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Environment, Development and Sustainability

, Volume 11, Issue 3, pp 639–654 | Cite as

Spatial identification by satellite imagery of the crop–fallow rotation cycle in northern Laos

  • Yukiyo Yamamoto
  • Thomas Oberthür
  • Rod Lefroy
Article

Abstract

In the mountainous regions of northern Laos, shifting cultivation, or slash-and-burn agriculture, is widely practiced. However, the crop–fallow rotation cycle is becoming shorter owing to forest conservation policies and population pressure, causing loss of productivity that deleteriously affects farmers’ livelihoods in the region. To investigate regional land use conditions, we have developed a method of identifying the crop–fallow rotation cycle from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) data. We assessed the impact of the identified cycle on plant production measured by Normalized Difference Vegetation Index (NDVI). The study site was an area in Luang Prabang Province. Using eight TM and ETM+ images acquired annually from 1995 to 2003, except for 1998, when cloud-free data were not collected, we classified land use in each year as crop or fallow by the presence of vegetation in the late dry season. Conformity with fallow age determined by field investigation was 69.1%. The cultivation frequency from 1995 to 2002 showed that 77,000 ha (17.3% of the study site) had not been used for cropping during the period, but 41,000 ha (9.2%) had been used every year. Of the study site, 129,000 ha (29.1%) was cultivated one or two times, 83,000 ha (18.7%) was three or four times, and 54,000 ha (12.2%) was five or six times. The NDVI of crops in November did not provide sufficient evidence to prove the assumption that a longer fallow period would result in better crop yields. Instead, the regeneration of fallow vegetation was evidenced by the higher NDVI values after longer fallow. More than 8 years would be needed to reach the same NDVI as forest. From the produced maps indicating fallow age and cultivation frequency, we found that areas with high potential for regeneration decreased as cultivation frequency increased. Areas near rivers were intensively used, and fallow length was accordingly short. Low-potential areas were found in the western basin of the Mekong River. This spatial information can be used to detect areas where biomass productivity is at high risk of deteriorating.

Keywords

Crop–fallow rotation cycle Fallow age Laos NDVI Shifting cultivation 

Notes

Acknowledgements

This research was implemented as a part of the JIRCAS International Collaborative Research Project entitled “Increasing Economic Options in Rainfed Agriculture in Indochina through Efficient Use of Water Resources”. The authors would like to extend their thanks to Mr. Xaysana Xayarath at CIAT, Mr. Kongkeo Phachomphon at the Soil Survey and Land Classification Center, and the CIAT National staff in the Vientiane and Luang Prabang offices for their assistance.

