areas of Inner Bangkok

Bad urban ventilation is a major intimidation to Bangkok urban air environments. Nowadays, not only lack of research progress in this area due to the complexity of urban fabric, but also lack of awareness and knowledge about affiliations between urban morphological properties and human level air ventilation at microscale is a key factor that contravenes mitigation efforts in dense areas. The objective of this study is to find a relationship between urban morphological properties and urban air ventilation in various intensely developed environments of inner Bangkok that are investigated through computer simulation of field measurements secondary data input and flow analysis calculation on the basis of the geometry of the urban fabric. With regard to the innumerable number of parameters collected throughout a literature review, this study aims to identify the most important urban morphological parameters at urban block level (at the human level above ground) that affect air ventilation in Bangkok area. This study is the first part of the identification process of urban morphological properties of block types that may correlate with urban air ventilation. The results are as follows: “high density - low rise” type with parallel-to-prevailing-wind orientation (block no. 26) has the best urban ventilation efficiency, followed by “high density - high rise” type with a single, large and tall building, “high density - low rise” type with deviating-from-prevailing-wind orientation, “high density - high rise” type with parallel-to-prevailing-wind orientation, and “high density - high rise” type with deviating-from-prevailing-wind orientation, respectively. Building height and orientation are the two factors that are attributed to the parameterization of human level air ventilation

Monitoring sustainable urban development using builtup area indicators: a case study of Stellenbosch, South Africa

Abstract: Rapid urbanisation in many developing countries causes land transformation from agricultural, rural, and natural landscapes into urban areas. Data to monitor this transformation is often out of date, unreliable, not in standard format, cumbersome and expensive to collect or simply unavailable. This inhibits local authorities and other stakeholders’ capacity to monitor and leverage resources toward sustainable urban development. This paper investigates the use of earth observation (EO) data for supporting sustainable urban development planning. The study demonstrates that EO adds value to sustainable urban development by providing area-wide and up-to-date thematic and geometric characterisation of the urban built-up area, which would be difficult to obtain from other data sources. This helps local planning authorities to monitor urban growth and sustainability, facilitate evidence-based decision making and an array of other practical uses

Computational design contributions of integrative architectural and urban digital design methodology based on satellite images

This study is based on innovative researches and combines technology, methodologies and means which are used in different sciences and processes such as informatics and computational processes, telegeoprocessing–telegeomonitoring technologies, survey science methodologies, urban and regional planning techniques in order to describe computational processes of innovative digital integrative design. This procedure refers to aspects of digital integrative design – modeling and simulation of a built-up architectural or urban area. These aspects concern a modeling process based on satellite images and specifically developed computational interfaces adapted to a CAD system, such as DTM’s mesh control points, conversion from geodetic to cartesian coordinates, bitmap adjustment to the buildings facades and surfaces normals handling, taking into account techniques that refer to other sciences such as survey, maths, astronomy and computer science. This modeling process is supported by an innovated proposed procedure that transfers remotely spatial data collected from the field (geographical coordinates and relative measurements taken in place) directly into a modeling system in order to model architectural entities and simulate simultaneously qualitative characteristics of an urban space (sound, temperature, humidity, etc) in real-time

Monitoring sustainable urban development using builtup area indicators : a case study of Stellenbosch, South Africa

Rapid urbanisation in many developing countries causes land transformation from agricultural, rural, and natural landscapes into urban areas. Data to monitor this transformation are often out of date, unreliable, not in standard format, cumbersome and expensive to collect or simply unavailable. This inhibits local authorities and other stakeholders’ capacity to monitor and leverage resources towards sustainable urban development. This paper investigates the use of earth observation (EO) data for supporting sustainable urban development planning. The study demonstrates that EO adds value to sustainable urban development by providing area-wide and up-to-date thematic and geometric characterisation of the urban built-up area, which would be difficult to obtain from other data sources. This helps local planning authorities to monitor urban growth and sustainability, and facilitate evidence-based decision-making and an array of other practical uses

A Study on Image Registration between High Resolution Optical and SAR Images Using SAR-SIFT and DLSS

