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Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change.
Haowei Mu; Xuecao Li; Xiaoping Du; Jianxi Huang; Wei Su; Tengyun Hu; Yanan Wen; Peiyi Yin; Yuan Han; Fei Xue. Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018. Remote Sensing 2021, 13, 1849 .
AMA StyleHaowei Mu, Xuecao Li, Xiaoping Du, Jianxi Huang, Wei Su, Tengyun Hu, Yanan Wen, Peiyi Yin, Yuan Han, Fei Xue. Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018. Remote Sensing. 2021; 13 (9):1849.
Chicago/Turabian StyleHaowei Mu; Xuecao Li; Xiaoping Du; Jianxi Huang; Wei Su; Tengyun Hu; Yanan Wen; Peiyi Yin; Yuan Han; Fei Xue. 2021. "Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018." Remote Sensing 13, no. 9: 1849.
Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.
Xiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing 2021, 13, 477 .
AMA StyleXiaoting Li, Tengyun Hu, Peng Gong, Shihong Du, Bin Chen, Xuecao Li, Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing. 2021; 13 (3):477.
Chicago/Turabian StyleXiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. 2021. "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method." Remote Sensing 13, no. 3: 477.