Assessing environmental impacts of urban growth using remote sensing

ABSTRACT

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.     

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R² value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.     

High spatial resolution image object classification for terrestrial oil spill contamination mapping in West Siberia

This work is a part of the OSCaR pilot study (Oil Spill Contamination mapping in Russia). A synergetic concept for an object based and multi temporal mapping and classification system for terrestrial oil spill pollution using a test area in West Siberia is presented. An object oriented image classification system is created to map contaminated soils, vegetation and changes in the oil exploration well infrastructure in high resolution data. Due to the limited spectral resolution of Quickbird data context information and image object structure are used as additional features building a structural object knowledge base for the area. The distance of potentially polluted areas to industrial land use and infrastructure objects is utilized to classify crude oil contaminated surfaces. Additionally the potential of Landsat data for dating of oil spill events using change indicators is tested with multi temporal Landsat data from 1987, 1995 and 2001. OSCaR defined three sub-projects: (1) high resolution mapping of crude oil contaminated surfaces, (2) mapping of industrial infrastructure change, (3) dating of oil spill events using multi temporal Landsat data. Validation of the contamination mapping results has been done with field data from Russian experts provided by the Yugra State University in Khanty-Mansiyskiy. The developed image object structure classification system has shown good results for the severely polluted areas with good overall classification accuracy. However it has also revealed the need for direct mapping of hydrocarbon substances. Oil spill event dating with Landsat data was very much limited by the low spatial resolution of Landsat TM 5 data, small scale character of oil spilled surfaces and limited information about oil spill dates.     

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales. However, the rapid growth of urban areas exerts effects on climate, biodiversity, hydrology, and natural ecosystems worldwide. Therefore, regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local, regional, and potentially global scales. This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data, including Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013, population density of 2012, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights of 2012, and constructed Impervious Surface Area (ISA) data of 2010. The Eurasian urban map was created using the threshold method for these data, combined with references of fine resolution Landsat and Google Earth imagery. The resultant map was compared with nine global urban maps and was validated using random sampling method. Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%. This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.     

Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.     

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (?0.8%–1.6% using the 2002 segment model to ?5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.     

Evaluation of multisource data for glacier terrain mapping: a neural net approach

Spectrally similar nature of land covers in a glacierized terrain hampers their automated mapping from multispectral satellite data, which may be overcome by using multisource data. In the present study, an artificial neural network (ANN)-based information extraction approach was applied for mapping the Kolahoi glacier and adjoining areas, using Landsat TM (Thematic Mapper) data and several ancillary layers such as image transformations and topographic attributes. Results reveal that ANN (highest overall accuracy (OA): 83.74%) outperforms maximum likelihood classifier (highest OA: 66.90%) and the incorporation of ancillary data into the classification process significantly enhances the mapping accuracy (>9%), particularly the addition of Near Infrared Red/Short Wave Infrared (NIR/SWIR) data to the spectral data. A nine-band combination dataset (spectral data, slope, Red/NIR and decorrelation stretch) was found to be the best multisource dataset. Results of the Z-tests (at 95% confidence level) also corroborate and statistically validate the above findings.     

Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people's interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.     

Observations of urban and suburban environments with global satellite scatterometer data

A global and consistent characterization of land use and land change in urban and suburban environments is crucial for many fundamental social and natural science studies and applications. Presented here is a dense sampling method (DSM) that uses satellite scatterometer data to delineate urban and intraurban areas at a posting scale of about 1 km. DSM results are analyzed together with information on population and housing censuses, with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, and with Defense Meteorological Satellite Program (DMSP) night-light data. The analyses include Dallas-Fort Worth and Phoenix in the United States, Bogotá in Colombia, Dhaka in Bangladesh, Guangzhou in China, and Quito in Ecuador. Results show that scatterometer signatures correspond to buildings and infrastructures in urban and suburban environments. City extents detected by scatterometer data are significantly smaller than city light extents, but not all urban areas are detectable by the current SeaWinds scatterometer on the QuikSCAT satellite. Core commercial and industrial areas with high buildings and large factories are identified as high-backscatter centers. Data from DSM backscatter and DMSP nighttime lights have a good correlation with population density. However, the correlation relations from the two satellite datasets are different for different cities indicating that they contain complementary information. Together with night-light and census data, DSM and satellite scatterometer data provide new observations to study global urban and suburban environments and their changes. Furthermore, the capability of DSM to identify hydrological channels on the Greenland ice sheet and ecological biomes in central Africa demonstrates that DSM can be used to observe persistent structures in natural environments at a km scale, providing contemporaneous data to study human impacts beyond urban and suburban areas.     

