Impervious Surface Estimation Research Articles

Slowing the expansion of impervious surfaces: the key to promoting high-quality and sustainable regional development.

Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4-5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City.

The effect modification of greenspace and impervious surface on the heat-mortality association: Differences by the dissimilarity index

Recent studies have shown that heat-mortality risk differs by level of greenspace and impervious surface. However, these studies do not consider both green spaces and impervious surfaces simultaneously, and further did not fully consider community- and individual-level characteristics. In this study we explored the effect modification of greenspace and impervious surface on the association between heat and mortality and how it differs by race/ethnicity dissimilarity index levels in North Carolina, USA. We aggregated datasets for greenspace, impervious surface estimates, temperature, and mortality for 1275 census tracts for North Carolina, USA, for 2000 to 2016 for 5 warm months (May to September). We used distributed lag non-linear models to estimate the heat-mortality relationship in each census tract. Heat-mortality relative risk (RR) was higher for census tracts with low greenspace than high greenspace (RR comparing risk at 99th temperature and minimum mortality temperature: 1.08 (1.02, 1.15) for low greenspace and 0.97 (0.87, 1.08) for high greenspace). Heat-mortality RR was higher for tracts with high impervious surface than low impervious surface (1.04 (1.00, 1.09) for high impervious surface and 0.94 (0.84, 1.05) for low impervious surface). Census tracts with high dissimilarity value and low greenspace had the highest heat-mortality risk compared to the tracts with high dissimilarity value with and high greenspace (1.13 (1.02, 1.24) for high dissimilarity index and 0.97 (0.86, 1.09) for low dissimilarity index). Communities with low greenspace or high impervious surfaces had higher heat-mortality associations, and this effect modification was higher for high race/ethnicity dissimilarity regions.

Monitoring of subpixel impervious surface dynamics using seasonal time series Landsat 8 OLI imagery

Since impervious surface is one of the core urban ecological indices, its high-precision time series monitoring at regional or global scales is highly desirable. Spectral features, spatial features, and temporal features have been successfully applied to regional impervious surface monitoring. However, few studies focus on the extraction of optimal features for subpixel impervious surface monitoring at the regional or global scales. In order to solve the above problem, this study proposes a new method for subpixel time series impervious surface estimation based on optimal spectral-temporal features. The proposed method first extracts various spectral indices from time series Landsat imagery to construct a spectral-temporal feature library. A feature scoring model is then established to optimize spectral-temporal features from the feature library. Based on the optimal spectral-temporal features, a modified semi-supervised Fuzzy C-means (FCM) method is proposed to identify stable pixels and extract endmembers from stable pixels. Finally, the endmembers are applied to the Fully Constrained Least Squares (FCLS) for time series impervious surface abundance extraction. The optimal spectral-temporal features can effectively distinguish impervious surfaces from pervious surfaces through feature optimization selection, and improve the feature utilization performances, thereby obtaining high-precision impervious surface mapping. The proposed method is applied in Wuhan, China, from 2013 to 2021, using seasonal time series Landsat 8 OLI images. Experimental results show that the impervious surface area of Wuhan expands from 567.27 km2 in winter 2013 to 1056.95 km2 in winter 2021 at the pixel scale. The impervious surface area expands from 545.80 km2 in winter 2013 to 921.01 km2 in winter 2021 at the subpixel scale. The subpixel impervious surface monitoring effectively solve the overestimation and underestimation problem of the pixel-scale impervious surface monitoring. This study characterizes the long-term dynamics of impervious surfaces at the subpixel scale and can provide reliable urban growth information for evaluating the impact of urbanization on the ecological environment.

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.

Quantifying spatiotemporal changes of the urban impervious surface of Dhaka District using Remote sensing Technology

Dhaka, the capital of Bangladesh, is one of the world's fastest-growing cities where imperviousness expanding in tandem. Therefore, accurate estimation of impervious surfaces is essential for urban planning and management. This paper attempts to quantify the changes of urban impervious surfaces in Dhaka district from 1990 to 2020 using remote sensing technology. Satellite images of 1990, 1995, 2000, 2005, 2010, 2015, and 2020 have been taken from the Landsat TM, ETM+, OLI sensor. Unsupervised classification with k-means clustering and three different RS indices NDVI, NDBI, and BUI was used to delineate the actual impervious area of Dhaka city. This study reveals that due to urbanization a net increase of 67.30 sq. miles impervious area is added to the existing amount over the study period. In 2020 total 300.749 sq. miles which contain 51.02% of the total land were occupied by impervious surfaces compared to the 233.446 sq. miles in 1990. Instantaneously taking appropriate strategies is crucial for sustainable urban growth.