References

  1. Agricultural Census Office. (2000). Lao agricultural census 1998/99 highlights. Vientiane: Agricultural Census Office.Google Scholar
  2. Awaya, Y., & Tanaka, K. (2003). Evaluation of forest monitoring using Landsat TM data: Logging and growth monitoring in Sugi cedar plantations. Photogrametry and Remote Sensing, 42(5), 60–70 [Japanese].Google Scholar
  3. Bhandari, S. P., Hussin, Y. A., & Sharifi, M. A. (2004). Detection and characterization of selective logging using remote sensing and GIS in tropical forest of East Kalimantan. Asian Journal of Geoinformatics, 4(3), 57–70.Google Scholar
  4. Bohlman, S., Adams, A. J. B., Smith, M. O., & Peterson, D. L. (1998). Seasonal foliage changes in the Eastern Amazon Basin detected from Landsat Thematic Mapper satellite images. Biotropica, 30(3), 376–391.CrossRefGoogle Scholar
  5. Burgers, P. (2006). Fallow management strategies in Southeast Asia: Some benefits and constraints. Newsletter on Soil Fertility and Fallow Management in the SEA. Retrieved September 5, 2006, from http://www.worldagroforestry.org/SEA/networks/ifm/NText2.htm#2
  6. Bustanul, A. (1998). Does shifting cultivation really cause deforestation? Lesson from communal forest area in Sumatra, Indonesia. International Association for the Study of Common Property, Canada. Retrieved September 5, 2006, from http://www.indiana.edu/~iascp/Drafts/arifin.pdf
  7. Cohen, W. B., Spies, T. A., & Fiorella, M. (1995). Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A. International Journal of Remote Sensing, 16(4), 721–746.CrossRefGoogle Scholar
  8. Committee for Planning, Co-operation. (2003). The national poverty eradication programme—a comprehensive approach to growth and development. Vientiane: Committee for Planning and Co-operation.Google Scholar
  9. Crist, E. P., Laurin, R., & Cicone, R. C. (1986). Vegetation and soils information contained in transformed Thematic Mapper data. Proceedings of IGARSS’ 86 Symposium, 1465-70. Retrieved August 10, 2007, from http://www.ciesin.columbia.edu/docs/005-419/005-419.html
  10. Curran, P. J. (1983). Multispectral remote sensing for the estimation of green leaf area index. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 309, 257–270.CrossRefGoogle Scholar
  11. D’Arrigo, R. D., Malmstrom, C. M., Jacoby, G. C., Los, S. O., & Bunker, D. E. (2000). Correlation between maximum latewood density of annual tree rings and NDVI based estimates of forest productivity. International Journal of Remote Sensing, 21(11), 2329–2336.CrossRefGoogle Scholar
  12. Dawson, T. P., North, P. R. J., Plummer, S. E., & Curran, P. J. (2003). Forest ecosystem chlorophyll content: Implications for remotely sensed estimates of net primary productivity. International Journal of Remote Sensing, 24(3), 611–617.CrossRefGoogle Scholar
  13. Dept. of Planning in Ministry of Agriculture, Forestry. (2004). Agricultural statistics year book 2003. Vientiane: Ministry of Agriculture and Forestry.Google Scholar
  14. Dymond, C. C., Mladenoff, D. J., & Radeloff, V. C. (2002). Phenological differences in Tasseled Cap indices improve deciduous forest classification. Remote Sensing of Environment, 80, 460–472.CrossRefGoogle Scholar
  15. Estreguil, C., & Lambin, E. F. (1996). Mapping forest-cover disturbances in Papua New Guinea with AVHRR data. Journal of Biogeography, 23(6), 757–773.Google Scholar
  16. Foody, G. M., & Curran, P. J. (1994). Estimation of tropical forest extent and regenerative stage using remotely sensed data. Journal of Biogeography, 21, 223–244.CrossRefGoogle Scholar
  17. Franca, H., & Setzer, W. (1998). AVHRR temporal analysis of savanna site in Brazil. International Journal of Remote Sensing, 19(16), 3127–3140.CrossRefGoogle Scholar
  18. Fujisaka, S. (1991). A diagnostic survey of shifting cultivation in northern Laos: Targeting research to improve sustainability and productivity. Agroforestry Systems, 13, 95–109.CrossRefGoogle Scholar
  19. Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Joel, G., Penuelas, J., & Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1), 28–41.CrossRefGoogle Scholar
  20. Govind, A., Bhavanarayana, M., Kumari, J., & Govind, A. (2005). Efficacy of different indices derived from spectral reflectance of wheat for nitrogen stress detection. Journal of Plant Interactions, 1(2), 93–105.CrossRefGoogle Scholar
  21. Helmer, E. H., Brown, S., & Cohen, W. B. (2000). Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery. International Journal of Remote Sensing, 21(11), 2163–2183.CrossRefGoogle Scholar
  22. Hibbard, K., & Sahagian, D. (1995). Net primary productivity model intercomparison activity. IGBP/GAIM Report Series, Report #5. Retrieved August 13, 2007, from http://www.gaim.sr.unh.edu/Products/Reports/Report_5/
  23. Hill, M. J., & Donald, G. E. (2003). Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series. Remote Sensing of Environment, 84(3), 367–384.CrossRefGoogle Scholar
  24. Hountondji, Y. C., Sokpon, N., & Ozer, P. (2006). Analysis of the vegetation trends using low resolution remote sensing data in Burkina Faso (1982–1999) for the monitoring of desertification. International Journal of Remote Sensing, 27(5), 871–884.CrossRefGoogle Scholar
  25. Kimes, D. S., Newcomb, W. W., Tucker, C. J., Zonneveld, I. S., Van Wijngaarden, W., Deleeuw, J., & Epema, G. F. (1985). Directional reflectance factor distributions for cover types of Northern Africa. Remote Sensing of Environment, 18(1), 1–20.CrossRefGoogle Scholar
  26. Lambin, E. F. (1999). Monitoring forest degradation in tropical regions by remote sensing: Some methodological issues. Global Ecology and Biogeography, 8, 191–198.CrossRefGoogle Scholar
  27. Liu, Y., Nishiyama, S., Yano, T., & Kusaka, T. (2004). Change detection in a forest well-maintained area using Landsat TM data. Asian Journal of Geoinformatics, 4(3), 3–8.Google Scholar
  28. Maselli, F., Barbati, A., Chiesi, M., Chirici, G., & Corona, P. (2006). Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy. Remote Sensing of Environment, 100(4), 563–575.CrossRefGoogle Scholar
  29. Mertz, O. (2002). The relationship between length of fallow and crop yields in shifting cultivation: A rethinking. Agroforestry Systems, 55, 149–159.CrossRefGoogle Scholar
  30. Ministry of Agriculture and Forestry. (2005a). Agricultural statistics—year book 2004 Vientiane: Ministry of Agriculture and Forestry.Google Scholar
  31. Ministry of Agriculture and Forestry. (2005b). Forestry strategy to the year 2020 of the Lao PDR. Vientiane: Ministry of Agriculture and Forestry.Google Scholar
  32. Nagasawa, R. (2002). Evaluation of land resources by remote sensing. Tokyo: Kokinsyoin [Japanese].Google Scholar
  33. National Space Development Agency of Japan. (2000). LANDSAT7 ETM+ data format guidebook. Tokyo: National Space Development Agency of Japan [Japanese].Google Scholar
  34. Numata, I., Soares, J. V. , Roberts, D. A., Leonidas, F. C., Chadwick, O. A., & Batista, G. T. (2003). Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rondônia, Brazil. Remote Sensing of Environment, 87(4), 446–455.CrossRefGoogle Scholar
  35. Paruelo, J. M., & Lauenroth, W. K. (1998). Interannual variability of NDVI and its relationship to climate for North American shrublands and grasslands. Journal of Biogeography, 25, 721–733.CrossRefGoogle Scholar
  36. Patel, N. R., Manjunath, M. N., Shukla, M., & Pande, L. M. (2004). Discrimination and empirical modeling of wheat and sugarcane crops using remote sensing and ground observations. Asian Journal of Geoinformatics, 4(4), 13–24.Google Scholar
  37. Rasul, G., & Thapa, G. B. (2003). Shifting cultivation in the mountains of South and Southeast Asia: Regional patterns and factors influencing the change. Land degradation & Development, 14, 495–508.CrossRefGoogle Scholar
