Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

Journal of Integrative Agriculture

Volumn 16, Issue 2, 2017, Pages 286-297

  LI, Zhen wang ^a   XIN, Xiao ping ^a   TANG, Huan ^a   YANG, Fan ^a   CHEN, Bao rui ^a   ZHANG, Bao hui ^a  

^a INSTITUTE OF PLANT PROTECTION   (China)

Author keywords

Hulunber; leaf area index; Random Forests grassland; remote sensing

Indexed keywords

EID: 85012255167     PISSN: 20953119     EISSN: None     Source Type: Journal    
DOI: 10.1016/S2095-3119(15)61303-X     Document Type: Article

References (53)

1. Atzberger, C, Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sensing of Environment 93 (2004), 53–67.
2. Atzberger, C, Darvishzadeh, R, Immitzer, M, Schlerf, M, Skidmore, A, Le Maire, G, Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation 43 (2015), 19–31.
3. Bader, M Y, Ruijten, J J, A topography-based model of forest cover at the alpine tree line in the tropical Andes. Journal of Biogeography 35 (2008), 711–723.
4. Baret, F, Weiss, M, Allard, D, Garrigues, S, Leroy, M, Jeanjean, H, Fernandes, R, Myneni, R, Privette, J, Morisette, J, VALERI: A network of sites and a methodology for the validation of medium spatial resolution land satellite products. [2013-5-18]. http://w3.avignon.inra.fr/valeri/documents/VALERI-RSESubmitted.pdf, 2005.
5. Breiman, L, Bagging predictors. Machine Learning 24 (1996), 123–140.
6. Breiman, L, Random forests. Machine Learning 45 (2001), 5–32.
7. Chen, J M, Pavlic, G, Brown, L, Cihlar, J, Leblanc, S G, White, H P, Hall, R J, Peddle, D R, King, D J, Trofymow, J A, Swift, E, Van der sanden, J, Pellikka, P K E, Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment 80 (2002), 165–184.
8. Combal, B, Baret, F, Weiss, M, Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies. Agronomie 22 (2002), 205–215.
9. Dobrowski, S Z, Safford, H D, Cheng, Y B, Ustin, S L, Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification. Applied Vegetation Science 11 (2008), 499–508.
10. Evans, J S, Cushman, S A, Gradient modeling of conifer species using random forests. Landscape Ecology 24 (2009), 673–683.
11. Fan, J, Zhong, H, Liang, B, Shi, P, Yu, G, Carbon stock in grassland ecosystem and its affecting factors. Grassland of China 25 (2003), 51–58 (in Chinese).
12. Fan, W, Liu, Y, Xu, X, Chen, G, Zhang, B, A new FAPAR analytical model based on the law of energy conservation: A case study in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (2014), 3945–3955.
13. Fan, W J, Xu, X R, Liu, X C, Yan, B Y, Cui, Y K, Accurate LAI retrieval method based on PROBA/CHRIS data. Hydrology and Earth System Sciences 14 (2010), 1499–1507.
14. Ge, Y, Bai, H, Wang, J, Cao, F, Assessing the quality of training data in the supervised classification of remotely sensed imagery: A correlation analysis. Journal of Spatial Science 57 (2012), 135–152.
15. Ge, Y, Wang, J H, Heuvelink, G B M, Jin, R, Li, X, Wang, J F, Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River basin, China. International Journal of Geographical Information Science 29 (2015), 92–110.
16. Genuer, R, Poggi, J M, Tuleau-Malot, C, Variable selection using random forests. Pattern Recognition Letters 31 (2010), 2225–2236.
17. Genuer, R, Poggi, J M, Tuleau, C, Random Forests: some methodological insights. [2015-9-18]. http://arxiv.org/pdf/0811.3619v1.pdf, 2008.
18. Gitelson, A A, Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 161 (2004), 165–173.
19. 2 flux in maize. Geophysical Research Letters 30 (2003), 1486–1489.
20. Gitelson, A A, Vi, A A, Arkebauer, T J, Rundquist, D C, Keydan, G, Leavitt, B, Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters 30 (2003), 1248–1251.
21. Haboudane, D, Miller, J R, Pattey, E, Zarco-tejada, P J, Strachan, I B, Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90 (2004), 337–352.
22. Hapfelmeier, A, Ulm, K, A new variable selection approach using random forests. Computational Statistics & Data Analysis 60 (2013), 50–69.
23. Heung, B, Bulmer, C E, Schmidt, M G, Predictive soil parent material mapping at a regional-scale: A random forest approach. Geoderma 214–215 (2014), 141–154.
24. Hotelling, H, Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 1933, 417.
25. Hultquist, C, Chen, G, Zhao, K, A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests. Remote Sensing Letters 5 (2014), 723–732.
26. Jacquemoud, S, Bacour, C, Poilv, H, Frangi, J P, Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sensing of Environment 74 (2000), 471–481.
27. Jacquemoud, S, Baret, F, PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment 34 (1990), 75–91.
28. Jolliffe, I, Principal Component Analysis, 2002, John Wiley & Sons, Ltd., New York, United States of America.
29. Jordan, C F, Derivation of leaf-area index from quality of light on the forest floor. Ecology 50 (1969), 663–666.
30. Kaufman, Y J, Tanre, D, Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing 30 (1992), 261–270.
31. De Kauwe, M G, Disney, M I, Quaife, T, Lewis, P, Williams, M, An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest. Remote Sensing of Environment 115 (2011), 767–780.
32. Kilibarda, M, Hengl, T, Heuvelink, G, Graler, B, Pebesma, E, Tadic, M P, Bajat, B, Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research (Atmospheres) 119 (2014), 2294–2313.
33. Kopecký, M, Čížková, Š, Using topographic wetness index in vegetation ecology: Does the algorithm matter?. Applied Vegetation Science 13 (2010), 450–459.
34. Lemaire, G, Wilkins, R, Hodgson, J, Challenges for grassland science: managing research priorities. Agriculture, Ecosystems & Environment 108 (2005), 99–108.
35. Liang, L, Di, L, Zhang, L, Deng, M, Qin, Z, Zhao, S, Lin, H, Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment 165 (2015), 123–134.
36. Liaw, A, Wiener, M, Classification and regression by randomForest. [2015-9-18]. https://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf, 2002.
37. Liu, M, Liu, X, Li, J, Ding, C, Jiang, J, Evaluating total inorganic nitrogen in coastal waters through fusion of multitemporal RADARSAT-2 and optical imagery using random forest algorithm. International Journal of Applied Earth Observation and Geoinformation 33 (2014), 192–202.
38. Liu, Y, Ju, W, Zhu, G, Chen, J, Xing, B, Zhu, J, Zhou, Y, Retrieval of leaf area index for different grasslands in Inner Mongolia prairie using remote sensing data. Acta Ecologica Sinica 39 (2011), 5159–5170 (in Chinese).
39. Morisette, J T, Baret, F, Privette, J L, Myneni, R B, Nickeson, J E, Garrigues, S, Shabanov, N V, Weiss, M, Fernandes, R A, Leblanc, S G, Kalacska, M, Sanchez-azofeifa, G A, Chubey, M, Rivard, B, Stenberg, P, Rautiainen, M, Voipio, P, Manninen, T, Pilant, A N, Lewis, T E, et al. Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup. IEEE Transactions on Geoscience and Remote Sensing 44 (2006), 1804–1817.
40. Prasad, A M, Iverson, L R, Liaw, A, Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems 9 (2006), 181–199.
41. Qin C, Zhu A X, Yang L, Li B, Pei T. 2007. Topographic wetness index computed using multiple flow direction algorithm and local maximum downslope gradient. In: The 7th International Workshop of Geographical Information System. September 12–14, 2007. Beijing, China.
42. Ramoelo, A, Cho, M A, Mathieu, R, Madonsela, S, Van de kerchove, R, Kaszta, Z, Wolff, E, Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. International Journal of Applied Earth Observation and Geoinformation 43 (2015), 43–54.
43. Rondeaux, G, Steven, M, Baret, F, Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55 (1996), 95–107.
44. Rouse Jr, J W, Haas, R, Schell, J, Deering, D, Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 1974, 309.
45. Running, S W, Nemani, R R, Peterson, D L, Band, L E, Potts, D F, Pierce, L L, Spanner, M A, Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology 70 (1989), 1090–1101.
46. Sellers, P J, Dickinson, R E, Randall, D A, Betts, A K, Hall, F G, Berry, J A, Collatz, G J, Denning, A S, Mooney, H A, Nobre, C A, Sato, N, Field, C B, Henderson-sellers, A, Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275 (1997), 502–509.
47. Svetnik, V, Liaw, A, Tong, C, Culberson, J C, Sheridan, R P, Feuston, B P, Random forest: A classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences 43 (2003), 1947–1958.
48. Van Niel, K P, Laffan, S W, Lees, B G, Effect of error in the DEM on environmental variables for predictive vegetation modelling. Journal of Vegetation Science 15 (2004), 747–756.
49. Verrelst, J, Schaepman, M E, Koetz, B, Kneubühler, M, Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment 112 (2008), 2341–2353.
50. Verrelst, J, Schaepman, M E, Malenovsk, Z, Clevers, J G, Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval. Remote Sensing of Environment 114 (2010), 647–656.
51. Viña, A A, Gitelson, A A, Nguy-robertson, A L, Peng, Y, Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment 115 (2011), 3468–3478.
52. Wang, J, Ge, Y, Heuvelink, G, Zhou, C, Spatial sampling design for estimating regional GPP with spatial heterogeneities. IEEE Geoscience and Remote Sensing Letters 11 (2014), 539–543.
53. Welles, J M, Norman, J M, Instrument for indirect measurement of canopy architecture. Agronomy Journal 83 (1991), 818–825.