Review of Land Use Change Detection—A Method Combining Machine Learning and Bibliometric Analysis
Abstract
:1. Introduction
2. Data Collection and Cleaning
2.1. Data Sources
2.2. Data Cleaning
3. Research Methodology
4. Results
4.1. Analysis of the Number of Articles Issued and the Publishing Journal
4.1.1. Annual Trends in the Number of Publications
4.1.2. Analysis of Publishing Journals
4.2. Analysis of Key Countries and Institutions
4.3. Keyword Analysis
4.3.1. High-Frequency Keyword Analysis
4.3.2. Multiple Correspondence Analysis of High-Frequency Keywords
4.4. Analysis of Highly Cited Papers
Reference | DOI | Year | Local Citations (LC) | LC Per Year | Global Citations (GC) | GC Per Year | LC/GC Ratio (%) |
---|---|---|---|---|---|---|---|
[23] | 10.1080/0143116031000139863 | 2004 | 521 | 26.05 | 1824 | 91.2 | 28.56 |
[24] | 10.1080/0143116031000101675 | 2004 | 394 | 19.7 | 1395 | 69.75 | 28.24 |
[9] | 10.1016/j.isprsjprs.2013.03.006 | 2013 | 215 | 19.5455 | 830 | 75.4545 | 25.90 |
[76] | 10.1080/014311699213659 | 1999 | 201 | 8.04 | 654 | 26.16 | 30.73 |
[19] | 10.1016/j.rse.2014.01.011 | 2014 | 190 | 19 | 773 | 77.3 | 24.58 |
[56] | 10.1016/j.rse.2005.08.006 | 2005 | 182 | 9.5789 | 649 | 34.1579 | 28.04 |
[20] | 10.1016/j.rse.2010.07.008 | 2010 | 162 | 11.5714 | 942 | 67.2857 | 17.20 |
[75] | 10.1109/36.843009 | 2000 | 151 | 6.2917 | 855 | 35.625 | 17.66 |
[13] | 10.1016/j.rse.2009.08.014 | 2010 | 149 | 10.6429 | 969 | 69.2143 | 15.38 |
[51] | 10.1016/S0034-4257(00)00169-3 | 2001 | 135 | 5.8696 | 1228 | 53.3913 | 10.99 |
4.5. Analysis of the Development and Evolution of LUCD
4.5.1. Development of LUCD Data Sources
4.5.2. Development of LUCD Methods
Data Sources | Study Region | Year | Change Detection Methods | Overall Accuracy (%) | References |
---|---|---|---|---|---|
AVHRR on NOAA−9 and NOAA−11 | West Africa | 1987–1989 | Change-vector analysis (CVA) | -/- | [54] |
Aerial images | Carolina bay and bay-like wetlands | 1951, 1992 | Visual interpretation | -/- | [89] |
Landsat TM images | USA | 1988, 1994 | Generalized linear models (GLMs) | -/- | [96] |
Land cover map | Northern Patagonia, Argentina | 1913, 1985 | Visual interpretation and field investigation | 84.4 | [88] |
Landsat TM images | Neuse River Basin | 1993, 2000 | Multiband image difference | 80–91 | [97] |
Spot images | Lusitu, the Southern Province of Zambia | 1986, 1992, 1997 | Maximum likelihood classification | 83 | [78] |
Landsat TM images | Minnesota, USA | 1990–1995 | Fitting Landsat TM images with topographic maps | 89 | [91] |
Landsat TM images | Moist tropical region of the Amazon | 1994, 1998 | Principal component differencing | 92–99 | [98] |
MODIS Vegetative cover conversion (VCC) | Idaho, Montana, New Mexico, Cambodia, Thailand, and Brazil | 2000 | Decision trees | 55–90 | [79] |
Landsat TM images | Minnesota, USA | 1986, 1991, 1998, and 2002 | Maximum likelihood classification | 80–90 | [56] |
MODIS NDVI | Albemarle, Pamlico Estuary System (APES) region of the US | 2001–2005 | Threshold method | 88 | [92] |
Landsat TM images | Oregon, USA | 1984–2004 | Trajectory-based change detection | 77–91 | [99] |
Landsat TM/ETM + and Landsat MSS | Horqin Sandy Land, China | 1975, 1987, 1999, and 2007 | Self-organizing mapping neural network method and subspace method | 70.66–86 | [90] |
Landsat ETM + | Mountainous area in Mexico | 1999, 2006 | Object-based maximum likelihood (ML) and standard nearest-neighbor (SNN) | 71–77 | [100] |
Landsat | Savannah River | 2001–2004 | The continuous monitoring of forest disturbance algorithm (CMFDA) | 95 | [82] |
