Publications
1.
Patel, Nirav N.; Angiuli, Emanuele; Gamba, Paolo; Gaughan, Andrea; Lisini, Gianni; Stevens, Forrest R.; Tatem, Andrew J.; Trianni, Giovanna
Multitemporal settlement and population mapping from Landsat using Google Earth Engine Journal Article
In: International Journal of Applied Earth Observation and Geoinformation, vol. 35, pp. 199-208, 2015, ISSN: 0303-2434.
Abstract | Links | BibTeX | Tags: Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization
@article{PATEL2015199,
title = {Multitemporal settlement and population mapping from Landsat using Google Earth Engine},
author = {Nirav N. Patel and Emanuele Angiuli and Paolo Gamba and Andrea Gaughan and Gianni Lisini and Forrest R. Stevens and Andrew J. Tatem and Giovanna Trianni},
url = {https://www.sciencedirect.com/science/article/pii/S0303243414001998},
doi = {https://doi.org/10.1016/j.jag.2014.09.005},
issn = {0303-2434},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {35},
pages = {199-208},
abstract = {As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.},
keywords = {Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization},
pubstate = {published},
tppubtype = {article}
}
As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.
2.
Mertes, C. M.; Schneider, A.; Sulla-Menashe, D.; Tatem, A. J.; Tan, B.
Detecting change in urban areas at continental scales with MODIS data Journal Article
In: Remote Sensing of Environment, vol. 158, pp. 331-347, 2015, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: Change detection, Cities, Classification, Data fusion, Decision fusion, Decision trees, Land cover, Machine learning, Urban areas, Urbanization
@article{MERTES2015331,
title = {Detecting change in urban areas at continental scales with MODIS data},
author = {C. M. Mertes and A. Schneider and D. Sulla-Menashe and A. J. Tatem and B. Tan},
url = {https://www.sciencedirect.com/science/article/pii/S003442571400368X},
doi = {https://doi.org/10.1016/j.rse.2014.09.023},
issn = {0034-4257},
year = {2015},
date = {2015-01-01},
journal = {Remote Sensing of Environment},
volume = {158},
pages = {331-347},
abstract = {Urbanization is one of the most important components of global environmental change, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify c. 2010 urban extent; and (3) mapping newly built areas (2000–2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East–Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socioeconomic/political and ecological/climatic conditions with good accuracy (70–91% overall accuracy by country, 69–89% by biome). The 250m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of the error in the change detection can be related to definitional issues or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as for providing a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.},
keywords = {Change detection, Cities, Classification, Data fusion, Decision fusion, Decision trees, Land cover, Machine learning, Urban areas, Urbanization},
pubstate = {published},
tppubtype = {article}
}
Urbanization is one of the most important components of global environmental change, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify c. 2010 urban extent; and (3) mapping newly built areas (2000–2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East–Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socioeconomic/political and ecological/climatic conditions with good accuracy (70–91% overall accuracy by country, 69–89% by biome). The 250m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of the error in the change detection can be related to definitional issues or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as for providing a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.
3.
Tatem, A. J.; Noor, A. M.; Hay, S. I.
Assessing the accuracy of satellite derived global and national urban maps in Kenya Journal Article
In: Remote Sensing of Environment, vol. 96, no. 1, pp. 87-97, 2005, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: Accuracy assessment, Urban area mapping, Urbanization
@article{TATEM200587,
title = {Assessing the accuracy of satellite derived global and national urban maps in Kenya},
author = {A. J. Tatem and A. M. Noor and S. I. Hay},
url = {https://www.sciencedirect.com/science/article/pii/S0034425705000702},
doi = {https://doi.org/10.1016/j.rse.2005.02.001},
issn = {0034-4257},
year = {2005},
date = {2005-01-01},
journal = {Remote Sensing of Environment},
volume = {96},
number = {1},
pages = {87-97},
abstract = {Ninety percent of projected global urbanization will be concentrated in low income countries. This will have considerable environmental, economic and public health implications for those populations. Objective and efficient methods of delineating urban extent are a cross-sectoral need complicated by a diversity of urban definition rubrics world-wide. Large-area maps of urban extents are becoming increasingly available in the public domain, as are a wide-range of medium spatial resolution satellite imagery. Here we describe the extension of a methodology based on Landsat ETM and Radarsat imagery to the production of a human settlement map of Kenya. This map was then compared with five satellite imagery-derived, global maps of urban extent at Kenya national-level, against an expert opinion coverage for accuracy assessment. The results showed the map produced using medium spatial resolution satellite imagery was of comparable accuracy to the expert opinion coverage. The five global urban maps exhibited a range of inaccuracies, emphasising that care should be taken with use of these maps at national and sub-national scale.},
keywords = {Accuracy assessment, Urban area mapping, Urbanization},
pubstate = {published},
tppubtype = {article}
}
Ninety percent of projected global urbanization will be concentrated in low income countries. This will have considerable environmental, economic and public health implications for those populations. Objective and efficient methods of delineating urban extent are a cross-sectoral need complicated by a diversity of urban definition rubrics world-wide. Large-area maps of urban extents are becoming increasingly available in the public domain, as are a wide-range of medium spatial resolution satellite imagery. Here we describe the extension of a methodology based on Landsat ETM and Radarsat imagery to the production of a human settlement map of Kenya. This map was then compared with five satellite imagery-derived, global maps of urban extent at Kenya national-level, against an expert opinion coverage for accuracy assessment. The results showed the map produced using medium spatial resolution satellite imagery was of comparable accuracy to the expert opinion coverage. The five global urban maps exhibited a range of inaccuracies, emphasising that care should be taken with use of these maps at national and sub-national scale.
