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

Multisource Remote Sensing based Impervious Surface Mapping

Impervious surface (IS) not only serves as a key indicator of urbanization, but also affects the micro-ecosystem. Therefore, it is essential to monitor IS distribution timely and accurately. Remote sensing is an effective approach as it can provide straightforward and consistent information over large area with low cost. This thesis integrates multi-source remote sensing data to interpretate urban patterns and provide more reliable IS mapping results. Registration of optical daytime and nighttime lights (NTL) data is developed in the first contribution. An impervious surface based optical-to-NTL image registration algorithm with iterative blooming effect reduction (IS_iBER) algorithm is proposed. This coarse-to-fine procedure investigates the correlation between optical and NTL features. The iterative registration and blooming effect reduction method obtains precise matching results and reduce the spatial extension of NTL. Considering the spatial transitional nature of urban-rural fringes (URF) areas, the second study proposed approach for URF delineation, namely optical and nighttime lights (NTL) data based multi-scale URF (msON_URF).The landscape heterogeneity and development vitality derived from optical and NTL features are analyzed at a series of scales to illustrate the urban-URF-rural pattern. Results illustrate that msON_URF is effective and practical for not only concentric, but also polycentric urban patterns. The third study proposes a nighttime light adjusted impervious surface index (NAISI) to detect IS area. Parallel to baseline subtraction approaches, NAISI takes advantage of features, rather than spectral band information to map IS. NAISI makes the most of independence between NTL-ISS and pervious surface to address the high spectral similarity between IS and bare soil in optical image. An optical and NTL based spectral mixture analysis (ON_SMA) is proposed to achieve sub-pixel IS mapping result in the fourth study. It integrates characteristics of optical and NTL imagery to adaptively select local endmembers. Results illustrate the proposed method yields effective improvement and highlight the potential of NTL data in IS mapping. In the fifth study, GA-SVM IS mapping algorithm is investigated with introduction of the achieved urban-URF-rural spatial structure. The combination of optical, NTL and SAR imagery is discussed. GA is implemented for feature selection and parameter optimization in each urban scenario

Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions.Peer Reviewe

Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models

Cityscape, poverty and crime: a quantitative assessment using VHR imagery

[EN] The first part of this work reviews the potential applications of satellite remote sensing to regional science research in urban settings. The availability of satellite remote sensing data has increased significantly in the last two decades. The increasing spatial resolution of commercial satellite imagery has influenced the emergence of new research and applications of regional science in urban settlements because it is now possible to identify individual objects of the urban fabric. The most common applications found in the literature are the detection of urban deprivation hot spots, quality of life index assessment, urban growth analysis, house value estimation, urban population estimation, urban social vulnerability assessment, and the variability of intra-urban crime rates. The satellite remote sensing imagery used in these applications has medium, high or very high spatial resolution (Landsat MSS, Landsat TM and ETM+, SPOT, ASTER, IRS, Ikonos and QuickBird). Consistent relationships between socio-economic variables derived from censuses and field surveys and proxy variables of vegetation coverage measured from satellite remote sensing data have been found in several cities in the US. Different approaches and techniques have been applied successfully around the world, but local research is always needed to account for the unique elements of each place. Spectral mixture analysis, object-oriented classifications and image texture measures are some of the techniques of image processing that have been implemented with good results. This work contributes empirical evidence about the usefulness of remote sensing imagery to quantify the degree of poverty at the intra-urban scale. This concept is based on two premises: first, that the physical appearance of an urban settlement is a reflection of the society; and second, that the people who reside in urban areas with similar physical housing conditions have similar social and demo- graphic characteristics. We evaluate the potential of the image-derived urban fabric descriptors to explain a measure of poverty known as the Slum Index. We found that these variables explain up to 59% of the variability in the Slum Index. Similar approaches could be used to lower the cost of socioeconomic surveys by developing an econometric model from a sample and applying that model to the rest of the city and to perform intercensal or intersurvey estimates of intra-urban Slum Index maps. The last part of this work analyzes the relation between the urban layout and crime. The link between place and crime is at the base of social ecology theories of crime that focus in the relationship of the characteristics of geographical areas and crime rates. The broken windows theory states that visible cues of physical and social disorder in a neighborhood can lead to an increase in more serious crime. Based on the premise that a settlement's appearance is a reflection of the society, we ask whether a neighbor- hood's design has a quantifiable imprint when seen from space using urban fabric descriptors computed from VHR imagery. The percentage of impervious surfaces other than clay roofs, the fraction of clay roofs to impervious surfaces, two structure descriptors related to the homogeneity of the urban layout, and the uniformity texture descriptor were all statistically significant. Areas with higher homicide rates tended to have higher local variation and less general homogeneity; that is, the urban layouts were more crowded and cluttered, with small dwellings with different roofing materials located in close proximity to one another, and these regions often lacked other homogeneous surfaces such as open green spaces, wide roads, or large facilities. These results seem to be in agreement with the broken windows theory and CPTED in the sense that more heterogeneous and disordered urban layouts are associated with higher homicide rates.[ES] La primera parte aporta una revisión de las aplicaciones de la teledetección satelital en la investigación de ciencia regional en entornos urbanos. La disponibilidad de imágenes satelitales se ha incrementado significativamente en las dos últimas décadas, al tiempo que la resolución espacial ha venido aumentando, lo que ha influenciado el surgimiento de investigaciones y aplicaciones de ciencia regional en zonas urbanas. Las aplicaciones más comunes son la detección de hot spots de pobreza urbana, la evaluación de índices de calidad de vida, el análisis del crecimiento urbano, la estimación de valores de vivienda, la estimación de población urbana, la evaluación de la vulnerabilidad social y las variaciones intra-urbanas en tasas de crimen. Las imágenes satelitales usadas tienen resolución espacial media, alta o muy alta (Landsat MSS, Landsat TM y ETM+, SPOT, ASTER, IRS, Ikonos y Quickbird). Se han encontrado relaciones consistentes entre variables socio-económicas obtenidas de censos y encuestas y variables de la cobertura de vegetación en varias ciudades de Estados Unidos. Algunas de las técnicas que se han implementado y obtenido buenos resultados son el análisis de mezcla espectral, las clasificaciones orientadas a objetos y las medidas de textura de la imagen. Se aporta evidencia empírica acerca de la utilidad de las imágenes satelitales para cuantificar el grado de pobreza a escala intra-urbana. Se basa en dos premisas: primero, que la apariencia física de un asentamiento urbano es un reflejo de la sociedad que lo habita; y segundo, que la población de áreas urbanas con condiciones físicas de vivienda parecidas tiene características sociales y demográficas similares. Evaluamos el potencial de los descriptores del tejido urbano extraídos de la imagen para explicar una medida de pobreza conocida como el índice Slum. Encontramos que esas variables explican hasta un 59% de la variabilidad en el índice Slum. Aproximaciones similares a esta podrían usarse para disminuir el costo de encuestas socioeconómicas por medio del desarrollo de un modelo econométrico usando una muestra y luego aplicando el modelo al resto de la ciudad, y para elaborar estimaciones inter-censales o inter-encuestas de mapas intra-urbanos del índice Slum. La última parte analiza la relación entre el trazado urbano y crimen. El enlace entre el lugar y el crimen está en la base de las teorías socio-ecológicas de crimen que se enfocan en la relación de las características de las áreas geográficas y las tasas de crimen. La teoría de las ventanas rotas afirma que las evidencias visibles de desorden físico y social en un barrio pueden llevar al incremento de crímenes más serios. Con base en la premisa de que la apariencia de un asentamiento es un reflejo de la sociedad, nos preguntamos si el diseño del barrio tiene un impacto cuantificable cuando se observa desde el espacio usando descriptores del tejido urbano obtenidos de imágenes de muy alta resolución. El porcentaje de superficies impermeables diferentes a los techos de arcilla, la fracción de techos de arcilla sobre las superficies impermeables, dos variables de estructura relacionadas con la homogeneidad del trazado urbano y la variable de textura uniformidad resultaron estadísticamente significativas. Las áreas con tasas de homicidio más altas tienden a tener mayor variación local y menor homogeneidad general; esto es, los trazados urbanos son más desordenados y hacinados, con pequeñas viviendas que tienen materiales diferentes en sus techos localizadas muy cerca unas de otras, y estas áreas carecen a menudo de otras superficies homogéneas tales como espacios verdes abiertos, vías amplias y grandes construcciones industriales o institucionales. Estos resultados parecen estar en acuerdo con la teoría de las ventanas rotas y CPTED en el sentido de que los trazados urbanos más desordenados y heterogéneos están asociados con tasas de homicid[CA] La primera part aporta una revisió de les potencials aplicacions de la teledetecció espacial a la investigació en ciència regional en entorns urbans. La disponibilitat de dades de percepció remota des de satèl·lits s'ha incrementat significativament a les dues últimes dècades. La resolució espacial de les imatges de satèl·lit comercials també han anat augmentant i això, ha influït en l'aparició de investigacions i aplicacions a la ciència regional en assentaments urbans. Les aplicacions més comunes trobades a la literatura són la detecció de punts calents de pobresa urbana, l'avaluació dels índex de qualitat de vida, les anàlisis de creixement urbà, l'avaluació de la vulnerabilitat social i les variacions intraurbanes de les taxes de crims. Les imatges de satèl·lit emprades tenen resolució espacial mitjana, alta o molt alta (Landsat MSS, Landsat TM i ETM+, SPOT, ASTER, IRS, Ikonos y Quickbird). S'han torbat relacions consistents entre variables socioeconòmiques obtingudes de censos i enquestes i variables de la cobertura de vegetació en varies ciutats del Estats Units. Algunes de les tècniques que s'han implementat i han donat bons resultats són l'anàlisi de mescla espectral, les classificacions orientades a objecte i les mesures de textura de les imatges. Es aporta evidència empírica sobre la utilitat de les imatges de satèl·lit per quantificar el grau de pobresa a escala intraurbana. Es bassa en dues premisses: primer, que l'aparença física d'un assentament urbà n'és un reflex de la societat que l'habita; i segon, que les persones que resideixen en àrees urbanes amb condicions físiques de vivenda paregudes tenen també característiques socials i demogràfiques similars. Avaluem el potencial dels descriptors del teixit urbà extrets de la imatge per explicar una mesura de pobresa coneguda com index Slum. Trobem que aquestes variables expliquen fins un 59% de la variabilitat de l'índex Slum. Aproximacions semblants a aquesta es podrien emprar per a disminuir el cost de les enquestes socioeconòmiques mitjançant el desenvolupament d'un model economètric utilitzant una mostra i després aplicant el model a la resta de la ciutat, i per elaborar estimacions inter-censals o inter-enquestes de mapes intraurbans de l'índex Slum. La darrera part analitza la relació entre el traçat urbà i el crim. L'enllaç entre el lloc i el crim està a la base de les teories socio-ecològiques del crim que es centren en la relació de les característiques de les àrees geogràfiques i les taxes de crims. La teoria de les finestres trencades afirma que les evidències visibles de desordre físic i social d'un barri pot portar a l'augment de crims més greus. Basant-se en la premissa de que l'aparença d'un assentament n'és el reflex de la societat, ens hi preguntem si el disseny del barri té un impacte quantificable quan s'observa des de el espai, utilitzant descriptors del teixit urbà obtinguts de imatges de molt alta resolució. Han resultat estadísticament significatius el percentatge de superfícies impermeables diferents a les teulades de argila, la fracció de teulades d'argila sobre les superfícies impermeables, dues variables d'estructura relacionades amb la homogeneïtat del traçat urbà i la variable de textura de uniformitat. Les àrees amb taxes d'homicidi més altes tendeixen a presentar una major variació local i una menor homogeneïtat general; és a dir, el traçats urbans són més desordenats i amuntonats, amb petites vivendes que tenen materials diferents a les seues teulades localitzades molt prop unes d'altres, i aquestes àrees manquen sovint d'altres superfícies homogènies, com ara espais verds oberts, vies amplies i grans construccions industrials o institucionals. Aquests resultats pareixen estar-hi d'acord amb la teoria de les finestres trencades i CPTED en el sentit de que els traçats urbans més desordenats i heterogenis estan associats amb taxes d'homicides mPatiño Quinchía, JE. (2015). Cityscape, poverty and crime: a quantitative assessment using VHR imagery [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59453TESI

