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Research Article

Quantifying oil palm expansion in Southeast Asia from 2000 to 2015: A data fusion approach

, &
Pages 26-46 | Received 05 Jul 2021, Accepted 14 Dec 2021, Published online: 15 Feb 2022

References

  • Ahrends, A., Hollingsworth, P.M., Ziegler, A.D., Fox, J.M., Chen, H., Su, Y., & Xu, J. (2015). Current trends of rubber plantation expansion may threaten biodiversity and livelihoods. Global Environmental Change, 34, 48–58. https://doi.org/10.1016/j.gloenvcha.2015.06.002
  • Austin, K.G., Schwantes, A., Gu, Y., & Kasibhatla, P.S. (2019). What causes deforestation in Indonesia? Environmental Research Letters, 14(2), 024007. https://doi.org/10.1088/1748-9326/aaf6db
  • Azhar, B., Saadun, N., Prideaux, M., & Lindenmayer, D.B. (2017). The global palm oil sector must change to save biodiversity and improve food security in the tropics. Journal of Environmental Management, 203, 457–466. https://doi.org/10.1016/j.jenvman.2017.08.021
  • Barcelos, E., Rios, S.D.A., Cunha, R.N., Lopes, R., Motoike, S.Y., Babiychuk, E., Skirycz, A., & Kushnir, S. (2015). Oil palm natural diversity and the potential for yield improvement. Frontiers in Plant Science, 6, 190. https://doi.org/10.3389/fpls.2015.00190
  • Byerlee, D., Falcon, W.P., & Naylor, R. (2017). The Tropical Oil Crop Revolution: Food, Feed, Fuel, and Forests. Oxford Univ. Press.
  • Caughlin, T.T., Barber, C., Asner, G.P., Glenn, N.F., Bohlman, S.A., & Wilson, C.H. (2021). Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series. Ecological Applications, 31(1), e02208. https://doi.org/10.1002/eap.2208
  • Cheng, Y., Yu, L., Xu, Y., Liu, X., Lu, H., Cracknell, A.P., Kanniah, K., & Gong, P. (2018). Towards global oil palm plantation mapping using remote-sensing data. International Journal of Remote Sensing, 39(18), 5891–5906. https://doi.org/10.1080/01431161.2018.1492182
  • Chong, K.L., Kanniah, K.D., Pohl, C., & Tan, K.P. (2017). A review of remote sensing applications for oil palm studies. Geo-spatial Information Science, 20(2), 184–200. https://doi.org/10.1080/10095020.2017.1337317
  • Coburn, C.A., & Roberts, A.C. (2004). A multiscale texture analysis procedure for improved forest stand classification. International Journal of Remote Sensing, 25(20), 4287–4308. https://doi.org/10.1080/0143116042000192367
  • Curtis, P.G., Slay, C.M., Harris, N.L., Tyukavina, A., & Hansen, M.C. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108–1111. https://doi.org/10.1126/science.aau3445
  • Damian, J.M., Durigan, M.R., Cherubin, M.R., Maia, S.M.F., Ogle, S.M., de Camargo, P.B., Ferreira, J.N., de Oliveira Júnior, R.C., & Cerri, C.E.P. (2021). Deforestation and land use change mediate soil carbon changes in the eastern Brazilian Amazon. Regional Environmental Change, 21(3), 1–12. https://doi.org/10.1007/s10113-021-01796-w
  • De Alban, J., Connette, G., Oswald, P., & Webb, E. (2018). Combined Landsat and L-band SAR data improves land cover classification and change detection in dynamic tropical landscapes. Remote Sensing, 10(2), 306. https://doi.org/10.3390/rs10020306
  • DeFries, R.S., Houghton, R.A., Hansen, M.C., Field, C.B., Skole, D., & Townshend, J., 2002. Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s. Proceedings of the National Academy of Sciences, 99(22), 14256–14261.
