Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A/1B CCD images and recurrent neural network

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 102, Issue , 2015, Pages 148-160

  Chen, Bangqian ^a   Wu, Zhixiang ^a   Wang, Jikun ^a   Dong, Jinwei ^b   Guan, Liming ^a   Chen, Junming ^a   Yang, Kai ^c   Xie, Guishui ^a  

^a INSTITUTE OF TROPICAL BIOSCIENCE AND BIOTECHNOLOGY   (China)

^b UNIVERSITY OF OKLAHOMA   (United States)

^c Department of Corporate Affairs CFPA Microfinance China Foundation for Poverty Alleviation   (China)

Author keywords

HJ 1A 1B CCD; Leaf area index; NARX; Recurrent neural network; Rubber plantation

Indexed keywords

DYNAMICS; FORECASTING; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; REGRESSION ANALYSIS; REMOTE SENSING; RUBBER; STATISTICAL TESTS; VEGETATION;

ECOLOGICAL STUDIES; HEVEA BRASILIENSIS; LEAF AREA INDEX; NARX; PRODUCTION MANAGEMENT; SPATIO-TEMPORAL PREDICTION; STEPWISE REGRESSION; TEMPORAL RESOLUTION;

RUBBER PLANTATIONS;

ARTIFICIAL NEURAL NETWORK; DATA SET; IMAGE ANALYSIS; LANDSAT THEMATIC MAPPER; LEAF AREA INDEX; MODIS; PLANTATION FORESTRY; PREDICTION; PRODUCTIVITY; REMOTE SENSING; RUBBER; SPATIOTEMPORAL ANALYSIS; VEGETATION INDEX;

CHINA; HAINAN;

HEVEA BRASILIENSIS;

EID: 84923017610     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.12.011     Document Type: Article

References (69)