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 건설환경공학부, 2018. 8. 김용일.최근 위성센서 기술의 발달로 다양한 센서를 탑재한 지구관측위성이 발사되면서, 다중센서 위성영상을 융합 분석하는 연구가 활발히 진행되고 있다. 특히, 광학영상과 SAR영상은 취급하는 파장대가 달라 동시에 활용할 경우 지표면에 대해 보다 구체적인 정보를 취득할 수 있으며, 더 나아가, 객체 추출, 변화탐지, 재난재해 모니터링 등 원격탐사 분야에 폭넓게 적용이 가능하다. 이를 위해서는 전처리 작업으로 두 영상 간 정합이 필수적으로 이루어져야 한다. 그러나, 광학영상과 SAR영상은 영상 취득시 위성센서 자세 및 취급하는 파장대의 상이함으로 기하 및 분광 정보 차이를 유발하여 영상 정합에 있어 유독 어려움이 존재한다. 이러한 차이는 건물이 밀집된 도심지역에서 부각되며, 중·저해상도 영상보다 고해상도 영상에서 두드러진다. 따라서, 본 연구에서는 도심지역에 대한 고해상도 광학영상과 SAR영상 간 정합에 효과적인 방법론을 제안하였다. 기존 광학영상과 SAR영상 간 정합 관련 연구는 크게 특징기반 정합기법과 강도기반 정합기법으로 진행되었다. 강도기반 정합기법은 분광 특성이 다른 영상 간 정합에 효과적이나, 영상 간 왜곡이 존재하지 않거나 기하학적 위치 차이가 적을 때에만 적용 가능하다. 고해상도 광학영상과 SAR영상은 지역적 왜곡이 존재하며, 두 영상 간 수십m 이상의 기하학적 위치 차이가 발생할 수 있다. 따라서, 고해상도 광학영상과 SAR영상 간 정합 연구는 강도기반 정합기법 보다 특징기반 정합기법이 중점적으로 진행되고 있다. 그러나, 특징기반 정합기법은 분광 특성이 다른 광학영상과 SAR영상에서 오정합쌍을 다수 추출하여 정합 성능이 떨어진다. 이를 해결하기 위해, 강도기반 정합기법과 특징기반 정합기법을 결합한 기법들이 제안되었으나, 도심지역에서 원 형상이 존재하는 지역이나 건물밀집지역을 제외한 지역 등과 같이 제한된 지역에서만 적용 가능하다는 한계점을 보였다. 이를 개선하기 위해, 본 연구에서는 특징기반 정합기법인 SAR-SIFT 기법과 강도기반 정합기법인 DLSS 기법을 결합한 정합기법을 제안하였다. 또한, 정합쌍을 추출하기 위해, 전처리 단계, 후보 정합쌍 추출 단계, 정밀 정합쌍 추출 단계인 총 세 단계를 추가하였다. 고해상도 광학영상과 SAR영상 간 정합을 위해서, SAR-SIFT 기법을 이용하여 특징점을 추출하고, 추출된 특징점에서 DLSS 기법을 이용하여 정합쌍을 추출하였다. 그러나, 추출된 정합쌍에 다수의 오정합쌍이 포함되는 문제점이 존재하였다. 이를 해결하기 위해, 추출된 정합쌍에서 임계치와 특징점 간 변위량을 이용한 전처리 단계와 후보 정합쌍 추출 단계를 통해 후보 정합쌍을 추출하고, 후보 정합쌍에 RANSAC 기법을 적용하여 정밀 정합쌍을 추출하는 방법을 제안하였다. 최종적으로 추출된 정밀 정합쌍을 이용하여 어핀 변환식(affine transformation)을 구성하고, 이를 적용하여 광학영상에 정합된 SAR영상을 생성하였다. 본 연구의 정확도를 검증하기 위하여, 대표적인 고해상도 광학영상인 KOMPSAT-2영상과 고해상도 SAR영상인 TerraSAR-X, Cosmo-SkyMed영상을 사용하였고, 시각적, 정량적 평가를 진행하였다. 시각적 평가를 위해서 모자이크 영상을 생성하였으며, 두 영상 간 경계에서 객체의 형상이 유지됨을 통해 정합이 우수하게 수행됨을 확인하였다. 정량적 평가를 위해서 수동 검사점을 통한 RMSE Ⅰ과 교차검증을 통한 RMSE Ⅱ를 사용하였으며, 모든 실험지역에 대해 RMSE Ⅰ은 1.51m에서 2.04m, RMSE Ⅱ는 1.34m에서 1.69m로 정확도가 도출되었다. 이는, 선행연구결과와 비교하였을 때 우수한 수준의 정확도로 확인되었다. 이를 통해, 제안 기법이 고해상도 광학영상과 SAR영상 간 정합에 효과적이며, 두 영상 간 융합 분석을 위해 효과적인 정합 기술로 활용될 것으로 사료된다.1. 서 론 1 1.1 연구배경 1 1.2 연구동향 4 1.3 연구의 목적 및 범위 7 2. 특징점 추출 10 2.1 영상 전처리 10 2.2 SAR-SIFT 기법을 통한 특징점 추출 11 2.2.1. SIFT 기법의 문제점 11 2.2.2. SAR-SIFT 기법 15 3. 정합쌍 추출 18 3.1 DLSS 기법을 통한 정합쌍 추출 18 3.1.1. 형상 서술자 LSS 19 3.1.2. 형상 서술자 벡터 DLSS 21 3.1.3. DLSS 기법의 문제점 22 3.2 제안된 정합쌍 추출 방법 24 3.2.1. 전처리 단계 24 3.2.2. 후보 정합쌍 추출 단계 26 3.2.3. 정밀 정합쌍 추출 단계 28 3.3 정합 및 정확도 평가 방법 29 3.3.1. 어핀 변환식 29 3.3.2. 정확도 평가 방법 31 4. 실험의 적용 및 평가 32 4.1 실험지역 및 자료 32 4.2 특징점 추출 결과 35 4.2.1. SIFT 기법을 통한 특징점 추출 결과 35 4.2.2. SAR-SIFT 기법을 통한 특징점 추출 결과 37 4.3 정합쌍 추출 결과 40 4.3.1. 기존 기법을 통한 정합쌍 추출 결과 40 4.3.2. 제안 기법을 통한 정합쌍 추출 결과 44 4.4 정합 결과 및 평가 49 5. 결론 55 Abstract 67Maste

Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery

The study of post-disaster recovery requires an understanding of the reconstruction process and growth trend of the impacted regions. In case of earthquakes, while remote sensing has been applied for response and damage assessment, its application has not been investigated thoroughly for monitoring the recovery dynamics in spatially and temporally explicit dimensions. The need and necessity for tracking the change in the built-environment through time is essential for post-disaster recovery modeling, and remote sensing is particularly useful for obtaining this information when other sources of data are scarce or unavailable. Additionally, the longitudinal study of repeated observations over time in the built-up areas has its own complexities and limitations. Hence, a model is needed to overcome these barriers to extract the temporal variations from before to after the disaster event. In this study, a method is introduced by using three spectral indices of UI (urban index), NDVI (normalized difference vegetation index) and MNDWI (modified normalized difference water index) in a conditional algebra, to build a knowledge-based classifier for extracting the urban/built-up features. This method enables more precise distinction of features based on environmental and socioeconomic variability, by providing flexibility in defining the indices’ thresholds with the conditional algebra statements according to local characteristics. The proposed method is applied and implemented in three earthquake cases: New Zealand in 2010, Italy in 2009, and Iran in 2003. The overall accuracies of all built-up/non-urban classifications range between 92% to 96.29%; and the Kappa values vary from 0.79 to 0.91. The annual analysis of each case, spanning from 10 years pre-event, immediate post-event, and until present time (2019), demonstrates the inter-annual change in urban/built-up land surface of the three cases. Results in this study allow a deeper understanding of how the earthquake has impacted the region and how the urban growth is altered after the disaster