  • DeVries, B., Verbesselt, J., Kooistra, L., & Herold, M. (2015). Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sensing of Environment, 161, 107–121. https://doi.org/10.1016/j.rse.2015.02.012
  • Dong, J., Xiao, X., Chen, B., Torbick, N., Jin, C., Zhang, G., & Biradar, C. (2013). Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. Remote Sensing of Environment, 134, 392–402. https://doi.org/10.1016/j.rse.2013.03.014
  • Galiatsatos, N., Donoghue, D. N., Watt, P., Bholanath, P., Pickering, J., Hansen, M. C., & Mahmood, A. R. (2020). An assessment of global forest change datasets for national forest monitoring and reporting. Remote Sensing, 12(11), 1790. 10.3390/rs12111790
  • Gatto, M., Wollni, M., & Qaim, M. (2015). Oil palm boom and land-use dynamics in Indonesia: The role of policies and socioeconomic factors. Land Use Policy, 46, 292–303. https://doi.org/10.1016/j.landusepol.2015.03.001
  • Gaveau, D.L., Sheil, D., Salim, M.A., Arjasakusuma, S., Ancrenaz, M., Pacheco, P., & Meijaard, E. (2016). Rapid conversions and avoided deforestation: Examining four decades of industrial plantation expansion in Borneo. Scientific Reports, 6(1), 1–13. https://doi.org/10.1038/srep32017
  • Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x
  • Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., & Wang, C. (2018). Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205, 253–275. https://doi.org/10.1016/j.rse.2017.11.026
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  • Gutiérrez-Vélez, V.H., DeFries, R., Pinedo-Vásquez, M., Uriarte, M., Padoch, C., Baethgen, W., Fernandes, K., & Lim, Y. (2011). High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environmental Research Letters, 6(4), 044029. https://doi.org/10.1088/1748-9326/6/4/044029
  • Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., & Townshend, J.R.G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850–853 doi:10.1126/science.1244693.
  • Hansen, M.C., Stehman, S.V., & Potapov, P.V., 2010. Quantification of global gross forest cover loss. Proceedings of the National Academy of Sciences, 107(19), 8650–8655 doi:10.1073/pnas.0912668107.
  • Hansen, M. C., Stehman, S. V., Potapov, P. V., Arunarwati, B., Stolle, F., & Pittman, K. (2009). Quantifying changes in the rates of forest clearing in Indonesia from 1990 to 2005 using remotely sensed data sets. Environmental Research Letters, 4(3), 034001.
  • Hansen, M.C., Stehman, S.V., Potapov, P.V., Loveland, T.R., Townshend, J.R., DeFries, R.S., Pittman, K.W., Arunarwati, B., Stolle, F., Steininger, M.K., & Carroll, M., 2008. Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data. Proceedings of the National Academy of Sciences, 105(27), 9439–9444 doi:10.1073/pnas.0804042105.
  • Hesselbarth, M.H., Sciaini, M., With, K.A., Wiegand, K., & Nowosad, J. (2019). landscapemetrics : An open-source R tool to calculate landscape metrics. Ecography, 42(10), 1648–1657. https://doi.org/10.1111/ecog.04617
  • Hu, Y., & Dong, Y. (2018). An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 347–359. https://doi.org/10.1016/j.isprsjprs.2018.10.008
  • Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
  • Hurni, K., Schneider, A., Heinimann, A., Nong, D.H., & Fox, J. (2017). Mapping the expansion of boom crops in mainland Southeast Asia using dense time stacks of Landsat data. Remote Sensing, 9(4), 320. https://doi.org/10.3390/rs9040320
  • Imai, N., Furukawa, T., Tsujino, R., Kitamura, S., & Yumoto, T. (2018). Correction: Factors affecting forest area change in Southeast Asia during 1980-2010. PloS One, 13(6), e0199908. https://doi.org/10.1371/journal.pone.0199908
  • Jensen, J.R. (2009). Remote sensing of the environment: An earth resource perspective (2nd ed.). Pearson Prentice Hall.
  • Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M.R., Kuemmerle, T., Meyfroidt, P., Mitchard, E.T., & Reiche, J. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70. https://doi.org/10.3390/rs8010070
  • Kennedy, R.E., Yang, Z., & Cohen, W.B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment, 114(12), 2897–2910. https://doi.org/10.1016/j.rse.2010.07.008
  • Khatun, R., Reza, M.I.H., Moniruzzaman, M., & Yaakob, Z. (2017). Sustainable oil palm industry: The possibilities. Renewable and Sustainable Energy Reviews, 76, 608–619. https://doi.org/10.1016/j.rser.2017.03.077
  • Koh, L.P., Miettinen, J., Liew, S.C., & Ghazoul, J., 2011. Remotely sensed evidence of tropical peatland conversion to oil palm. Proceedings of the National Academy of Sciences, 108(12), 5127–5132 doi:10.1073/pnas.1018776108.
