Optimizing kNN for mapping vegetation cover of arid and semi-arid areas using landsat images

Remote Sensing

Volumn 10, Issue 8, 2018, Pages

  Sun, Hua ^a,b,c   Wang, Qing ^d   Wang, Guangxing ^a,b,c,d   Lin, Hui ^a,b,c   Luo, Peng ^e   Li, Jiping ^a,b,c   Zeng, Siqi ^a,b,c   Xu, Xiaoyu ^a,b,c   Ren, Lanxiang ^a,b,c  

^a CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY   (China)

^b Key Lab of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province   (China)

^c Key Lab of State Forestry Administration on Forest Resources Management   (China)

^d SOUTHERN ILLINOIS UNIVERSITY   (United States)

^e RESEARCH INSTITUTE OF FORESTRY   (China)

Author keywords

Duolun County; Kangbao County; Land degradation; Landsat image; Optimized k nearest neighbors; Percentage vegetation cover

Indexed keywords

ARID REGIONS; DECISION TREES; FORECASTING; INFRARED DEVICES; MAPPING; MEAN SQUARE ERROR; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; REGRESSION ANALYSIS; VEGETATION;

DUOLUN COUNTY; K-NEAREST NEIGHBORS; KANGBAO COUNTY; LAND DEGRADATION; LANDSAT IMAGES; VEGETATION COVER;

IMAGE ENHANCEMENT;

EID: 85051632898     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10081248     Document Type: Article

References (74)

