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Article

Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing

1
Division of Geoinformatics, Department of Urban Planning and Environment, KTH Royal Institute of Technology, Teknikringen 10A, 100 44 Stockholm, Sweden
2
Centre for Geographic Information Systems and Remote Sensing, College of Science and Technology, University of Rwanda, KN 67 Street, Nyarugenge, Po Box 3900 Kigali, Rwanda
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 2883; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182883
Received: 17 July 2020 / Revised: 2 September 2020 / Accepted: 3 September 2020 / Published: 5 September 2020
Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions. View Full-Text
Keywords: Landsat time series; LandTrendr; trajectory segmentation; urban land cover change dynamics; Google Earth Engine; cloud computing Landsat time series; LandTrendr; trajectory segmentation; urban land cover change dynamics; Google Earth Engine; cloud computing
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MDPI and ACS Style

Mugiraneza, T.; Nascetti, A.; Ban, Y. Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing. Remote Sens. 2020, 12, 2883. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182883

AMA Style

Mugiraneza T, Nascetti A, Ban Y. Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing. Remote Sensing. 2020; 12(18):2883. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182883

Chicago/Turabian Style

Mugiraneza, Theodomir, Andrea Nascetti, and Yifang Ban. 2020. "Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing" Remote Sensing 12, no. 18: 2883. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182883

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