Multitemporal impervious surface estimation via an optimized stable/change pixel detection approach

ABSTRACT Remote sensing techniques have proved its efficacy for the impervious surface mapping, which is a significant indicator of urbanization process and environmental status. However, systematic and random errors in the existing methods still impact the reliability of subpixel impervious surface estimation, generating compounded errors when conducting multitemporal monitoring. The compounded errors of the conventional methods often significantly impact the temporal consistency of the results. In this study, a novel method based on a straightforward pixel change detection approach was put forward to improve the estimation of multitemporal impervious surface area. Two experimental areas located in Rome in Italy and Shenzhen in China were chosen to testify the generality of the proposed method to estimate different types of impervious surfaces worldwide. By reducing the compounded errors, the proposed method demonstrated its efficiency in achieving higher accuracy in both study areas without involving extensive data sources and intensive manual tasks. Compared with the conventional classification and regression tree algorithm, the overall mean average error and root mean square error of this study declined by more than 15.55% and 8.63%, respectively, and R2 increased from approximately 0.93 to 0.96. The proposed method also drastically reduced the standard deviation of the multitemporal percent ISA of the stable pixels. The accurate change estimation of percent ISA has been a fundamental but challenging issue associated with monitoring and understanding the urban environment. Therefore, our proposed method, with its improved ability to estimate impervious surface change both spatially and temporally, can provide accurate information required for urban environment research.

Comparison of Impervious Surface Dynamics through Vegetation/High-Albedo/Low-Albedo/Soil Model and Socio-Economic Factors

Hong Kong and Shenzhen have entirely different land-use development policies, resulting in a disparity in the increase rate of impervious surface area. Impervious surface estimation is a significant method for evaluating urbanization, so that countries and cities can deal with their growing populations. The impervious surface area was estimated through Landsat Thematic Mapper (TM) image extraction, the V-H-L-S (vegetation, high-albedo, low-albedo, and soil) model, and linear spectral un-mixing analysis (LSUM). Changes in fractions of endmembers over periods of time were identified and employed to analyze changes in land use and land cover (LULC). The research adopting the V-H-L-S model for classifying land cover and exploring the association of change in impervious surface areas and socio-economic growth over a period of time is limited. In this study, impervious surface estimations for Hong Kong and Shenzhen in 1995, 2005, and 2016 were compared, selecting vegetation, high-albedo, low-albedo, and soil as endmembers. The change rate of the fractions in the four endmembers was calculated to identify changes in land use and land cover during these three specific time periods. The impervious surface was determined to constitute a combination of high-albedo and low-albedo. Moreover, a proportional relationship exists between the increase in impervious surface area, population rate, GDP, and GDP per capita in both Hong Kong and Shenzhen. However, there was a difference in the increase in impervious surface area between Hong Kong and Shenzhen due to the different land-use policies in the country’s two systems.

Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

ABSTRACT Impervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.

Improving LSMA for impervious surface estimation in an urban area

ABSTRACT Linear spectral mixture analysis (LSMA) and regression analysis are the two most conventionally used methods to estimate impervious surfaces at the subpixel scale in an urban area. However, LSMA lacks the sensitivity to pixel brightness, which leads to inter variability of endmembers and affects the ability to distinguish features with a similar spectral signature. This research aims to develop LSMA aided by a regression analysis model to estimate impervious surfaces with higher accuracy. A spectral angle mapping (SAM) based regression analysis model is introduced to reduce errors. Based on high-resolution images and field survey data, the SAM-based regression analysis can estimate non-impervious surface and high-impervious surface densities with high accuracy, while less accurate in impervious surfaces with low/medium density. In contrast, LSMA is able to estimate low/medium-density impervious surfaces with higher accuracy. We propose an improved approach by integrating the two methods, regression analysis aided LSMA, for impervious surface estimation. The proposed method increases the overall accuracy of the impervious surface estimation to 85.24%, which is significantly greater than that of the conventional methods.

Extracting impervious surfaces from full polarimetric SAR images in different urban areas

ABSTRACTAccurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited.