Mapping regional land cover and land use change using MODIS time series

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations

Spatio-Temporal Modeling of Earthquake Recovery

The recovery process after a major disaster or disruption, is impacted by the inequality of risk prior to and post event. In the past decades there has been few efforts to model the recovery process and the focus is mainly on staged models (i.e. emergency, restoration, and reconstruction). The overarching research question asks how a non-stage-like model could apply to the recovery process. This study poses three broad questions: 1) what are the indicators suitable for monitoring the recovery process; 2) what are the driving factors of differential recovery trends; and 3) what are the predicted development trajectories for communities if there was no disruption? To address the research questions, a new model is proposed for tracking the recovery process as the “Tempo-variant Model of Disaster Recovery” (TMDR), which is implemented for six case studies of recoveries post-earthquakes, in a continuous trend through time (case studies from: Chile, New Zealand, India, Iran, China, and Italy). The recovery process is monitored through a set of proposed indicators representing the changes in six main categories of housing, socio-economic, agriculture, infrastructural, institutional, and development. Satellite imagery is used as a congruent data source to monitor urban land surface change that is implemented with a new model and conditional algebra for change detection. A new method is then developed by combining the satellite imagery data with social indicators, which provides quantitative/relative measure of recovery trend (spatially and temporally) where ground assessments are impractical. The results of implementing the new TMDR model in this cross-cultural comparative study, further highlights the drivers of recovery process across time and nations. The difference between post-event and pre-event trends (i.e. recovery progress) shows significant association with instantaneous impact of the event on community development dynamics in all cases. The spatio-temporal analysis shows majority of the study area in Chile is recovered, but there are regions in the other cases that are still recovering. The comparative view on TMDR results indicates that impact of event is more significant for recovery progress in the initial years post-event, while additional indicators of access to basic infrastructure is more predictive in the long-term. Therefore, this new model provides a case-dependent baseline and an operational tool for monitoring the recovery process

Scenarios of Urban Growth in Kenya Using Regionalised Cellular Automata based on Multi temporal Landsat Satellite Data

