Paper Info
Title
Aging brick kilns in the asian brick belt using a long time series of Landsat sensor data to inform the study of modern day slavery
Abstract
The brick-making industry of the `Brick Belt' (spanning mostly parts of Bangladesh, Nepal, India and Pakistan) is a major employer, providing work for tens of millions people. Unfortunately, it is known via locally-based human rights groups that many of those working in the brick kilns are modern-day slaves. However, reliable and timely, spatially explicit and scalable data on the full extent of this slavery is currently unavailable. Since brick kilns leave a visible impact on the Earth's land cover that can be detected from space, and given that satellite sensor images are readily available globally for the past >40 years, using these data could afford a better understanding of the spatio-temporal dynamics of this slavery activity, affording optimal intervention. This research focuses on aging of brick kilns in the `Brick Belt' based on Landsat time series data, in order to reflect the evolution of brick kilns. Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+) and Landsat 8 Operational Land Imager (OLI) time series data from 1984 to 2018 were downloaded from Google Earth Engine according to the coordinates of a collection of kilns identified in the `Brick Belt'. A break detection method applied to the time series of data and random forest classifications were used to age the kilns. Results showed that the accuracy of kiln aging, determined using Google Earth as ground data, was approximately 83%, and the root-mean-square-error (RMSE) between the Landsat prediction date and Google Earth date was about 3.60 years, both highlighting the potential to age kilns across the broader region. The results also show that many kilns were younger than 10 years, indicating that the brick making industry has been highly active in recent years, being driven by the demand for bricks as the region's economy grows.
Year
DOI
Venue
2019
10.1109/IGARSS.2019.8898981
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
Kiln aging,Landsat,time series,break detection,random forests
Physical geography,Time series,Thematic Mapper,Satellite,Computer science,Remote sensing,Brick,Operational land imager,Land cover,Kiln
Conference
ISSN
ISBN
Citations 
2153-6996
978-1-5386-9155-7
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaodong Li117116.82
Giles M. Foody237439.06
Doreen S. Boyd311713.48
Feng Ling420921.29