Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

abstract: This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics

Discriminant Analysis with Spatial Weights for Urban Land Cover Classification

Classifying urban area images is challenging because of the heterogeneous nature of the urban landscape resulting in mixed pixels and classes with highly variable spectral ranges. Approaches using ancillary data, such as knowledge based or expert systems, have shown to improve the classification accuracy in urban areas. Appropriate ancillary data, however, may not always be available. The goal of this study is to compare the results of the discriminant analysis statistical technique with discriminant analysis with spatial weights to classify urban land cover. Discriminant analysis is a statistical technique used to predict group membership for a target based on the linear combination of independent variables. Strict per pixel statistical analysis however does not consider the spatial dependencies among neighbouring pixels. Our study shows that approaches using ancillary data continue to outperform strict spectral classifiers but that using a spatial weight improved the results. Furthermore, results show that when the discriminant analysis technique works well then the spatially weighted approach performs better. However, when the discriminant analysis performs poorly, those poor results are magnified in the spatially weighted approach in the same study area. The study shows that for dominant classes, adding spatial weights improves the classification accuracy.

Modelling and monitoring tools to evaluate the Urban Heat Island's contribution to the risk of indoor overheating

The growth of cit ies increases urban surface areas and anthropogenic heat generation, causing an Urban Heat Island (UHI) effect. In the UK , UHI effects may cause positive (winter) and negative (summer) health , comfort and energy consumption consequences . With the increasing focus on climate change - related heat exposure and consequent increased mortality risk, there is a need to better investigate the UHI during hot seasons. This paper reviews the current literature regarding UHI characterisation using monitoring, modelling, and remote sensing approaches, their limitations, and applications in building simulation and population heat exposure models . Ongoing and future research is briefly introduced in which downscaling techniques are proposed that provide higher temporal and spatial information to assess and locate heat - associated health risk in London

Spatial characteristics of the remotely-sensed surface urban heat island in Baton Rouge, LA: 1988-2003

Our understanding of urban effects on local climate remains unsatisfactory due to several difficulties: 1) the inherent complexity of the city-atmosphere system, 2) lack of a clear conceptual theoretical framework for inquiry, and 3) the high expense and enormous difficulties of acquiring a sufficient quantity of high-quality, high-resolution (both spatially and temporally) observations in cities. Using remotely-sensed data, this study analyzes urban heat islands (UHI) that are manifested through an elevation in the surface thermal emissions within urban regions known as surface heat islands (SHI). The study area for this research endeavor is Baton Rouge, Louisiana. Whereas the surface air temperature-derived UHI did not portray an accurate representation of distinct changes in surface temperature across the study area, the remotely-sensed surface temperature-derived SHI proved to reveal microscale differences that the surface air temperature-derived UHI was unable to depict. This study also provided verification that altering amounts of vegetation within a given land cover over time can reveal changes in surface temperature values, thus providing a means to reconstruct and predict future SHIs. This was achieved through regression equations predicting surface temperatures from known NDVI values. Finally, the moist static energy parameter was evaluated to test for a better indicator of the UHI over time throughout the study area. A decreasing temporal trend in MSE was identified throughout the study period (1988 - 2003) whereas no significant linear trend occurred in air temperature. This is supported by change detection rates generated from a comparison of the 1988 and 2003 LANDSAT data sets, as well as the range in 1988 and 2003 predicted surface temperatures (as a function of land cover)

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Determination of Urban Thermal Characteristics on an Urban/Rural Land Cover Gradient Using Remotely Sensed Data

The transformation from natural to impervious surfaces in an urbanization process and the urban heat island (UHI) phenomenon is known to significantly compromise urban environmental quality and has been linked to climate change and associated impacts. Whereas the existence of UHI is common knowledge, the implication of urban land use land cover (LULC) gradient on intra-urban thermal characteristics is often poorly understood. A recent proliferation of remotely sensed datasets offer great potential in understanding the relationship  between urban LULCs and their respective thermal characteristics, a critical basis for urban environmental  management and designing climate change mitigation measures. This study explores the potential of  multispectral remotely sensed dataset in determining the influence of rural/urban LULC gradient on urban  thermal characteristics. A rectangular eleven band Landsat 8 image subset was delineated from the central  business district to the rural periphery and classified into most dominant LULCs and a corresponding Landsat 8 thermal layer used to determine the LULCs thermal characteristics. Digitized point data was used to  determine differences in land surface temperature (LST) over gradient's LULC types. Results showed that  there was varied contribution of LULCs to the LST. As expected, the density of built up surfaces and LST  decreased towards the city’s periphery while a decline in vegetation density from the periphery led to an  increase in LST. These results provide valuable insights into the value of remotely sensed datasets in  understanding the implication of intra-urban LULC gradient on LST characteristics. Specifically, the study  demonstrates the value of remotely sensed data as aids to sustainable urban environmental planning

Spatio-temporal Analysis of Urban Built-up Land in the Hanoi Metropolitan Area (Vietnam) using Remotely Sensed Images

Rapid and unplanned urbanization leads to temperature rise, urban vegetation decrease, and built-up land increase, forming an urban heat island. It is, therefore, the change of built-up land plays an important role in surface urban heat island studies. This study aims to analyze spatio-temporal changes of urban built-up land in the Hanoi Metropolitan Area (HMA), Vietnam, using Landsat remotely sensed images acquired in 1996 and 2016. Landsat time-series images were first pre-processed preprocessed to account for sensor, solar, atmospheric, and topographic effects. Urban built-up land was then extracted based on an NDBI based continuous built-up index (BUc). Spatio-temporal changes of built-up land were finally analyzed by means of Geographic Information System (GIS). It was found that the urban built-up land area had increased from 4063.1 hectares in 1996 to 7163.2 hectares in 2016 which account for 13.3% and 23.4% of the total area, respectively. The built-up land area had increased by about 10.1% of the total area in 20 years. On average, 0.5% of the urban built-up area increases each year. The urban built-up land tends to expand to the west, southwest, and south of the HMA. These findings demonstrate the effectiveness of the proposed method for spatio-temporal analysis of built-up land in urban areas using remotely sensed images