1. Veron, S.R.; Paruelo, J.M.; Oesterheld, M. Assessing desertification. J. Arid Environ. 2006, 66, 751-763
2. Reynolds, J.F.; Smith, D.M.S.; Lambin, E.F.; Turner, B.L.; Mortimore, M.; Batterbury, S.P.J.; Downing, T.E.; Dowlatabadi, H.; Fernández, R.J.; Herrick, J.E.; et al. Global desertification: building a science for dryland development. Science 2007, 316, 847-851
3. Mariano, D.A.; dos Santos, C.A.C.; Wardlow, B.D.; Anderson, M.C.; Schiltmeyer, A.C.; Tadesse, T.; Svoboda, M.D. Use of remote sensing indicators to assess effects of drought and human induced land degradation on ecosystem health in Northeastern Brazil. Remote Sens. Environ. 2018, 213, 129-143
4. Dymond, J.R.; Stephens, P.R.; Newsome, P.F.; Wilde, R.H. Percentage percentage vegetation cover of a degrading rangeland from SPOT. Int. J. Remote Sens. 1992, 13, 1999-2007
5. Eklundh, L.; Olsson, L. Vegetation index trends for the African Sahel in 1982-1999. Geophys. Res. Lett. 2003, 30, 1430-1434
6. Schucknecht, A.; Erasmi, S.; Niemeyer, I.; Matschullat, J. Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series. Eur. J. Remote Sens. 2013, 46, 40-59
7. Lehnert, L.W.; Meyer, H.;Wang, Y.; Miehe, G.; Thies, B.; Reudenbach, C.; Bendix, J. Retrieval of grassland plant coverage on the Tibetan Plateau based on a multi-scale, multi-sensor and multi-method approach. Remote Sens. Environ. 2015, 164, 197-207
8. Symeonakis, E.; Drake, N. Monitoring desertification and land degradation over sub-Saharan Africa. Int. J. Remote Sens. 2004, 25, 573-592
9. Wessels, K.J.; Prince, S.D.; Malherbe, J.; Small, J.; Frost, P.E.; VanZyl, D. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. J. Arid Environ. 2007, 68, 271-297
10. Tchuenté, A.T.K.; De Jong, S.M.; Roujean, J.L.; Favier, C.; Mering, C. Ecosystem mapping at the African continent scale using a hybrid clustering approach based on 1-km resolution multi-annual data from SPOT/VEGETATION. Remote Sens. Environ. 2011, 115, 452-464
11. Boschetti, M.; Nutini, F.; Brivio, P.A.; Bartholomé, E.; Stroppiana, D.; Hoscilo, A. Identification of environmental anomaly hot spots in West Africa from time series of NDVI and rainfall. ISPRS J. Photogramm. Remote Sens. 2013, 78, 26-40
12. Landmann, T.; Dubovyk, O. Spatial analysis of human-induced vegetation productivity decline over eastern Africa using a decade (2001-2011) of medium resolution MODIS time-series data. Int. J. Appl. Earth Obs. Geoinf. 2014, 33, 76-82
13. Chen, Y.; Gillieson, D. Evaluation of Landsat TM vegetation indices for estimating percentage vegetation cover on semi-arid rangelands: a case study from Australia. Can. J. Remote Sens. 2009, 35, 435-446
14. Wang, T.; Yan, C.Z.; Song, X.; Xie, J.L. Monitoring recent trends in the area of aeolian desertified land using Landsat images in China's Xinjiang region. ISPRS J. Photogramm. Remote Sens. 2012, 68, 184-190
15. Jia, K.; Liang, S.; Gu, X.; Baret, F.; Wei, X.; Wang, X.; Yao, Y.; Yang, L.; Li, Y. Fractional percentage vegetation cover estimation algorithm for Chinese GF-1 wide field view data. Remote Sens. Environ. 2016, 177, 184-191
16. Halperin, J.; LeMay, V.; Coops, N.; Verchot, L.; Marshall, P.; Lochhead, K. Canopy cover estimation in miombo woodlands of Zambia: comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods. Remote Sens. Environ. 2016, 179, 170-182
17. Becker, F.; Choudhury, B.J. Relative sensitivity of normalized difference vegetation index (NDVI) and microwave polarization difference index (MPDI) for vegetation and desertification monitoring. Remote Sens. Environ. 1988, 24, 297-311
18. Carreiras, J.M.; Pereira, J.M.; Pereira, J.S. Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. For. Ecol. Manag. 2006, 223, 45-53
19. Wang, Z.; Xiao, X.; Yan, X. Modeling gross primary production of maize cropland and degraded grassland in northeastern China. Agric. For. Meteorol. 2010, 150, 1160-1167
20. del Barrio, G.; Puigdefabregas, J.; Sanjuan, M.E.; Stellmes, M.; Ruiz, A. Assessment and monitoring of land condition in the Iberian Peninsula, 1989-2000. Remote Sens. Environ. 2010, 114, 1817-1832
21. Lamchin, M.; Lee, J.Y.; Lee, W.K.; Lee, E.J.; Kim, M.; Lim, C.H.; Choi, H.A.; Kim, S.R. Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Adv. Space Res. 2016, 57, 64-77
22. Munson, S.M.; Long, A.L.; Wallace, C.S.; Webb, R.H. Cumulative drought and land-use impacts on perennial vegetation across a North American dryland region. Appl. Veg. Sci. 2016, 19, 430-441