Urban Impervious Surface Extraction Based on Multi-Features and Random Forest

Impervious surface data has become an indicator of the degree of urbanization and the environmental quality, which inspires the widely use of remote sensing technology in extracting impervious surface. In order to reduce the confusion between impervious surface and other landcover types, this paper proposed an effective urban impervious surface extraction method based on multi-features and Random Forest (RF). First, Sentinel-2 multispectral data and Luojia 1–01 images are employed to extracted multi-features, including spectral features, texture features and temporal features. Then, a feature selection method with null importance is proposed to remove irrelevant features. Finally, Probabilistic Label Relaxation (PLR) is introduced into RF to obtain the classification results and impervious surface. The main urban areas of Zhengzhou and Hangzhou are selected as study areas. The experiment results show that the integration of the multi-features can significantly improve the overall accuracy of classification and the extraction accuracy of impervious surface. And the classification accuracy can be further improved after the feature selection with null importance. Besides, the PLR method can effectively reduce the salt-and-pepper phenomenon of classification results using random forest, which presupposes the optimal number of iterations. The method proposed in this paper can effectively improve the estimation of impervious surface and provide an important reference for the extraction of impervious surface based on pixel level.

Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites

Urban impervious surface is considered one of main factors affecting urban heat island and urban waterlogging. It is commonly extracted utilizing the original linear spectral mixture analysis (LSMA) model. However, due to the deficiencies of this method, many improvements and modifications have been proposed. In this paper, a modified dynamic endmember linear spectral mixture analysis (DELSMA) model was introduced and tested in Zhengzhou, China, using different images of Landsat series satellites. The accuracy and performance of DELSMA model was evaluated in terms of R M S E , r and R 2 . Results show that (1) the DELSMA model performed equally well for Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) images, and obtained better accuracy by using Landsat-8 Operational Land Imager (OLI) than Landsat TM/ETM+; (2) the DELSMA model achieved a better performance than the original LSMA model consistently, using images of Landsat from different sensors. Based exclusively on the overall accuracy reports, the DELSMA model proved to be a more efficient method for extracting impervious surface. Our study will provide a reliable method of impervious surface estimation for the urban planner and management in monitoring urban expansion, revealing urban heat island, and estimating urban surface runoff, using time-series Landsat imagery.

Improving Impervious Surface Estimation by Using Remote Sensed Imagery Combined With Open Street Map Points-of-Interest (POI) Data

As a key indicator of urban environments, the accurate mapping of impervious surface is essential. With the availability of high spatial resolution remote sensing imagery, such as Sentinel-2, it is feasible to produce fine-scale impervious surface maps. For mapping high-resolution impervious surfaces, object-based image analysis (OBIA) classification method demonstrated its efficiency and accuracy by combining spectral information and spatial information. Although with some success, impervious surface estimation remains challenging because different land cover types share similar spectral information. With the emergence of affordable GPS-enabled devices (such as a smartphone) and web 2.0 development, more and more people are getting involved in sharing their locations with others or posting on the Internet. These volunteered geographical information (VGI) data provide a brand-new prospect for mapping urban impervious surfaces. This research proposes an optimized method for impervious surface mapping based on Sentinel-2 multispectral imagery and open street map (OSM) points-of-interest (POI) data. The proposed method was tested in Milwaukee county, US, and the results show that the overall accuracy of the proposed OBIA increases from 82.57% to 87.02% compared with the conventional OBIA. Thus, this study provides an effective means of combining OBIA and the relatively new VGI POIs data to extract impervious surface with higher spatial resolution.

Synergistic Use of Optical and Dual-Polarized SAR Data With Multiple Kernel Learning for Urban Impervious Surface Mapping

Accurate mapping of impervious surface distribution is important but challenging. Integrating optical and SAR data to improve urban impervious surface estimation has recently shown promising performance. Further investigation and development on this multisensory approach are conducted in this study. A novel multiple kernel learning (MKL) framework is proposed to integrate heterogeneous features from Landsat-8 and Sentinel-1A data effectively. A linearly weighted combination of basic kernels built using each group of features is learned as the optimal kernel, while the hyperparameters and the weight of each basic kernel are determined simultaneously by using the differential evolution algorithm. Then, the optimal kernel is embedded into the support vector regression algorithm, and the impervious surface abundance of the study area is estimated by applying the developed multiple kernel support vector regression (MKSVR) model. The impervious surface ground truth at a subpixel level is derived from a high-resolution image by means of object-oriented classification. The experimental results indicate that the synergistic use of optical and dual-pol SAR data by employing MKSVR achieves a noteworthy improvement for impervious surface estimation compared to that using optical image alone, the root mean square error is decreased by 4.30%, and the coefficient of determination ( R 2) is increased by 9.47%, and that the incorporation of optical and SAR does not guarantee the improved performance, simply stacking all features of multisource data into a vector is not a good choice, and the MKL is a powerful tool to apply as demonstrated by the experiments conducted in this study.

