Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches


Autoři: Sumin Park aff001;  Haemi Park aff001;  Jungho Im aff001;  Cheolhee Yoo aff001;  Jinyoung Rhee aff003;  Byungdoo Lee aff004;  ChunGeun Kwon aff004
Působiště autorů: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea aff001;  Institute of Industrial Science, The University of Tokyo, Tokyo, Japan aff002;  Climate Analytics Department, APEC Climate Center, Busan, South Korea aff003;  Forest Conservation Department, National Institute of Forest Science, Seoul, South Korea aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223362

Souhrn

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Köppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC).

Klíčová slova:

Artificial neural networks – Climate change – Climate modeling – Machine learning – Meteorology – Snow – Summer – Winter


Zdroje

1. Beck C, Grieser J, Kottek M, Rubel F, Rudolf B. Characterizing global climate change by means of Köppen climate classification. Klimastatusbericht. 2005;51:139–49.

2. Köppen WP. Das geographische System der Klimate: mit 14 Textfiguren: Borntraeger; 1936.

3. Trewartha G, Horn L. Köppen's classification of climates. An Introduction to climate McGraw-Hill, New York. 1980:397–403.

4. Bunkers MJ, Miller JR Jr, DeGaetano AT. Definition of climate regions in the Northern Plains using an objective cluster modification technique. Journal of Climate. 1996;9(1):130–46.

5. Malmgren BA, Winter A. Climate zonation in Puerto Rico based on principal components analysis and an artificial neural network. Journal of climate. 1999;12(4):977–85.

6. Rhee J, Im J, Carbone GJ, Jensen JR. Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas. Remote Sensing of Environment. 2008;112(6):3099–111.

7. Cannon AJ. Regression-guided clustering: a semisupervised method for circulation-to-environment synoptic classification. Journal of Applied Meteorology and Climatology. 2012;51(2):185–90.

8. Geiger R, Pohl W. Eine neue Wandkarte der Klimagebiete der Erde nach W. Köppens Klassifikation (A New Wall Map of the Climatic Regions of the World According to W. Köppen's Classification). Erdkunde. 1954;8:58–61.

9. Peel MC, Finlayson BL, McMahon TA. Updated world map of the Köppen-Geiger climate classification. Hydrology and earth system sciences discussions. 2007;4(2):439–73.

10. Lohmann U, Sausen R, Bengtsson L, Cubasch U, Perlwitz J, Roeckner E. The Köppen climate classification as a diagnostic tool for general circulation models. Climate Research. 1993;3:177–93.

11. Feng S, Hu Q, Huang W, Ho C-H, Li R, Tang Z. Projected climate regime shift under future global warming from multi-model, multi-scenario CMIP5 simulations. Global and Planetary Change. 2014;112:41–52.

12. Peel MC, McMahon TA, Finlayson BL, Watson FG. Identification and explanation of continental differences in the variability of annual runoff. Journal of Hydrology. 2001;250(1–4):224–40

13. Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P, Podzun R, et al. Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. Atmosphere. 2012;3(1):181–99.

14. Teutschbein C, Seibert J. Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrology and Earth System Sciences. 2013;17(12):5061–77.

15. Belda M, Holtanová E, Halenka T, Kalvová J. Climate classification revisited: from Köppen to Trewartha. Climate research. 2014;59(1):1–13.

16. Rubel F, Brugger K, Haslinger K, Auer I. The climate of the European Alps: Shift of very high resolution Köppen-Geiger climate zones 1800–2100. Meteorologische Zeitschrift. 2017;26(2):115–25.

17. Unal Y, Kindap T, Karaca M. Redefining the climate zones of Turkey using cluster analysis. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2003;23(9):1045–55.

18. Hijmans RJ, Graham CH. The ability of climate envelope models to predict the effect of climate change on species distributions. Global change biology. 2006;12(12):2272–81.

19. Willmes C, Becker D, Brocks S, Hütt C, Bareth G. High Resolution Köppen‐Geiger Classifications of Paleoclimate Simulations. Transactions in GIS. 2017;21(1):57–73.

20. Hutchinson MF, McKenney DW, Lawrence K, Pedlar JH, Hopkinson RF, Milewska E, et al. Development and testing of Canada-wide interpolated spatial models of daily minimum–maximum temperature and precipitation for 1961–2003. Journal of Applied Meteorology and Climatology. 2009;48(4):725–41.

