Incorporating nocturnal UAV side-view images with VIIRS data for accurate population estimation: a test at the urban administrative district scale

Kong, Wenyuan; Cheng, Jingyi; Liu, Xin; Zhang, Fan; Fei, Teng

doi:10.1080/01431161.2019.1615653

Cited by 9 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. 2018, Regean 2018, and new studies are starting to explore the potential of acquiring data on night time lights from drones, given the flexibility in deploying them at different times during the night, the ability to acquire multi-angular images (Kong et al, 2019), and their potential for providing some near-sensing validation to space borne measurements. To some extent, the limited use of drones for remote sensing of night lights may be due to regulations restricting the use of drones at night over urban areas.…”

Section: Airborne Remote Sensing Of Night Lightsmentioning

confidence: 99%

Remote sensing of night lights: A review and an outlook for the future

Levin

Kyba

Zhang³

et al. 2020

Remote Sensing of Environment

583

247

View full text Add to dashboard Cite

Remote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind day-time sensors, we demonstrate that the field is in a stage of rapid development.The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use.

show abstract

Section: Airborne Remote Sensing Of Night Lightsmentioning

confidence: 99%

Remote sensing of night lights: A review and an outlook for the future

Levin

Kyba

Zhang³

et al. 2020

Remote Sensing of Environment

583

247

View full text Add to dashboard Cite

show abstract

“…High-precision population data for all types of spatial accessibility are mostly not available (Mao and Nekorchuk, 2013), so researchers must often estimate such data by simulations based on public population databases. NighttimelightPop is clearly better than AveragePop, both theoretically and statistically, as echoed by many researches (Huang et al, 2016;Gao et al, 2019;Kong et al, 2019). Additionally, night-time light data are real-time and relatively easy to obtain.…”

Section: Reconmmendation Of Population Distribution Measuresmentioning

confidence: 88%

The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: A case study from China

2021

View full text Add to dashboard Cite

Improvement of the equality of geographical allocation of limited health-care resources requires an accurate evaluation of spatial accessibility of the facilities. The adoption of appropriate population distribution measures is one of the leading factors affecting such an evaluation. Using primary health-care institutions in Hainan, China as an example, this study aimed to explore the disparities embedded in spatial accessibility evaluations based on six common measures of population distribution, namely community/ village population (VillagePop), average population distribution (AveragePop), population distribution by night-time light intensity (NighttimelightPop) together with the public population databases LandScan, WorldPop and PoiPop for construction of the weights. The enhanced two-step floating catchment area method, two-way analysis of variance (ANOVA), Dunnett test, root mean square error and the mean absolute error were employed to assess and compare spatial accessibilities based on these different population distribution measures. The spatial accessibility of primary health-care institutions in Hainan was found to vary when plotted using the various population distribution measures mentioned. As indicated by the statistical outcomes of both ANOVA and the Dunnett test, using the spatial accessibility calculated by VillagePop as reference, those calculated by AveragePop and PoiPop were found to be significantly different. In addition, the spatial accessibilities calculated by AveragePop and PoiPop demonstrated higher error rates in the identification of underserved areas compared with the reference. Considering the limitations of public population databases, the adoption of night-time light data is highly recommended for estimating population distribution in the absence of high-resolution data.

show abstract

“…In this scenario, autonomous mobile robots represent a solution with great potential, as they can overcome all these limitations and challenges and fight fires from the outset without risking human lives. Moreover, these robots can be equipped with devices and sensors that allow them to operate at night and at short distances, thereby increasing the firefighting efficiency [17], [18]. An example of a special type of robot is a UAVs, or drones that move in a three-dimensional space.…”

Section: A Forest Fire Suppression In Large Areas: Methods and Techno...mentioning

confidence: 99%

Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption

et al. 2023

View full text Add to dashboard Cite

Wildfires are among the biggest problems faced worldwide. They are increasing in severity and frequency, causing economic losses, human death, and significant environmental damage. Environmental factors, such as wind and large forest areas, contribute to the fire spreading over multiple fire spots, all of which grow continuously, making fire suppression extremely difficult. Therefore, fire spots should be coverage simultaneously to contain the spread and prevent coalescence. Therefore, this study presents a new model based on the principles of nature-inspired metaheuristics that uses Swarm Intelligence (SI) to test the effectiveness of using an autonomous and decentralized behaviour for a swarm of Unmanned Aerial Vehicles (UAVs) or drones to detect all distributed fire spots and extinguishing them cooperatively. To achieve this goal, we used the improved random walk algorithm to explore the distributed fire spots and a self-coordination mechanism based on the stigmergy as an indirect communication between the swarm drones, taking into account the collision avoidance factor, the amount of extinguishing fluid, and the flight range of the drones. Numerical analysis and extensive simulations were performed to investigate the behaviour of the proposed methods and analyze their performance in terms of the area-coverage rate and total energy required by the drone swarm to complete the task. Our quantitative tests show that the improved model has the best coverage (95.3%, 84.3% and 65.8%, respectively) compared to two other methods Levy Flight (LF) algorithm and Particle Swarm Optimization (PSO), which use the same initial parameter values. The simulation results show that the proposed model performs better than its competitors and saves energy, especially in more complicated situations.

show abstract

Incorporating nocturnal UAV side-view images with VIIRS data for accurate population estimation: a test at the urban administrative district scale

Cited by 9 publications

References 28 publications

Remote sensing of night lights: A review and an outlook for the future

Remote sensing of night lights: A review and an outlook for the future

The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: A case study from China

Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption

Contact Info

Product

Resources

About