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.     

Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty

The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes.     

Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

光谱指数趋势面的城市地表温度降尺度转换

针对城市及周边区域建造区和自然地表交织分布的特点,探讨了利用归一化植被指数(NDVI)和归一化建造指数(NDBI)构造趋势面的地表温度(LST)降尺度方法,以北京市市区及周边较平坦区域为例实现了LST自960 m向120 m的降尺度转换。分析了LST空间分布特征及NDVI、NDBI对地物的指示性特征;以北京市四至六环为界分析NDVI、NDBI趋势面对地表温度的拟合程度及各自的适用区域;在120 m、240 m、480 m和960 m 4个尺度上评价了NDVI、NDBI和NDVI+NDBI趋势面对LST的拟合程度和趋势面转换函数的尺度效应;对NDVI、NDBI和NDVI NDBI等3种方法的降尺度结果分覆盖类型、分区域对比评价。实验结果表明结合两种光谱指数的NDVI NDBI方法降尺度转换精度有所改善,改善程度取决于地表覆盖类型组合。     

Bi-temporal characterization of land surface temperature in relation to impervious surface area,NDVI and NDBI,using a sub-pixel image analysis

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.     

Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.     

Assessing the potential of integrated Landsat 8 thermal bands,with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge.     

Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data

ABSTRACT

We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30?m) resolution down to 2–4?m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83?K prior to and 0.76–1.21?K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.     

Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi,India

The knowledge of the surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change and human-environment interactions. The study analyses land surface temperature (LST) estimation using temporal ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) datasets (day time and night time) over National Capital Territory Delhi using the surface emissivity information at pixel level. The spatial variations of LST over different land use/land cover (LU/LC) at day time and night time were analysed and relationship between the spatial distribution of LU/LC and vegetation density with LST was developed. Minimum noise fraction (MNF) was used for LU/LC classification which gave better accuracy than classification with original bands. The satellite derived emissivity values were found to be in good agreement with literature and field measured values. It was observed that fallow land, waste land/bare soil, commercial/industrial and high dense built-up area have high surface temperature values during day time, compared to those over water bodies, agricultural cropland, and dense vegetation. During night time high surface temperature values are found over high dense built-up, water bodies, commercial/industrial and low dense built-up than over fallow land, dense vegetation and agricultural cropland. It was found that there is a strong negative correlation between surface temperature and NDVI over dense vegetation, sparse vegetation and low dense built-up area while with fraction vegetation cover, it indicates a moderate negative correlation. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban area. The analysis also indicates that the relationship between the spatial distribution of LU/LC and vegetation density is closely related to the development of urban heat islands (UHI).     

A Bayesian characterization of urban land use configurations from VHR remote sensing images

The composition and arrangement of spatial entities, i.e., land cover objects, play a key role in distinguishing land use types from very high resolution (VHR) remote sensing images, in particular in urban environments. This paper presents a new method to characterize the spatial arrangement for urban land use extraction using VHR images. We derive an adjacency unit matrix to represent the spatial arrangement of land cover objects obtained from a VHR image, and use a graph convolutional network to quantify the spatial arrangement by extracting hidden features from adjacency unit matrices. The distribution of the spatial arrangement variables, i.e., hidden features, and the spatial composition variables, i.e., widely used land use indicators, are then estimated. We use a Bayesian method to integrate the variables of spatial arrangement and composition for urban land use extraction. Experiments were conducted using three VHR images acquired in two urban areas: a Pleiades image in Wuhan in 2013, a Superview image in Wuhan in 2019, and a GeoEye image in Oklahoma City in 2012. Our results show that the proposed method provides an effective means to characterize the spatial arrangement of land cover objects, and produces urban land use extractions with overall accuracies (i.e., 86% and 93%) higher than existing methods (i.e., 83% and 88%) that use spatial arrangement information based on building types on the Pleiades and GeoEye datasets. Moreover, it is unnecessary to further categorize the dominant land cover type into finer types for the characterization of spatial arrangement. We conclude that the proposed method has a high potential for the characterization of urban structure using different VHR images, and for the extraction of urban land use in different urban areas.     

Comparison and relative quality assessment of the GLC2000, GLOBCOVER,MODIS and ECOCLIMAP land cover data sets at the African continental scale

Land cover dynamics at the African continental scale is of great importance for global change studies. Actually, four satellite-derived land cover maps of Africa now available, e.g. ECOCLIMAP, GLC2000, MODIS and GLOBCOVER, are based on images acquired in the 2000s. This study aims at stressing the compliances and the discrepancies between these four land cover classifications systems. Each of them used different mapping initiatives and relies on different mapping standards, which supports the present investigation. In order to do a relative comparison of the four maps, a preamble was to reconcile their thematic legends into more aggregated categories after a projection into the same spatial resolution. Results show that the agreement between the four land cover products is between 56 and 69%. While all these land cover datasets show a reasonable agreement in terms of surface types and spatial distribution patterns, mapping of heterogeneous landscapes in the four products is not very successful. Land cover products based on remote sensing imagery can indeed significantly be improved by using smarter algorithms, better timing of image acquisition, improved class definitions. Either will help to improve the accuracy of future land cover maps at the African continental scale. Data producers may use the areas of spatial agreement for training area selection while users might need to verify the information in the areas of disagreement using additional data sources.     

