| Records |
Author  |
Beresford, A.E.; Donald, P.F.; Buchanan, G.M. |
| Title |
Repeatable and standardised monitoring of threats to Key Biodiversity Areas in Africa using Google Earth Engine |
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Journal Article |
| Year |
2020 |
Publication |
Ecological Indicators |
Abbreviated Journal |
Ecological Indicators |
| Volume |
109 |
Issue |
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Pages |
105763 |
| Keywords |
Remote Sensing |
| Abstract |
Key Biodiversity Areas (KBAs) are sites that make significant contributions to the global persistence of biodiversity, but identification of sites alone is not sufficient to ensure their conservation. Monitoring is essential if pressures on these sites are to be identified, priorities set and appropriate responses developed. Here, we describe how analysis of freely available data on a cloud-processing platform (Google Earth Engine) can be used to assess changes in three example remotely sensed threat indicators (fire frequency, tree loss and night-time lights) over time on KBAs in Africa. We develop easily repeatable methods with shared code that could be applied across any geographic area and could be adapted and applied to other datasets as they become available. Fire frequency was found to have increased significantly on 12.4% of KBAs and 15.9% of ecoregions, whilst rates of forest loss increased significantly on 24.3% of KBAs and 22.6% of ecoregions. There was also evidence of significant increases in night-time lights on over half (53.3%) of KBAs and 39.6% of ecoregions between 1992 and 2013, and on 11.6% of KBAs and 53.0% of ecoregions between 2014 and 2018. |
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1470160X |
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GFZ @ kyba @ |
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2707 |
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| Author |
Chen, J.; Fan, W.; Li, K.; Liu, X.; Song, M. |
| Title |
Fitting Chinese cities’ population distributions using remote sensing satellite data |
Type |
Journal Article |
| Year |
2019 |
Publication |
Ecological Indicators |
Abbreviated Journal |
Ecological Indicators |
| Volume |
98 |
Issue |
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Pages |
327-333 |
| Keywords |
Remote Sensing |
| Abstract |
Remote sensing satellite data from 2012 to 2013 are used to fit the Chinese cities’ population distributions over the same period in order to verify the population distribution in China from a relatively objective perspective. Most scholars have used nighttime light data and vegetation indexes to fit the population distribution, but the fitting effect has not been satisfactory. In this paper, processed Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data, net primary productivity of vegetation (NPP), and average slope data were used to fit the population distribution from the three dimensions of economic growth, ecological environment, and topographic factors, respectively. The fitting effect was significantly improved compared with other studies (R2 values of 0.9244 and 0.9253 in 2012 and 2013, respectively). Therefore, this method provides a practical and effective way to fit the population distribution for remote cities or areas lacking census data. Furthermore, there is important practical significance for the government to formulate its population policies rationally, optimize the spatial distribution of population, and improve the ecological quality of the city. |
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1470160X |
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GFZ @ kyba @ |
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2071 |
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Neri, L.; Coscieme, L.; Giannetti, B.F.; Pulselli, F.M. |
| Title |
Imputing missing data in non-renewable empower time series from night-time lights observations |
Type |
Journal Article |
| Year |
2018 |
Publication |
Ecological Indicators |
Abbreviated Journal |
Ecological Indicators |
| Volume |
84 |
Issue |
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Pages |
106-118 |
| Keywords |
Remote Sensing |
| Abstract |
Emergy is an environmental accounting tool, with a specific set of indicators, that proved to be highly informative for sustainability assessment of national economies. The empower, defined as emergy per unit time, is a measure of the overall flow of resources used by a system in order to support its functioning. Continuous time-series of empower are not available for most of the world countries, due to the large amount of data needed for its calculation year by year. In this paper, we aim at filling this gap by means of a model that facilitates reconstruction of continuous time series of the non-renewable component of empower for a set of 57 countries of the world from 1995 to 2012. The reconstruction is based on a 3 year global emergy dataset and on the acknowledged relationships between the use of non-renewables, satellite observed artificial lights emitted at night, and Gross Domestic Product. Results show that this method provides accurate estimations of non-renewable empower at the country scale. The estimation model can be extended onward and backward in time and replicated for more countries, also using higher-resolution satellite imageries newly available. Besides representing an important advancement in emergy theory, this information is helpful for monitoring progresses toward Sustainable Development and energy use international goals. |
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LoNNe @ kyba @ |
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1706 |
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Xu, P.; Wang, Q.; Jin, J.; Jin, P. |
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An increase in nighttime light detected for protected areas in mainland China based on VIIRS DNB data |
Type |
Journal Article |
| Year |
2019 |
Publication |
Ecological Indicators |
Abbreviated Journal |
Ecological Indicators |
| Volume |
107 |
Issue |
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Pages |
105615 |
| Keywords |
Remote Sensing |
| Abstract |
Protected areas, a globally accepted conservation strategy, play a fundamental role in biodiversity and species conservation. There are increasing concerns about the ecological influence of nighttime light within protected areas due to the emergence of more light-related ecological issues. Previous approaches for detecting nighttime light mainly used the traditional data source released by the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS), but its coarse spatial resolution and limited radiometric resolution dramatically hamper prompt detection. In this study, we used data from a new source, the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) to detect nighttime light disturbance within protected areas of mainland China. Protected areas extracted from Landsat 8 Operational Land Imager and the Thermal Infrared Sensor (OLI-TIRS) images served as ground truths to assess detection accuracy. We found that the VIIRS DNB data provided more and better details compared with the traditional DMSP/OLS data. Pixel-based trend analysis clearly indicated that within the protected areas lighted pixels existed extensively and increased significantly from 2012 to 2017. This study provides a new solution to better understand human activities within protected areas. |
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1470160X |
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GFZ @ kyba @ |
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2612 |
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