Janet E. Nichol

Remote Sensing of Urban Heat Islands by Day and Night

A night-time thermal image from the ASTER satellite sensor, of the western New territories of Hong Kong is compared with a daytime Landsat Enhanced Thematic Mapper Plus (ETM� ) thermal image obtained nineteen days earlier. Densely built high rise areas which appear cool on daytime images are conversely, relatively warm on nighttime images, though the temperature differences are not well developed at night. Lower temperature gradients between different land cover types observed on the night time image result in meso-scale, rather than micro-scale climatic patterns being dominant, suggestive of processes operating in the Urban Boundary Layer (UBL), as opposed to the Urban Canopy Layer (UCL) which is dominant in the daytime. Thus, at night, proximity to extensive cool surfaces such as forested mountain slopes appears to be influential in maintaining cooler building temperatures. The relevance of satellite-derived surface temperatures for studies of urban microclimate is supported by field data of surface and air temperatures collected in the study area. Comparison of the ASTER Kinetic Temperature standard product with a thermal image processed using locally derived emissivity and atmospheric data indicated higher accuracy for the latter.

An Emissivity Modulation Method for Spatial Enhancement of Thermal Satellite Images in Urban Heat Island Analysis

This study examines and validates a technique for spatial enhancement of thermal satellite images for urban heat island analysis, using a nighttime ASTER satellite image. The technique, termed Emissivity Modulation, enhances the spatial resolution while simultaneously correcting the image derived temperatures for emissivity differences of earth surface materials. A classified image derived from a higher resolution visible wavelength sensor is combined with a lower resolution thermal image in the emissivity correction equation in a procedure derived from the Stephan Bolzmann law. This has the effect of simultaneously correcting the image-derived “Brightness Temperature” (Tb) to the true Kinetic Temperature (Ts), while enhancing the spatial resolution of the thermal data. Although the method has been used for studies of the urban heat island, it has not been validated by comparison with “in situ” derived surface or air temperatures, and researchers may be discouraged from its use due to the fact that it creates sharp boundaries in the image. The emissivity modulated image with 10 m pixel size was found to be highly correlated with 18 in situ surface and air temperature measurements and a low Mean Absolute Difference of 1 K was observed between image and in situ surface temperatures. Lower accuracies were obtained for the Ts and Tb images at 90 m resolution. The study demonstrates that the emissivity modulation method can increase accuracy in the computation of kinetic temperature, improve the relationship between image values and air temperature, and enable the observation of microscale temperature patterns.

Urban vegetation monitoring in Hong Kong using high resolution multispectral images

Very high resolution (VHR) satellite remote sensing systems are now capable of providing imagery with similar spatial detail to aerial photography, but with superior spectral information. This research investigates the hypothesis that it should be possible to use multispectral IKONOS images to quantify urban vegetation, obtaining similar accuracy to that achieved from false colour aerial photographs. Two parameters, vegetation cover and vegetation density are used to represent biomass in the study area (Kowloon, Hong Kong), for which data is collected for 41 field quadrats. Regression equations relating the field measurements of vegetation density to image wavebands obtained similar high correlations for both image types and lower but significant correlations for vegetation cover. Vegetation density is a quantifiable measure of vegetation in multiple layers above ground, representing the total amount of biomass and is thus well able to indicate the diverse structural types of vegetation found in urban areas. Furthermore it can be accurately measured using the IKONOS green/red ratio (Chlorophyll Index). The superiority of the latter to the more commonly used Normalized Difference Vegetation Index (NDVI), is attributed to the sub‐optimal timing of the imagery during the dry season, and its greater sensitivity to multiple layering within the vegetation canopy. A time and cost comparison between the two image types suggests that the use of IKONOS images is much more cost effective than aerial photographs for urban vegetation monitoring.

Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment

Precise atmospheric correction is important for applications where small differences in surface reflectance (SR) are significant, such as biomass estimation, crop phenology, and retrieval of water quality parameters. It also enables direct comparison between different image dates and different sensors. As a precursor to monitoring different parameters of water quality around the coastline of Hong Kong using medium-resolution sensors Landsat TM/ETM, and HJ-1A/B, this study evaluated the performance of five atmospheric correction methods. The estimated SR of the first four reflective bands of Landsat 7 ETM+ and of the identical bands of the HJ-1A/B satellites was compared with in situ multispectral radiometer (MSR) SR measurements over sand, artificial turf, grass, and water surfaces for the five atmospheric correction methods – second simulation of the satellite signal in the solar spectrum (6S), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), atmospheric correction (ATCOR), dark object subtraction (DOS), and the empirical line method (ELM). Among the five methods, 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in-situ-measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites as compared to the DOS and equalled the good performance of 6S over the high reflective sand surfaces. The study also evaluated the new standard Landsat SR product Landsat ecosystem disturbance adaptive processing system (LEDAPS) using the in situ measured SR data for the three land surface types – sand, artificial turf, and grass. For the highly and moderately reflecting bright sand and artificial turf, LEDAPS performed poorly, while for the darker grass site it performed better, although still inferior to 6S and ELM methods. This is probably due to the variable aerosol types and atmospheric conditions of Hong Kong, as LEDAPS was mainly compiled with reference to larger continental landmass areas.

Empirical correction of low Sun angle images in steeply sloping terrain: a slope‐matching technique

A technique based on slope matching is presented, which corrects the topographic effect on images with very low illumination due to very steep terrain, very low Sun angle, or both. The technique is a modification of Civcos (1989) two‐stage normalization correction. The modified correction arose from the failure of existing correction methods to eliminate dark shadows on wintertime IKONOS images, in the steeply sloping terrain of our study area. The shadows, which were particularly severe in the near‐infrared (NIR) band preclude accurate habitat classification due to the importance of this band on IKONOS multispectral images: the remaining three bands being highly correlated. Since the objective of topographic correction is to equalize the radiance between shady and sunny slopes, the slope‐matching technique normalizes the radiance values to the mean of the sunny slope, rather than to the overall mean, as in the two‐stage normalization correction of Civco (1989). This enables a more appropriate correction factor to be computed, suitable for the wide range of values encountered for the incident angle of illumination. The slope‐matching correction was able to reduce intra‐class variance significantly more than the two‐stage normalization correction, as well as increase classification accuracy by 7%.

Evaluation of MODIS aerosol retrieval algorithms over the Beijing-Tianjin-Hebei region during low to very high pollution events

This study evaluates the performance of different MODerate resolution Imaging Spectroradiometer (MODIS) aerosol algorithms during fine particle pollution events over the Beijing-Tianjin-Hebei region using Aerosol Robotic Network aerosol optical depth (AOD). These algorithms include the Deep Blue (DB) Collection 5.1 (C5) and Collection 6 (C6) algorithms at 10 km resolution, the Dark Target (DT) C5 and C6 algorithms at 10 km, the DT C6 algorithm at 3 km, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m, 3 km, and 10 km resolutions. The DB C6 retrievals have 34–39% less uncertainties, 2–3 times smaller root-mean-square error (RMSE), and 3–4 times smaller mean absolute error (MAE) than DB C5 retrievals. The DT C6 has 4–8% lower bias, 4–12% less overestimation, and smaller RMSE and MAE errors than DT C5. Due to underestimation of surface reflectance and the use of inappropriate aerosol schemes, 87–89% of the collocations of the DT C6 at 3 km fall above the expected error (EE), with overestimation of 64–79% which is 15–27% higher than that for the DT C6 at 10 km. The results suggest that the DT C6 at 3 km resolution is less reliable than that at 10 km. The SARA AOD has small RMSE and MAE errors with 90–96% of the collocations falling within the EE. Overall, the SARA showed 15–16% less uncertainty than the DB C6 (10 km), 69–72% less than the DT C6 (10 km), and 79–83% less than the DT C6 (3 km) retrievals.

