Our research Detecting change in urban areas at continental scales with MODIS data is now available in the January 2015 issue of of Remote Sensing of Environment (click here for more information).
This work demonstrates a new methodology for monitoring urban land expansion at
continental to global scales using Moderate Resolution Imaging
Spectroradiometer (MODIS) data. We tested the method in 15 countries in East-Southeast Asia experiencing
different rates and manifestations of urban expansion, finding accuracies ranging from 70-95% at the country level. A companion article describing the spatial and temporal trends in urban growth across the 15 countries, A new urban landscape in East-Southeast Asia, 2000-2010, has been accepted at Environmental Research Letters.
Caitlin Kontgis presented her work, Understanding transitions in rice paddy extent and management in the Vietnamese Mekong River Delta using Landsat data,
at the 2014 AGU meeting in San Francisco, California. This work
demonstrates how to map rice agriculture using fine-scale Landsat data
by generating dense time stacks over multiple growing seasons. Second,
the new approach differentiates between single-, double-, and
triple-cropped rice paddies in the region, again by exploiting
temporally dense EVI trajectories. The results show that this new
approach can map rice paddy agriculture with over 90% accuracy at a much
finer spatial resolution than has ever been produced. In addition,
this research demonstrates that triple-cropped rice fields have nearly
doubled in area from 2000 to 2010, from one-third of total rice area to
nearly two-thirds of paddy area.
Caitlin will submit this work for publication in Remote Sensing of Environment.
Our new research assessing the urban transformation in Western China has been published in this month's issue of Landscape & Urban Planning. The work, funded by a NASA Land Cover-Land Use Change grant, focuses on the post-reform period (1988–2009) in four major metropolitan areas: Chengdu, Xi’an, Kunming, and Urumqi (shown below). The analysis exploits recent land change maps, satellite images, socioeconomic data, and master planning documents, and draws on a variety of spatial and statistical measures to estimate urban patterns through space and time. Check out the full article here for more information.
Caitlin Kontgis was
selected to receive the prestigious Twin Cities Women's Philanthropy
Council student travel grant. Caitlin is the very first recipient of
this grant, and was selected based on the high ratings from peer
reviewers she received on her Vilas Travel Award application. This
spring, Caitlin will use the grant to spend 10 weeks in Vietnam working
with collaborators at the Can Tho University Research Institute for
Climate Change. This grant will enable her to collect data necessary to
parameterize and validate an agricultural model that will help her
understand how future climate fluctuations could impact rice paddy
yields in the Mekong River Delta.
Graduate Research Assistant or Project Assistant, Remote Sensing of Urban and Peri-Urban Expansion
The Land Cover Change Lab at the Center for Sustainability and the Global Environment (SAGE) has funding for one RA or PA position starting this fall, and continuing through spring and summer, 2015, to conduct research to monitor urban expansion and agricultural land loss across East-Southeast Asia using multiple remote sensing data sets.
The overall goal of the project is to monitor land cover change, and work will be completed using a combination of ENVI, ArcGIS, and command line programming tools. The student will focus on analysis of time series MODIS satellite data to understand global trends in urban expansion. For more information, please click here.
Application deadline: October 27, 2014
Check out our recent research Detecting change in urban areas at continental scales with MODIS data in this upcoming issue of Remote Sensing of Environment. This work, completed as part of Carly Mertes' Master's thesis, demonstrates 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 250 m annual maximum enhanced vegetation index (EVI). The method is tested in 15 countries in East-Southeast Asia experiencing different rates and manifestations of urban expansion.
Interested in learning how to map the world with satellite imagery? Or detect change in dynamic landscapes using advanced machine learning methods? If yes, then please join us for Introduction to Environmental Remote Sensing this fall, Tuesdays and Thursdays, 2:30-4:00 pm in room 104 Russell Labs. For more information, please visit the class website.
