Rubber Tree Distribution Mapping in Northeast Thailand

Zhe Li, Jefferson M. Fox

.

DOI: 10.4236/ijg.2011.24060                

Abstract

In many parts of mainland Southeast Asia rubber plantations are expanding rapidly in areas where the crop was not historically found. Monitoring and mapping the distribution of rubber trees in the region is necessary for developing a better understanding of the consequences of land-cover and land-use change on carbon and water cycles. In this study, we conducted rubber tree growth mapping in Northeast Thailand using Landsat 5 TM data. A Mahalanobis typicality method was used to identify different age rubber trees. Landsat 5 TM 30 m non-thermal reflective bands, NDVI and tasseled cap transformation components were selected as the model input metrics. The validation was carried out using provincial level agricultural statistical data on the rubber tree growth area. At regional (Northeast Thailand) and provincial scales, the estimates of mature and middle-age rubber stands produced from 30 m Landsat 5 TM data compared well (high statistical significance) with the provincial rubber tree growth statistical data.

Keywords

Northeast Thailand, Rubber Tree Mapping, Land-Use and land-Cover Change, Mahalanobis Typicality, Kauth-Thomas Transformation, Landsat 5 TM

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. Fox and J. Vogler, “Land-Use and Land-Cover Change in Montane Mainland Southeast Asia,” Environmental Management, Vol. 36, No. 3, 2005, pp. 394-403. doi:10.1007/s00267-003-0288-7
[2]	A. D. Ziegler, J. M. Fox and J. Xu, “The Rubber Juggernaut,” Science, Vol. 324, No. 5930, 2009, pp. 1024-1025. doi:10.1126/science.1173833
[3]	C. C. Mann, “Addicted to Rubber,” Science, Vol. 325, No. 5940, 2009, pp. 565-566. doi:10.1126/science.325_564
[4]	S. J. Walsh, B. Entwisle, R. R. Rindfuss and P. H. Page, “Spatial Simulation Modeling of Land Use/Land Cover Change Scenarios in Northeastern Thailand: A Cellular Automata Approach,” Journal of Land Use Science, Vol. 1, No. 1, 2006, pp. 5-28. doi:10.1080/17474230600604213
[5]	K. Watanabe, T. Yamamotob, T. Yamada, T. Sakuratani, E. Nawata, C. Noichana, A. Sributta and H. Higuchi, “Changes in Seasonal Evapotranspiration, Soil Water Content, and crop Coefficients in Sugarcane, Cassava, and Maize Fields in Northeast Thailand,” Agricultural Water Management, Vol. 67, No. 2, 2004, pp. 133-143. doi:10.1016/j.agwat.2004.02.004
[6]	J. Xu, J. M. Fox, J. B. Vogler, P. Zhang, Y. Fu , L. Yang, J. Qian and S. Leisz, “Land-Use and Land-Cover Change and Farmer Vulnerability in Xishuangbanna Prefecture in Southwestern China,” Environment Management, Vol. 36, No. 3, 2005, pp. 404-413. doi:10.1007/s00267-003-0289-6
[7]	H. Li, Y. Ma, T. M. Aide and W. Liu, “Past, Present and Future Land-Use in Xishuangbanna, China and the Implications for Carbon Dynamics,” Forest Ecology and Management, Vol. 255, No. 1, 2008, pp. 16-24. doi:10.1016/j.foreco.2007.06.051
[8]	H. Hu, W. Liu and M. Cao, “Impact of Land Use and Land Cover Changes on Ecosystem Services in Menglun, Xishuangbanna, Southwest China,” Environmental Moni- toring and Assessment, Vol. 46, No. 1-3, 2008, pp. 147- 156. doi:10.1007/s10661-007-0067-7
[9]	D. Lawrence, D. R. Peart and M. Leighton, “The Impact of Shifting Cultivation on a Rainforest Landscape in West Kalimantan: Spatial and Temporal Dynamics,” Landscape Ecology, Vol. 13, No. 3, 1998, pp. 135-148. doi:10.1023/A:1007985915187
[10]	H. Li, T.M. Aide, Y. Ma and W. Liu, “Demand for Rubber Is Causing the Loss of High Diversity Rain Forest in SW China,” Biodiversity Conservation, Vol. 16, No. 6, 2007, pp. 1731-1745. doi:10.1007/s10531-006-9052-7
[11]	A. Ekandinata, A. Widayati and G. Vincent, “Rubber Agroforest Identification Using Object-Based Classifi- cation in Bungo District, Jambi, Indonesia,” The 25th Asian Conference on Remote Sensing, Chiang Mai, Thailand, 22-26 November 2004.
[12]	K. Hurni, “Rubber in Laos: Detection of Actual and As- Sessment of Potential Plantations in Lao PDR Using GIS and Remote Sensing Technologies, Master Thesis,” Cen- tre for Development and Environment, University of Bern, Bern, 2008.
[13]	E. Van Ranst, H. Tang, R. Groenemans and S. Sinthurahat, “Application of Fuzzy Logic to Land Suitability for Rubber Production in Peninsular Thailand,” Geodema, Vol. 70, No. 1, 1996, pp. l-19. doi:10.1016/0016-7061(95)00061-5
[14]	C. Bishop, “Pattern Recognition and Machine Lear- ning,” Springer, New York, 2006.
[15]	J. R. Quinlan, “Induction of Decision Trees. Machine Learning, Vol. 1, No. 1, 1986, pp. 81-106. doi:10.1007/BF00116251
[16]	D. L. Civco, “Artificial Neural Networks for Land Cover Classification and Mapping,” International Journal of Geographical Information Science, Vol. 7, No. 2, 1993, pp. 173-186. doi:10.1080/02693799308901949
[17]	Z. Li and J. R. Eastman, “Commitment and Typicality Measures for the Self-Organizing Map,” International Journal of Remote Sensing, Vol. 31, No. 16, 2010, pp. 4265-4280. doi:10.1080/01431160903246725
[18]	C. D. Lippitt, J. Rogan, Z. Li, J. R. Eastman and T. G. Jones, “Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms,” Photogrammetric Engineering and Remote Sensing, Vol. 74, No. 10, 2008, pp. 1201-1211.
[19]	Z. Li and J. M. Fox, “Mapping Rubber Tree Growth in Mainland Southeast Asia Using Time-Series MODIS 250 m NDVI and Statistical Data,” Applied Geography, Vol. 32, No. 2, 2011, pp. 420-432. doi:10.1016/j.apgeog.2011.06.018
[20]	F. Sangermano and R. Eastman, “Linking GIS and Ecology: The Use of Mahalanobis Typicalities to Model Species Distribution,” In: G. D. Buzai, Eds., Memorias XI Conferencia Iberoamericana de Sistemas de Información Geográfica, Buenos Aires, 2007.
[21]	P. A. Hernandez, I. Franke, S. K. Herzog, V. Pacheco, L. Paniagua, H. L. Quintana, A. Soto, J. J. Swenson, C. Tovar, T. H. Valqui, J. Vargas and B. E. Young, “Pre- dicting Species Distributions in Poorly-Studied Landscapes,” Biodiversity and Conservation, Vol. 17, No. 6, 2008, pp. 1353-1366. doi:10.1007/s10531-007-9314-z
[22]	J. Chang, M. C. Hansen, K. Pittman, M. Carroll and C. Di-Miceli, “Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets,” Agronomy Journal, Vol. 99, No. 6, 2007, pp. 654-1664. doi:10.2134/agronj2007.0170
[23]	R. Katawatin, P. H. Crown and D. L. Klita, “Mapping Dry Season Crops in Thailand Using Landsat-5 TM Data,” Canadian Journal of Remote Sensing, Vol. 22, No. 4, 1996, pp. 349-472.
[24]	A. J. W. De Wit and J. G. P. W. Clevers, “Efficiency and Accuracy of Per-Field Classification for Operational Crop Mapping,” International Journal of Remote Sensing, Vol. 25, No. 20, 2004, pp. 4091-4112. doi:10.1080/01431160310001619580
[25]	M. A. Wulder, S. M. Ortlepp, J. C. White and S. Maxwell, “Evaluation of Landsat-7 SLC-off Image Products for Forest Change Detection,” Canadian Journal of Remote Sensing, Vol. 34, No. 2, 2008, pp. 93-99. doi:10.5589/m08-020
[26]	F. Rembold and F. Maselli, “Estimating Inter-Annual Crop Area Variation Using Multi-Resolution Satellite Sensor Images,” International Journal of Remote Sensing, Vol. 25, 2004, pp. 2641-2647. doi:10.1080/01431160310001657614
[27]	F. Rembold and F. Maselli, “Estimation of Inter-Annual Crop Area Variation by the Application of Spectral Angle Mapping to Low Resolution Multitemporal NDVI Images,” Photogrammetric Engineering and Remote Sensing, Vol. 72, No. 1, 2006, pp. 55-62.
[28]	Z. Li and J. M. Fox, “Integrating Mahalanobis Typicalities with a Neural Network for Rubber Distribution Mapping,” Remote Sensing Letters, Vol. 2, No. 2, 2011, pp. 157-166. doi:10.1080/01431161.2010.505589
[29]	P. S. Chavez, “Image-Based Atmospheric Corrections: Revisited and Improved,” Photogrammetric Engineering and Remote Sensing, Vol. 62, No. 9, 1996, pp. 1025- 1036.
[30]	C. Song, C. E. Woodcock, K. C. Seto, M. Pax-Lenney and S. A. Macomber, “Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?” Remote Sensing of Environment, Vol. 75, No. 2, 2001, pp. 230-244. doi:10.1016/S0034-4257(00)00169-3
[31]	M. C. Hansen, D. P. Roy, E. J. Lindquist, B. Adusei, C. O. Justice and A. Altstatt, “A method for Integrating MODIS and Landsat Data for Systematic Monitoring of Forest Cover and Change in the Congo Basin,” Remote Sensing of Environment, Vol. 112, No. 5, 2008, pp. 2495-2513. doi:10.1016/j.rse.2007.11.012
[32]	J. Wang, P. M. Rich, K. P. Price and W. D. Kettle, “Relations between NDVI and Tree Productivity in the Central Great Plains,” International Journal of Remote Sensing, Vol. 25, No. 16, 2004, pp. 3127-3138. doi:10.1080/0143116032000160499
[33]	Y. Yamamoto, T. Oberthür and R. Lefroy, “Spatial Identifycation by Satellite Imagery of the Crop-Fallow Rotation Cycle in Northern Laos,” Environment, Development and Sustainability, Vol. 11, No. 3, 2009, pp. 639-654. doi:10.1007/s10668-007-9134-z
[34]	E. P. Crist, “ATM Tasselled Cap Equivalent Transformation for Reflectance Factor Data,” Remote Sensing of Environment, Vol. 17, No. 3, 1985, pp. 301-306. doi:10.1016/0034-4257(85)90102-6
[35]	T. Han, M. A. Wulder, J. C. White, N. C. Coops, M. F. Alvarez and C. Butson, “An Efficient Protocol to Process Landsat Images for Change Detection With Tasselled Cap Transformation,” IEEE Geoscience and Remote Sensing Letters, Vol. 4, No. 1, 2007, pp. 147-151. doi:10.1109/LGRS.2006.887066
[36]	C. Huang, B. Wylie, L. Yang, H. Collin and G. Zylstra, “Derivation of a Tasseled Cap Transformation Based on Landsat 7 at-Satellite Reflectance,” (Raytheon ITSS, USGS EROS Data Center, Sioux Falls, SD 57198, USA). http://landcover.usgs.gov/pdf/tasseled.pdf.
[37]	J. M. B. Carreiras, J. M. C. Pereira and J. S. Pereira, “Estimation of tree Canopy Cover in Evergreen Oak Woodlands Using Remote Sensing,” Forest Ecology and Management, Vol. 223, No. 1-3, 2006, pp. 45-53. doi:10.1016/j.foreco.2005.10.056
[38]	G. M. Foody, N. A. Campbell, N. M. Trodd and T. F. Wood, “Derivation and Applications of Probabilistic Measures of Class Membership from the Maximum Likelihood Classification,” Photogrammetric Engineering and Remote Sensing, Vol. 58, No. 9, 1992, pp. 1335-1341.
[39]	J. R. Eastman, J. Toledano, S. Crema, H. Zhu and H. Jiang, “In-Process Classification Assessment of Remotely Sensed Imagery,” GeoCarto International, Vol. 20, No. 4, 2005, pp. 33-44. doi:10.1080/10106040508542362
[40]	G. M. Foody, “Hard and Soft Classifications by a Neural Network with a Non-Exhaustively Defined Set of Classes,” International Journal of Remote Sensing, Vol. 23, No. 18, 2002, pp. 3853-3864. doi:10.1080/01431160110109570
[41]	J. R. Eastman, “IDRISI: The Taiga Edition,” Clark Labs, Clark University, Worcester, 2009.