Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Study Overview
3.2. Data Collection
3.3. Data Collection and Processing
3.4. Classification Process
3.5. LULCC Analysis
3.6. Classification Accuracy Validation
4. Results
4.1. LULC Classification Accuracy Assessment
4.2. LULC Classification Results
4.3. LULC Change Detection
4.4. Relation with Terrace Field and Elevation
5. Discussion
5.1. Interpretation and Explanation of the Forest Change in North Korea
5.2. Importance of Spatial Analysis and Future Research Directions
5.3. Advantage of Time Series Image Composite on Google Earth Engine
5.4. Classification Limits and Advantages in North Korea
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- McKinley, D.C.; Ryan, M.; Birdsey, R.A.; Giardina, C.P.; Harmon, M.E.; Heath, L.S.; Houghton, R.A.; Jackson, R.B.; Morrison, J.F.; Murray, B.C.; et al. A synthesis of current knowledge on forests and carbon storage in the United States. Ecol. Appl. 2011, 21, 1902–1924. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hosonuma, N.; Herold, M.; De Sy, V.; De Fries, R.S.; Brockhaus, M.; Verchot, L.; Angelsen, A.; Romijn, E. An assessment of deforestation and forest degradation drivers in developing countries. Environ. Res. Lett. 2012, 7, 044009. [Google Scholar] [CrossRef]
- Guadalupe, V.; Sotta, E.D.; Santos, V.F.; Aguiar, L.J.G.; Vieira, M.; de Oliveira, C.P.; Siqueira, J.V.N. REDD+ implementation in a high forest low deforestation area: Constraints on monitoring forest carbon emissions. Land Use Policy 2018, 76, 414–421. [Google Scholar] [CrossRef]
- Murad, C.A.; Pearse, J. Landsat study of deforestation in the Amazon region of Colombia: Departments of Caquetá and Putumayo. Remote Sens. Appl. Soc. Environ. 2018, 11, 161–171. [Google Scholar] [CrossRef]
- Jeong, S.G.; Park, J.; Park, C.H.; Lee, D.K. Terrace fields classification in North Korea using modis multi-temporal image data. J. Korea Soc. Environ. Restor. Technol. 2016, 19, 73–83. [Google Scholar] [CrossRef] [Green Version]
- Jin, Y.; Sung, S.; Lee, D.K.; Biging, G.S.; Jeong, S. Mapping deforestation in North Korea using phenology-based multi-index and random forest. Remote Sens. 2016, 8, 997. [Google Scholar] [CrossRef] [Green Version]
- Choi, W.; Kang, S.; Choi, J.; Larsen, J.J.; Oh, C.; Na, Y.-G. Characteristics of deforestation in the Democratic People’s Republic of Korea (North Korea) between the 1980s and 2000s. Reg. Environ. Chang. 2016, 17, 379–388. [Google Scholar] [CrossRef]
- Kang, S.; Choi, W. Forest cover changes in North Korea since the 1980s. Reg. Environ. Chang. 2014, 14, 347–354. [Google Scholar] [CrossRef] [Green Version]
- Lim, C.-H.; Choi, Y.; Kim, M.; Jeon, S.W.; Lee, W.-K. Impact of deforestation on agro-environmental variables in Cropland, North Korea. Sustainability 2017, 9, 1354. [Google Scholar] [CrossRef] [Green Version]
- De Sousa, C.; Fatoyinbo, L.; Neigh, C.; Boucka, F.; Angoue, V.; Larsen, T. Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon. PLoS ONE 2020, 15, e0227438. [Google Scholar] [CrossRef]
- Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 2012, 67, 93–104. [Google Scholar] [CrossRef]
- Hu, Y.; Hu, Y. Land cover changes and their driving mechanisms in Central Asia from 2001 to 2017 supported by google earth engine. Remote Sens. 2019, 11, 554. [Google Scholar] [CrossRef] [Green Version]
- Qu, L.; Chen, Z.; Li, M.; Zhi, J.; Wang, H. Accuracy improvements to pixel-based and object-based lulc classification with auxiliary datasets from Google Earth Engine. Remote Sens. 2021, 13, 453. [Google Scholar] [CrossRef]
- Inglada, J.; Vincent, A.; Arias, M.; Tardy, B.; Morin, D.; Rodes, I. Operational high resolution land cover map production at the country scale using satellite image time series. Remote Sens. 2017, 9, 95. [Google Scholar] [CrossRef] [Green Version]
