Assessment of the impact of the 2010 Haiti earthquake on human activity based on DMSP/OLS time series nighttime light data

2019 ◽  
Vol 13 (04) ◽  
pp. 1 ◽  
Author(s):  
Xue Li ◽  
Zhumei Liu ◽  
Xiaolin Chen ◽  
Qingxiang Meng
Keyword(s):  
Time Series ◽  
Human Activity ◽  
Haiti Earthquake ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
The Impact

Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products

2019 ◽  
Vol 12 (4) ◽  
pp. 1143-1153 ◽  
Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Yuyu Zhou ◽  
Hongxing Liu ◽  
Chengshu Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Urban Areas ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals Modifiable reporting unit problems and time series of long-term human activity

2020 ◽  
Vol 376 (1816) ◽  
pp. 20190726 ◽  
Author(s):  
A. Bevan ◽  
E. R. Crema
Keyword(s):  
Time Series ◽  
Human Activity ◽  
Real World ◽  
Population Change ◽  
Theme Issue ◽  
The Impact

This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenomena. Drawing inspiration from real-world examples from prehistoric Britain, Greece and Japan, we explore their consequences and possible mitigation via a reproducible set of tactical simulations. This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.

scholarly journals A time series of urban extent in China using DSMP/OLS nighttime light data

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0198189 ◽  
Author(s):  
Yao Yao ◽  
Dongsheng Chen ◽  
Le Chen ◽  
Huan Wang ◽  
Qingfeng Guan
Keyword(s):  
Time Series ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Urban Extent

scholarly journals Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

2016 ◽  
Author(s):  
Guannan Geng ◽  
Qiang Zhang ◽  
Randall Martin ◽  
Jintai Lin ◽  
Hong Huo ◽  
...  
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Urban Areas ◽  
Road Transport ◽  
Emission Inventories ◽  
Bottom Up ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Current Emission ◽  
The Impact

Abstract. Spatial proxies used in bottom-up emission inventories to derive the spatial distributions of emissions are usually empirical and involve additional levels of uncertainty. Although uncertainties in current emission inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. In this work, we investigate the impact of spatial proxies on the representation of gridded emissions by comparing five gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem modeled tropospheric NO2 columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 columns are sensitive to the spatial proxies used in the gridded emission inventories. The total population density is less suitable for allocating NOx emissions than nighttime light data because population density tends to allocate more emissions to rural areas. Determining the exact locations of large emission sources could significantly strengthen the correlation between modeled and observed NO2 columns. When applying industrial gross domestic product (GDP) values and an updated road network map as proxies for the industrial and on-road transport sectors respectively, modeled NO2 columns could better capture pollution hotspots in urban areas and exhibit best performance of the five cases comparing to satellite-based NO2 columns (slope = 1.01 and R2 = 0.85). This analysis provides a framework for information from satellite observations to inform bottom-up inventory development. In the future, more effort should be devoted to the representation of spatial proxies to improve spatial patterns in bottom-up emission inventories.

scholarly journals Assessing Spatiotemporal Characteristics of Urbanization Dynamics in Southeast Asia Using Time Series of DMSP/OLS Nighttime Light Data

2018 ◽  
Vol 10 (2) ◽  
pp. 47 ◽  
Author(s):  
Min Zhao ◽  
Weiming Cheng ◽  
Chenghu Zhou ◽  
Manchun Li ◽  
Kun Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Southeast Asia ◽  
Spatiotemporal Characteristics ◽  
Nighttime Light Data ◽  
Nighttime Light
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