  38. Rasmussen, M. S. (1997). Operational yield forecast using AVHRR NDVI data: Reduction of environmental and inter-annual variability. International Journal of Remote Sensing, 18(5), 1059–1077.CrossRefGoogle Scholar
  39. Roder, W. (1997). Slash-and burn rice systems in transition: Challenges for agricultural development in the hills on northern Laos. Mountain Research and Development, 17(1), 1–10.CrossRefGoogle Scholar
  40. Roder, W., Phengchanh, S., & Keoboualapha, B. (1995). Relationship between soil, fallow period, weeds and rice yield in slash-and-burn systems of Laos. Plant and soil, 176, 27–36.CrossRefGoogle Scholar
  41. Roder, W., Phengchanh, S., & Maniphone, S. (1997a). Dynamics of soil and vegetation during crop and fallow period in slash-and-burn fields on northern Laos. Geoderma, 76, 131–144.CrossRefGoogle Scholar
  42. Sandewall, M., Ohlsson, B., & Sawathvong, S. (2001). Assessment of historical land use changes for purposes of strategic planning—a case study in Laos. Ambio, 30(1), 55–61.Google Scholar
  43. Sarkar, S., & Kafatos, M. (2004). Interannual variability of vegetation over the Indian sub-continent and its relation to the different meteorological parameters. Remote Sensing of Environment, 90(2), 268–280.CrossRefGoogle Scholar
  44. Sunderlin, W. D. (1997). Shifting cultivation and deforestation in Indonesia: Steps toward overcoming confusion in the Debate. Network paper 21b. Retrieved September 5, 2006, from http://www.mekonginfo.org/mrc/rdf-odi/english/papers/rdfn/21b.pdf
  45. Tieszen, L. L., Reed, B. C., Bliss, N. B., Wylie, B. K., & DeJong, D. D. (1997). NDVI, C3 and C4 production, and distributions in Great Plains grassland land cover classes. Ecological Applications, 7(1), 59–78.Google Scholar
  46. Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht K. S., & Briggs, J. M. (1999). Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70(1), 52–68.CrossRefGoogle Scholar
  47. Wang, J., Rich, P. M., Price, K. P., & Kettle, W. D. (2004). Relations between NDVI and tree productivity in the central Great Plains. International Journal of Remote Sensing, 25(16), 3127–3138.CrossRefGoogle Scholar
  48. Waring, R. H., Coops, N. C., Ohmann, J. L., & Sarr, D. A. (2002). Interpreting woody plant richness from seasonal ratios of photosynthesis. Ecology, 83(11), 2964–2970.CrossRefGoogle Scholar
  49. Williams, M., Rastetter, E. B., Shaver, G. R., Hobbie, J. E., Carpino, E., & Kwiatkowski, B. L. (2001). Primary production of an Arctic watershed: An uncertainty analysis. Ecological Applications, 11(6), 1800–1816.CrossRefGoogle Scholar
  50. Wulder, M. A., Skakun, R. S., Kurz, W. A., & White, J. C. (2004). Estimating time since forest harvest using segmented Landsat ETM+ imagery. Remote Sensing of Environment, 93, 179–187.CrossRefGoogle Scholar
  51. Xavier, A. C., & Vettorazzi, C. A. (2004). Mapping leaf area index through spectral vegetation indices in a subtropical watershed. International Journal of Remote Sensing, 25(9), 1661–1672.CrossRefGoogle Scholar
  52. Yamamoto, Y., Lefroy, R., & Phachomphon, K. (2004b). Impact of the crop-fallow rotation cycle in northern Laos. Proceedings of Asian Conference on Remote Sensing, 1, 818–821 (Paper presented at the 25th Asian Conference on Remote Sensing, Chiang Mai).Google Scholar
  53. Yamamoto, Y., Oberthür, T., & Lefroy, R. (2006). Rainfed agriculture in northern Laos—identification of land use cycles in slash-and-burn agriculture by satellite imagery. JIRCAS Working Report, 47, 1–6.Google Scholar
  54. Yamamoto, Y., Souza, O. C., & Araujo, M. R. (2004a). Diagnosis of pasture degradation by multi-temporal satellite imagery. JIRCAS Working Report, 36, 49–55.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Japan International Research Center for Agricultural Sciences (JIRCAS)TsukubaJapan
  2. 2.International Center for Tropical Agriculture (CIAT)CaliColombia
  3. 3.International Center for Tropical Agriculture in Asia (CIAT in Asia)VientianeLao PDR

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