- Adam, J.O.; Prasad, S.T.; Pardhasaradhi, T.; Xiong, J.; Murali, K.G.; Russell, G.C.; Kamini, Y. Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud. Int. J. Appl. Earth Obs. Geoinf. 2019, 81, 110–124, ISSN 0303-2434. [Google Scholar] [CrossRef]
- Bagan, H.; Millington, A.; Takeuchi, W.; Yamagata, Y. Spatiotemporal analysis of deforestation in the Chapare region of Bolivia using landsat images. Land Degrad. Dev. 2020, 31, 3024–3039. [Google Scholar] [CrossRef]
- Margono, B.A.; Turubanova, S.; Zhuravleva, I.; Potapov, P.; Tyukavina, A.; Baccini, A.; Goetz, S.; Hansen, M.C. Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010. Environ. Res. Lett. 2012, 7, 034010. [Google Scholar] [CrossRef]
- Souza, C.M., Jr.; Siqueira, J.V.; Sales, M.H.; Fonseca, A.V.; Ribeiro, J.G.; Numata, I.; Cochrane, M.; Barber, C.; Roberts, D.; Barlow, J. Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens. 2013, 5, 5493–5513. [Google Scholar] [CrossRef] [Green Version]
- Hütt, C.; Koppe, W.; Miao, Y.; Bareth, G. Best accuracy land use/land cover (lulc) classification to derive crop types using multitemporal, multisensor, and multi-polarization sar satellite images. Remote Sens. 2016, 8, 684. [Google Scholar] [CrossRef] [Green Version]
- Ma, J.; Ding, Y.; Cheng, J.C.; Jiang, F.; Tan, Y.; Gan, V.J.; Wan, Z. Identification of high impact factors of air quality on a national scale using big data and machine learning techniques. J. Clean. Prod. 2020, 244, 118955. [Google Scholar] [CrossRef]
- Inglada, J.; Arias, M.; Tardy, B.; Hagolle, O.; Valero, S.; Morin, D.; Dedieu, G.; Sepulcre, G.; Bontemps, S.; Defourny, P.; et al. Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sens. 2015, 7, 12356–12379. [Google Scholar] [CrossRef] [Green Version]
- Müller, H.; Rufin, P.; Griffiths, P.; Siqueira, A.; Hostert, P. Mining dense landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sens. Environ. 2015, 156, 490–499. [Google Scholar] [CrossRef] [Green Version]
- Talukdar, S.; Singha, P.; Mahato, S.; Shahfahad; Pal, S.; Liou, Y.-A.; Rahman, A. Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sens. 2020, 12, 1135. [Google Scholar] [CrossRef] [Green Version]
- Gudex-Cross, D.; Pontius, J.; Adams, A. Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal landsat imagery. Remote Sens. Environ. 2017, 196, 193–204. [Google Scholar] [CrossRef]
- Tamiminia, H.; Salehi, B.; Mahdianpari, M.; Quackenbush, L.; Adeli, S.; Brisco, B. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 2020, 164, 152–170. [Google Scholar] [CrossRef]
- Phan, T.N.; Kuch, V.; Lehnert, L. Land cover classification using Google Earth Engine and random forest classifier—The role of image composition. Remote Sens. 2020, 12, 2411. [Google Scholar] [CrossRef]
- Dong, J.; Xiao, X. Evolution of regional to global paddy rice mapping methods: A review. ISPRS J. Photogramm. Remote Sens. 2016, 119, 214–227. [Google Scholar] [CrossRef] [Green Version]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Carrasco, L.; O’Neil, A.W.; Morton, R.D.; Rowland, C.S. Evaluating combinations of temporally aggregated sentinel-1, sentinel-2 and landsat 8 for land cover mapping with Google Earth Engine. Remote Sens. 2019, 11, 288. [Google Scholar] [CrossRef] [Green Version]
- Praticò, S.; Solano, F.; Di Fazio, S.; Modica, G. Machine learning classification of mediterranean forest habitats in Google Earth Engine based on seasonal sentinel-2 time-series and input image composition optimisation. Remote Sens. 2021, 13, 586. [Google Scholar] [CrossRef]
- Richards, D.R.; Belcher, R.N. Global changes in urban vegetation cover. Remote Sens. 2020, 12, 23. [Google Scholar] [CrossRef] [Green Version]