Remote sensing of urban vegetation life form by spectral mixture analysis of high-resolution IKONOS satellite images

This paper evaluates the techniques of linear spectral unmixing (LSU), comparing high‐ and medium‐resolution images for their ability to obtain separate estimates of tree and grassy surfaces in urban areas. It demonstrates that, unlike on medium‐resolution images, tree and grassy surfaces each constitute distinct endmembers on high‐resolution images. This is because at high resolution, shadows in the urban scene approximate pixel size and therefore can be separately masked, thus avoiding the spectral similarities between shadow and tree canopies on the one hand, and low albedo surfaces on the other. In this study, the ability to mask shadow on IKONOS VHR images removes these spectral overlaps. Spatial autocorrelation, applied to find the characteristic scale lengths of vegetated patches in the study area, demonstrated that at the 4 m spatial resolution of IKONOS almost two thirds of pixels would be mixed, and at the 20 m resolution of SPOT all pixels would be mixed. Accuracies of the tree and grass fractions were found to be very high in the case of IKONOS, with 87% confidence that both the grass and tree fractions within each pixel were within 10% of the actual amount. The somewhat lower accuracy for SPOT supports previous studies based on medium‐resolution sensors, which have noted that trees do not constitute an endmember.

Validation of MODIS 3 km Resolution Aerosol Optical Depth Retrievals Over Asia

This study evaluates the new Aqua MODIS Dark Target (DT) Collection 6 (C6) Aerosol Optical Depth (AOD) (MYD04_3K) retrieval algorithm at 3 km resolution over Asian countries that have recently experienced severe and increasing air pollution. Retrievals showed generally low accuracy compared with the AErosol RObotic NETwork (AERONET), with only 55% of retrievals within the expected error (EE). The uncertainty appears mainly due to systematic overestimation at both low and high AOD levels. This is attributed to under-prediction of surface reflectance, similar to, but more severe than, the C6 DT product at 10-km resolution. This is because MYD04_3K observes more noise in the surface reflectance computations, due to retention of some bright pixels in the retrieval window which would be discarded at 10 km. Greatest uncertainty was observed at urban sites, especially those dominated by coarse aerosols. Results suggest that the DT at 3 km is less reliable than MODIS C6 AOD products at 10 km.

High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression

Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5 and AOD. Take the core portion of the Beijing-Hebei-Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5 concentrations. The GWR model shows significant spatial variations in PM2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM2.5 concentrations demonstrates that PM2.5 concentrations predicted by the GWR model (R2 = 0.75, RMSE = 10 μg/m3) correlate better than those by the OLS model (R2 = 0.53, RMSE = 16 μg/m3). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5 concentrations over urban areas.

Spatial variability of air temperature and appropriate resolution for satellite-derived air temperature estimation

This study investigates the spatial variability of air temperature over Hong Kong using in situ air temperature recorded from a mobile traverse combined with an ASTER thermal satellite image. Three different degrees of urbanization in Hong Kong, including city downtown (Kowloon), suburban areas (Yuen Long and Shatin), and rural countryside (Tai Mo Shan and Lam Tsuen) are analysed. The spatially variable relationship between air and surface temperature was evaluated using two spatial averaging techniques, namely spatial resampling and buffering around air temperature points. The strength of the correlation coefficient was tested for every decreasing resolution and the appropriate spatial scales of air temperature in urban, suburban and rural areas were found to be 200 m, 450 m and 700 m, respectively. The differences in the spatial scales of air temperature in these regions are attributed mainly to structural factors of land cover such as city block size, building density and percentage of green areas, and secondarily to the climatic conditions being operating in, and which commonly typify these individual regions. Thus small scale lengths in the urban area corresponded to heterogeneous land cover, a well developed urban boundary layer, low wind speeds and a low lapse rate, whereas longer scale lengths were observed in suburban and rural areas having more homogeneous land cover, higher wind speeds and higher lapse rate.