Sustainable Urbanization in the China‐Indochinese Peninsula Economic Corridor

Countries in the China‐Indochinese Peninsula are home to rich human and natural resource endowments and have the potential to be one of the world\u27s fastest growing areas. Sustainable urbanization in the China‐Indochinese Peninsula Economic Corridor is important for the regional economic development and prosperity. Taking the advantages of the remote sensing and Geographic Information System (GIS) technologies, this chapter is first presents a general overview of urbanization procession in this region and monitors the spatiotemporal dynamics of the urban environment; the second objective is to present the multiple driving force factor analysis for urban development in countries of the China‐Indochinese Peninsula Economic Corridor using statistical models. The results indicated that the China‐Indochinese Peninsula Economic Corridor has experienced a rapid urbanization process during the past 15 years both in terms of urban areas and urban population (UP). In addition to socioeconomic factors, there is also a noticeable correlation between foreign direct investment (FDI) and international trade and urban development in the China‐Indochinese Peninsula Economic Corridor. Active participation in international trade and attracting foreign investment are helpful for the regional urbanization. As a neighboring country, China\u27s economic and trade activity also has a significant impact on the urbanization in countries of the China‐Indochinese Peninsula Economic Corridor. Furthermore, as the launch of the Silk Road Economic Belt and the 21st Century Maritime Silk Road and the Asian Infrastructure Investment Bank (AIIB), the China‐Indochinese Peninsula Economic Corridor will witness a more rapid urbanization progress in the next decade. This study has its characteristics in focusing on the region of the Indochinese Peninsula in which the most rapid urbanization is occurring, presenting the state‐of‐the‐art techniques for monitoring urban expansion and probing into the driving factors of the urban expansion in the China‐Indochinese Peninsula Economic Corridor by multiple principles and multiple‐level data. It is expected to benefit policymakers in urban development and also provide a basis for further studies of sustainable urbanization in the China‐Indochinese Peninsula Economic Corridor

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

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Detecting and Modeling the Changes of Land Use/Cover for Land Use Planning in Da Nang City, Viet Nam

The geometrical accuracy of georeferenced digital surfacemodels (DTM) obtained fromimages captured bymicro-UAVs and processed by using structure frommotion (SfM) photogrammetry depends on several factors, including flight design, camera quality, camera calibration, SfM algorithms and georeferencing strategy. This paper focusses on the critical role of the number and location of ground control points (GCP) used during the georeferencing stage. A challenging case study involving an area of 1200+ ha, 100+ GCP and 2500+ photos was used. Three thousand, four hundred and sixty-five different combinations of control points were introduced in the bundle adjustment, whilst the accuracy of the model was evaluated using both control points and independent check points. The analysis demonstrates how much the accuracy improves as the number of GCP points increases, as well as the importance of an even distribution, how much the accuracy is overestimated when it is quantified only using control points rather than independent check points, and how the ground sample distance (GSD) of a project relates to the maximum accuracy that can be achieved

Coastal vulnerability assessment: a case study in Kien Giang, western part of the Mekong River Delta in Vietnam

Climate change, particularly sea-level rise, threatens low-lying coastal systems, such as small islands on coral atolls, and deltas where millions of people are living. The Mekong River Delta is considered especially at risk. Although most of the delta is only a few metres above sea level, there have been few assessments of vulnerability at local scale. The aim of this thesis is to provide quantitative and qualitative information to guide the process of adaptation and provide visualisations that will enhance local authority’s decision making to adapt to climate change, particularly sea-level rise. It focuses on the seven coastal districts within Kien Giang province in the western, micro-tidal section of the delta. A framework is adopted that integrates biophysical effects and socioeconomic stressors for the case study area and consists of three main components of vulnerability: exposure, sensitivity, and adaptive capacity. The analytical hierarchical process (AHP) method of multi-criteria decision making was integrated directly into a geographic information system (GIS) to derive a composite vulnerability index that indicated areas or hotspots most likely to be vulnerable to sea-level rise. The hierarchical structure comprised three components: exposure, sensitivity and adaptive capacity (level 1); and eight sub-components (level 2): seawater incursion, flood risk, shoreline change, population characteristics, landuse, as well as socioeconomic, infrastructure, and technological capability. The Digital Shoreline Analysis System (DSAS) tool was used to calculate rates of shoreline change along the Kien Giang coast over time in order to derive the shoreline change sub-component that contributed to the exposure component. Beyond this, a further 22 variables (level 3) and 24 sub-variables (level 4) related to vulnerability were also mapped. Based on the weights of variables derived from AHP pair-wise comparisons, a final map was generated to visualise areas reported into five categories of relative vulnerability; very low, low, moderate, high to very high vulnerability. Several regional patterns emerged. Relatively high exposure to seawater incursion, flood risk, and moderate loss of mangroves characterised the coastal fringe of each district. Those areas found to be most sensitive tended to have moderate population density, generally with a large rural population and high proportions of ethnic households with limited availability of agricultural land. Many aspects of adaptive capacity could only be represented at district scale, with the least adaptable areas consisting of large proportions of poor households, low income, and moderate densities of transport, irrigation, and drainage systems. Finally, most coastal districts were determined to be of moderate to relatively high vulnerability, with scattered hotspots along the Kien Giang coast, which coincided with settlement areas. The results obtained, enable identification and prioritisation of the areas, or hotspots most likely to be vulnerable, for which site-specific assessments might further assist the local authorities and communities in better coastal management and conservation. However, the limitations of data accessible at an entire district can influence the outcome. Social vulnerability remains a challenge because it is changing over time and space

An Evaluation of Surface Urban Heat Islands in Two Contrasting Cities

This thesis presents a comparative study on surface urban heat islands effects in Baghdad and Perth. The first part evaluates expansion of built-up areas and quantifies its effects on land surface temperature patterns. The second part examines the extent to which the urban thermal environment is influenced by spatial patterns of land use and land cover (LULC) categories. The final part investigates the thermophysical behaviour of various urban LULC categories using albedo and LST parameters

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing