Please wait a minute...
Journal of Integrative Agriculture  2017, Vol. 16 Issue (02): 286-297    DOI: 10.1016/S2095-3119(15)61303-X
Section 2: Agricultural quantitative remote sensing Advanced Online Publication | Current Issue | Archive | Adv Search |
Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China
LI Zhen-wang, XIN Xiao-ping, TANG Huan, YANG Fan, CHEN Bao-rui, ZHANG Bao-hui
National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
Abstract  Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance.  The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems.  The Random Forests (RF) method aggregates an ensemble of decision trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods.  This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. 
Parameter optimization and variable reduction were conducted before model prediction.  Two variable reduction methods were examined: the Variable Importance Value method and the principal component analysis (PCA) method.  Finally, the sensitivity of RF to highly correlated variables was tested.  The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956.  The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information.  After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the variables with all vegetation indices or those selected based on the most important one.  The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.
Keywords:  leaf area index      Random Forests grassland      remote sensing      Hulunber  
Received: 15 October 2015   Accepted:

The study was funded by the Key Technologies Research and Development Program of China (2013BAC03B02, 2012BAC19B04), the International Science and Technology Cooperation Project of China (2012DFA31290), and the Earmarked Fund for Modern Agro-industry Technology Research System, China (CARS-35).

Corresponding Authors:  ZHANG Bao-hui, Tel: +86-10-82109618, E-mail:   
About author:  LI Zhen-wang, Tel: +86-10-82109618, E-mail:

Cite this article: 

LI Zhen-wang, XIN Xiao-ping, TANG Huan, YANG Fan, CHEN Bao-rui, ZHANG Bao-hui. 2017. Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China. Journal of Integrative Agriculture, 16(02): 286-297.

