Mapping rubber stand ages in Luangnamtha district (Northern Laos) using NDVI and LSWI from Landsat images
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Published:
Jun 19, 2019
Keywords:
Rubber stand age Landsat 8 OLI Google Earth Imagery
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Abstract
Over the recent past, rubber tree plantations have rapidly expanded across vast tracts of Northern Laos. Remotely assessing rubber stand age is invaluable to monitor and predict the impacts of such an extensive and rapid land use change. This study presents the classification of rubber plantation areas versus non-rubber plantation areas, and the potential of two vegetation indices, namely NDVI and LSWI, for mapping rubber stand ages. Spectral signatures were derived from Landsat OLI images of Luangnamtha district acquired on 5th March and 14th March 2017, including Band 4 (RED), Band 5 (NIR), and Band 7 (SWIR), captured at the time of the year corresponding to the beginning of the foliation of rubber tree, with a spatial resolution of 30m. The work shows that the classification has a high accuracy of 92% and 0.89 for the overall accuracy and the Kappa coefficient, respectively. The producer’s and user’s accuracies of rubber stand ages were 75-92%, and 75-91% for the three age groups (< 7, 7-12, and > 12 years old). Overall accuracy and Kappa coefficients were 87 and 80%, and 0.79 and 0.67 for NDVI and LSWI indices, respectively. We also found that LSWI values were larger than NDVI when rubber stand age is greater than 7 years old, but NDVI showed better overall accuracy than LSWI at the beginning of the rubber tree foliation.
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How to Cite
Sayavong, S., Kaewjampa, N., Katawatin, R., Boonthai Iwai, C., Moukomla, S., Oszwald, J., & Pierret, A. (2019). Mapping rubber stand ages in Luangnamtha district (Northern Laos) using NDVI and LSWI from Landsat images. Asia-Pacific Journal of Science and Technology, 24(2), APST–24. https://doi.org/10.14456/apst.2019.18
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Research Articles
References
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[2] Suratman MN, Bull GQ, Leckie DG, Lemay VM, Marshall PL, Mispan MR. Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data. Int For Rev. 2004(1):1-12.
[3] Li Z, Fox JM. Rubber Tree Distribution Mapping in Northeast Thailand. Int J Geosci. 2011;02(04):573–84.
[4] Fox JM, Castella J-C, Ziegler AD, Westley SB. Rubber plantations expansion in mountanous Southeast Asia: What are the consequences for the environment?. 2014.
[5] Agriculture and Forestry Policy Research Center. Rubber Expansion in Lao PDR. National Agriculture and Forestry Research Institute (NAFRI) and Upland Research and Capacity Development Programme, Vientiane, Lao PDR. 2010.
[6] Liu X, Jiang L, Feng Z, Li P. Rubber Plantation Expansion Related Land Use Change along the Laos-China Border Region. Sustainability. 2016;8(10):1011.
[7] Alton C, Bluhm D, Sananikone S. Para Rubber Study Hevea brasiliensis Lao P.D.R. Lao - German Program Rural Development in Mountainous Areas of Northern Lao PDR. 2005.
[8] Shi W. Rubber Boom in Luang Namtha. Transnatl Perspect GTZ RDMA. 2008.
[9] Rawat JS, Biswas V, Kumar M. Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt J Remote Sens Space Sci. 2013;16(1):111–7.
[10] Lao Statistics Bureau. Results of Population and Housing Census 2015. The 4th Population and Housing Census (PHC) 2015, Ministry of Planning and Investment. 2015.
[11] Thongmanivong S, Fujita Y. Recent Land Use and Livelihood Transitions in Northern Laos. Mt Res Dev. 2006;26(3):237–44.
[12] Centre for Research and Information on Land and Natural Resources, Faculty of Social Sciences and Foundation for Ecological Recovery. Research evaluation of economic, social, and ecological implications of the programme for commercial tree plantations: case study of rubber in the south of Laos PDR. Centre for Research and Information on Land and Natural Resources, National Land Management Authority, Office of Prime Minister, Lao PDR, Faculty of Social Sciences, Chiang Mai University, Thailand, Foundation for Ecological Recovery, Bangkok Thailand Vientiane Capital: Lao PDR. 2009.
[13] Manivong V, Cramb RA. Economics of smallholder rubber production in Northern Laos. Submitt Agrofor Syst. 2007.
