Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Area
2.2. Data and Pre-Processing
2.2.1. Landsat Imagery
2.2.2. Vegetation Index
2.3. Algorithms for Identifying Time Series of Built-Up Land
2.3.1. Spectral Variability between Built-Up Land and Other Lands Based on Phenology
2.3.2. Annual Composite Feature Enhancement Images
2.3.3. Deep Learning for Identifying Built-Up Land Per Year
Res-UNet++ Model for Identifying Built-Up Land
Deeplab-v3 and FCN Models for Comparison
Parameters and Design of Deep Learning Training
2.3.4. Correction of Time Series Built-Up Land by Temporal Segmentation Algorithm
2.4. Accuracy Assessment and Area Estimation
3. Results
3.1. Model Comparison Experiment Result
3.2. Assessment of Built-Up Land Maps from 1991 to 2020
3.3. Spatial-Temporal Changes of Built-Up Land in Guangdong
4. Discussion
4.1. Algorithms for Mapping Annual Built-Up Land Maps
4.2. Comparison with Different Datasets
4.3. Advantages of Combining GEE and Deep Learning Methods
4.4. Implications and Future Work
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Year | Number | Year | Number | Year | Number | Year | Number | Year | Number |
---|---|---|---|---|---|---|---|---|---|
1991 | 267 | 1997 | 271 | 2003 | 427 | 2009 | 335 | 2015 | 359 |
1992 | 289 | 1998 | 281 | 2004 | 377 | 2010 | 210 | 2016 | 371 |
1993 | 225 | 1999 | 368 | 2005 | 271 | 2011 | 195 | 2017 | 387 |
1994 | 236 | 2000 | 603 | 2006 | 285 | 2012 | 202 | 2018 | 394 |
1995 | 250 | 2001 | 585 | 2007 | 232 | 2013 | 273 | 2019 | 341 |
1996 | 290 | 2002 | 530 | 2008 | 297 | 2014 | 372 | 2020 | 352 |
Model | OA | Kappa | UA | PA | ||
---|---|---|---|---|---|---|
N-BL | BL | N-BL | BL | |||
Res-UNet++ | 0.99 | 0.96 | 0.99 | 0.97 | 1.00 | 0.83 |
Deeplab-v3 | 0.94 | 0.78 | 0.93 | 0.97 | 1.00 | 0.53 |
FCN | 0.95 | 0.82 | 0.95 | 0.93 | 1.00 | 0.55 |
Sub-Regions | Reference | |||||
---|---|---|---|---|---|---|
N-BL | BL | Total | UA | |||
WG | Map | N-BL | 0.94 | 0.01 | 0.95 | 0.99 |
BL | 0.00 | 0.05 | 0.05 | 0.97 | ||
Total | 0.94 | 0.06 | 1.00 | |||
PA | 1.00 | 0.86 | OA = 0.99 | |||
NG | Map | N-BL | 0.94 | 0.01 | 0.95 | 0.99 |
BL | 0.00 | 0.05 | 0.05 | 0.97 | ||
Total | 0.94 | 0.06 | 1.00 | |||
PA | 1.00 | 0.86 | OA = 0.99 | |||
EG | Map | N-BL | 0.93 | 0.02 | 0.95 | 0.98 |
BL | 0.00 | 0.05 | 0.05 | 1.00 | ||
Total | 0.93 | 0.067 | 1.00 | |||
PA | 1.00 | 0.76 | OA = 0.98 | |||
PRD | Map | N-BL | 0.94 | 0.01 | 0.95 | 0.99 |
BL | 0.00 | 0.05 | 0.05 | 0.97 | ||
Total | 0.94 | 0.06 | 1.00 | |||
PA | 1.00 | 0.86 | OA = 0.99 |
Year | OA | Kappa | UA | PA | Area (95% CI) (×103 km2) | ||
---|---|---|---|---|---|---|---|
BL | N-BL | BL | N-BL | ||||
1995 | 0.98 | 0.90 | 0.93 | 0.99 | 0.55 | 1.00 | 4.98 (±0.94) |
2000 | 0.99 | 0.92 | 0.94 | 0.99 | 0.63 | 1.00 | 5.37 (±1.76) |
2005 | 0.98 | 0.91 | 0.94 | 0.98 | 0.66 | 1.00 | 6.93 (±1.02) |
2010 | 0.99 | 0.94 | 0.94 | 0.99 | 0.85 | 1.00 | 7.87 (±0.71) |
2015 | 0.98 | 0.93 | 0.97 | 0.98 | 0.73 | 1.00 | 9.17 (±1.90) |
2020 | 0.99 | 0.96 | 0.97 | 0.99 | 0.83 | 1.00 | 10.65 (±1.56) |
Regions | Reference | |||||
---|---|---|---|---|---|---|
BL | N-BL | |||||
Bare Land | Sand | Mixed Pixel | Crop Land | |||
Mapped as BL | WG | 30 | 0 | 0 | 0 | 0 |
NG | 29 | 1 | 0 | 0 | 0 | |
EG | 29 | 0 | 0 | 1 | 0 | |
PRD | 29 | 0 | 0 | 0 | 1 | |
Total | 117 | 1 | 0 | 1 | 1 |
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Xu, H.; Xiao, X.; Qin, Y.; Qiao, Z.; Long, S.; Tang, X.; Liu, L. Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine. Remote Sens. 2022, 14, 3562. https://doi.org/10.3390/rs14153562
Xu H, Xiao X, Qin Y, Qiao Z, Long S, Tang X, Liu L. Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine. Remote Sensing. 2022; 14(15):3562. https://doi.org/10.3390/rs14153562
Chicago/Turabian StyleXu, Han, Xiangming Xiao, Yuanwei Qin, Zhi Qiao, Shaoqiu Long, Xianzhe Tang, and Luo Liu. 2022. "Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine" Remote Sensing 14, no. 15: 3562. https://doi.org/10.3390/rs14153562
APA StyleXu, H., Xiao, X., Qin, Y., Qiao, Z., Long, S., Tang, X., & Liu, L. (2022). Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine. Remote Sensing, 14(15), 3562. https://doi.org/10.3390/rs14153562