Normalized Difference Impervious Surface Index Research Articles

Semi-automatic mapping of anthropogenic impervious surfaces in an urban/suburban area using Landsat 8 satellite data

Impervious surfaces have a significant impact on urban runoff, groundwater, base flow, water quality, and climate. Increase in Anthropogenic Impervious Surfaces (AIS) for a region is a true representation of urban expansion. Monitoring of AIS in an urban region is helpful for better urban planning and resource management. Cost effective and efficient maps of AIS can be obtained for larger areas using remote sensing techniques. In the present study, extraction of AIS has been carried out using Double window Flexible Pace Search (DFPS) from a new index named as Normalized Difference Impervious Surface Index (NDAISI). NDAISI is developed by enhancing Biophysical Composition Index (BCI) in two stages using a new Modified Normalized Difference Soil Index (MNDSI). MNDSI has been developed from Band 7 and Band 8 (PAN) of Landsat 8 data. In comparison to existing impervious surface extraction methods, the new NDAISI approach is able to improve Spectral Discrimination Index (SDI) for bare soil and AIS significantly. Overall accuracy of mapping of AIS, using NDAISI approach has been found to be increased by nearly 23% when compared with existing impervious surface extraction methods.

Multi-dimensional histogram-based information capacity analysis of urban heat island effect using Landsat 8 data

ABSTRACTThe vegetation and impervious surface area (ISA) are the most key indicators for the urban heat islands (UHI). This study used the normalized difference vegetation index (NDVI) as the indicator of vegetation abundance, the modified normalized difference impervious surface index (MNDISI) as the indicator of impervious surface fraction to estimate the land surface temperature (LST)–vegetation relationship and LST–ISA relationship. The land surface cover types were obtained by classification and regression tree (CART). Results demonstrate and verify that LST possessed strong negative correlations with NDVI and positive correlations with MNDISI at various spatial resolutions (30–960 m). Both correlation coefficients reached their strongest points at 30 m resolution, which is believed to be the operational scale of LST, NDVI and MNDISI. Further, information capacity (IC), as a spatial index, is used to characterize the spatial pattern of UHI. Results show that the IC of LST (LSTIC) possessed strong positive correlations with the IC of NDVI (NDVIIC) and the IC of MNDISI (MNDISIIC). It is suggested that the spatial pattern of UHI has a direct correspondence with the vegetation and ISA.

Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices

Since the mid-1990s, various spectral indices have been proposed for the rapid and accurate classification of built-up lands from satellite imageries. However, a comprehensive comparison between these indices as applied to various satellite imageries is still lacking. Hence, this study examines and compares the performance of six spectral indices in the classification and change detection of built-up lands from Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) imageries. It includes three mid-infrared (MIR)-based indices, i.e. the urban index (UI), the normalized difference built-up index (NDBI), and the index-based built-up index (IBI), two proposed visible (Vis)-based indices, i.e. the VrNIR-BI and VgNIR-BI or the visible red/green-based built-up indices, and one thermal infrared (TIR)-based index, i.e. the normalized difference impervious surface index (NDISI). In addition, a water index, i.e. the modified normalized difference water index (MNDWI), was also derived. Otsu's method was used to separate water from the non-water areas on the MNDWI map. Subsequently, a water mask was produced and used to mask all the built-up index maps, leaving only the non-water areas. Using the same thresholding method, the non-water areas of all the built-up index maps were classified into built-up and non-built-up classes. The classification accuracy was assessed using 5000 reference points for each image. The results show that the VrNIR-BI, with an overall accuracy of 92.50% (Landsat-7) and 92.28% (Landsat-8), and the VgNIR-BI, with 92.78% (Landsat-7) and 92.14% (Landsat-8) overall accuracy, were more robust and superior. They were more accurate than the other indices by up to 8% for the Landsat-7 ETM+ image and 10% for the Landsat-8 image OLI/TIRS image. The qualitative assessment results also supported these quantitative findings. The results also show indications that the detected spatiotemporal urban LUC changes (i.e. built-up expansions) based on the VrNIR-BI and VgNIR-BI were also the most accurate. These indices, i.e. the proposed Vis-based indices, have better potential in separating built-up lands from dry vegetation, which has been an important challenge in the application of spectral indices for classifying built-up lands from satellite imageries.

