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Wang, L.; Wang, S.; Zhou, Y.; Liu, W.; Hou, Y.; Zhu, J.; Wang, F. |
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Title |
Mapping population density in China between 1990 and 2010 using remote sensing |
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Journal Article |
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Year |
2018 |
Publication |
Remote Sensing of Environment |
Abbreviated Journal |
Remote Sensing of Environment |
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210 |
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269-281 |
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Remote Sensing |
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Abstract |
Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. |
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0034-4257 |
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GFZ @ kyba @ |
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2480 |
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Goldblatt, R.; Stuhlmacher, M.F.; Tellman, B.; Clinton, N.; Hanson, G.; Georgescu, M.; Wang, C.; Serrano-Candela, F.; Khandelwal, A.K.; Cheng, W.-H.; Balling Jr, R.C. |
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Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover |
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Journal Article |
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2018 |
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Remote Sensing of Environment |
Abbreviated Journal |
Remote Sensing of Environment |
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205 |
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253-275 |
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Keywords |
Remote Sensing |
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Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time. |
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0034-4257 |
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3403 |
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Gwiazdsinski, L.; Straw, W. |
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Nights and Mountains. Preliminary Explorations of a Double Frontier |
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2018 |
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Revue de géographie alpine |
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rga |
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106 |
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1 |
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Commentary; Darkness |
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0035-1121 |
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GFZ @ kyba @ |
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1866 |
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Challéat, S.; Lapostolle, D.; Milian, J. |
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Title |
The Night-time Environment in French Mountain Areas. A Resource and a Transition Operator Towards Sustainability |
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2018 |
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Revue de géographie alpine |
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rga |
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106 |
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1 |
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Darkness; Society |
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This article presents our approach to construct the night-time environment (NE) as an interdisciplinary research subject. Understood within the framework of various French mountain areas, we show that the NE is highly indicative of different development trajectories. We analyse them by combining traditional social science research into territory with the ecosystem approaches of the experimental sciences. We show how the NE resource (NER) is transformed into an operator that facilitates the transition towards sustainability. By highlighting three of the NE’s specifications, this work lays the groundwork for a transdisciplinary approach. |
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0035-1121 |
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GFZ @ kyba @ |
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1867 |
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Charlier, B. |
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“You Know the Pyrenees by Day – Come See Them by Night...” Reflections on in visu Artialisation of Nocturnal Skyscapes in the Pyrenees |
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2018 |
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Revue de géographie alpine |
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rga |
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106 |
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1 |
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Society; Economics; Darkness |
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“Nocturnal skyscapes. You know the Pyrenees by day – come see them by night... ”: thus the title of an exhibition of photographs set up in 2012 by the Pays de Lourdes et des Vallées des Gaves (Hautes-Pyrénées département) to help raise public awareness about the project for the Pic du Midi International Dark Sky Reserve (IDSR), mainly among the local population and stakeholders in the areas concerned.
Although its evocative title might suggest otherwise, this is rather more than an exhibition on the iconic sites of the Pyrenees seen at night. What it seems to do is to bring out new landscapes that are not just “mountainscapes at night”, or simply night-time versions of landscapes seen by day.
The night skies that characterise these landscapes therefore represent an new category, they need to be considered in their entirety as conveying a meaning that encompasses all that is both construed and material in our relationships with landscape. As in many areas with similar projects either in place (North America, Europe) or emerging (the Cévennes and Mercantour national parks in France, for example), the creation of the Pic du Midi IDSR will have helped to bring a new kind of “landscape object” (Besse, 2009) into being in the Pyrenean region. |
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0035-1121 |
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GFZ @ kyba @ |
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1869 |
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