23. Huerta, E.; van derWal, H. Soil macroinvertebrates' abundance and diversity in home gardens in Tabasco, Mexico, vary with soil texture, organic matter and percentage vegetation cover. Eur. J. Soil Biol. 2012, 50, 68-75
24. Jakob, S.; Bühler, B.; Gloaguen, R.; Breitkreuz, C.; Eliwa, H.A.; El Gameel, K. Remote sensing based improvement of the geological map of the Neoproterozoic Ras Gharib segment in the Eastern Desert (NE-Egypt) using texture features. J. Afr. Earth Sci. 2015, 111, 138-147
25. Dubovyk, O.; Menz, G.; Conrad, C.; Kan, E.; Machwitz, M.; Khamzina, A. Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling. Environ. Monit. Assess. 2013, 185, 4775-4790
26. Foody, G.M. Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI-rainfall relationship. Remote Sens. Environ. 2003, 88, 283-293
27. Keshkamat, S.S.; Tsendbazar, N.E.; Zuidgeest, M.H.P.; Shiirev-Adiya, S.; van der Veen, A.; van Maarseveen, M.F.A.M. Understanding transportation-caused rangeland damage in Mongolia. J. Environ. Manag. 2013, 114, 433-444
28. Serra, P.; Pons, X.; Saurí, D. Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Appl. Geogr. 2008, 28, 189-209
29. Fleming, A.; Wang, G.; McRoberts, R.E. Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and Landsat TM images. Eur. J. For. Res. 2015, 134, 125-137
30. Wang, G.; Oyana, T.; Zhang, M.; Adu-Prah, S.; Zeng, S.; Lin, H.; Se, J. Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images. For. Ecol. Manag. 2009, 258, 1275-1283
31. Wang, G.; Zhang, M.; Gertner, G.Z.; Oyana, T.; McRoberts, R.E.; Ge, H. Uncertainties of mapping aboveground forest carbon due to plot locations using national forest inventory plot and remotely sensed data. Scand. J. For. Res. 2011, 26, 360-373
32. Howard, H.R.;Wang, G.; Singer, S.; Singer, A.B. Anderson. Modeling and Prediction of Land Condition for Fort Riley Military Installation. Trans. ASABE 2013, 56, 643-652
33. Zhao, P.; Lu, D.;Wang, G.;Wu, C.; Huang, Y.; Yu, S. Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. Remote Sens. 2016, 8, 469
34. Zhu, J.; Huang, Z.; Sun, H.;Wang, G. Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods. Remote Sens. 2017, 9, 241
35. Collado, A.D.; Chuvieco, E.; Camarasa, A. Satellite remote sensing analysis to monitor desertification processes in the crop-rangeland boundary of Argentina. J. Arid Environ. 2002, 52, 121-133
36. Shrestha, D.P.; Margate, D.E.; Van der Meer, F.; Anh, H.V. Analysis and classification of hyperspectral data for mapping land degradation: An application in southern Spain. Int. J. Appl. Earth Obs. Geoinf. 2005, 7, 85-96
37. Sohn, Y.; McCoy, R.M. Mapping desert shrub rangeland using spectral unmixing and modeling spectral mixtures with TM data. Photogramm. Eng. Remote Sens. 1997, 63, 707-716
38. Thorp, K.R.; French, A.N.; Rango, A. Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA). Remote Sens. Environ. 2013, 132, 120-130
39. Xiao, J.; Moody, A. A comparison of methods for estimating fractional green percentage vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sens. Environ. 2005, 98, 237-250
40. Zhang, X.; Shang, K.; Cen, Y.; Shuai, T.; Sun, Y. Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method. Int. J. Appl. Earth Obs. Geoinf. 2014, 31, 86-94
41. Archibald, S.; Roy, D.P.; WILGEN, V.; Brian, W.; SCHOLES, R.J. What limits fire? An examination of drivers of burnt area in Southern Africa. Glob. Chang. Biol. 2009, 15, 613-630
42. Zaher, M.A.; Senosy, M.M.; Youssef, M.M.; Ehara, S. Thickness variation of the sedimentary cover in the South Western Desert of Egypt as deduced from Bouguer gravity and drill-hole data using neural network method. Earth Planets Space 2009, 61, 659-674
43. Hassan, S.M.; Soliman, O.S.; Mahmoud, A.S. Optimized data input for the support vector machine classifier using ASTER data. Case study: Wadi Atalla area, Eastern Desert, Egypt. Carpath. J. Earth Environ. 2015, 10, 15-26
44. Rayegani, B.; Barati, S.; Sohrabi, T.A.; Sonboli, B. Remotely sensed data capacities to assess soil degradation. Egypt. J. Remote Sens. Space Sci. 2016, 19, 207-222
45. McRoberts, R.E.; Nelson, M.D.; Wendt, D.G. Stratified estimation of forest area using satellite imagery, inventory data, and the k-Nearest Neighbors technique. Remote Sens. Environ. 2002, 82, 457-468
46. McRoberts, R.E.; Magnussen, S.; Tomppo, E.O.; Chirici, G. Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data. Remote Sens. Environ. 2011, 115, 3165-3174