Feature selection methods in extracting impervious surface from Landsat TM image

Impervious surface(s) (ISs) as an important ecological index has been widely used in the study of urban ecological environment evaluation, urban hydrology and urban vegetation mapping. In this paper, methods in feature selection, including principal component analysis (PCA) and optimum index factor (OIF), as well as indices approaches such as the normalized difference impervious surface index (NDISI) and normalized difference built-up index (NDBI) were evaluated. A new approach, experimental band combination (EBC), was proposed to extract ISs in Taiyuan city of Shanxi Province, China, using Landsat Thematic Mapper (TM) imagery. The same classification procedure was applied on original TM image, PCA image, OIF image and EBC image respectively to extract IS; the results were also compared with those extracted from the aforementioned indices approaches. Accuracy assessments were conducted on each of them using 253 randomly selected sampling points. By comparison and analysis, it was found that the EBC method obtained the highest producer’s accuracy (87.63%), user’s accuracy (90.43%) and overall accuracy (87.75%), with the Kappa statistic of 0.85 in IS extraction.

Impervious Surface Information Extracting Approach Using Landsat TM Remote Sensing Image

In this paper, we present a new approach to improve extracting accuracy of impervious surfaces. One Landsat TM image of Taiyuan city, Shanxi Province of China was used. After doing test work and analyzing using optimum bands analysis, principal component analysis, and normalized difference impervious surface index, we present the method, optimized band combination. Both unsupervised and supervised classification methods were used to classify the original image, principal component analysis image, normalized difference impervious surface index image, and optimized band combination images we present. The accuracies result of these classifications were assessed by using 256 randomly selected sampling points, and it was found that the overall accuracy the accuracy of optimized band combination method can be reach 87.72%, with the Kappa statistic of 0.85 in impervious surface extraction, it was better than other three methods can get.

MNDISI: a multi-source composition index for impervious surface area estimation at the individual city scale

Impervious surface is a key indicator for monitoring urban land cover changes and human-environment interaction. Although the normalized difference impervious surface index (NDISI) has shown the potential to extract impervious surface areas (ISA) from multi-spectral imagery, it may lack robustness due to the spectral heterogeneity within urban impervious materials and confusion between other land covers. In this letter, a multi-source composition index is proposed to overcome the limitations of the original method. Three data sources: night-time light luminosity, land surface temperature and multi-spectral reflectance are integrated to create a modified NDISI (MNDISI), which aims to enhance impervious surfaces and suppress other unwanted land covers. Experimental results reveal that the MNDISI offers a stable and close relationship with ISA and is shown to be an effective index for mapping and estimating impervious surfaces in heterogeneous urban land cover environment.

福建省长汀县河田盆地区近35年来地表裸土变化的遥感时空分析

The Hetian basinal area in County Changting of Fujian province is one of the most typical soil loss areas in the reddish soil regions of southern China.Local people and governments have been struggling for the control of the soil loss for decades.This study used remote sensing technology to analyze spatiotemporal dynamics of the bare soil cover in the Hetian basinal area since 1976.The bare soil features were extracted from the Landsat images of 1976,1988,1999,2004 and 2010 using two thematic indices-normalized difference soil index(NDSI) and normalized difference impervious surface index(NDISI).After converting the original digital number values of the images to at-satellite reflectance,the soil and impervious surface features were enhanced using the two indices,respectively.The soil features were then extracted from the soil-enhanced images.However,the extracted soil information was found to have been mixed with built-up land information due to the spectral confusion between the two categories.Therefore,the NDISI was further used to filter out the unwanted built-up land information from the soil images using a logical tree algorithm,as the NDISI is the index specially designed for highlight impervious surface features represented mainly by built-up lands.Results showed that the nearly thirty-five years of fight for controlling soil loss have greatly reduced bare soil covers in the basinal area.The area of the bare soil cover has been greatly reduced from 159.17 km2 in 1976 to 51.98 km2 in 2010.In the three observed periods,1976—1988,1988—1999,and 1999—2010,the decrease in bare soil cover was in an accelerate speed,which was-2.45 km2 per year,-2.75 km2 per year,and-4.33 km2 per year for the three periods,respectively.This corresponds to the major policies issued by the government for the soil loss treatment of the area.Government policies accompanied with effective actions have played a key role in the reducing of the bare soil cover in the Hetian basinal area.

