Abstract: Nesting sea turtles appear to avoid brightly lit beaches and often turn back to sea prematurely when exposed to artificial light. Observations and experiments have noted that nesting turtles prefer darker areas where buildings and high dunes act as light barriers. As a result, sea turtles often nest on darker beaches, creating spatial concentrations of nests. Artificial nighttime light, or light pollution, has been quantified using a variety of methods. However, it has proven challenging to make accurate measurements of ambient light at fine scales and on smaller nesting beaches. Additionally, light has traditionally been measured from stationary tripods perpendicular to beach vegetation, disregarding the point of view of a nesting sea turtle. In the present study, nighttime ambient light conditions were assessed on three beaches in central North Carolina: a developed coastline of a barrier island, a nearby State Park on the same barrier island comprised of protected and undeveloped land, and a completely uninhabited wilderness on an adjacent barrier island in the Cape Lookout National Seashore. Using an autonomous terrestrial rover, high resolution light measurements (mag/arcsec2) were collected every minute with two ambient light sensors along transects on each beach. Spatial comparisons between ambient light and nesting density at and between these locations reveal that highest densities of nests occur in regions with lowest light levels, supporting the hypothesis that light pollution from coastal development may influence turtle nesting distribution. These results can be used to support ongoing management strategies to mitigate this pressing conservation issue.

Abstract: Night-time light (NTL) data provides a great opportunity to monitor human activities and settlements. Currently, global-scale NTL data are acquired by two satellite sensors, i.e., DMSP-OLS and VIIRS, but the data collected by the satellites are not compatible. To address this issue, we proposed a method for generating long-term and consistent NTL data. First, a logistic model was employed to estimate and smooth the missing DMSP-OLS data. Second, the Lomb-Scargle Periodogram technique was used to statistically examine the presence of seasonality of monthly VIIRS time series. The seasonal effect, noisy and unstable observations in VIIRS were eliminated by the BFAST time-series decomposition algorithm. Then, we proposed a residuals corrected geographically weighted regression model (GWRc) to generate DMSP-like VIIRS data. A consistent NTL time series from 1996 to 2017 was formed by combining the DMSP-OLS and synthetic DMSP-like VIIRS data. Our assessment shows that the proposed GWRc model outperformed existing methods (e.g., power function model), yielding a lower regression RMSE (6.36), a significantly improved pixel-level NTL intensity consistency (SNDI = 82.73, R2 = 0.986) and provided more coherent results when used for urban area extraction. The proposed method can be used to extend NTL time series, and in conjunction with the upcoming yearly VIIRS data and Black Marble daily VIIRS data, it is possible to support long-term NTL-based studies such as monitoring light pollution in ecosystems, and mapping human activities.

Abstract: Satellite-observed night-time light in urban areas has been widely used as an indicator for socioeconomic development and light pollution. Up to present, the diurnal dynamics of city light during the night, which are important to understand the nature of human activity and the underlying variables explaining night-time brightness, have hardly been investigated by remote sensing techniques due to limitation of the revisit time and spatial resolution of available satellites. In this study, we employed a consumer-grade unmanned aerial vehicle (UAV) to monitor city light in a study area located in Wuhan City, China, from 8:08 PM, April 15, 2019 to 5:08 AM, April 16, 2019, with an hourly temporal resolution. By using three ground-based Sky Quality Meters (SQMs), we found that the UAV-recorded light brightness was consistent with the ground luminous intensity measured by the SQMs in both the spatial (R2 = 0.72) and temporal dimensions (R2 > 0.94), and that the average city light brightness was consistent with the sky brightness in the temporal dimension (R2 = 0.98), indicating that UAV images can reliably monitor the city's night-time brightness. The temporal analysis showed that different locations had different patterns of temporal changes in their night-time brightness, implying that inter-calibration of two kinds of satellite images with different overpass times would be a challenge. Combining an urban function map of 18 classes and the hourly UAV images, we found that urban functions differed in their temporal light dynamics. For example, the outdoor sports field lost 97.28% of its measured brightness between 8: 08 PM – 4:05 AM, while an administrative building only lost 4.56%, and the entire study area lost 61.86% of its total brightness. Within our study area, the period between 9:06 PM and 10:05 PM was the period with largest amount of light loss. The spectral analysis we conducted showed that city light colors were different in some urban functions, with the major road being the reddest region at 8:08 PM and becoming even redder at 4:05 AM. This preliminary study indicates that UAVs are a good tool to investigate city light at night, and that city light is very complex in both of the temporal and spatial dimensions, requiring comprehensive investigation using more advanced UAV techniques, and emphasizing the need for geostationary platforms for night-time light sensors.