The exponential increase of urban areas in Africa during the last decade has become a major concern in the context of local climatic change and the increasing amount of impervious surface. Major African cities such as Nairobi and Nakuru have undergone rapid urban growth in comparison to the rest of the world. In this research we investigated the land-use changes and used the results in urban growth modelling which integrates cellular automata (CA), remote sensing (RS) and geographic information systems (GIS) in order to simulate urban growth up to the year 2030. We used multi-temporal Landsat imageries for the years 1986, 2000 and 2010 to map urban land-use changes in Nairobi and Nakuru. The use of multi-sensor imageries was also explored incorporating World view 2, and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping in Nakuru. We conducted supervised classification using support vector machine (SVM) which performed better than maximum likelihood classification. Land-use change estimates were obtained indicating increased urban growth into the year 2010. We used the land-use change analysis information to model urban growth in Nairobi and Nakuru. Our urban growth model (UGM) utilised various datasets in modelling urban growth namely urban land-use extracted from land-use maps, road network data, slope data and exclusion layer defining areas excluded from development. The Monte-Carlo technique was used in model calibration. The model was validated using Multiple Resolution Validation (MRV) technique. Prediction of urban land-use was done up to the year 2030 when Kenya plans to attain Vision 2030. Three scenarios were explored in the urban modelling process; unmanaged growth with no restriction on environmental areas, managed growth with moderate protection, and a managed growth with maximum protection on forest, agricultural areas, and urban green. Furthermore, we explored the spatial effects of varying UGM parameters using the city of Nairobi. The objective here was to investigate the contribution of each model parameter in simulating urban growth. The results obtained indicate that varying model coefficients leads to urban growth in different directions and magnitude. However, several model parameters were observed to be highly correlated namely; spread, breed and road. The lowest spatial effect was achieved by at least maintaining spread, breed and road while varying the other parameters. The highest spatial effect was observed by at least keeping slope constant while varying the other four parameters. Additionally, we used kappa statistics to compare the simulation maps. High values of Khisto indicated high similarity between the maps in terms of quantity and location thus indicating the lowest spatial effect obtained. Kenya plans to achieve Vision 2030 in the year 2030 and information on spatial effects of our UGM can help in identifying different scenarios of future urban growth. It is thus possible to discover areas that are likely to experience; spontaneous growth, edge growth, road influenced growth or new spreading centres growth. Policy makers can see the influence of establishing new infrastructure such as housing and road in new areas compared to existing settlements. Moreover, the outcome of this research indicates that Nairobi and Nakuru are experiencing fast urban sprawl with urban land-use consuming the available land. The results obtained illustrate the possibility of urban growth modelling in addressing regional planning issues. This can help in comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social equity, economic efficiency and environmental sustainability. Hence, cellular automata are a worthwhile approach for regional modelling of African cities such as Nairobi and Nakuru. This provides opportunities for other cities in Africa to be studied using UGM and its adaptability noted accordingly.Das exponentielle Wachstum afrikanischer Städte im letzten Jahrzehnt ist mit Blick auf die lokalen klimatischen Veränderungen und der zunehmenden Menge an versiegelten Oberflächen von besonderer Tragweite. Im Vergleich zu anderen Metropolen erfuhren afrikanische Städte wie Nairobi und Nakuru ein extensives Wachstum der urbanen Flächen. Die vorliegende Arbeit setzt sich mit dem urbanen Landnutzungswandel auseinander und modelliert die Siedlungsflächenausdehnung für das Jahr 2030 mit Hilfe eines Zellulären Automaten (CA), Fernerkundungsdaten (RS) sowie Geographischen Informationssystemen (GIS). Zur Kartierung der Siedlungsflächenausdehnung von Nairobi und Nakuru wurden multitemporale Landsat-Daten der Jahre 1986, 2000 und 2010 verwendet. Zusätzlich wurden multisensorale Daten von World View 2 und ALOS PALSAR für Nakuru eingesetzt. Die Landnutzungsklassifikation erfolgte mit support vector machines (SVM). Dieses Verfahren zeigte bessere Ergebnisse als eine Maximum-Likelihood-Klassifikation. Auf Basis der klassifizierten Satellitendaten erfolgte die Landnutzungsmodellierung für Nairobi und Nakuru. Hierzu wurde die von Goetzke (2011) modifizierte Version von Clarke’s Urban Growth Model (Clarke, Hoppen, & Gaydos, 1997) benutzt. Neben den Landnutzungskarten fungieren Informationen zum Verkehrsnetz, zur Hangneigung und zu Ausschlussflächen als Hauptinputdaten. Die Kalibration erfolgte mit Hilfe von Monte Carlo Iterationen. Zur Validation des Modells wurde eine Multiple Resolution Validation (MRV) durchgeführt. Die Siedlungsflächenausdehnung wurde für das Jahr 2030 simuliert. Zu diesem Zeitpunkt plant das Land Kenia die Umsetzung des Vision 2030 Programmes. Es wurden insgesamt drei Szenarien mit dem Wachstumsmodell gerechnet: (1) Wachstum ohne Planungszwänge, so dass auch Siedlungsflächen in Naturschutzgebieten entstehen dürfen. (2) Siedlungsflächenausdehnung unter moderaten Planungsbedingungen. (3) Wachstum mit sehr restriktiven Planungsbedingungen, unter Einschluss des Schutzes von Wald-, Grün- und- Agrarflächen. Des Weiteren wurde eine Sensitivitätsanalyse der modelleigenen Wachstumsparameter am Beispiel von Nairobi durchgeführt. Es konnte gezeigt werden, welchen Einfluss die Parameter auf die Intensität und das Muster der modellierten Siedlungsflächenausdehnung ausüben. Dabei zeigten die Wachstumskoeffizienten „spread“, „breed“ und „road“ eine signifikante Korrelation. Zur weiteren Analyse der erzielten Modellierungsergebnisse und zum Vergleich der räumlichen Muster wurden Kappa-Statistiken herangezogen. Die Arbeit sieht sich als Beitrag zum Vision 2030 Diskurs der kenianischen Regierung. Die simulierten Szenarien der Siedlungsflächenausdehnung von Nairobi und Nakuru identifizieren die für eine Urbanisierung wahrscheinlich in Frage kommenden Regionen. Die Studie zeigt zudem, dass sich die Siedlungsflächenausdehnung von Nairobi und Nakuru schnell und mit hohen Wachstumsraten vollzieht. Der Einsatz von CA Modellen ist ein wertvoller Ansatz zur regionalen Modellierung nicht nur von kenianischen sondern auch von afrikanischen Städten. Die Arbeit kann somit Entscheidungsträger aus Politik und Verwaltung unterstützen, indem sie die räumlichen Auswirkungen des zukünftigen Ausbaus der Infrastruktur und von Wohnflächen aufzeigt. Eine umfassende Planung von Landnutzungswandel und ein integriertes Management sind essentiell auf dem Weg zu einem bewussteren Umgang mit der Ressource Land sowie zu einer sozialen Gleichheit, wirtschaftlichen Effizienz und einer ökologischen Nachhaltigkeit