21. Cuervo‐Robayo AP, Téllez‐Valdés O, Gómez‐Albores MA, Venegas‐Barrera CS, Manjarrez J, Martínez‐Meyer E. An update of high‐resolution monthly climate surfaces for Mexico. International Journal of Climatology. 2014;34(7):2427–37.

22. Diaz HF, Eischeid JK. Disappearing “alpine tundra” Köppen climatic type in the western United States. Geophysical Research Letters. 2007;34:L18707.

23. Zhang X, Yan X. Spatiotemporal change in geographical distribution of global climate types in the context of climate warming. Climate dynamics. 2014;43(3–4):595–605.

24. Sathiaraj D, Huang X, Chen J. Predicting climate types for the Continental United States using unsupervised clustering techniques. Environmetrics. 2019;30(4):e2524.

25. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift. 2006;15(3):259–63

26. Kwon E-H, Sohn B-J, Chang D-E, Ahn M-H, Yang S. Use of numerical forecasts for improving TMI rain retrievals over the mountainous area in Korea. Journal of Applied Meteorology and Climatology. 2008;47(7):1995–2007.

27. Zeng L, Wardlow B, Tadesse T, Shan J, Hayes M, Li D, et al. Estimation of daily air temperature based on MODIS land surface temperature products over the corn belt in the US. Remote Sensing. 2015;7(1):951–70.

28. Yoo C, Im J, Park S, Quackenbush LJ. Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data. ISPRS journal of photogrammetry and remote sensing. 2018;137:149–62.

29. Noi P, Degener J, Kappas M. Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of MODIS LST data. Remote Sensing. 2017;9(5):398.

30. Zhang H, Zhang F, Ye M, Che T, Zhang G. Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data. Journal of Geophysical Research: Atmospheres. 2016;121(19):11,425–11,41.

31. Zakšek K, Schroedter-Homscheidt M. Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments. ISPRS Journal of Photogrammetry and Remote Sensing. 2009;64(4):414–21.

32. Rubel F, Kottek M. Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorologische Zeitschrift. 2010;19(2):135–41.

33. Harris I, Jones P. CRU TS4. 00: Climatic Research Unit (CRU) Time-Series (TS) version 4.00 of high resolution gridded data of month-by-month variation in climate (Jan. 1901–Dec. 2015). Centre for Environmental Data Analysis. 2017;25.

34. Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B. GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology. 2014;115(1–2):15–40.

35. de Barros Soares D, Lee H, Loikith PC, Barkhordarian A, Mechoso CR. Can significant trends be detected in surface air temperature and precipitation over South America in recent decades? International Journal of Climatology. 2017;37(3):1483–93.

36. Gao J, Meng B, Liang T, Feng Q, Ge J, Yin J, et al. Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China. ISPRS Journal of Photogrammetry and Remote Sensing. 2019;147:104–17.

37. Geiß C, Pelizari PA, Marconcini M, Sengara W, Edwards M, Lakes T, et al. Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques. ISPRS journal of photogrammetry and remote sensing. 2015;104:175–88

38. Georganos S, Grippa T, Vanhuysse S, Lennert M, Shimoni M, Kalogirou S, et al. Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application. GIScience & remote sensing. 2018;55(2):221–42.

39. Ozdogan M. A practical and automated approach to large area forest disturbance mapping with remote sensing. PloS one. 2014;9(4):e78438. doi: 10.1371/journal.pone.0078438 24717283

40. Adelabu S, Mutanga O, Adam EE, Cho MA. Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image. Journal of Applied Remote Sensing. 2013;7(1):073480.

41. Kim YH, Im J, Ha HK, Choi J-K, Ha S. Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GIScience & Remote Sensing. 2014;51(2):158–74.

42. Omer G, Mutanga O, Abdel-Rahman EM, Adam E. Performance of support vector machines and artificial neural network for mapping endangered tree species using WorldView-2 data in Dukuduku forest, South Africa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015;8(10):4825–40.