Impervious Surface Extraction From Multispectral Images via Morphological Attribute Profiles Based on Spectral Analysis

Impervious surfaces exhibit unique spatial characteristics in urban environments. The estimation of impervious surfaces is critical for the analysis of these environments. In this paper, we propose a new technique based on morphological attribute profiles for mapping impervious surfaces under a spectral mixture analysis model. A main feature of our newly developed method is that it can model different kinds of structural information, which represents an important competitive advantage over existing techniques. As a result, considering the special characteristics of urban environments, our new method for impervious surface extraction exhibits the potential to model complex urban backgrounds. Four kinds of remotely sensed data, including Landsat ETM+, GF-1, IKONOS and sentinel-2 collected over Guangzhou, China, are used in this work to test the performance of our approach in the task of extracting imperviousness from images with different spatial resolution. Our experimental results illustrate that the proposed method exhibits very good performance in the task of estimating the impervious surface distribution, with relatively high precision. Root-mean-square error (RMSE) was 10.89%, mean absolute error (MAE) was 8.37% and Bias was 1.4% for the ETM+ data. RMSE was 11.49%, MAE was 6.25% and Bias was 2.34% for the GF-1 data. The RMSE was 7.72%, MAE was 7.67% and Bias was 3.91% for the IKONOS data, respectively. These results are superior to those provided by other state-of-the-art methods. Furthermore, our results also show the effectiveness of the method in distinguishing bright impervious surface from the dark impervious surface, especially in high resolution remotely sensed images.

Annual dynamics of impervious surfaces at city level of Pearl River Delta metropolitan

ABSTRACTHuman activities have transformed natural land surface to artificial constructions, which causes the urban regions increasingly expanding. Urban development can be reflected by the dynamic changes of impervious surfaces, which can be assessed using time-series satellite data. The Pearl River Delta (PRD) metropolitan has witnessed fast urban development over past decades. Even though many studied have focused on monitoring the urbanization of megacities, there is still a lack of comprehensive understanding about the long-term annual dynamics of urban impervious surfaces at city level in PRD. In this study, MOD09A1 (MODIS Surface Reflectance 8 Day L3 Global 500 m) products were chosen to assess the annual dynamics of impervious surface at city level in PRD. Temporal filtering was utilized to improve the accuracy of the impervious surface estimation. Moreover, a new index, normalized difference urban development index (NDUDI) was proposed to investigate relative development of impervious surface in PRD. Additionally, the economy factor, Gross Domestic Product (GDP) was introduced to reveal the correlation with the impervious surfaces. Results demonstrated that the overall accuracy for impervious surface extraction was about 91.00–94.00%. The impervious surfaces area in PRD dramatically increased from 6000 km2 to 115,840 km2 in the past 16 years. Guangzhou and Foshan have the fastest developing speed, while Dongguan, Shenzhen and Foshan sacrificed much more lands to the construction of PRD than other cities. Besides, areas of impervious surface per city show very good correlations with GDP in city of Guangzhou, Huizhou, Foshan, Zhongshan, Hong Kong and Zhuhai-Macau. The scatter plot between NDUDI and GDP data display different clustering pattern between different cities.

Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data

Directly mapping impervious surface area (ISA) at national and global scales using nighttime light data is a challenge due to the complexity of land surface components and the impacts of unbalanced economic conditions. Previous research mainly used the coarse spatial resolution Defense Meteorological Satellite Program’s Operational Linescan System (DMSP OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI) data for ISA mapping; the improved spatial resolution and data quality in the Suomi National Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS DNB) and in Proba-V data provide a new opportunity to accurately map ISA distribution at the national scale, which has not been explored yet. This research aimed to develop a new index – modified impervious surface index (MISI) – based on VIIRS DNB and Proba-V data to improve ISA estimation and to compare the results with those from the combination of VIIRS DNB and MODIS NDVI data. Landsat data were used to develop ISA data for the typical sites for use as reference data. Regression analysis was used to establish the ISA estimation model in which the dependent variable was from the Landsat data and the independent variable was from the MISI, as well as the previously used Large-scale Impervious Surface Index (LISI). The results indicate that the major error is from the very small or very large proportion of ISA in a unit; improvement of spatial resolution through use of higher spatial resolution nighttime light data (e.g., VIIRS DNB) or NDVI (e.g., Proba-V NDVI) data is an effective approach to improve ISA estimation. Although different indices for the combination of nighttime light and NDVI data have been used, the MISI is especially valuable for reducing the estimation errors for the regions with a small or large ISA proportion.

Improving impervious surface estimation: an integrated method of classification and regression trees (CART) and linear spectral mixture analysis (LSMA) based on error analysis

Classification and regression tree (CART) has been widely implemented to estimate impervious surface, an important indicator of urbanization and environmental quality. Although the CART algorithm gains higher overall accuracy than linear regression models, only very few studies have noticed that reliability of CART is affected by systematic errors. Especially, CART typically overestimates impervious surfaces in low-density urban areas and underestimates them in high-density urban areas. The primary objective of this study is to develop an improved integrated method to estimate impervious surface with higher accuracy by reducing the systematic errors of CART. This improved method was applied to three urban areas, Chicago (United States), Venice (Italy), and Guangzhou (China) to examine its effectiveness. When compared with the conventional CART, overall mean average error (MAE) and root mean square error (RMSE) of improved method are decreased by 22.64% and 20.93%, respectively, and R2 rises from 0.9 to 0.96. In high-density impervious surfaces, where intensely developed urban area is located, the MAE and RMSE for the improved method are 0.066 and 0.088, respectively, largely improved from 0.100 to 0.130. Since accurate estimation of high-density impervious surfaces is the fundamental issue for monitoring and understanding the urban environment, the improved method demonstrated in this study is significant.

Subpixel urban impervious surface mapping: the impact of input Landsat images

Due to the heterogeneity of urban environments, subpixel urban impervious surface mapping is a challenging task in urban environmental studies. Factors, such as atmospheric correction, climate conditions, seasonal effect, urban settings, substantially affect fractional impervious surface estimation. Their impacts, however, have not been well studied and documented. In this research, we performed direct and comprehensive examinations to explore the impacts of these factors on subpixel estimation when using an effective machine learning technique (Random Forest) and provided solutions to alleviate these influences. Four conclusions can be drawn based on the repeatable experiments in three study areas under different climate conditions (humid continental, tropical monsoon, and Mediterranean climates). First, the performance of subpixel urban impervious surface mapping using top-of-atmosphere (TOA) reflectance imagery is comparable to, and even slightly better than, the surface reflectance imagery provided by U.S. Geological Services in all seasons and in all testing regions. Second, the effect of images with leaf-on/off season varies, and is contingent upon different climate regions. Specifically, humid continental areas may prefer the leaf-on imagery (e.g., summer), while the tropical monsoon and Mediterranean regions seem to favor the fall and winter imagery. Third, the overall estimation performance in the humid continental area is somewhat better than the other regions. Finally, improvements can be achieved by using multi-season imagery, but the increments become less obvious when including more than two seasons. The strategy and results of this research could improve and accommodate regional/national subpixel land cover mapping using Landsat images for large-scale environmental studies.

USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT

Abstract. surfaces has increasingly roused widely interests of researchers in monitoring urban development and determining the overall environmental health of a watershed. However, studies on the impervious surface using multi-spectral imageries is insufficient and inaccurate due to the complexity of urban infrastructures base on the need to further recognize these impervious surface materials in a finer scale. Hyperspectral imageries have been proved to be sensitive to subtle spectral differences thus capable to exquisitely discriminate these similar materials while limited to the low spatial resolution. Coupled nonnegative matrix factorization (CNMF) unmixing method is one of the most physically straightforward and easily complemented hyperspectral pan-sharpening methods that could produce fused data with both high spectral and spatial resolution. This paper aimed to exploit the latent capacity and tentative validation of CNMF on the killer application of mapping urban impervious surfaces in complexed metropolitan environments like Hong Kong. Experiments showed that the fusion of high spectral and spatial resolution image could provide more accurate and comprehensive information on urban impervious surface estimation.