47. Tomppo, E.; Olsson, H.; Stahl, G.; Nilsson, M.; Hagner, O.; Katila, M. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens. Environ. 2008, 112, 1982-1999
48. Thessler, S.; Sesnie, S.; Bendaña, Z.S.R.; Ruokolainen, K.; Tomppo, E.; Finegan, B. Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica. Remote Sens. Environ. 2008, 112, 2485-2494
49. 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 J. Photogramm. Remote Sens. 2015, 105, 19-29
50. Mura, M.; McRoberts, R.E.; Chirici, G.; Marchetti, M. Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique. Remote Sens. Environ. 2016, 186, 678-686
51. Franco-Lopez, H.; Ek, A.R.; Bauer, M.E. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sens. Environ. 2001, 77, 251-274
52. Labrecque, S.; Fournier, R.A.; Luther, J.E.; Piercey, D. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. For. Ecol. Manag. 2006, 226, 129-144
53. Fuchs, H.; Magdon, P.; Kleinn, C.; Flessa, H. Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory. Remote Sens. Environ. 2009, 113, 518-531
54. Stümer, W.; Kenter, B.; Köhl, M. Spatial interpolation of in situ data by self-organizing map algorithms (neural networks) for the assessment of carbon stocks in European forests. For. Ecol. Manag. 2010, 260, 287-293
55. Tomppo, E.; Halme, M. Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach. Remote Sens. Environ. 2004, 92, 1-20
56. Katila, M.; Tomppo, E. Selecting estimation parameters for the Finnish multisource National Forest Inventory. Remote Sens. Environ. 2001, 76, 16-32
57. Tomppo, E.O.; Gagliano, C.; De Natale, F.; Katila, M.; McRoberts, R.E. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery. Remote Sens. Environ. 2009, 113, 500-517
58. McRoberts, R.E.; Næsset, E.; Gobakken, T. Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data. Remote Sens. Environ. 2015, 163, 13-22
59. Tokola, T.; Pitkänen, J.; Partinen, S.; Muinonen, E. Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials. Int. J. Remote Sens. 1996, 17, 2333-2351
60. Hall, P.; Park, B.U.; Samworth, R.J. Choice of neighbor order in nearest-neighbor classification. Ann. Stat. 2008, 36, 2135-2152
61. James, G.; Witten, D.; Hastie, T.; Tibshirani, R. Introduction to Statistical Learning: With Applications in R; Springer: New York, NY, USA, 2017
62. Alimjan, G.; Sun, T.; Liang, Y.; Jumahun, H.; Guan, Y. A New Technique for Remote Sensing Image Classification Based on Combinatorial Algorithm of SVM and KNN. Int. J. Pattern Recognit. Artif. Intell. 2018, 32, 1-23
63. Jenson, J.R. Introductory digital image processing: A remote sensing perspective. J. Geocarto Int. 2008, 2, 65
64. Sun, H.; Qie, G.; Wang, G.; Tan, Y.; Li, J.; Peng, Y.; Ma, Z.; Luo, C. Increasing the accuracy of mapping urban forest carbon density by combining spatial modeling and spectral unmixing analysis. Remote Sens. 2015, 7, 15114-15139
65. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32
66. Hao, P.; Zhan, Y.; Wang, L.; Niu, Z.; Shakir, M. Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA. Remote Sens. 2015, 7, 5347-5369
67. Koreen Millard, K.; Richardson, M. On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping. Remote Sens. 2015, 7, 8489-8515
68. Sharma, R.C.; Tateishi, R.; Hara, K.; Iizuka, K. Production of the Japan 30-m Land Cover Map of 2013-2015 Using a Random Forests-Based Feature Optimization Approach. Remote Sens. 2016, 8, 429
69. Tin Kam, H. Random decision forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 14-16 August 1995; pp. 278-282
70. Tin Kam, H. The Random Subspace Method for Constructing Decision Forests. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20, 832-844
71. Wessels, K.J.; van den Bergh, F.; Roy, D.P.; Salmon, B.P.; Steenkamp, K.C.; MacAlister; Swanepoel, D.; Jewitt, D. Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers. Remote Sens. 2016, 8, 888
72. Chen, T.; Trinder, J.C.; Niu, R. Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China. Remote Sens. 2017, 9, 333
73. de Castro, A.I.; Torres-Sánchez, J.; Peña, J.M.; Jiménez-Brenes, F.M.; Csillik, O.; López-Granados, F. An Automatic Random Forest-OBIA Algorithm for EarlyWeed Mapping between and within Crop Rows Using UAV Imagery. Remote Sens. 2018, 10, 285
74. Halme, M.; Tomppo, E. Improving the accuracy of multisource forest inventory estimates to reducing plot location error-A multicriteria approach. Remote Sens. Environ. 2001, 78, 321-327