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

https://doi.org/10.1016/j.jag.2015.02.010Get rights and content

Highlights

  • We develop a ratio normalized difference soil index (RNDSI) to enhance soil cover.

  • Normalized difference soil index (NDSI) is formulated using TM bands 7 and 2.

  • RNDSI was constructed using the ratio of NDSI and the first Tasseled Cap component.

  • Qualitative and quantitative analyses are used to examine RNDSI's performance.

  • RNDSI is promising in separating soil from impervious surfaces and vegetation.

Abstract

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.

Introduction

Land use land cover change (LULCC) plays an important role in shaping our living environments. While LULCC is associated with economic benefits and improved quality of life, it also brings numerous environmental problems to human societies, such as urban heat island, biodiversity loss, water, air, and soil pollutions, etc. For examining the intensity and patterns of LULCC, multi-temporal remotely sensed variables (e.g., reflectance, spectral indices) have been typically applied at different geographical scales. Within these variables, spectral indices, computed using the spectral signatures of two or more bands of remotely sensed imagery, are one of the most convenient means for extracting land properties (e.g., vegetation, impervious surfaces). Specified land cover information can be highlighted and thereafter separated from other types by computing the ratio (Major et al., 1990), normalize difference (Gao, 1996, Townshend and Justice, 1986), and difference of spectral signatures of two bands. Taking vegetation as an example, numerous vegetation indices, including normalized difference vegetation index (NDVI) (Becker and Choudhury, 1988), ratio vegetation index (RVI) (Major et al., 1990), soil adjusted vegetation index (Huete, 1988) have been developed to examine the condition of vegetation growth. Similarly, for impervious surfaces, normalized different built-up index (NDBI) (Zha et al., 2003), normalized difference impervious surface index (NDISI) (Xu, 2010), and biophysical composition index (BCI) (Deng and Wu, 2012), have been developed to examine the quantity and distribution of urban impervious surfaces. In addition to vegetation and impervious surfaces, soil is also an important biophysical component in urban/suburban environments (Raison, 1979). For urban/suburban environments, Ridd (1995) developed a conceptual vegetation-impervious surface-soil (V-I-S) model to characterize dynamic urban land use land covers. Besides, soil is a key parameter in examining agriculture production (Magdoff and Weil, 2004), hydrological process (Zhang et al., 2002), and sandstorm statues (Yang et al., 2010).

The developments of soil indices, however, are difficult due to several reasons. First, soil is a complex material with various chemical and physical compositions, and the spectra of soil are very complex that prevents direct connections between soil properties and its spectral responses (Ben-Dor, 2002). The formation of an efficient spectral index always relies on the identification of spectral bands with distinct spectral properties (Mahlein et al., 2013). Such characteristically spectral signatures, however, cannot be found in the spectra of soil (Ge et al., 2011). Second, soil’s spectral signatures are varied with its construction, texture, moisture, color, and surface roughness (Lõhmus et al., 1989), and significantly different spectral signatures may exist among different types of soil. For example, moist soil has a similar spectral signatures when compared to those of water, shadow, and dark urban impervious surfaces, majorly due to the large amount of water content in the soil (Wu, 2004). Comparatively, dry bare soil’s spectral signatures are comparable to those of bright urban impervious surfaces and bare rocks (Weng and Lu, 2008). As a result, the confusions between moist soil and water, shadow, and dark impervious surfaces, as well as between dry bare soil and bright urban impervious surfaces and bare rock are always considered as essential problems, especially for analyzing urban and suburban environments.

To address the aforementioned problems, this paper proposed a three-step empirical approach to generate a soil index, named ratio normalized difference soil index (RNDSI). In particular, the first step involves the generations of random samples of three major land cover types of urban/suburban areas, e.g., vegetation, impervious surfaces, and soil. With the spectral signatures of these samples, we empirically examined a number of spectral variables for developing normalized difference indices, and identified the one that can highlight soil covers. The last step involves the construction of a ratio index to further highlight soil information. This empirically created index can be considered as an integration of the normalized difference index and the ratio index. The remainder of this paper is organized as follows. The next section introduces the study area and data sources. Section 3 describes the methods applied for developing the RNDSI, as well as comparative analyses with other relevant indices. Results and comparative analyses are reported in Section 4. Finally, discussion and conclusions are provided in Section 5 and Section 6.

Section snippets

Study area and data

Two counties of Wisconsin, Milwaukee and Waukesha, in the United States were chosen as the study area. These two counties are located in the Great Lake Region (Fig. 1) with a humid continental climate. Milwaukee and Waukesha have geographic areas of 607 km2 and 2058 km2 and are with total population of 947,735 and 389,891 respectively. Within these two counties, Milwaukee is majorly covered by urban and suburban land uses (e.g., commercial, industrial, residential etc.), and Waukesha is dominated

Generation of stratified random samples

Due to the complexity of soil compositions and their corresponding spectral signatures, direct connections between soil properties and their spectral responses could not be constructed. To address this problem, we developed an empirical approach to formulate the soil index with samples chosen through applying a stratified random sampling strategy. The selection of random samples is essential for constructing an effective index. Theoretically, it would be ideal to select a large number of

Development of RNDSI

Through applying Eq. (1), the normalized difference soil index (NDSI) was calculated using the combination of bands 7 and 2 of the TM imagery. Results (see Fig. 5A) indicate that NDSI can, to a certain degree, separate soil with other land cover types. In particular, the values of NDSI for soil are clustered in the range of 0.1 to 0.4, while ISA and vegetation are with lower NDSI values. Therefore, with NDSI, soil information has been enhanced and could be, to some degree, separated from

Discussion

Due to convenience of spectral indices in highlighting a particular land cover type, a large number of indices, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Impervious surface Index (NDISI), Ratio Vegetation Index (RVI), have been developed in the past decades. Few indices, however, have been developed to enhance soil information directly. A major reason is associated with the complexity of soil properties and their

Conclusions

As a major biophysical component of urban/suburban environments, soil abundance and spatial distribution are essential for LULC studies. Soil indices may serve as a convenient means of extracting soil information from remotely sensed imagery. The development of soil indices, however, is extremely difficult due to the complexity of soil spectra, and their dependency on soil’s chemical and physical compositions, construction, texture, moisture, color, and surface roughness. In order to address

Acknowledgements

This research was partially supported by the One-hundred Talent Award of Chinese Academy of Sciences and the Graduate School Research Committee Award of University of Wisconsin-Milwaukee. The authors would like to thank anonymous reviewers for their constructive comments.

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