- Kang, B.-S. Hydrometeorological climate change trend. Meteor. Technol. Pol. Meteor. Admin. 2009, 2, 61–63. (In Korean) [Google Scholar]
- Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. The shuttle radar topography mission. Rev. Geophys. 2007, 45, 583–585. [Google Scholar] [CrossRef] [Green Version]
- Elvidge, C.D.; Baugh, K.E.; Kihn, E.A.; Kroehl, H.W.; Davis, E.R. Mapping city lights with nighttime data from the DMSP operational linescan system. Photogramm. Eng. Remote Sens. 1997, 63, 727–734. [Google Scholar]
- Miller, S.D.; Straka, W.; Mills, S.P.; Elvidge, C.D.; Lee, T.F.; Solbrig, J.; Walther, A.; Heidinger, A.K.; Weiss, S.C. Illuminating the capabilities of the suomi national polar-orbiting partnership (NPP) visible infrared imaging radiometer suite (VIIRS) day/night band. Remote Sens. 2013, 5, 6717–6766. [Google Scholar] [CrossRef] [Green Version]
- Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef] [Green Version]
- Chen, J.; Chen, J.; Liao, A.; Cao, X.; Chen, L.; Chen, X.; He, C.; Han, G.; Peng, S.; Lu, M.; et al. Global land cover mapping at 30m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 2015, 103, 7–27. [Google Scholar] [CrossRef] [Green Version]
- Gong, P.; Wang, J.; Yu, L.; Zhao, Y.; Zhao, Y.; Liang, L.; Niu, Z.; Huang, X.; Fu, H.; Liu, S.; et al. Finer resolution observation and monitoring of global land cover: First mapping results with landsat TM and ETM+ data. Int. J. Remote Sens. 2012, 34, 2607–2654. [Google Scholar] [CrossRef] [Green Version]
- Li, C.; Gong, P.; Wang, J.; Zhu, Z.; Biging, G.S.; Yuan, C.; Hu, T.; Zhang, H.; Wang, Q.; Li, X.; et al. The first all-season sample set for mapping global land cover with landsat-8 data. Sci. Bull. 2017, 62, 508–515. [Google Scholar] [CrossRef] [Green Version]
- Oliphant, A.; Thenkabail, P.; Teluguntla, P.; Xiong, J.; Congalton, R.; Yadav, K.; Smith, C. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) Cropland Extent 2015. Southeast Asia 30 m V001; ICRISAT: Hyderabad, India, 2017; Available online: http://oar.icrisat.org/id/eprint/10981 (accessed on 20 November 2020).
- Foody, G.M.; Mathur, A. Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification. Remote Sens. Environ. 2004, 93, 107–117. [Google Scholar] [CrossRef]
- Prasad, A.M.; Iverson, L.R.; Liaw, A. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems 2006, 9, 181–199. [Google Scholar] [CrossRef]
- Richards, J.A.; Richards, J. Remote Sensing Digital Image Analysis; Springer: Berlin/Heidelberg, Germany, 1999; Volume 3. [Google Scholar]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Naghibi, S.A.; Pourghasemi, H.R.; Dixon, B. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ. Monit. Assess. 2016, 188, 1–27. [Google Scholar] [CrossRef] [PubMed]
- Shabani, S.; Jaafari, A.; Bettinger, P. Spatial modeling of forest stand susceptibility to logging operations. Environ. Impact Assess. Rev. 2021, 89, 106601. [Google Scholar] [CrossRef]
- Abdullah, A.Y.M.; Masrur, A.; Adnan, M.S.G.; Baky, M.; Al, A.; Hassan, Q.K.; Dewan, A. Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sens. 2019, 11, 790. [Google Scholar] [CrossRef] [Green Version]
- DeFries, R.S.; Townshend, J.R.G. NDVI-derived land cover classifications at a global scale. Int. J. Remote Sens. 1994, 15, 3567–3586. [Google Scholar] [CrossRef]
- Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Xiong, J.; Thenkabail, P.S.; Tilton, J.C.; Gumma, M.K.; Teluguntla, P.; Oliphant, A.; Congalton, R.G.; Yadav, K.; Gorelick, N. Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using sentinel-2 and landsat-8 data on Google Earth Engine. Remote Sens. 2017, 9, 1065. [Google Scholar] [CrossRef] [Green Version]