Mapping urban growth of the capital city of Honduras from Landsat data using the impervious surface fraction algorithm

This study developed an impervious surface fraction algorithm (ISFA) for automatic mapping of urban areas from Landsat data. We processed the data for 2001 and 2014 to trace the urbanization of Tegucigalpa, the capital city of Honduras, using a four-step procedure: (1) data pre-processing to perform image reflectance normalization, (2) quantification of impervious surface area (ISA) using ISFA, (3) accuracy assessment of mapping results and (4) change analysis of urban growth. The mapping results compared with the ground reference data confirmed the validity of ISFA for automatic delineation of ISA in the study region. The overall accuracy and Kappa coefficient achieved for 2001 were 92.8% and 0.86, while the values for 2014 were 91.8% and 0.84, respectively. The results of change detection between the classification maps indicated that ISA increased approximately 1956.7 ha from 2001 to 2014, mainly attributing to the increase of the city’s population.     

新疆巴里坤ETM数据遥感地质填图的探索

 对新疆巴里坤县八墙子一带ETM数据进行主成分分析、彩色，空间变换及假彩色合成等图像处理，同时，依据野外踏勘、实测 剖面等资料不断调整图像处理方法； 以最大程度突出已知岩性单元间影像区别为目的，通过全色谱段融合方式将图像比例尺提高到1 ︰5万； 在ArcView等平台上进行综合分析、解译和填图，并经过实地查验进一步修改填图方法与结果，最后编辑形成地质图。     

European forest cover mapping with high resolution satellite data: The Carpathians case study

The aim of the study was to elaborate a methodology for forest mapping based on high resolution satellite data, relevant for reporting on forest cover and spatial pattern changes in Europe. The Carpathians were selected as a case study area and mapped using 24 Landsat scenes, processed independently with a supervised approach combining image segmentation, knowledge-based rules to extract a training set and the maximum likelihood decision rule. Validation was done with available very high resolution imagery. Overall accuracies per scene ranged from 93 to 96%. The labelling disagreement in overlapping areas of adjacent scenes was 6.8% on average.     

Improving Large Area Population Mapping Using Geotweet Densities

Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo‐located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo‐located tweets in 1x1 km grid cells over a 2‐month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests‐based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media‐derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.     

Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover

Abstract

A methodology is presented for estimating percent coverage of impervious surface (IS) and forest cover (FC) within Landsat thematic mapper (TM) pixels of urban areas. High-resolution multi-spectral images from Quickbird (QB) play a key role in the sub-pixel mapping process by providing information on the spatial distributions of ISs and FCs at 2.4 m ground sampling intervals. Thematic classifications, also derived from the Landsat imagery, have then been employed to define relationships between 30 m Landsat-derived greenness values and percent IS and FC. By also utilizing land cover/land use classification derived from Landsat and defining unique relationships for urban sub-classes (i.e. residential, commercial/industrial, open land), confusion between impervious and fallow agricultural lands has been overcome. Test results are presented for Ottawa-Gatineau, an urban area that encompasses many aspects typical of the North American urban landscape. Multiple QB scenes have been acquired for this urban centre, thereby allowing us to undertake an in-depth study of the error budgets associated with the fractional inference process.     

The urban greenness score: A satellite-based metric for multi-decadal characterization of urban land dynamics

