Title: Monitoring wildfires in the northeastern Peruvian amazon using Landsat-8 and sentinel-2 imagery in the GEE platform
During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.
Elgar Barboza, an environmental engineer by profession, specialist in geographic information systems from the Universidad Distrital de Caldas (Colombia) and is currently pursuing a Master's degree in Territorial Planning and Urban Development at the Pedro Ruiz Gallo National University (Perú). He is a researcher in the Research Institute for Sustainable Development in Highland Forests.
Title: Saving energy while maintaining the feeling of safety associated with urban street lighting
Street lighting (SL) forms a major share of municipal energy consumption and should thus be energy efficient. However, question of how much energy can be saved on SL without compromising on the feeling of safety (FoS), which SL helps to provide for pedestrians, poses a major challenge. To tackle this challenge, in this study, we attempt to determine the amount of energy that can potentially be saved by a proper selection of SL attributes, such as light color and uniformity, while preserving FoS by pedestrians. As the study indicates, using warmer lights and increasing light uniformity can result in 30–50% energy savings on SL. Using this assessment, we estimate that for medium-size cities with population of 200–400 K residents, energy savings on SL can reach 8–23 MWh per annum, which is equal to the output of a small power plant. As we conclude, the study findings help to design more efficient SL systems that can promote urban residents’ well-being, while saving energy and contributing to a cleaner and more sustainable urban environment. To the best of our knowledge, this study is the first that shows that energy on SL can be saved by using warmer lights and increasing light uniformity.
Portnov holds PhD in urban and regional planning from the central research institute of town planning (1987; Moscow, USSR) and D.Sc. (2nd Russian doctoral degree) in urban and regional planning from the Moscow Architectural Institute (1994). Since 2012, he holds the position of a full professor at the department of natural resources and environmental management, University of Haifa (tenured).