- Oad, V.K.; Mustafa, M.R.U.; Takaijudin, H.B.; Nabi, G.; Hussain, M. Monitoring trends of land use and land cover changes in rajang river basin. In Proceedings of the 2020 Second International Sustainability and Resilience Conference: Technology and Innovation in Building Designs(51154), Sakheer, Bahrain, 11–12 November 2020; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2020; Volume 51154, pp. 1–8. [Google Scholar] [CrossRef]
- Lu, D.; Li, G.; Moran, E.; Hetrick, S. Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon. Int. J. Remote Sens. 2013, 34, 5953–5978. [Google Scholar] [CrossRef] [Green Version]
- Tian, Y.; Yin, K.; Lu, D.; Hua, L.; Zhao, Q.; Wen, M. Examining land use and land cover spatiotemporal change and driving forces in Beijing from 1978 to 2010. Remote Sens. 2014, 6, 10593–10611. [Google Scholar] [CrossRef] [Green Version]
- Ghosh, A.; Sharma, R.; Joshi, P. Random forest classification of urban landscape using landsat archive and ancillary data: Combining seasonal maps with decision level fusion. Appl. Geogr. 2014, 48, 31–41. [Google Scholar] [CrossRef]
- Liu, D.; Chen, N.; Zhang, X.; Wang, C.; Du, W. Annual large-scale urban land mapping based on landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin. ISPRS J. Photogramm. Remote Sens. 2020, 159, 337–351. [Google Scholar] [CrossRef]
- Bock, G.-W.; Zmud, R.W.; Kim, Y.-G.; Lee, J.-N. Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Q. 2005, 29, 87–111. [Google Scholar] [CrossRef]
- Fielding, A.H.; Bell, J.F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 1997, 24, 38–49. [Google Scholar] [CrossRef]
- Statistics Korea. Major Statistics Indicators of North Korea; Statistics Korea: Daejeon, Korea, 2019. [Google Scholar]
- National Institute of Forest Science. Development of a Method of Constructing North Korean Forest Information Using Satellite Imagery and AI; National Institute of Forest Science: Seoul, Korea, 2020. (In Korean) [Google Scholar]
- CRED. Annual Disaster Statistical Review 2015; CRED: Bengaluru, India, 2016. [Google Scholar]
- Han, S.H. Statistics on Livestock and Fishery Products in North Korea; Agricultural Village Research Institute: Ansan, Korea, 2017. (In Korean) [Google Scholar]
- Park, K.; Lee, S.; Park, S. A study on the North Korea’s change of forest policy since the economic crisis in 1990s. Korean J. Unification Aff. 2009, 21, 459–492. [Google Scholar]
- Oh, S.; Kim, E.; Kim, K. Characteristics of forest policy in the Kim Jong-Un era. North Korean Stud. 2018, 14, 101–133. [Google Scholar]
- Yu, J.; Park, H.; Kim, K.; Lee, S.-H. Review of slope criteria and forestland restoration plan in North Korea. J. Korea Soc. Environ. Restor. Technol. 2016, 19, 19–28. [Google Scholar] [CrossRef] [Green Version]
- Lee, S. North Korean Industrial Statistics; KIET: Sejong, Korea, 2015. (In Korean) [Google Scholar]
- Lee, B.-R.; Oh, S.-B.; Byun, H.-R. The characteristics of drought occurrence in North Korea and its comparison with drought in South Korea. Theor. Appl. Clim. 2015, 121, 199–209. [Google Scholar] [CrossRef]
- Ryu, J.-H.; Han, K.-S.; Lee, Y.-W.; Park, N.-W.; Hong, S.; Chung, C.-Y.; Cho, J. Different agricultural responses to extreme drought events in neighboring counties of South and North Korea. Remote Sens. 2019, 11, 1773. [Google Scholar] [CrossRef] [Green Version]
- Kim, K.-H.; Choi, K.-W.; Kim, I.-S.; Noh, S.-I. North Korea’s agricultural irrigation system. J. Korean Soc. Agric. Eng. 2014, 2014, 129. [Google Scholar]
- Kim, K.-H.; Jin, W.-G. Water resources of North Korea. J. Korean Soc. Agric. Eng. 2013, 102. [Google Scholar]
- Lee, S. North Korea’s Industry Trends in 2017; [KDI] North Korean Economy [Review, 20]; KIET: Sejong, Korea, 2018. (In Korean) [Google Scholar]
