A Remote Sensing Approach to Measuring Artificial Night Time Light at North Carolina Sea Turtle Nesting Beaches Chelsey Stephenson College of Earth, Ocean, and Atmospheric Science, Oregon State University GEOG 580 Remote Sensing INTRODUCTION Artificial night time light (ANTL) can have detrimental impacts to nesting adult sea turtles and emerging sea turtle hatchlings. Nocturnally nesting sea turtles can be discouraged from nesting on their natal beaches in areas of high light pollution. Artificial light can also cause disorientation of sea turtle hatchlings, leading to dehydration and death of these federally protected marine reptiles. As humans and wildlife compete for limited waterfront resources, the impacts of ANTL on nocturnal animals needs to be assessed and mitigated. Remotely sensed satellite night time images can be used to quantify the level of light pollution along sea turtle nesting habitat. Although night time light radiance levels are below the “noise” level of most visible wavelength instruments, there are two sensors designed for night time detection. Prior to the launch of the Suomi NPP satellite in 2011, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) was the only instrument able to capture visible imagery at night. World composite night time images from DMSP-OLS are currently available from 1992-2013 at a spatial resolution of approximately 1 km. However, the Visible Instrument Imaging Radiometer Suite (VIIRS) instrument aboard the Suomi NPP now provides higher spatial and radiometric resolution images through its Day/Night Band (DNB) channel. These two instruments can be used to evaluate levels of ANTL at a global scale. Hypothesis: Average levels of ANTL along with North Carolina coast will increase over time from 1992 to 2013. Areas of high ANTL will be less productive for sea turtle nesting. Project Objectives: 1. Compare DMPS-OLS and VIIRS DNB data for utility in analyzing ANTL 2. Use DMPS-OLS data to quantify changes in ANTL on North Carolina barriers islands from 1992-2013 3. Determine if there is a correlation between sea turtle nesting productivity and ANTL BACKGROUND AND PREVIOUS WORK Previous studies have used DMSP-OLS data to evaluate the relationship between ANTL and sea turtle nesting patterns in Florida and found that nest densities were negatively influenced by ANTL (Weishampel et al, 2015). However, Weishampel et al. found that although urbanization on the Florida coast increased from 1992 to 2013, the majority of beaches exhibited decreased light levels through this time period. Kamrowski et al. produced a similar study in Australia and found that while ANTL did increase at some nesting beaches, 60% of study sites did not change significantly from 1992 to 2010 (Kamrowski et al., 2014). Both papers mention the potential benefits of finer spatial and temporal resolution of VIIRS data for evaluating ANTL impacts to nesting sea turtles. In this project, I explore the utility of higher spatial, temporal, and radiometric resolution VIIRS data in evaluating the relationship between ANTL and nesting densities in coastal North Carolina. Due to the competing influences of increased development and enhanced conservation measures (such as lighting ordinances) it is expected that ANTL changes will be site specific. I also use historical DMSP data to evaluate long-term ANTL changes from 1992 to 2013. METHODOLOGY The study site consists of North Carolina barrier islands from the Virginia border to the South Carolina border (figure 1). The North Carolina barrier island ecosystem provides an important nesting ground at the northern portion of sea turtle’s nesting range. ENVI software was used to clip to Regions of Interest (ROIs), calculate image statistics, and perform map algebra. Data: DMSP-OLS Version 4 Series annual cloud-free composite nighttime images from 1992-2013. This data has been preprocessed to exclude non-stable lights such as gas flares, moonlit/sunlit data, and clouds. VIIRS DNB Version 1 nighttime lights annual composite from 2015 NOAA high resolution vector shoreline data 2015 sea turtle nesting data from SeaTurtle.org RESULTS SOURCES OF ERROR The DMSP-OLS data is pulled from 6 different satellites over the 21 year period. Because there is no onboard calibration, light sensitivity may differ among different years or among different satellites. This may introduce error in the change detection results. Further intercalibration, as described by Eldridge et al. (2014) would produce a more accurate analysis. Pixel saturation is common in DMSP-OLS, losing information above a certain light level. The map algebra analysis likely underestimates the number of pixels that increased from 1992 to 2013, as many of these pixels likely read at the saturated values of 63 for both years. The large pixel size of both DMSP-OLS (~1km) and VIIRS (~750m) makes it difficult to measure ANTL along narrow features. This resolution is much coarser than most beaches, making it difficult to equate