Defense Meteorological Satellite Program-Operational Linescan System Data Research Articles

Does China's City-Size Distribution Present a Flat Distribution Trend? A Socioeconomic and Spatial Size Analysis From DMSP-OLS Nighttime Light Data

Inconsistent measurements of city-size and a lack of time-series information on urban socioeconomic development have hindered determining whether China's city-size distribution (CD) follows a Pareto distribution according to multiple perspectives. This article has attempted to evaluate China's CD based on the defense meteorological satellite program- operational line-scan system (DMSP-OLS) nighttime light data in terms of socioeconomic size (SS) and spatial size (SC). First, city size was defined from the DMSP-OLS data. Then, whether China's CD followed a Pareto distribution was evaluated from different perspectives. The results show that China's CD from 1995 to 2015 presents a flat distribution trend; the flat distribution trend of the SC is more obvious than that of the SS; “borrowed size” has become an important reason for the flat trend of China's CD; and residential suburbanization, transportation cost reductions, local government policies, and land finances could effectively explain the CD differences in the flat trends between the SS and SC. This article offers an effective means for quantifying and comparing CD in long time series at a large scale (e.g., national scale or regional scale) and provides a scientific decision basis for governments to build a reasonable CD system in China.

Identifying and Correcting Signal Shift in DMSP-OLS Data

Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data has become a key tool of the environmental and social scientific fields, but suffers from several validity problems. We highlight one such problem—shifts in the digital number position in DMSP-OLS composites in the same satellite. We present techniques for identifying the problem, using moving window raster correlation and visual inspection, and for solving the problem, by assigning control points and manually shifting raster positions. To illustrate the importance of accounting for signal shift, we re-examine a recent analysis of the relationship between public goods provision and patterns of violence in the 2011 Syrian uprising and ensuing civil war. We find the statistical results change considerably when correcting for signal shift. We attribute this change to the systematic undercounting of light intensity in heavily populated areas. We close by identifying the types of research that would most benefit from our correction and suggest future refinements to our technique through automation.

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy.

Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data

In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light composite data were released. Up to present, few studies have been conducted to evaluate the ability of NPP-VIIRS data to estimate the amount of freight traffic. This paper provides an exploratory evaluation on the NPP-VIIRS data for estimating the total freight traffic (TFT) in China, in comparison with the results derived from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime stable light composite data. We first corrected the original NPP-VIIRS data by employing a simple method to remove the outliers. The total nighttime light (TNL) which is measured by the sum value of all pixels from the nighttime light composite data was then regressed on TFT at the provincial level of China. Finally, the spatial distribution patterns of TFT were produced from the corrected NPP-VIIRS and DMSP-OLS data, respectively, and validated by the TFT statistics of 244 prefectures. The results have demonstrated that the corrected NPP-VIIRS data are more suitable for modeling TFT in China than the DMSP-OLS data.

High-resolution mapping of anthropogenic heat in China from 1992 to 2010.

Anthropogenic heat generated by human activity contributes to urban and regional climate warming. Due to the resolution and accuracy of existing anthropogenic heat data, it is difficult to analyze and simulate the corresponding effects. This study exploited a new method to estimate high spatial and temporal resolutions of anthropogenic heat based on long-term data of energy consumption and the US Air Force Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data from 1992 to 2010 across China. Our results showed that, throughout the entire study period, there are apparent increasing trends in anthropogenic heat in three major metropoli, i.e., the Beijing-Tianjin region, the Yangzi River delta and the Pearl River delta. The annual mean anthropogenic heat fluxes for Beijing, Shanghai and Guangzhou in 2010 were 17 Wm−2, 19 and 7.8 Wm−2, respectively. Comparisons with previous studies indicate that DMSP-OLS data could provide a better spatial proxy for estimating anthropogenic heat than population density and our analysis shows better performance at large scales for estimation of anthropogenic heat.

Forest fire monitoring and burnt area mapping using satellite data: a study over the forest region of Kerala State, India

The present study describes the night-time active forest fire detection capabilities of Defense Meteorological Satellite Program – Operational Linescan System (DMSP-OLS) satellite data over the forest region of Kerala State, India in 2004. Kerala State had a high incidence of forest fires during 2004 because of the extended dry season, with the unusual absence of intermittent rainfall from January to April. DMSP-OLS data were processed to detect active night-time forest fires over Kerala State and validated with ground data and fine-resolution Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS) satellite imagery. DMSP-OLS-derived night-time fire products were compared with synchronous Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daytime fire products to check for the spatial agreement and continuity of fires. To estimate the burnt areas, different atmospheric correction algorithms were applied to the IRS-P6 AWiFS dataset; these included the cosine approximation model (COST), ATCOR2 in ERDAS Imagine and the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) code. Atmospheric corrections to the satellite data indicated significant improvement in burnt area estimates. The results of the study suggest a good correlation between AWiFS data-derived burnt areas, DMSP-OLS-derived fire counts and MODIS-derived fire products. The fire occurrences derived from DMSP-OLS and MODIS data were validated with field records on fire occurrences over the study area.