Atzberger C. 2004. Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sensing of Environment, 93, 53–67.
Atzberger C, Darvishzadeh R, Immitzer M, Schlerf M, Skidmore A, Le Maire G. 2015. 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, 19–31.
Bader M Y, Ruijten J J. 2008. A topography-based model of forest cover at the alpine tree line in the tropical Andes. Journal of Biogeography, 35, 711–723.
Baret F, Weiss M, Allard D, Garrigues S, Leroy M, Jeanjean H, Fernandes R, Myneni R, Privette J, Morisette J. 2005. VALERI: A network of sites and a methodology for the validation of medium spatial resolution land satellite products. [2013-5-18].
Breiman L. 1996. Bagging predictors. Machine Learning, 24, 123–140.
Breiman L. 2001. Random forests. Machine Learning, 45, 5–32.
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. 2002. 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, 165–184.
Combal B, Baret F, Weiss M. 2002. Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies. Agronomie, 22, 205–215.
Dobrowski S Z, Safford H D, Cheng Y B, Ustin S L. 2008. Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification. Applied Vegetation Science, 11, 499–508.
Evans J S, Cushman S A. 2009. Gradient modeling of conifer species using random forests. Landscape Ecology, 24, 673–683.
Fan J, Zhong H, Liang B, Shi P, Yu G. 2003. Carbon stock in grassland ecosystem and its affecting factors. Grassland of China, 25, 51–58. (in Chinese)
Fan W, Liu Y, Xu X, Chen G, Zhang B. 2014. 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, 3945–3955.
Fan W J, Xu X R, Liu X C, Yan B Y, Cui Y K. 2010. Accurate LAI retrieval method based on PROBA/CHRIS data. Hydrology and Earth System Sciences, 14, 1499–1507.
Ge Y, Bai H, Wang J, Cao F. 2012. Assessing the quality of training data in the supervised classification of remotely sensed imagery: A correlation analysis. Journal of Spatial Science, 57, 135–152.
Ge Y, Wang J H, Heuvelink G B M, Jin R, Li X, Wang J F. 2015. 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, 92–110.
Genuer R, Poggi J M, Tuleau-Malot C. 2010. Variable selection using random forests. Pattern Recognition Letters, 31, 2225–2236.
Genuer R, Poggi J M, Tuleau C. 2008. Random Forests: some methodological insights. [2015-9-18].
Gitelson A A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161, 165–173.
Gitelson A A, Verma S B, Vi A A, Rundquist D C, Keydan G, Leavitt B, Arkebauer T J, Burba G G, Suyker A E. 2003a. Novel technique for remote estimation of CO2 flux in maize. Geophysical Research Letters, 30, 1486–1489.
Gitelson A A, Vi A A, Arkebauer T J, Rundquist D C, Keydan G, Leavitt B. 2003b. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30, 1248–1251.
Haboudane D, Miller J R, Pattey E, Zarco-tejada P J, Strachan I B. 2004. 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, 337–352.
Hapfelmeier A, Ulm K. 2013. A new variable selection approach using random forests. Computational Statistics & Data Analysis, 60, 50–69.
Heung B, Bulmer C E, Schmidt M G. 2014. Predictive soil parent material mapping at a regional-scale: A random forest approach. Geoderma, 214–215, 141–154.
Hotelling H. 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417.
Hultquist C, Chen G, Zhao K. 2014. A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests. Remote Sensing Letters, 5, 723–732.
Jacquemoud S, Bacour C, Poilv H, Frangi J P. 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sensing of Environment, 74, 471–481.
Jacquemoud S, Baret F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75–91.
Jolliffe I. 2002. Principal Component Analysis. John Wiley & Sons, Ltd.  New York, United States of America.
Jordan C F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50, 663–666.
Kaufman Y J, Tanre D. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30, 261–270.
De Kauwe M G, Disney M I, Quaife T, Lewis P, Williams M. 2011. An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest. Remote Sensing of Environment, 115, 767–780.
Kilibarda M, Hengl T, Heuvelink G, Graler B, Pebesma E, Tadic M P, Bajat B. 2014. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research (Atmospheres), 119, 2294–2313.
Kopecký M, ?í?ková Š. 2010. Using topographic wetness index in vegetation ecology: Does the algorithm matter? Applied Vegetation Science, 13, 450–459.
Lemaire G, Wilkins R, Hodgson J. 2005. Challenges for grassland science: managing research priorities. Agriculture, Ecosystems & Environment, 108, 99–108.
Liang L, Di L, Zhang L, Deng M, Qin Z, Zhao S, Lin H. 2015. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment, 165, 123–134.
Liaw A, Wiener M. 2002. Classification and regression by randomForest. [2015-9-18].
Liu M, Liu X, Li J, Ding C, Jiang J. 2014. Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 33, 192–202.
Liu Y, Ju W, Zhu G, Chen J, Xing B, Zhu J, Zhou Y. 2011. Retrieval of leaf area index for different grasslands in Inner Mongolia prairie using remote sensing data. Acta Ecologica Sinica, 39, 5159–5170. (in Chinese)
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. 2006. 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, 1804–1817.
Prasad A M, Iverson L R, Liaw A. 2006. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9, 181–199.
