scholarly journals Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4319 ◽  
Author(s):  
Hongsheng Zhang ◽  
Ting Wang ◽  
Yuhan Zhang ◽  
Yiru Dai ◽  
Jiangjie Jia ◽  
...  

Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011–2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology.

2020 ◽  
Vol 12 (18) ◽  
pp. 2883
Author(s):  
Theodomir Mugiraneza ◽  
Andrea Nascetti ◽  
Yifang Ban

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.


2012 ◽  
Vol 27 (6) ◽  
pp. 479-497 ◽  
Author(s):  
George Xian ◽  
Collin Homer ◽  
Brett Bunde ◽  
Patrick Danielson ◽  
Jon Dewitz ◽  
...  

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