BCI: A biophysical composition index for remote sensing of urban environments

Understanding urban environments and their spatio-temporal changes is essential for regional and local planning and environmental management. To facilitate monitoring and analyzing urban environments, remotely sensed data have been applied extensively because of its synoptic view and repeat coverage over large geographic areas. Compared with traditional per-pixel and sub-pixel image analyses, spectral indices have apparent advantages due to their easy implementation. However, most spectral indices are designed to highlight only one land cover, and confusion between other land cover types, in particular impervious surfaces and bare soil, has not been successfully addressed. This study proposes a biophysical composition index (BCI) for simple and convenient derivation of urban biophysical compositions for practical applications following Ridd's conceptual vegetation – impervious surface – soil triangle model by a reexamination of the Tasseled Cap (TC) transformation. Further, this research explores the applicability of BCI in various remotely sensed images at different spatial resolutions. Results indicate that, BCI has a closer relationship with impervious surface abundance than those of normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference impervious surface index (NDISI), with correlation coefficients of approximately 0.8 at various resolutions. Also, the performances of BCI in quantifying vegetation abundance are comparable with NDVI at all three spatial scales. Additionally, with much higher values of separability metrics than any other index, the study confirms that BCI was shown to be the most effective index of the four evaluated for separating impervious surfaces and bare soil.

The impact of impervious surface development on land surface temperature in a subtropical city: Xiamen, China

AbstractImpervious surface coverage is a quantifiable environmental indicator correlating closely with the urban thermal environment. This study measured the impervious surface development in the subtropical coastal area of Xiamen, southeastern China, and investigated its impact on the regional thermal environment. The Normalized Difference Impervious Surface Index was used to extract impervious surface features of the Xiamen area from the Landsat Thematic Mapper images of 1989, 1996, and 2009, respectively. Multivariate regression analysis was implemented to examine how land surface temperature (LST) was related to impervious surface and to some other urban biophysical descriptors such as vegetation and water. Results show that the impervious surface of the study area increased by approximately eight times during the 20 study years. Detailed relationship analysis can find that (1) impervious surface is positively exponentially correlated with LST, while vegetation and water are inversely related to LST; (2) the impervious surface contribution to regional LST change can be up to six times greater than the sum of vegetation and water contributions and (3) each decrement of 10% impervious surface cover with additional 10% green or water space could lower LST by up to 2.9 or 2.5 °C, respectively. These findings would facilitate the regional urban planning strategies to minimize the local temperature rise in future urban growth. Copyright © 2012 Royal Meteorological Society

Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI)

The fast urban expansion has led to replacement of natural vegetation-dominated land surfaces by various impervious materials. This has a significant impact on the environment due to modification of heat energy balance. Timely understanding of spatiotemporal information of impervious surface has become more urgent as conventional methods for estimating impervious surface are very limited. In response to this need, this paper proposes a new index, normalized difference impervious surface index (NDISI), for estimating impervious surface. The application of the index to the Landsat ETM+ image of Fuzhou City and the ASTER image of Xiamen City in China has shown that the new index can efficiently enhance and extract impervious surfaces from satellite imagery, and the normalized NDISI can represent the real percentage of impervious surface. The index was further used as an indicator to investigate the impact of impervious surface on urban heat environment by examination of its quantitative relationship with land surface temperature ( LST ), vegetation, and water using multivariate statistical analysis. The result reveals that impervious surface has a positive exponential relationship with LST rather than a simple linear one. This suggests that the areas with high percent impervious surface will accelerate LST rise and urban heat island development.