Monitoring plastic-mulched farmland by Landsat-8 oli imagery using spectral and textural features

Remote Sensing

Volumn 8, Issue 4, 2016, Pages

  Hasituya, H ^a   Chen, Zhongxin ^a   Wang, Limin ^a   Wu, Wenbin ^a   Jiang, Zhiwei ^b   Li, He ^a  

^a INSTITUTE OF PLANT PROTECTION   (China)

^b NATIONAL SATELLITE METEOROLOGICAL CENTER   (China)

Author keywords

Landsat 8; OLI imagery; Plastic mulched farmland; Spectral features; Support vector machine; Textural features

Indexed keywords

AGRICULTURAL MACHINERY; AGRICULTURE; CULTIVATION; MAXIMUM LIKELIHOOD; MONITORING; REMOTE SENSING; SUPPORT VECTOR MACHINES;

AGRICULTURAL PRODUCTIONS; GREY-LEVEL CO-OCCURRENCE MATRIXES; LANDSAT; MAXIMUM LIKELIHOOD CLASSIFIERS; MINIMUM DISTANCE CLASSIFIERS; OLI IMAGERY; SPECTRAL FEATURE; TEXTURAL FEATURE;

FARMS;

EID: 84971667903     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8040353     Document Type: Article

References (52)

1. Bai, L.; Hai, J.; Han, Q.; Jia, Z. Effects of mulching with different kinds of plastic film on growth and water use efficiency of winter wheat in Weibei Highland. Agric. Res. Arid Areas 2010, 28, 135-139
2. Takakura, T. Climate under Cover: Digital Dynamic Simulation in Plant Bio-Engineering; Springer Science+Business Media: Berlin, Germany, 1993
3. Yan, C.; Liu, E.; Shu, F.; Liu, Q.; Liu, S.; He, W. Review of agricultural plastic mulching and its residual pollution and prevention measures in China. J. Agric. Res. Environ. 2014, 31, 95-102
4. Yan, C.; He, W.; Liu, E.; Lin, T.; Pasquale, M.; Liu, S.; Liu, Q. Concept and estimation of crop safety period of plastic film mulching. Trans. Chin. Soc. Agric. Eng. 2015, 31, 1-4
5. Yan, C.; Mei, X.; He, W.; Zheng, S. Present situation of residue pollution of mulching plastic film and controlling measures. Trans. Chin. Soc. Agric. Eng. 2006, 22, 269-272
6. Moreau, S.; Bosseno, R.; Gu, X.F.; Baret, F. Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR. Remote Sens. Environ. 2003, 85, 516-529
7. Nordberg, M.L.; Evertson, J. Monitoring change in mountainous dry-heath vegetation at a regional scale using multitemporal Landsat TM data. AMBIO 2003, 32, 502-509
8. Gao, J. Quantification of grassland properties: How it can benefit from geoinformatic technologies? Int. J. Remote Sens. 2006, 27, 1351-1365
9. Haihui, W. Study on the Polarized Reflectance Characteristics of Agricultural Thin Membrane. Master Thesis, Northeast Normal University, Changchun, China, 2007
10. Lu, L.; Di, L.; Ye, Y. A Decision-Tree classifier for extracting transparent Plastic-Mulched landcover from landsat-5 TM images. IEEE JSTARS 2014, 7, 4548-4558
11. Lu, L.; Hang, D.; Di, L. Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: A case study in southern Xinjiang, China. J. Appl. Remote Sens. 2015
12. Carvajal, F.; Crisanto, E.; Aguilar, F.J.; Aguera, F.A.; Aguilar, M.A. Greenhouses detection using an artificial neural network with a very high resolution satellite image. Proc. ISPRS Tech. Comm. II Symp. 2006
13. Aguera, F.; Aguilar, F.J.; Aguilar, M.A. Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J. Photogramm. Remote Sens. 2008, 63, 635-646
14. Aguera, F.; Liu, J.G. Automatic greenhouse delineation from QuickBird and Ikonos satellite images. Comput. Electron. Agric. 2009, 66, 191-200
15. Koc-San, D. Evaluation of different classification techniques for the detection of glass and plastic greenhouses fromWorldView-2 satellite imagery. J. Appl. Remote Sens. 2013
16. Zhao, G.X.; Li, J.; Li, T.; Yue, Y.D.;Warner, T. Utilizing Landsat TM imagery to map greenhouses in Qingzhou, Shandong Province, China. Pedosphere 2004, 14, 363-369
17. Bin, S. Quality Inspection and Quality Analysis of Hebei Province Jizhou City Environment. Master Thesis, Northwest Agriculture & Forestry University, Yangling, China, 2014
18. Zhao, Y. The Principle and Method of Remote Sensing Application; Science Press: Beijing, China, 2003; pp. 204-205
19. Fodor, I.K. A survey of dimension reduction techniques. Opt Commun. 2002, 205, 351-359
20. Li, N.; Bruzzone, L.; Chen, Z.; Liu, F. A novel technique based on the combination of labeled Co-Occurrence matrix and variogram for the detection of built-up areas in High-Resolution SAR images. Remote Sens. 2014, 6, 3857-3878
21. Guindon, B.; Zhang, Y.; Dillabaugh, C. Landsat urban mapping based on a combined spectral-spatial methodology. Remote Sens. Environ. 2004, 92, 218-232