[14] Fox J. Crossing borders, changing landscapes: Land-use dynamics in the Golden Triangle. Asia Pacific 2009;(92):1–8.
[15] Phanvilay K, Thongmanivong S, Fujita Y, Fox J, Center EW. Agrarian Land Use Transformation in Upland Areas of Northern Laos. In: SSLWM workshop. 2006.
[16] Leinenkugel P, Oppelt N, Kuenzer C. A new land cover map for the Mekong: Southeast Asia’s largest transboundary river basin. Pacific Geographies 2014;(41):10-15.
[17] Suratman MN, LeMay VM, Gary Q, Donald GL, Walsworth N, Peter LM. Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping. Sci Lett. 2005;2(1):79-85.
[18] Wasana C. An approach for estimating area of rubber plantation: integrating satellite and physical data over the Northeast Thailand. In: Proceedings of the 31st Asian Conference on Remote Sensing Vietnam, Hanoi, Vietnam. 2010.
[19] Putklang W, Maneechot S, Mongkolsawat C. Assessing Thaichote Satellite Data in Support of Mapping Rubber Tree Plantation in Northeast Thailand. The 33rd Asian Conference on Remote Sensing. 2012.
[20] Koedsin W, Huete A. Mapping Rubber Tree Stand Age using Pléiades Satellite Imagery: A Case Study in Talang District, Phuket, Thailand. Eng J. 2015;19(4):45-56.
[21] Li Z, Fox JM. Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data. Appl Geogr. 2012;32(2):420-32.
[22] Li P, Zhang J, Feng Z. Mapping rubber tree plantations using a Landsat-based phenological algorithm in Xishuangbanna, southwest China. Remote Sens Lett. 2015;6(1):49-58.
[23] Senf C, Pflugmacher D, van der Linden S, Hostert P. Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series. Remote Sens. 2013;5(6):2795-812.
[24] Kou W, Xiao X, Dong J, Gan S, Zhai D, Zhang G, et al. Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images. Remote Sens. 2015;7(1):1048-73.
[25] Agricultural Land Research Center. Results of Agriculture and Forestry Allocation and Plan to 2020 in Luangnamtha District and Province. National Agriculture and Forestry Research Institute (NAFRI), Vientiane Capital, Lao PDR. 2008.
[26] Unesco, editor. Impact: the effect of tourism on culture and the environment in Asia and the Pacific: alleviating poverty and protecting cultural and natural heritage through community-based ecotourism in Luang Namtha, Lao PDR. Bangkok, Thailand: UNESCO Bangkok, Asia and Pacific Regional Bureau for Education; 2008. p. 121
[27] Luangnamtha Department of Information and Culture. Luangnamtha 30 Year Anniversary. Luangnamtha, Lao PDR. 2006.
[28] Vermote E, Justice C, Claverie M, Franch B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ. 2016;185:46-56.
[29] U.S. Department of the Interior and U.S. Geological Survey. Using the USGS Landsat 8 Product. [Internet]. 2013 [cited 2019 May 17]. Available: https://landsat.usgs.gov/Landsat8_Using_Product.php
[30] D’Allestro P, Parente C. GIS application for NDVI calculation using Landsat 8 OLI images. International Journal of Applied Engineering Research; 2015;(10): 42099–42102
[31] Richards JA, Jia X. Remote sensing digital image analysis: an introduction. 4th ed. Berlin: Springer; 2006. p. 439
[32] Xiao X, Zhang Q, Saleska S, Hutyra L, De Camargo P, Wofsy S, et al. Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest. Remote Sens Environ. 2005;94(1):105–22.
[33] Dibs H, Idrees MO, Alsalhin GBA. Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery. Egypt J Remote Sens Space Sci. 2017;20(1):21–30.
[34] Chen Y, Wang Q, Wang Y, Duan S-B, Xu M, Li Z-L. A Spectral Signature Shape-Based Algorithm for Landsat Image Classification. ISPRS Int J Geo-Inf. 2016;5(9):154.
[35] R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Internet]. 2017 [cited 2019 April 02]. Available: https://www.R-project.org/.
[36] Congalton RG, Green K. Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed. Boca Raton: CRC Press/Taylor & Francis; 2009. p. 183
[37] Dong J, Xiao X, Chen B, Torbick N, Jin C, Zhang G, et al. Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. Remote Sens Environ. 2013;134:392–402.