1. Aosier, B., Kaneko, M., Takada, M., 2007. Evaluation of the forest damage by typhoon using remote sensing technique. In: Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International, Barcelona, pp. 3022-3026.
2. Bacour C., Baret F., Béal D., Weiss M., Pavageau K. Neural network estimation of LAI, fAPAR, fCover and LAIxCab, from top of canopy MERIS reflectance data: principles and validation. Remote Sens. Environ. 2006, 105:313-325.
3. Beale, M.H., Hagan, M.T., Demuth, H.B., 2011. Neural Network Toolbox User's Guide. MathWorks Inc., Natick, MA. <. http://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf.
4. Berterretche M., Hudak A.T., Cohen W.B., Maiersperger T.K., Gower S.T., Dungan J. Comparison of regression and geostatistical methods for mapping leaf area index (LAI) with Landsat ETM+ data over a boreal forest. Remote Sens. Environ. 2005, 96:49-61.
5. Broge N.H., Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 2000, 76:156-172.
6. Chai L., Qu Y., Zhang L., Liang S., Wang J. Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs. Int. J. Remote Sens. 2012, 33:5712-5731.
7. Chander G., Brian L.M., Dennis L.H. Summary of currentradiometriccalibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113:893-903.
8. Chen J.M., Cihlar J. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens. Environ. 1996, 55:153-162.
9. Chen X., Vierling L., Rowell E., DeFelice T. Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sens. Environ. 2004, 91:14-26.
10. Chen C., Wang R., Jiang J. Variation of soil fertility and carbon sequestration by planting hevea brasiliensis in Hainan Island, China. J. Environ. Sci. 2007, 19:348-352.
11. Chen J., Mo Y., Chen M., Wei H., Sun J. Potential analyse of natural rubber production in China. Trop. Agric. Eng. 2010, 90-98.
12. Chen X., Meng J., Du X., Zhang F., Zhang M., Wu B. The monitoring of the winter wheat leaf area index based on HJ-1 CCD data. Remote Sens. Land Resour. 2010, 55-58.
13. Chen B., Cao J., Wang J., Wu Z., Tao Z., Chen J., Yang C., Xie G. Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: a case study in Hainan Island. China. Forest Ecol. Manage. 2012, 274:222-230.
14. Diaconescu E. The use of NARX neural networks to predict chaotic time series. WSEAS Trans. Comput. Res. 2008, 3:182-191.
15. Dong J., Xiao X., Chen B., Torbick N., Jin C., Zhang G., Biradar C. Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. Remote Sens. Environ. 2013, 134:392-402.
16. Fang H., Liang S. Retrieving leaf area index with a neural network method: simulation and validation. IEEE Trans. Geosci. Remote Sens. 2003, 41:2052-2062.
17. Fang H., Liang S. A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sens. Environ. 2005, 94:405-424.
18. Fassnacht K.S., Gower S.T., MacKenzie M.D., Nordheim E.V., Lillesand T.M. Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper. Remote Sens. Environ. 1997, 61:229-245.
19. Foody G.M., Cutler M.E., Mcmorrow J., Pelz D., Tangki H., Boyd D.S., Douglas I. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecol. Biogeogr. 2001, 10:379-387.
20. Fu F., Zhang D. Rubber tree windstorm insurance and its improvement. Trop. Agric. Sci. Technol. 2010, 33:38-42.
21. González-Sanpedro M.C., Le Toan T., Moreno J., Kergoat L., Rubio E. Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data. Remote Sens. Environ. 2008, 112:810-824.
22. Gray J., Song C. Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors. Remote Sens. Environ. 2012, 119:173-183.
23. He K., Huang Z. Rubber Culture in the Northern Part of Tropical Area 1987, Guangdong Science & Technology Press, Guangdong.
24. Houborg R., Soegaard H., Boegh E. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sens. Environ. 2007, 106:39-58.
25. Hu Y., Wang Z., Shu Y., Wu Y. Studies on photosynthesis of hevea population II. A mathematical model for estimating the photosynthetic output of hevea population. Chinese J. Trop. Crops. 1982, 3:41-48.
26. Hu Z., Zhiyuan P., Shangjie M., Lianlin W., Zhiping M. Retrieving leaf area index of winter wheat using HJ-1-A/B CCD2 data. Trans. Chinese Soc. Agric. Eng. 2012, 28:172-176.
27. Jensen J.R., Qiu F., Ji M. Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. Int. J. Remote Sens. 1999, 20:2805-2822.
28. Jensen J.L.R., Humes K.S., Hudak A.T., Vierling L.A., Delmelle E. Evaluation of the MODIS LAI product using independent lidar-derived LAI: a case study in mixed conifer forest. Remote Sens. Environ. 2011, 115:3625-3639.
29. Jonckheere I., Fleck S., Nackaerts K., Muys B., Coppin P., Weiss M., Baret F.D.R. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agric. Forest Meteorol. 2004, 121:19-35.
30. Ju-sheng J., Ru-song W. Hydrological eco-service of rubber plantations in Hainan Island and its effect on local economic development. J. Environ. Sci. 2003, 15:701-709.
31. le Maire G., Marsden C., Verhoef W., Ponzoni F.J., Lo Seen D., Bégué A., Stape J., Nouvellon Y. Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations. Remote Sens. Environ. 2011, 115:586-599.
32. Li Z., Fox J.M. Integrating Mahalanobis typicalities with a neural network for rubber distribution mapping. Remote Sensing Lett. 2011, 2:157-166.
33. Li W., Li H., Zhao L. Estimating rice yield by HJ-1A satellite images. Rice Sci. 2011, 18:142-147.
34. Liang S., Liang S. Quantitative Remote Sensing of Land Surfaces 2003, Wiley Interscience.
35. LI-COR, I., 1992. LAI-2000 Plant Canopy Analyzer Operating Manual. LI-COR Inc., Lincoln, NE. <. ftp://ftp.licor.com/perm/env/LAI-2000/Manual/LAI-2000_Manual.pdf.
36. LI-COR, I., 2012. LAI-2200 Plant Canopy Analyzer Instruction Manual. LI-COR, Inc., Lincoln, NE. <. ftp://ftp.licor.com/perm/env/LAI-2200/Manual/LAI-2200_Manual.pdf.
37. Lin T., Horne B.G., Tino P., Giles C.L. Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans. Neural Networks 1996, 7:1329-1338.
38. Linna C., Yonghua Q., Lixin Z., Shunlin L., Jindi W. Estimating time series leaf area index based on recurrent neural networks. Adv. Earth Sci. 2009, 24:756-768.
39. Lu S., Wu B., Yan N., Wang H. Water body mapping method with HJ-1A/B satellite imagery. Int. J. Appl. Earth Obs. Geoinf. 2011, 13:428-434.
40. Maass J., Vose J.M., Swank W.T., Martínez-Yrízar A. Seasonal changes of leaf area index (LAI) in a tropical deciduous forest in west Mexico. Forest Ecol. Manage. 1995, 74:171-180.
41. Menezes J.M.P., Barreto G.A. Long-term time series prediction with the NARX network: an empirical evaluation. Neurocomputing 2008, 71:3335-3343.
42. Mo Y. Production and marketing of nature rubber in 2009 and market forecast in 2010. China Trop. Agric. 2010, 12-15.
43. Murnion Shane D. Comparison of back propagation and binary diamond neural networks in the classification of a Landsat TM image. Comput. Geosci. 1996, 22:995-1001.
44. Muukkonen P., Heiskanen J. Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote Sens. Environ. 2005, 99:434-447.
45. Olthof I., King D.J. Development of a forest health index using multispectral airborne digital camera imagery. Can. J. Remote Sens. 2000, 166-176.
46. Pisoni E., Farina M., Carnevale C., Piroddi L. Forecasting peak air pollution levels using NARX models. Eng. Appl. Artif. Intel. 2009, 22:593-602.
47. Ricciardelli E., Romano F., Cuomo V. Physical and statistical approaches for cloud identification using meteosat second generation-spinning enhanced visible and infrared imager data. Remote Sens. Environ. 2008, 112:2741-2760.
48. Righi C., Bernardes M. The potential for increasing rubber production by matching tapping intensity to leaf area index. Agroforest. Syst. 2008, 72:1-13.
49. Sauter T., Weitzenkamp B., Schneider C. Spatio-temporal prediction of snow cover in the Black Forest mountain range using remote sensing and a recurrent neural network. Int. J. Climatol. 2010, 30:2330-2341.
50. Shupe S.M., Marsh S.E. Cover- and density-based vegetation classifications of the Sonoran Desert using Landsat TM and ERS-1 SAR imagery. Remote Sens. Environ. 2004, 93:131-149.
51. Siegelmann H.T., Horne B.G., Giles C.L. Computational capabilities of recurrent NARX neural networks. IEEE Trans. Syst. Man Cybern. B Cybern. 1997, 27:208-215.
52. Soudani K., François C., le Maire G., Le Dantec V., Dufrêne E. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sens. Environ. 2006, 102:161-175.
53. Sprintsin M., Cohen S., Maseyk K., Rotenberg E., Grünzweig J., Karnieli A., Berliner P., Yakir D. Long term and seasonal courses of leaf area index in a semi-arid forest plantation. Agric. Forest Meteorol. 2011, 151:565-574.
54. Sun Y., Gu X., Yu T., Gao H., Li J., Hu X. Research on the inversion of leaf area index based on radiative transfer model of HJ-1A CCD data. J. Anhui Agric. Sci. 2011, 39:5012-5015.
55. Tian Q., Luo Z., Chen J.M., Chen M., Hui F. Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images. J. Environ. Manage. 2007, 85:624-627.
56. Turner D.P., Cohen W.B., Kennedy R.E., Fassnacht K.S., Briggs J.M. Relationships between leaf area index and landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens. Environ. 1999, 70:52-68.
57. Walthall C., Dulaney W., Anderson M., Norman J., Fang H., Liang S. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sens. Environ. 2004, 92:465-474.
58. Wang B., Ono A., Muramatsu K., Fujiwara N. Automated detection and removal of clouds and their shadows from Landsat TM images. IEICE Trans. Inform. Syst. 1999, E82:453-460.
59. Watmough G.R., Atkinson P.M., Hutton C.W. A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+. Int. J. Appl. Earth Obs. Geoinf. 2011, 13:220-227.
60. Welles J.M., Norman J.M. Instrument for indirect measurement of canopy architecture. Agron. J. 1991, 83:818-825.
61. Xie G. Modeling of Photosynthesis and Dry Matter Accumulation of Rubber Tree and Analysis of the Potential for Rubber Production in Hainan Province 2009, China Agricultural University, Beijing.
62. Xie G., Chen B., Wang J., Wu Z., Huang Y. Studies on the simulation model of photosynthesis and dry matter accumulation for rubber tree. Chinese Agric. Sci. Bull. 2010, 26:317-323.
63. Yu W., Zhang M., Mai Q., Jiang J. Damage of Typhoon Damrey to the rubber industry in Hainan state farm bureau and its countermeasures for future development. Chinese J. Trop. Agric. 2006, 26:41-43.
64. Zhang D. Study on practice of rubber tree windstorm insurance in Hainan state farms. Issues Forest. Econ. 2011, 31:145-148.
65. Zhang J., Gu X., Wang J., Huang W., Xin H., Wang H. Analysis of consistency between HJ-CCD images and TM images in monitoring rice LAI. Trans. CSAE 2010, 26:186-193.
66. Zhang K., Song C., Chen Y. Records and disaster assessment of typhoons in East Hainan Island in recent 50years. J. Anhui Agric. Sci. 2010, 38:12880-12882.
67. Zhang Y., Meng Q., Wu J., Zhao F. Study of environmental vegetation index based on environmental satellite CCD data and LAI inversion. Spectrosc. Spectral Anal. 2011, 31:2789-2793.
68. Zheng G., Moskal L.M. Retrieving leaf area index (LAI) using remote sensing: theories methods and sensors. Sensors 2009, 2719-2745.
69. Zhu G., Li M., Ju W., Chen J., Wu Z. Difference in leaf area index in forests retrieved from HJ-1 CCD and TM data of Maoershan mountain, Heilongjiang province. J. Northeast Forest. Univ. 2011, 39:127-130.