- Daunt, A.B.P.; Silva, T.S.F. Beyond the park and city dichotomy: Land use and land cover change in the northern coast of São Paulo (Brazil). Landsc. Urban Plan. 2019, 189, 352–361. [Google Scholar] [CrossRef]
- Lone, S.; Mayer, I.A. Geo-spatial analysis of land use/land cover change and its impact on the food security in District Anantnag of Kashmir Valley. GeoJournal 2018, 84, 785–794. [Google Scholar] [CrossRef]
- Kim, E.-S.; Lee, S.-H.; Cho, H.-K. Segment-based land cover classification using texture information in degraded forest land of north korea. Korean J. Remote Sens. 2010, 26, 477–487. [Google Scholar]
- Song, X.-P.; Hansen, M.C.; Stehman, S.V.; Potapov, P.V.; Tyukavina, A.; Vermote, E.F.; Townshend, J.R. Global land change from 1982 to 2016. Nature 2018, 560, 639–643. [Google Scholar] [CrossRef] [PubMed]
- Muriithi, F.K. Land use and land cover (LULC) changes in semi-arid sub-watersheds of Laikipia and Athi River basins, Kenya, as influenced by expanding intensive commercial horticulture. Remote Sens. Appl. Soc. Environ. 2016, 3, 73–88. [Google Scholar] [CrossRef]
- Kleemann, J.; Baysal, G.; Bulley, H.N.; Fürst, C. Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa. J. Environ. Manag. 2017, 196, 411–442. [Google Scholar] [CrossRef] [PubMed]
Class | Data | Year | Term | Resolution |
---|---|---|---|---|
Satellite Image | Landsat | 2000–2019 | 16 days | 30 m |
Defense Meteorological Program (DMSP)/Operational Line-Scan System (OLS) | 2001–2012 | 1 year | 30 arc seconds | |
NPOESS Preparatory Project (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) | 2012–2017 | 1 month | 15 arc seconds | |
Shuttle Radar Topography Mission V3 product (SRTM3) | 2000 | - | 30 m | |
Land use and land cover (LULC) Product | MCD12Q1.006 | 2001–2018 | 1 year | 500 m |
Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) | 2017 | - | 30 m | |
Global land cover (GLC30) | 2010 | - | 30 m | |
Global Forest Change dataset (GFCD) | 2000 | - | 30 m | |
Global Food Security-support Analysis Data Extent Southeast and Northeast Asia (GFSAD30SEACE) | 2015 | - | 30 m |
Land Cover | User’s Accuracy | Producer’s Accuracy |
---|---|---|
Built-up | 91.7% ± 8.3% | 85.3% ± 14.7% |
Cropland | 95.5% ±4.5% | 97.8% ± 2.2% |
Forest | 98.9% ± 1.1% | 99.7% ± 0.3% |
Grassland | 72.9% ± 27.1% | 66.7% ± 33.3% |
Water bodies | 96.7% ± 3.3% | 95% ± 5% |
Overall Accuracy | 98.2% ± 1.6% | Kappa Coefficient 0.959 ± 0.037 |
Class | Area 2001 | Area 2018 | Change Rate |
---|---|---|---|
Built-up | 0.789% | 1.083% | 37.26% |
Cropland | 30.832% | 29.237% | −5.17% |
Forest | 61.917% | 63.520% | 2.59% |
Grassland | 5.126% | 4.761% | −7.11% |
Water bodies | 1.336% | 1.399% | 4.73% |
Total | 100.00% | 100.00% |
Class | 2001–2004 | 2004–2007 | 2007–2010 | 2010–2013 | 2013–2016 | 2016–2018 |
---|---|---|---|---|---|---|
C to C | 27.08% | 27.44% | 27.16% | 26.73% | 26.51% | 26.49% |
C to F | 2.40% | 2.46% | 2.64% | 1.86% | 1.77% | 0.73% |
C to G | 0.62% | 1.28% | 0.95% | 1.66% | 2.35% | 0.94% |
F to C | 2.32% | 2.51% | 2.14% | 2.94% | 0.78% | 0.65% |
F to F | 58.73% | 59.42% | 59.59% | 59.04% | 59.33% | 60.84% |
F to G | 0.82% | 1.25% | 1.12% | 1.52% | 1.89% | 0.94% |
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Piao, Y.; Jeong, S.; Park, S.; Lee, D. Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea. Remote Sens. 2021, 13, 3501. https://doi.org/10.3390/rs13173501
Piao Y, Jeong S, Park S, Lee D. Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea. Remote Sensing. 2021; 13(17):3501. https://doi.org/10.3390/rs13173501
Chicago/Turabian StylePiao, Yong, Seunggyu Jeong, Sangjin Park, and Dongkun Lee. 2021. "Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea" Remote Sensing 13, no. 17: 3501. https://doi.org/10.3390/rs13173501