43. Forkuor G, Hounkpatin OK, Welp G, Thiel MJPo. High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. 2017;12(1):e0170478. doi: 10.1371/journal.pone.0170478 28114334

44. Wang L, Chang Q, Yang J, Zhang X, Li FJPo. Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments. 2018;13(12):e0207624. doi: 10.1371/journal.pone.0207624 30517144

45. Breiman L. Random forests. Machine learning. 2001;45(1):5–32.

46. Jeong JH, Resop JP, Mueller ND, Fleisher DH, Yun K, Butler EE, et al. Random forests for global and regional crop yield predictions. PLoS One. 2016;11(6):e0156571. doi: 10.1371/journal.pone.0156571 27257967

47. Park S, Im J, Park S, Rhee J. Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology. 2017;237:257–69.

48. Rhee J, Im J. Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data. Agricultural and Forest Meteorology. 2017;237:105–22.

49. Ke Y, Im J, Park S, Gong H. Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sensing. 2016;8(3):215.

50. Sim S, Im J, Park S, Park H, Ahn M, Chan P-w. Icing detection over East Asia from geostationary satellite data using machine learning approaches. Remote Sensing. 2018;10(4):631.

51. Guo Z, Du S. Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data. GIScience & Remote Sensing. 2017;54(1):38–63.

52. Liu T., Abd-Elrahman A., Morton J., Wilhelm V. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience and Remote Sensing. 2018; 55:243–264.

53. Forkuor G., Dimobe K., Serme I., Tondoh J. Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience and Remote Sensing. 2018; 55:331–354.

54. Liaw A, Wiener M. Classification and regression by randomForest. R news. 2002;2(3):18–22.

55. Omrani H., Tayyebi A., Pijanowski B. Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework. GIScience and Remote Sensing. 2017; 54:283–304.

56. Callister KE, Griffioen PA, Avitabile SC, Haslem A, Kelly LT, Kenny SA, et al. Historical maps from modern images: using remote sensing to model and map century-long vegetation change in a fire-prone region. PloS one. 2016;11(3):e0150808. doi: 10.1371/journal.pone.0150808 27029046

57. Yang S, Feng Q, Liang T, Liu B, Zhang W, Xie H. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region. Remote Sensing of Environment. 2018;204:448–55.

58. Maxwell AE, Warner TA, Fang F. Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing. 2018;39(9):2784–817.

59. Yang J., Guo A., Li Y., Zhang Y., Li X. Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District. GIScience and Remote Sensing. 2019; 56:388–405.

60. Pham T., Yoshino K., Bui D. Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks. GIScience and Remote Sensing. 2017; 54: 329–353.

61. Huang K, Li S, Kang X, Fang L. Spectral–spatial hyperspectral image classification based on KNN. Sensing and Imaging. 2016;17(1):1.

62. Meng Q, Cieszewski CJ, Madden M, Borders BE. K nearest neighbor method for forest inventory using remote sensing data. GIScience & Remote Sensing. 2007;44(2):149–65.

63. Franco-Lopez H, Ek AR, Bauer ME. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote sensing of Environment. 2001;77(3):251–74.

64. Cao J, Li Y, Tian Y. Emotional modelling and classification of a large-scale collection of scene images in a cluster environment. PloS one. 2018;13(1):e0191064. doi: 10.1371/journal.pone.0191064 29320579

65. Lu D, Li G, Moran E, Kuang W. A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon. GIScience & Remote Sensing. 2014;51(6):695–709.

66. Borra S, Thanki R, Dey N. Satellite Image Analysis: Clustering and Classification: Springer; 2019.

67. Tan K, Hu J, Li J, Du P. A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination. ISPRS journal of photogrammetry and remote sensing. 2015;105:19–29.

68. Dou J, Bui DT, Yunus AP, Jia K, Song X, Revhaug I, et al. Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PloS one. 2015;10(7):e0133262. doi: 10.1371/journal.pone.0133262 26214691

69. Park S, Im J, Park S, Yoo C, Han H, Rhee J. Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sensing. 2018;10(3):447.

70. Tesfamichael S., Newete S., Adam E., Dubula B. Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants. GIScience and Remote Sensing. 2018; 55: 417–436.

71. Kim M, Park M-S, Im J, Park S, Lee M-I. Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data. Remote Sensing. 2019;11(10):1195.

72. Du X, Li Q, Shang J, Liu J, Qian B, Jing Q, et al. Detecting advanced stages of winter wheat yellow rust and aphid infection using RapidEye data in North China Plain. GIScience & Remote Sensing. 2019:1–21.

73. Chu H-J, Wang C-K, Kong S-J, Chen K-C. Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classification. GIScience & Remote Sensing. 2016;53(4):542–59.