pixel value with actual light levels experienced by nesting turtles. The North Carolina coast serves as the north end of the sea turtle nesting range. For a more accurate analysis of the relationship between nesting density and ANTL, geographic location of each nesting unit should be taken into account. Annual composite images will not capture seasonal conservation measure to reduce ANTL during the nesting season. DISCUSSION This analysis of ANTL supports both hypotheses that ANTL increased along the North Carolina coast from 1992 to 2013 and that nesting density is negatively correlated to ANTL levels. DMSP-OLS results shows that ANTL increased 31% from 1992 to 2013 along the eastern coast of North Carolina. The Map Algebra tool indicated that only 4% of pixels along the coast decreased in average DN value from 1992 to 2013. While 34% of pixels increased during this time series, this number is likely an underestimate as saturated pixels would should no change from year to year. Although course in spatial and radiometric resolution, the DMSP-OSL sensor demonstrates it’s value as being the only source of global historical ANTL data. The finer resolution VIIRS data was used to explore the relationship between nest density and average ANTL. The analysis found a weak negative correlation between nesting density and the average DN value within a nesting unit ROI. Although not explored in this study, NOAA provides composite VIIRS images on a monthly time-scale that would be useful in the analysis of ANTL specifically during the summer months. CONCLUSIONS This project supports the hypothesis that sea turtle nesting is negatively impacted by ANTL. It also reinforced the assumption that as the world population grows, ANTL levels will increase, posing additional threats to marine reptiles. ANTL not only poses a threat to sea turtles in metropolitan areas, but also less developed areas such as the North Carolina barrier islands. Remote sensing instruments provide an important tool for analyzing the effects of ANTL on the ecosystem. DMSP-OLS provides important historical data on ANTL, while the increased spatial, temporal, and radiometric resolution of the VIIRS instrument will continue to provide finer scale analysis of ANTL levels across the globe. References Earth Observation Group, NOAA National Geophysical Data Center. https://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html Elvidge, C. D.; Hsu F.; Baugh K.E., and and Ghosh, T. 2014. National trends in satellite-observed lighting 1992–2012. Pp. 97–119. in Q. Wend, ed. Global urban monitoring and assessment through earth observation. CRC Press, Boca Raton, FL. Kamrowski, R.L.; Limpus, C.; Jones, R.; Anderson, S., and Hamann, M., 2014. Temporal changes in artificial light exposure of marine turtle nesting areas. Global Change Biology¸ 20, 2437-2449. NOAA's National Geophysical Data Center https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html SEATURTLE.ORG, http://www.seaturtle.org/ Weishampel, Z.A.; Cheng, W., and Weishampel, J.F., 2016. Sea turtle nesting patterns in Florida vis-à-vis satellite-derived measures of artificial lighting. Remote Sensing in Ecology and Conservation, 2(1), 59-72. DMSP-OLS Results Global annual composites images were cropped to an ROI encompassing the North Carolina coast from 1992 to 2013 (figure 2). Five year increments were used from 1993 to 2013 due to the computational burden of working with large datasets in ENVI. The ROI Stats tool was used to calculate the average DN for the ROI for each year. The average DN for coastal North Carolina increased 31% between 1992 and 2013 (figure 3). Figure 2. ROI for North Carolina coast indicated by red polygon over 1992 clipped DMSP image. Figure 3. Average DN value for ROI versus year from 1993 to 2013. Dashed line is trend line showing increase over the 20 year period a. b. The Band Math tool revealed that 34% of pixels in the ROI increases in DN from 1992 to 2013, while only 4% of pixels decreased (figure 4). Figure 4. a) binary image with pixels that increased in DN from 1993 to 2013 indicated with white and b) with white pixel indicating decreases in DN VIIRS Results Sea turtle nesting beaches were separated into 17 nesting units, continuous stretches of beach with a comparable level of development (figure 5). ROIs were developed for each nesting unit using high resolution NOAA shoreline vector data. The ROI stats tool was used to calculate the average DN value over the ROI. Average DN’s were compared to nesting densities (nesting/km) for the 2015 nesting season. Nesting density was weakly negatively correlated with average DN with a correlation coefficient of -0.315 (figure 6). 0-10 10-20 20-40 40-50 melt ponds Figure 6. Nest density (nests/km) versus average DN for each nesting unit ROI. Trend line shows weak negative correlation (r = -0.315) Figure 1. Map of study area along North Carolina coast Figure 5. North Carolina nesting units and associated ROIs over VIIRS DNB image