  • Lambin, E.F., Geist, H.J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28(1), 205–241. https://doi.org/10.1146/annurev.energy.28.050302.105459
  • Lee, J.S., Wen, J.H., Ainsworth, T.L., Chen, K.S., & Chen, A.J. (2009). Improved sigma filter for speckle filtering of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(1), 202–213. https://doi.org/10.1109/TGRS.2008.2002881
  • Lee, J.S.H., Wich, S., Widayati, A., & Koh, L.P. (2016). Detecting industrial oil palm plantations on Landsat images with Google Earth Engine. Remote Sensing Applications: Society and Environment, 4, 219–224. https://doi.org/10.1016/j.rsase.2016.11.003
  • Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18–22.
  • Magliocca, N.R., Van Khuc, Q., de Bremond, A., & Ellicott, E.A. (2020). Direct and indirect land-use change caused by large-scale land acquisitions in Cambodia. Environmental Research Letters, 15(2), 024010. https://doi.org/10.1088/1748-9326/ab6397
  • Marceau, D.J., Howarth, P.J., Dubois, J.M.M., & Gratton, D.J. (1990). Evaluation of the grey-level co-occurrence matrix method for land-cover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 513–519. https://doi.org/10.1109/TGRS.1990.572937
  • Margono, B.A., Potapov, P.V., Turubanova, S., Stolle, F., & Hansen, M.C. (2014). Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change, 4(8), 730–735. https://doi.org/10.1038/nclimate2277
  • McGarigal, K., Cushman, S.A., & Ene, E., 2012. FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. University of Massachusetts, Amherst, MA, USA. Available at the following web sitehttp://www.umass.edu/landeco/research/fragstats/fragstats.html
  • McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157. https://doi.org/10.1007/BF02295996
  • Meijjard, E., Abrams, J., Juffe-Bignoli, D., Voigt, M., & Sheil, D. (2020). Coconut oil, conservation and the conscientious consumer. Current Biology, 30, 757–758 doi:10.1016/j.cub.2020.05.059.
  • Mekhilef, S., Siga, S., & Saidur, R. (2011). A review on palm oil biodiesel as a source of renewable fuel. Renewable and Sustainable Energy Reviews, 15(4), 1937–1949. https://doi.org/10.1016/j.rser.2010.12.012
  • Miettinen, J., Shi, C., & Liew, S.C. (2016). 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sensing Letters, 7(7), 701–710. https://doi.org/10.1080/2150704X.2016.1182659
  • Morel, A.C., Fisher, J.B., & Malhi, Y. (2012). Evaluating the potential to monitor aboveground biomass in forest and oil palm in Sabah, Malaysia, for 2000–2008 with Landsat ETM+ and ALOS-PALSAR. International Journal of Remote Sensing, 33(11), 3614–3639. https://doi.org/10.1080/01431161.2011.631949
  • Nangendo, G., Skidmore, A.K., & van Oosten, H. (2007). Mapping East African tropical forests and woodlands — A comparison of classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, 61(6), 393–404. https://doi.org/10.1016/j.isprsjprs.2006.11.003
  • Ordway, E.M., Naylor, R.L., Nkongho, R.N., & Lambin, E.F. (2019). Oil palm expansion and deforestation in Southwest Cameroon associated with proliferation of informal mills. Nature Communication, 10(1), 1–11. https://doi.org/10.1038/s41467-018-07915-2
  • Pirker, J., Mosnier, A., Kraxner, F., Havlík, P., & Obersteiner, M. (2016). What are the limits to oil palm expansion? Global Environmental Change, 40, 73–81 Accessed September 2016. https://doi.org/10.1016/j.gloenvcha.2016.06.007
  • Qaim, M., Sibhatu, K.T., Siregar, H., & Grass, I. (2020). Environmental, economic, and social consequences of the oil palm boom. Annual Review of Resource Economics, 12(1), 321–344. https://doi.org/10.1146/annurev-resource-110119-024922
