Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

Run Wang, Bo Wan, Qinghua Guo, Maosheng Hu, Shunping Zhou
2017 Remote Sensing  
The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer
more » ... ucts (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer's accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user's accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner. Remote Sens. 2017, 9, 862 2 of 25 natural habitat loss, threatening biodiversity, affecting local climate, etc. [5] [6] [7] [8] [9] [10] [11] . It is essential to obtain timely and accurate information on urbanized areas to monitor the environmental and socioeconomic processes [12, 13] . Land cover maps derived from remotely-sensed data are a valuable source for mapping urban area and monitoring urban dynamics [14] [15] [16] [17] [18] [19] . Images with high or medium spatial resolution are commonly used for characterizing the urban structures due to their detailed spatial observations of cities [20] [21] [22] . However, the coverage range of one scene in a high or medium spatial resolution image is limited. For example, IKONOS collects high-resolution imagery at 1 and 4 m resolution, and the swath of a single scene is 11 km × 11 km. When applied at the regional scale (e.g., China, covering approximately 9.6 million km 2 ), a large number of images is needed to cover the entire area. It is time consuming and labor intensive to process multi-fold pixels [23] . High-and medium-spatial resolution images are also frequently affected by cloud cover. It is challenging to select high-quality images around the same time for a large area. Additionally, the sensors collecting high-and medium-resolution images have a relatively long revisiting period. For example, Landsat sensors, providing medium-resolution imagery of 30 m, have a revisit rate at 16 days, which reduces the number of suitable images. Thus, at the national or continental scale, coarse imagery with wider swaths and higher revisit rates is the preferred imagery for macroscopically monitoring urban extents [24] . As the main data source of coarse images, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument can provide global imagery (500 to 1000 m spatial resolution) with 1-2-day temporal resolution. The dimensions of one scene are 2400 × 2400 rows/columns (about 1.4 million km 2 ). Many researchers have explored the capability of MODIS reflectance data in mapping urban areas [12, 18, 25] . The Normalized Difference Vegetation Index (NDVI) products derived from reflectance data are frequently used to rapidly characterize urban extent [26] [27] [28] [29] . Since urban areas are dominated by impervious surfaces, the vegetation distributions inside and outside cities have obvious differences, which can be reflected in the NDVI [30]. Multi-temporal or time series NDVI are often adopted to remove the cloud contamination, which always occurs in a single time image [31] . However, either MODIS reflectance data or the derived NDVI alone can reflect the physical properties of the land surface. Urban physical properties are similar to those of non-or low-vegetation land covers, and some non-urban blocks may have similar reflecting spectral curves as urban blocks, such as bare soils. Besides, urban areas can be regarded as the union of multi-features (e.g., trees, asphalt road, buildings, etc.), but the specific composition of each city is different, resulting in urban spectral features varying from city to city. There is no universal standard to distinguish city from the background. Particularly, pixels in coarse images cover a larger ground surface and contain more features. The spectral heterogeneity and homogeneity of urban land make it easily confused with other land covers, resulting in biased estimated (overestimated or underestimated). Thus, mapping urban extent from physical information alone is challenging. Another coarse data source, the nighttime lighting data, regarded as a sign of human activities, can provide different urban information from spectral data. It can distinguish human urban areas with artificial lights from the dark background at night [32] and has been employed to estimate economic conditions and energy consumption [33, 34] . The most widely-used nighttime light data are the stable Nighttime Light Data on the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS NLD) [35-39], but they have some limitations related to their coarse spatial resolution (30 arc seconds, 5 km × 5 km at nadir), blooming effects (the dispersion of light from built-up areas into non-light areas), saturation in urban areas and intra-sensor calibration problems, resulting in misestimates of urban land [40, 41] . Some studies have found that the combined use of spectral data and nighttime light data can reduce the blooming effect and pixel saturation of nighttime light products, as well as provide better performance than using individual nighttime light data [42, 43] . However, this integration, which provides only supplemental information for DMSP/OLS NLD, cannot eliminate data limitations, i.e., coarse spatial resolution of about 1 km and small data ranges from 0 to 63.
doi:10.3390/rs9080862 fatcat:qt65phoqbbaapjtgvgxhcaz2bm