A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland)

The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for digital images were compared, namely Support Vector Machines (SVM), Maximum Likelihood (ML) and Random Trees (RT) classifiers. The study area is one of the most prestigious and the largest housing estates in Warsaw (Poland), the Fort Bema housing complex, which is also an exemplary model for hydrological solutions. The study was prepared on the Geographic Information System platform (GIS) using aerial optical images, orthorectified and thus provided with a suitable coordinate system. The use of these data is therefore supported by the accuracy of the resulting infrared channel product with a pixel size of 0.25 m, making the results much more accurate compared to satellite imagery. The results of the SVM, ML and RT classifiers were compared using the confusion matrix, accuracy (Root Mean Square Error /RMSE/) and kappa index. This showed that the three algorithms were able to successfully discriminate between targets. Overall, the three classifiers had errors, but specifically for impervious surfaces, the highest accuracy was achieved with the SVM classifier (the highest percentage of overall accuracy), followed by ML and RT with 91.51%, 91.35% and 84.52% of the results, respectively. A comparison of the visual results and the confusion matrix shows that although visually the RT method appears to be the most detailed classification into pervious and impervious surfaces, the results were not always correct, e.g., water/shadow was detected as an impervious surface. The NDVI index was also mapped for the same spatial study area and its application in the evaluation of pervious surfaces was explained. The results obtained with the GIS platform, presented in this paper, provide a better understanding of how these advanced classifiers work, which in turn can provide insightful guidance for their selection and combination in real-world applications. The paper also provides an overview of the main works/studies dealing with impervious surface mapping, with different methods for their assessment (including the use of conventional remote sensing, NDVI, multisensory and cross-source data, ‘social sensing’ and classification methods such as SVM, ML and RT), as well as an overview of the research results

Remote sensing of night lights: a review and an outlook for the future

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordRemote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind day-time sensors, we demonstrate that the field is in a stage of rapid development. The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use.European Union Horizon 2020Helmholtz AssociationNatural Environment Research Council (NERC)Chinese Academy of ScienceLeibniz AssociationIGB Leibniz Institut