  • R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing. URL http://www.R-project.org/
  • Ramankutty, N., Evan, A.T., Monfreda, C., & Foley, J.A. (2008). Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22(GB1003). https://doi.org/10.1029/2007GB002952
  • Ramdani, F. (2019). Recent expansion of oil palm plantation in the most eastern part of Indonesia: Feature extraction with polarimetric SAR. International Journal of Remote Sensing, 40(19), 7371–7388. https://doi.org/10.1080/01431161.2018.1508924
  • Reiche, J., Verbesselt, J., Hoekman, D., & Herold, M. (2015). Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sensing of Environment, 156, 276–293. https://doi.org/10.1016/j.rse.2014.10.001
  • Rozendaal, D.M., & Zuidema, P.A. (2011). Dendroecology in the tropics: A review. Trees, 25(1), 3–16. https://doi.org/10.1007/s00468-010-0480-3
  • Sarzynski, T., Giam, X., Carrasco, L., & Lee, J.S.H. (2020). Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the google earth engine. Remote Sensing, 12(7), 1220. https://doi.org/10.3390/rs12071220
  • Shimada, M., & Osawa, Y., 2012. ALOS-2 science program and high resolution SAR applications. In: Proceedings of SPIE 8528: November 9 2012, Kyoto, Japan: SPIE Asia-Pacific Remote Sensing, 852812. https://doi.org/10.1117/12.979379.
  • Skole, D., & Tucker, C. (1993). Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988. Science, 260(5116), 1905–1910. https://doi.org/10.1126/science.260.5116.1905
  • Sodhi, N.S., Koh, L.P., Brook, B.W., & Ng, P.K. (2004). Southeast Asian biodiversity: An impending disaster. Trends in Ecology & Evolution, 19(12), 654–660. https://doi.org/10.1016/j.tree.2004.09.006
  • Stuhlmacher, M., Turner, B.L., Frazier, A.E., Kim, Y., & Leffel, J. (2020). Institutional shifts and landscape change: The impact of the Período Especial on Cuba’s land system architecture. Journal of Land Use Science, 15(5), 690–706. https://doi.org/10.1080/1747423X.2020.1829119
  • Torbick, N., Ledoux, L., Salas, W., & Zhao, M. (2016). Regional mapping of plantation extent using multisensor imagery. Remote Sensing, 8(3), 236. https://doi.org/10.3390/rs8030236
  • Tropek, R., Sedláček, O., Beck, J., Keil, P., Musilová, Z., Šímová, I., & Storch, D. (2014). Comment on High-resolution global maps of 21st-century forest cover change. Science, 344(6187), 981. https://doi.org/10.1126/science.1248753
  • United Nations, Department of Economic and Social Affairs, Population Division. (2019). World Population Prospects 2019. https://population.un.org/wpp/.
  • Vaiphasa, C., Skidmore, A.K., & de Boer, W.F. (2006). A post-classifier for mangrove mapping using ecological data. ISPRS Journal of Photogrammetry and Remote Sensing, 61(1), 1–10. https://doi.org/10.1016/j.isprsjprs.2006.05.005
  • Wicke, B., Sikkema, R., Dornburg, V., & Faaij, A. (2011). Exploring land use changes and the role of palm oil production in Indonesia and Malaysia. Land Use Policy, 28(1), 193–206. https://doi.org/10.1016/j.landusepol.2010.06.001
  • Woodcock, C.E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S.N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P.S., Vermote, E.F., Vogelmann, J., Wulder, M.A., & Wynne, R. (2008). Free access to Landsat imagery. Science, 320(5879), 1011. https://doi.org/10.1126/science.320.5879.1011a
  • Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
  • Zeng, Z., Estes, L., Ziegler, A.D., Chen, A., Searchinger, T., Hua, F., Guan, K., Jintrawet, A., & Wood, E.F. (2018). Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century. Nature Geoscience, 11(8), 556. https://doi.org/10.1038/s41561-018-0166-9