22. Pesaresi, M.; Gerhardinger, A.; Kayitakire, F. A robust Built-Up area presence index by anisotropic Rotation-Invariant textural measure. IEEE JSTARS 2008, 1, 180-192
23. Pesaresi, M.; Gerhardinger, A. Improved textural Built-Up presence index for automatic recognition of human settlements in arid regions with scattered vegetation. IEEE JSTARS 2011, 4, 16-26
24. Ghosh, A.; Joshi, P.K. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. Int. J. Appl. Earth Obs. 2014, 26, 298-311
25. Lu, D.; Hetrick, S.; Moran, E. Land cover classification in a complex Urban-Rural landscape with QuickBird imagery. Photogramm. Eng. Remote Sens. 2010, 76, 1159-1168
26. Angel Castillo-Santiago, M.; Ricker, M. Estimation of tropical forest structure from SPOT-5 satellite images. Int. J. Remote Sens. 2010, 31, 2767-2782
27. Kayitakire, F.; Hamel, C.; Defourny, P. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens. Environ. 2006, 102, 390-401
28. Ozdemir, I.; Norton, D.A.; Ozkan, U.Y.; Mert, A.; Senturk, O. Estimation of tree size diversity using object oriented texture analysis and aster imagery. Sensors 2008, 8, 4709-4724
29. Pasher, J.; King, D.J. Multivariate forest structure modelling and mapping using high resolution airborne imagery and topographic information. Remote Sens. Environ. 2010, 114, 1718-1732
30. Estes, L.D.; Okin, G.S.; Mwangi, A.G.; Shugart, H.H. Habitat selection by a rare forest antelope: A multi-scale approach combining field data and imagery from three sensors. Remote Sens. Environ. 2008, 112, 2033-2050
31. Estes, L.D.; Reillo, P.R.; Mwangi, A.G.; Okin, G.S.; Shugart, H.H. Remote sensing of structural complexity indices for habitat and species distribution modeling. Remote Sens. Environ. 2010, 114, 792-804
32. Beekhuizen, J.; Clarke, K.C. Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty. Int. J. Appl. Earth Obs. Geoinf. 2010, 12, 127-137
33. Johansen, K.; Coops, N.C.; Gergel, S.E.; Stange, Y. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sens. Environ. 2007, 110, 29-44
34. Pathak, V.; Dikshit, O. A new approach for finding an appropriate combination of texture parameters for classification. Geocarto Int. 2010, 25, 295-313
35. Pacifici, F.; Chini, M.E. A neural network approach using multiscale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sens. Environ. 2009, 113, 1276-1292
36. Bellman, R. Dynamic Programming, 2nd ed.; Dover Publications: Mineola, NY, USA, 2003
37. Saeys, Y.; Inza, I.; Larranaga, P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23, 2507-2517
38. Berberoglu, S.; Curran, P.J.; Lloyd, C.D.; Atkinson, P.M. Texture classification of Mediterranean land cover. Int. J. Appl. Earth Obs. Geoinf. 2007, 9, 322-334
39. Bazi, Y.; Melgani, F. Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Trans. Geosci. Remote Sens. 2006, 44, 3374-3385
40. Pal, M.; Foody, G.M. Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 2010, 48, 2297-2307
41. Rodriguez-Galiano, V.F.; Chica-Olmo, M.; Abarca-Hernandez, F.; Atkinson, P.M.; Jeganathan, C. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ. 2012, 121, 93-107
42. Chen, Y.; Zhao, X.; Lin, Z. Optimizing subspace SVM ensemble for hyperspectral imagery classification. IEEE JSTARS 2014, 7, 1295-1305
43. Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011, 66, 247-259
44. Pal, M.; Mather, P.M. Support vector machines for classification in remote sensing. Int. J. Remote Sens. 2005, 26, 1007-1011
45. Melgani, F.; Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci. Remote Sens. 2004, 42, 1778-1790
46. Huang, C.; Davis, L.S.; Townshend, J. An assessment of support vector machines for land cover classification. Int. J. Remote Sens. 2002, 23, 725-749
47. Heinzel, J.; Koch, B. Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 101-110
48. Angelo, J.J.; Duncan, B.W.;Weishampel, J.F. Using Lidar-Derived vegetation profiles to predict time since fire in an oak scrub landscape in East-Central florida. Remote Sens. 2010, 2, 514-525
49. Chen, C.H.; Ho, P.P. Statistical pattern recognition in remote sensing. Pattern Recognit. 2008, 41, 2731-2741
50. Duro, D.C.; Franklin, S.E.; Dube, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 2012, 118, 259-272
51. Vapnik, V.N. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 2000
52. Richards, J.A. Remote Sensing Digital Image Analysis; Springer-Verlag: Berlin, Germany, 1999