The use of motor-glider in topoclimatic studies

This paper reports on the advantages and disadvantages of motor-glider use in studying topoclimates. Despite the widespread use of images taken from low-altitude flying platforms (planes, helicopters, UAVs), the use of a motor-glider for imagery collection has not been reported in environmental studies. In presented study, the low-altitude remote sensing techniques were used to increase the spatial resolution of thermal maps derived from Landsat ETM+ thermal bands. Thermal images from motor-glider were taken by a thermovision camera. At the local scale, land surface temperature (LST) is one of the factors influencing topoclimatic diversity hence, by analysing LST distribution one can determine topoclimatic variability. Topoclimate has been the subject of previous studies, however, they have not used thermal remote sensing in the research process but instead relied on ground measurement network. The presented research contributes to better understanding of the thermal environment of the Earth by employing an innovative data collection method suitable for relatively large areas under specific weather conditions. The data collection with motor glider offers good spatial resolution of less than 1 m and facilitates the compilation of good quality LST maps. The paper discusses the influence of spatial resolution on LST variability and demonstrates gain in information granularity resulting from sub-meter resolution of collected data.     

Use of Landsat Thematic Mapper for urban land use/land cover mapping

Visual interpretation of Landsat Thematic Mapper data coupled with ground checking has been used to extract information for urban areas. The emphasis has been given on development of land use/land cover scheme and image interpretation keys for interpretation and delineation purposes using satellite remote sensing data. Lucknow city and its surroundings have been studied to evaluate the usefulness and potentiality of satellite data particularly Landsat Thematic Mapper for urban area studies. This study has demonstrated that remote sensing can provide a valuable tool for urban data acquisition.     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis,United States

This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying urban landscape changes in Indianapolis, Indiana, the United States, and for examining the environmental impact of such changes on land surface temperatures (LST). Three dates of Landsat TM/ETM+ images, acquired in 1991, 1995, and 2000, respectively, were utilized to document the historical morphological changes in impervious surface and vegetation coverage and to analyze the relationship between these changes and those occurred in LST. Three fraction endmembers, i.e., impervious surface, green vegetation, and shade, were derived with an unconstrained least-squares solution. A hybrid classification procedure, which combined maximum-likelihood and decision-tree algorithms, was developed to classify the fraction images into land use and land cover classes. Correlation analyses were conducted to investigate the changing relationships of LST with impervious surface and vegetation coverage. Results indicate that multi-temporal fraction images were effective for quantifying the dynamics of urban morphology and for deriving a reliable measurement of environmental variables such as vegetation abundance and impervious surface coverage. Urbanization created an evolved inverse relationship between impervious and vegetation coverage, and brought about new LST patterns because of LST's correlations with both impervious and vegetation coverage. Further researches should be directed to refine spectral mixture modeling by stratification, and by the use of multiple endmembers and hyperspectral imagery.     

The application of day and night time ASTER satellite imagery for geothermal and mineral mapping in East Africa

There is considerable interest in optimizing geothermal exploration techniques via the mapping of alteration and evaporate mineralisation, as well as of thermal emissions associated with geothermally active areas on the Earth’s surface. Optical and thermal satellite sensor technologies, improvements in processing algorithms and the means for large scale (e.g. 1:250,000) spatial data distribution are required for detecting both these attributes. The extensive visible, -near, -shortwave and thermal infrared (VNIR-SWIR-TIR) data archive acquired by the multi-spectral Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) provides a rich source of geoscience related imagery for geothermal exploration. Examples of generating large scale mosaicked ASTER imagery to provide province to continental mineral mapping have been undertaken in areas including such as Australia, western USA, Namibia and Zagros Mountains Iran. In addition, ASTER’s thermal infrared imagery also provides night time land surface temperature (LST) estimates relevant for detecting possible geothermal related anomalies.This study outlines existing methods for the application of ASTER data for geothermal exploration in East Africa. The study area encompasses a section of the East African Rift System across the Tanzanian and Kenyan border. The area includes rugged volcanic terrain which has had geological mapping of limited coverage at detailed scales, from various heritages and mapping agencies. This study summarizes the technology, the processing methodology and initial results in applying ASTER imagery for such compositional and thermal anomaly mapping related to geothermal activity. Fields observations have been used from the geothermal springs of Lake Natron, Tanzania, and compared with ASTER derived spectral composition and land surface temperature results. Published geothermal fields within the Kenyan portion of the study area have also been incorporated into this study.     

Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan,China

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.     

基于TM影像的城市建筑用地信息提取方法研究

本文选用金华市Landsat TM影像为研究的数据源,在归一化裸露指数基础上,利用归一化植被指数提取出非植被信息,通过图像二值化、叠加分析以及掩膜处理去除了低密度植被覆盖区域的噪音信息,自动提取了金华城市建筑用地信息。研究结果表明,归一化裸露指数和归一化植被指数相结合的方法弥补了单一利用归一化裸露指数来提取城市建筑用地信息的不足,提高了提取精度,而且结果客观可信,是一种不经人为干预的、快速有效的提取城市建筑用地方法。     

atmospheric corrections to thermal IR data for estimation of land surface temperature

Satellite data provides important inputs far estimating regional surface emisslviiy and surface temperature. The methodology for estimation of emissivity over heterogeneous areas is based on the calculation of fraction vegetation cover per pixel taking NDVI, reflectances of pure pixels as input. The surface temperature is calculated using a sptit-window equation, which depends on atmospheric water vapour, viewing angle and channel surface emissivities. In the present study model coefficients for atmospheric corrections to NOAA AVHRR thermal data Fqr tropical atmospheres have been derived with a view to operationally use the methodolpgy for generating land surface temperature information from satellite data. The results of the study show that the estimated temperature values are comparable with the ctimatological values over the region Suggesting the possible use of the methodology.