Canada’s urban areas have experienced extensive growth over the past quarter century; however, there has been no consistent, spatially explicit approach for quantifying the loss and gain of greenness in cities nationally. Herein, we developed a novel urban greenness score metric using greenness fractions from a multi-decadal time series (1984–2016) of spectrally unmixed annual Landsat satellite image composites to characterize final year (2016) greenness and its overall change for 18 major Canadian urban areas, summarized by census dissemination area (DA). The applied validation procedure confirmed correlation coefficients (ρ) ranging from 0.67 – 0.85 between reference and estimated greenness fractions, indicating that spectral unmixing is an appropriate method for extracting urban greenness from a time series of medium spatial resolution satellite imagery. Most DAs across Canada sustained a moderate (∼20 % – 40 %) or low (≲ 20 %) level of greenness between 1984 and 2016, but overall there was a decreasing trend in greenness. Eastern urban areas maintained the most greenness over time, while urban areas in the Prairies had the greatest increase in greenness. Densely populated urban areas experienced the greatest loss in greenness (16 % of DAs); whereas, urban areas with a moderately-low density experienced the greatest increase (14 % of DAs). In agreement with previous studies, we found that greenness was negatively associated with urban infilling, with lower greenness levels typically found in urban cores, and greenness loss most often found in the urban periphery in conjunction with urban expansion. Methods presented in this analysis take advantage of the open and longstanding Landsat archive, as well as multiple spatial scales, including sub-pixel unmixing techniques, pixel level greenness faction data summarized for management units, and analysis conducted nationally. The developed urban greenness score provides a comprehensive framework to understand current urban greenness and relate it to its recent past, which supports long-term strategic planning, and can be transferred to other regions across spatial and temporal scales.     

珞珈一号夜间灯光影像在建设用地提取中的应用:以武汉市为例

城市建设用地能够反映城市建设发展在地域空间上的分布形态,是规划主管部门监测城市建设和扩张的关键指标.2018-06-02发射的珞珈一号卫星可提供130 m分辨率的夜间灯光数据,在城市建设用地的提取方面具有较大潜力.首先整合珞珈一号夜间灯光影像与Landsat 8多光谱影像以及网络地图兴趣点数据;然后分别采用人类居住合成...     

城市扩展强度及其地表热特性遥感定量分析

提出定量确定城市扩展范围及其发展强度的方法——地图密度指数。以中部城市长沙为例,利用Landsat TM/ETM+数据定量评价城市扩张及其热环境特征的变化。首先,集成遥感光谱指数提取地表非渗透表面,然后利用移动窗口算法获得地图密度指数,再根据设定的阈值获得密度指数等级图,依此密度指数等级图识别城市扩展范围及其发展强度。再结合地表温度反演的数据,分析城市格局及其变化与地表热特性变化的定量关系。结果表明,自20世纪90年代以来,长沙市城市区域及其发展密度显著增加,城市发展的密度差异与地表温度相一致。地图密度指数能较好刻画城市扩展范围及其发展强度,并与地表温度空间分布存在较好的对应关系。     

Mapping impervious surface distribution in China using multi-source remotely sensed data

ABSTRACT

Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling.     

Monitoring Urban Landscape Dynamics Over Delhi (India) Using Remote Sensing (1998–2011) Inputs

Urbanization is increasingly becoming a widespread phenomenon at all scales of development around the globe. Be it developing or developed nations, all are witnessing urbanization at very high pace. In order to study its impacts, various methodologies and techniques are being implemented to measure growth of urban extents over spatial and temporal domains. But urbanization being a very dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. This paper aims at quantifying urban expansion in Delhi, the capital city of India. The process has been studied using urban land cover pattern derived from Landsat TM/ETM satellite data for two decades (1998–2011). These maps show that built-up increased by 417 ha in first time period (1998–2003) and 6,633 ha during next period (2003–2011) of study. For quantification of metrics for urban expansion, the Urban Landscape Analysis Tool (ULAT) was employed. Land cover mapping was done with accuracy of 92.67 %, 93.3 % and 96 % respectively for years 1998, 2003 and 2011. Three major land covers classes mapped are; (i) built-up, (ii) water and (iii) other or non-built-up. The maps were then utilized to extract degree of urbanization based on spatial density of built-up area consisting of seven classes, (i) Urban built-up, (ii) Suburban built-up,(iii) Rural built-up, (iv) Urbanized open land, (v) Captured open land, (vi) Rural open land and (vii) Water. These classes were demarcated based on the urbanness of cells. Similarly urban footprint maps were generated. The two time maps were compared to qualitatively and quantitatively capture the dynamics of urban expansion in the city. Along with urbanized area and urban footprint maps, the new development areas during the study time periods were also identified. The new development areas consisted of three major categories of developments, (i) infill, (ii) extension and (iii) leapfrog.