Biophysical Composition Index Research Articles

Combinational Biophysical Composition Index (CBCI) for Effective Mapping Biophysical Composition in Urban Areas

The current spectral indices for estimating urban biophysical compositions still have several limitations. First, most of those indices can highlight one urban biophysical composition, such as impervious surfaces, vegetation, or water. Furthermore, most of those indices depend on short-and mid-wave infrared and thermal infrared bands and thus have limited availability in high-resolution imagery. Finally, the separability between impervious surfaces and bare soil still needs to improve. Therefore, this paper proposed a combinational biophysical composition index (CBCI) for effective highlighting the four-major urban biophysical compositions. Correlation analyses were performed to estimate the relationship between the CBCI values and biophysical compositions, and the spectral discrimination index (SDI) was conducted to test the separability degree between impervious surfaces and bare soil. Moreover, normalized difference vegetation index (NDVI), normalized difference building index, BCI, and the combinational build-up index were utilized to perform the comparative analyses. Moreover, this paper investigated the applicability of the CBCI in different spatial resolution remote sensing images. Our results indicated that the CBCI had a much closer relationship with impervious surface fraction than the other four indices and had a stronger correlation with vegetation abundances when compared with the NDVI. In addition, the CBCI had much higher SDI values than the other indices. This paper proved that the CBCI was an effective index for highlighting the four-major urban biophysical compositions and had good performance for separating impervious surfaces and bare soil.

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.

Development of a modified bare soil and urban index for Landsat 8 satellite data

Mapping of urban area has always been a challenging task due to its similar spectral characteristics with bare soil. The spectral characteristics of urban and bare soil being similar, causes confusion and misclassification among themselves. A new modified normalized difference soil index (MNDSI) has been proposed using PAN and Band 7 of Landsat 8. PAN band of Landsat 8 provides increased contrast between vegetation and land areas without vegetation. Subsequently, MNDSI was used to develop a new normalized ratio urban index (NRUI) by enhancing the capability of biophysical composition index (BCI) in two stages. First, a ratio urban index (RUI) was developed which discriminates urban and soil better than BCI. Second, RUI was further enhanced, subsequently known as NRUI, which is able to discriminate urban area from soil even better than RUI. MNDSI and NRUI show a good discrimination between soil and urban and may be useful for such purposes.

Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments

Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model, and the one with the least estimation error (e.g., root mean square error) was chosen as the “best-fit” model. Such an approach may mistakenly include a land cover class in the model and overestimate its abundance, or it might omit a class from the model and subsequently lead to underestimation. To address this problem, this paper developed a land cover class-based multiple endmember spectral mixture analysis (C-MESMA) method. In particular, a support vector machine (SVM) method with reflectance spectra and spectral indices, including the normalized difference vegetation index (NDVI), the biophysical composition index (BCI), and the ratio normalized difference soil index (RNDSI), were employed to classify the image into six land cover classes: pure impervious surface area (ISA), pure vegetation, pure soil, ISA-vegetation, vegetation-soil, and vegetation-ISA-soil. With the information of land cover classes, an individual MESMA method was applied to each mixed class. Finally, the fractional maps were derived through integrating land cover fractions of each land cover class. Quantitative analysis of the resulting percent ISA (%ISA) and comparative analyses with traditional MESMA indicate that C-MESMA improved the estimation accuracy of %ISA.

Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information

DMSP/OLS nighttime light (NTL) image is a widely used data source for urbanization studies. Although OLS NTL data are able to map nighttime luminosity, the identification accuracy of distribution of urban areas (UAD) is limited by the overestimation of the lit areas resulting from the coarse spatial resolution. In view of geographical condition, we integrate NTL with Biophysical Composition Index (BCI) and propose a new spectral index, the BCI Assisted NTL Index (BANI) to capture UAD. Comparisons between BANI approach and NDVI-assisted SVM classification are carried out using UAD extracted from Landsat TM/ETM+ data as reference. Results show that BANI is capable of improving the accuracy of UAD extraction using NTL data. The average overall accuracy (OA) and Kappa coefficient of sample cities increased from 88.53% to 95.10% and from 0.56 to 0.84, respectively. Moreover, with regard to cities with more mixed land covers, the accuracy of extraction results is high and the improvement is obvious. For other cities, the accuracy also increased to varying degrees. Hence, BANI approach could achieve better UAD extraction results compared with NDVI-assisted SVM method, suggesting that the proposed method is a reliable alternative method for a large-scale urbanization study in China’s mainland.

Analysis of the Urban Heat Island Effect in Shijiazhuang, China Using Satellite and Airborne Data

The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect existed in the study area during summer days, with a SUHI intensity of 2–4 °C. The analyses reveal that ISA% can provide an additional metric for the study of SUHI, yet its association with LST is not straightforward and this should a focus in future work. It was also found that two physically based indices, VIUPD and BCI, have the potential to account for the variation in urban LST. The results concerning on TASI indicate that diversity of impervious surfaces (rooftops, concrete, and mixed asphalt) contribute most to the SUHI, among all of the land cover features. Moreover, the effect of impervious surfaces on LST is complicated, and the composition and arrangement of land cover features may play an important role in determining the magnitude and intensity of SUHI. Overall, the analysis of urban thermal signatures at two spatial scales complement each other and the use of airborne imagery data with higher spatial resolution is helpful in revealing more details for understanding urban thermal environments.

RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments

Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models.

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.