Landsat | Louisiana, Colorado, and Mississippi | 2006, 2011 | Comprehensive change detection method (CCDM) | 91 | [101] |
Landsat | New England | 1982–2011 | CCDC | 90 | [19] |
MODIS NDVI | Southeast Australia | 2000–2008 | Breaks for additive season and trend (BFAST) | -/- | [13] |
Landsat | Pacific Northwest of the USA | 1985–2007 | LandTrendr | 97 | [20] |
Sentinel−2 | Klingenberg, Saxony, Germany | 2016, 2018 | Fully convolutional neural network (FCN) and long short-term memory (LSTM) networks | 87 | [102] |
Unmanned aerial vehicle (0.2 m) | Guangzhou, China | 2009, 2019 | A deep multitasking learning frame- work for change detection (MTL-CD) | 92.97 | [86] |
Aerial imagery | -/- | 2012–2020 | Dual correlation attention-guided detector (DCA-Det) | 99.5 | [103] |
Landsat5, 7, 8 | Australia | 1985–2015 | Random forest (RF) | 93 | [104] |
Sentinel−2 | Western Cape Province of South Africa | 2016–2021 | Convolutional neural networks (CNN) and transformer | 89 | [22] |
Landsat TM/ETM+/OLI | Shenzhen, China | 1986–2017 | Temporal segmentation and trajectory classification | 93.33 | [105] |
0.2 m high-resolution images | -/- | -/- | Super-resolution-based change detection network (SRCDNet) | 85.66–90.02 | [84] |
5. Discussion
5.1. Future Research Directions for LUCD
- (1)
- Expanding the range of image data acquisition can be achieved by combining multiple data sources.
- (2)
- Cloud platforms should be utilized to conduct more precise, long-term, and large-scale land use change detection studies.
- (3)
- Further research is needed on the geometric registration and spectral differences of multi-source remote sensing images during the preprocessing stage.
- (4)
- Accuracy evaluation should be improved, and object-oriented and feature-based accuracy evaluation methods should be developed.
- (5)
- Future research should focus on studying optimal, adaptive, and full-scale image segmentation and threshold selection techniques.
- (6)
- Based on deep learning, LUCD methods have demonstrated great potential in recent years through their multi-level and deep network structures.
5.2. Advantages and Uncertainties
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sources | Articles | Total Citations (TC) | H-Index |
---|---|---|---|
Remote Sensing | 585 | 11,380 | 49 |
Remote Sensing of Environment | 313 | 36,064 | 98 |
International Journal of Remote Sensing | 242 | 13,400 | 50 |
IEEE Transactions on Geoscience and Remote Sensing | 153 | 7961 | 47 |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 110 | 2019 | 25 |
International Journal of Applied Earth Observation and Geoinformation | 88 | 2310 | 28 |
ISPRS Journal of Photogrammetry and Remote Sensing | 88 | 5593 | 38 |
Environmental Monitoring and Assessment | 65 | 1438 | 19 |
Journal of Applied Remote Sensing | 58 | 591 | 12 |
Photogrammetric Engineering and Remote Sensing | 58 | 2962 | 26 |
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Liu, B.; Song, W.; Meng, Z.; Liu, X. Review of Land Use Change Detection—A Method Combining Machine Learning and Bibliometric Analysis. Land 2023, 12, 1050. https://doi.org/10.3390/land12051050
Liu B, Song W, Meng Z, Liu X. Review of Land Use Change Detection—A Method Combining Machine Learning and Bibliometric Analysis. Land. 2023; 12(5):1050. https://doi.org/10.3390/land12051050
Chicago/Turabian StyleLiu, Bo, Wei Song, Zhan Meng, and Xinwei Liu. 2023. "Review of Land Use Change Detection—A Method Combining Machine Learning and Bibliometric Analysis" Land 12, no. 5: 1050. https://doi.org/10.3390/land12051050