Elsevier

Ecological Indicators

Volume 107, December 2019, 105615
Ecological Indicators

An increase in nighttime light detected for protected areas in mainland China based on VIIRS DNB data

https://doi.org/10.1016/j.ecolind.2019.105615Get rights and content

Highlights

  • VIIRS DNB data provide more details compared with DMSP/OLS data.

  • Most protected areas were detected with significant human light disturbance.

  • Lighted pixels changed differently in different types of protected areas.

  • Lighted pixels increased significantly from 2012 to 2017.

Abstract

Protected areas, a globally accepted conservation strategy, play a fundamental role in biodiversity and species conservation. There are increasing concerns about the ecological influence of nighttime light within protected areas due to the emergence of more light-related ecological issues. Previous approaches for detecting nighttime light mainly used the traditional data source released by the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS), but its coarse spatial resolution and limited radiometric resolution dramatically hamper prompt detection. In this study, we used data from a new source, the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) to detect nighttime light disturbance within protected areas of mainland China. Protected areas extracted from Landsat 8 Operational Land Imager and the Thermal Infrared Sensor (OLI-TIRS) images served as ground truths to assess detection accuracy. We found that the VIIRS DNB data provided more and better details compared with the traditional DMSP/OLS data. Pixel-based trend analysis clearly indicated that within the protected areas lighted pixels existed extensively and increased significantly from 2012 to 2017. This study provides a new solution to better understand human activities within protected areas.

Introduction

Protected areas play a key role in the protection of regions with recognized natural, ecological or cultural value (Joppa et al., 2008, Gaston et al., 2014a, Gaston et al., 2014b). This approach has been widely accepted as an important solution for biodiversity conservation due preliminary to the increased understanding of the necessity to consume natural resources in a sustainable manner (Phillips, 2004). According to the recent World Database on Protected Areas (WDPA), there are more than 209,000 legally designated protected areas all over the world, among which more than 2000 are located within mainland China. These areas cover nearly 1.6 million km2 or approximately 17.08% of China’s total surface area (WDPA, 2016).

Although protected, such areas are still suffering from various human interactions, including residences, education, resource exploration and tourist activities, all of which lead to serious human disturbances. Among diverse disturbances, severe night light problems with profound biological consequences on species and ecology have received increasing attention in recent years (Gaston et al., 2013). Nighttime light adds excessive pressure on light-sensitive species by altering their abundance and distribution and ecosystem functions, threatening biodiversity and accelerating species extinction (Aubrecht et al., 2010, Gaston et al., 2013, Hölker et al., 2010, Joppa et al., 2008, Longcore and Rich, 2004). Thus, accurate information on current and historical spatial distributions and intensities of artificial lights within protected areas are crucial for monitoring human activities, evaluating the impact of anthropogenic lights and maintaining protected area ecology.

Few studies on nighttime light detection and assessment have been performed for protected areas (Aubrecht et al., 2010, Gaston et al., 2014a, Gaston et al., 2014b, Xiang and Tan, 2017), due preliminarily to the lack of an easy and accurate light disturbance detection method. Remote sensing offers a proper solution to the dilemma. Traditional fine resolution satellites help map the spatial distribution of anthropogenic activities related to light emanation within protected areas, although the procedure is complex, labour intensive and usually limited to small regions (Shi et al., 2014a, Shi et al., 2014b, Shi et al., 2014c, Joppa et al., 2008, Lu et al., 2008). Recent use of nighttime light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) relieved this difficulty to a certain extent. For example, Aubrecht et al. (2010) presented a novel global assessment of light pollution impact on protected areas and claimed that light pollution affects most protected regions around the world. Using the same dataset, Gaston et al., 2014a, Gaston et al., 2014b quantified the erosion of natural darkness in global protected areas. Similarly, Xiang and Tan (2017) analyzed changes in light pollution in China’s protected areas from 1992 to 2012 and summarized the factors related to light changes.

DMSP/OLS images, however, always suffer from several disadvantages, such as coarse spatial resolution (about 1 km), blooming and pixel saturation that might underestimate the actual light in specific regions; all these factors dramatically hamper their wide applications (Letu et al., 2012, Zhang and Seto, 2011, Elvidge et al., 2009, Proville et al., 2017). In 2013, the US National Oceanic and Atmospheric Administration (NOAA) and National Geophysical Data Center (NGDC; https://www.ngdc.noaa.gov) released a new generation of nighttime light images with the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) dataset. Compared with the DMSP/OLS dataset, VIIRS DNB images provide better spatial resolution (15 arc-seconds), better radiometric resolution (14-bit vs. 6-bit digitisation) and a lower light imaging detection limit with high sensitivity over a broad dynamic radiance range (Miller et al., 2013). Further, the VIIRS DNB data employ onboard calibration to avoid bright light saturation that frequently occurs within the DMSP/OLS images (Elvidge et al., 2013, Small et al., 2011, Wu et al., 2013); this change helps to map nighttime light more accurately and may add new insights into nighttime light detection. The potential for VIIRS DNB to estimate socioeconomic activities was verified by several previous studies that included urbanization mapping, economic detection and ghost city identification among others (Jing et al., 2016, Kyba et al., 2015, Ma et al., 2014, Shi et al., 2014a, Shi et al., 2014b, Shi et al., 2014c). However, to the best of our knowledge, its performance in detecting nighttime light within protected areas has yet to be evaluated.

In this study, VIIRS DNB data were applied to detect anthropogenic nighttime light conditions in protected areas in mainland China. We attempted to provide an on-time assessment of nighttime light disturbance and dynamics in such areas using the new data source. Additionally, we compared the accuracy of extracted lighted area (LA) from VIIRS DNB with that from DMSP/OLS.

Section snippets

Research region

China, one of the largest countries in the world, has many protected areas that cover nearly 1.6 million km2. Among them, most areas are important habitats and shelters for wildlife and endangered species or places of national or international value (www.protectedplanet.net).

Information on protected areas was acquired from the WDPA. The database is updated monthly and is available online (www.protectedplanet.net). In this study, we used data from protected areas within mainland China in January

Accuracy of VIIRS DNB for detecting artificial light in protected areas

To assess the accuracy of VIIRS DNB data for detecting artificial light status in protected areas, we compared the identification results using either VIIRS DNB or DMSP/OLS for nine typical sites with different human interaction levels. For comparison, we used the maximum likelihood classification results of Landsat 8 OLI-TIRS images as ground truths for each site. We assumed regions that emanated light within protected areas at night mainly consisted of residences, mine sites and

Pros of VIIRS DNB for describing nighttime light distribution within protected areas

With a day/night band that covers a spectral range from 500 to 900 nm and finer spatial resolution, nocturnal brightness signals obtained from VIIRS provide much richer information for accurate light detection regarding human activities (Baugh et al., 2013). Such information has been tested, and its superiority was demonstrated in a series of recent studies (Bennett and Smith, 2017, Hillger et al., 2013, Stathakis and Baltas, 2018, Zheng et al., 2017). However, in most previous studies, light

Conclusions

Light disturbances within protected areas of mainland China from 2012 to 2017 have been assessed in this study using the VIIRS DNB nighttime light dataset. Although both nighttime light datasets (VIIRS DNB and DMSP/OLS) are applicable for detecting nighttime light disturbances, the VIIRS DNB data are apparent with much high potential to provide better results. Even so, there are several limitations that must be mentioned. First, to acquire accurate information about the ground surface from the

Acknowledgment

The first author appreciates the financial support from the China Scholarship Council for his joint Ph.D. Scholarship (Project No. 201706320300).

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