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.
Ramoelo A, Cho M A, Mathieu R, Madonsela S, Van de kerchove R, Kaszta Z, Wolff E. 2015. 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, 43–54.
Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.
Rouse Jr J W, Haas R, Schell J, Deering D. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309.
Running S W, Nemani R R, Peterson D L, Band L E, Potts D F, Pierce L L, Spanner M A. 1989. Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology, 70, 1090–1101.
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. 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 275, 502–509.
Svetnik V, Liaw A, Tong C, Culberson J C, Sheridan R P, Feuston B P. 2003. Random forest: A classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences, 43, 1947–1958.
Van Niel K P, Laffan S W, Lees B G. 2004. Effect of error in the DEM on environmental variables for predictive vegetation modelling. Journal of Vegetation Science, 15, 747–756.
Verrelst J, Schaepman M E, Koetz B, Kneubühler M. 2008. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, 112, 2341–2353.
Verrelst J, Schaepman M E, Malenovsk Z, Clevers J G. 2010. Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval. Remote Sensing of Environment, 114, 647–656.
Viña A A, Gitelson A A, Nguy-robertson A L, Peng Y. 2011. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115, 3468–3478.
Wang J, Ge Y, Heuvelink G, Zhou C. 2014. Spatial sampling design for estimating regional GPP with spatial heterogeneities. IEEE Geoscience and Remote Sensing Letters, 11, 539–543.
Welles J M, Norman J M. 1991. Instrument for indirect measurement of canopy architecture. Agronomy Journal, 83, 818–825.
[1] SONG Chao-yu, ZHANG Fan, LI Jian-sheng, XIE Jin-yi, YANG Chen, ZHOU Hang, ZHANG Jun-xiong. Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model[J]. >Journal of Integrative Agriculture, 2023, 22(6): 1671-1683.
[2] XIA Tian, WU Wen-bin, ZHOU Qing-bo, Peter H. VERBURG, YANG Peng, HU Qiong, YE Li-ming, ZHU Xiao-juan. From statistics to grids: A two-level model to simulate crop pattern dynamics[J]. >Journal of Integrative Agriculture, 2022, 21(6): 1786-1789.
[3] CHU Xiao-lei, LU Zhong, WEI Dan, LEI Guo-ping . Effects of land use/cover change (LUCC) on the spatiotemporal variability of precipitation and temperature in the Songnen Plain, China[J]. >Journal of Integrative Agriculture, 2022, 21(1): 235-248.
[4] ZHAO Yu, WANG Jian-wen, CHEN Li-ping, FU Yuan-yuan, ZHU Hong-chun, FENG Hai-kuan, XU Xin-gang, LI Zhen-hai. An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat[J]. >Journal of Integrative Agriculture, 2021, 20(9): 2535-2551.
[5] Jae-Hyun RYU, Dohyeok OH, Jaeil CHO. Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor[J]. >Journal of Integrative Agriculture, 2021, 20(7): 1969-1986.
[6] ZHANG Sha, Bai Yun, Zhang Jia-hua, Shahzad ALI. Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain[J]. >Journal of Integrative Agriculture, 2021, 20(2): 408-423.
[7] YANG Fei-fei, LIU Tao, WANG Qi-yuan, DU Ming-zhu, YANG Tian-le, LIU Da-zhong, LI Shi-juan, LIU Sheng-ping. Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters[J]. >Journal of Integrative Agriculture, 2021, 20(10): 2613-2626.
[8] ZHANG Yuan-meng, XUE Jun, ZHAI Juan, ZHANG Guo-qiang, ZHANG Wan-xu, WANG Ke-ru, MING Bo, HOU Peng, XIE Rui-zhi, LIU Chao-wei, LI Shao-kun. Does nitrogen application rate affect the moisture content of corn grains?[J]. >Journal of Integrative Agriculture, 2021, 20(10): 2627-2638.
[9] QI Dong-liang, HU Tian-tian, SONG Xue. Effects of nitrogen application rates and irrigation regimes on grain yield and water use efficiency of maize under alternate partial rootzone irrigation[J]. >Journal of Integrative Agriculture, 2020, 19(11): 2792-2806.
[10] LIU Chang-an, CHEN Zhong-xin, SHAO Yun, CHEN Jin-song, Tuya Hasi, PAN Hai-zhu. Research advances of SAR remote sensing for agriculture applications: A review[J]. >Journal of Integrative Agriculture, 2019, 18(3): 506-525.
[11] WU Cheng-yong, CAO Guang-chao, CHEN Ke-long, E Chong-yi, MAO Ya-hui, ZHAO Shuang-kai, WANG Qi, SU Xiao-yi, WEI Ya-lan. Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover[J]. >Journal of Integrative Agriculture, 2019, 18(2): 316-327.
[12] ZHANG Xi-wang, LIU Jian-feng, Zhenyue Qin, QIN Fen . Winter wheat identification by integrating spectral and temporal information derived from multi-resolution remote sensing data[J]. >Journal of Integrative Agriculture, 2019, 18(11): 2628-2643.
[13] QIANG Sheng-cai, ZHANG Fu-cang, Miles Dyck, ZHANG Yan, XIANG You-zhen, FAN Jun-liang. Determination of critical nitrogen dilution curve based on leaf area index for winter wheat in the Guanzhong Plain, Northwest China[J]. >Journal of Integrative Agriculture, 2019, 18(10): 2369-2380.
[14] Yanbo Huang, CHEN Zhong-xin, YU Tao, HUANG Xiang-zhi, GU Xing-fa. Agricultural remote sensing big data: Management and applications[J]. >Journal of Integrative Agriculture, 2018, 17(09): 1915-1931.
[15] TAO Zhi-qiang, WANG De-mei, MA Shao-kang, YANG Yu-shuang, ZHAO Guang-cai, CHANG Xu-hong. Light interception and radiation use efficiency response to tridimensional uniform sowing in winter wheat[J]. >Journal of Integrative Agriculture, 2018, 17(03): 566-578.
No Suggested Reading articles found!