74. Guo L, Chehata N, Mallet C, Boukir S. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing. 2011;66(1):56–66.

75. Williams G. Data mining with Rattle and R: The art of excavating data for knowledge discovery: Springer Science & Business Media; 2011.

76. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides. 2016;13(5):839–56.

77. Tatsumi K, Yamashiki Y, Torres MAC, Taipe CLR. Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Computers and Electronics in Agriculture. 2015;115:171–9.

78. Vogels M, De Jong SM, Sterk G, Addink EA. Agricultural cropland mapping using black-and-white aerial photography, object-based image analysis and random forests. International journal of applied earth observation and geoinformation. 2017;54:114–23.

79. Behnke R, Vavrus S, Allstadt A, Albright T, Thogmartin WE, Radeloff VC. Evaluation of downscaled, gridded climate data for the conterminous United States. Ecological applications. 2016;26(5):1338–51. doi: 10.1002/15-1061 27755764

80. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. International journal of climatology. 2005;25(15):1965–78.

81. Ham J, Chen Y, Crawford MM, Ghosh J. Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing. 2005;43(3):492–501.

82. Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing. 2012;67:93–104.

83. Kim HB, Sohn G. Point-based classification of power line corridor scene using random forests. Photogrammetric Engineering & Remote Sensing. 2013;79(9):821–33.

84. Shao Y, Lunetta RS. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing. 2012;70:78–87.

85. Suh J, Choi Y, Roh T-D, Lee H-J, Park H-D. National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea. Environmental Earth Sciences. 2011;63(3):619–32.

86. Kim G, Won S, Kim D. GIS based analysis of landslide effecting factors in the Pyeongchang area. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography. 2014;32(3):261–9.

87. Kim H-G, Kang Y-H, Kim J-Y. Evaluation of wind resource potential in mountainous region considering morphometric terrain characteristics. Wind Engineering. 2017;41(2):114–23.

88. Chen Z, Ye X, Huang P. Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks. Water. 2018;10(1):26.

89. Yun K-S, Heo K-Y, Chu J-E, Ha K-J, Lee E-J, Choi Y, et al. Changes in climate classification and extreme climate indices from a high-resolution future projection in Korea. Asia-Pacific Journal of Atmospheric Sciences. 2012;48(3):213–26.

90. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific data. 2018;5:180214. doi: 10.1038/sdata.2018.214 30375988

91. Arguez A, Vose RS. The definition of the standard WMO climate normal: The key to deriving alternative climate normals. Bulletin of the American Meteorological Society. 2011;92(6):699–704.

92. Chang H, Kwon W-T. Spatial variations of summer precipitation trends in South Korea, 1973–2005. Environmental Research Letters. 2007;2(4):045012.

93. Yang J, Ding Y, Chen R, Liu L. Fluctuations of the semi-arid zone in China, and consequences for society. Climatic change. 2005;72(1–2):171–88.

94. Kelly AE, Goulden ML. Rapid shifts in plant distribution with recent climate change. Proceedings of the National Academy of Sciences. 2008;105(33):11823–6.

95. Lough J. Shifting climate zones for Australia's tropical marine ecosystems. Geophysical Research Letters. 2008;35(14).


Článek vyšel v časopise

PLOS One


2019 Číslo 10

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Léčba bolesti v ordinaci praktického lékaře
nový kurz
Autoři: MUDr. PhDr. Zdeňka Nováková, Ph.D.

Revmatoidní artritida: včas a k cíli
Autoři: MUDr. Heřman Mann

Jistoty a nástrahy antikoagulační léčby aneb kardiolog - neurolog - farmakolog - nefrolog - právník diskutují
Autoři: doc. MUDr. Štěpán Havránek, Ph.D., prof. MUDr. Roman Herzig, Ph.D., doc. MUDr. Karel Urbánek, Ph.D., prim. MUDr. Jan Vachek, MUDr. et Mgr. Jolana Těšínová, Ph.D.

Léčba akutní pooperační bolesti
Autoři: doc. MUDr. Jiří Málek, CSc.

Nové antipsychotikum kariprazin v léčbě schizofrenie
Autoři: prof. MUDr. Cyril Höschl, DrSc., FRCPsych.

Všechny kurzy
Kurzy Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se