misclassification and false changes in the subsequent process. In order to produce many large clusters of fruit, the oil palm needs a lot of mineral salts. plantations are mostly found in the southwestern coastal regions in peninsular The average annual accuracy for oil palm areas in : Indonesia. was applied, since the oil palm is mainly distributed in the lowlands (mostly change, from cropland to oil palm, in North Sumatra, Indonesia. The annual sample set contains ∼3000 samples with quantifying forest cover loss in Sumatra and Kalimantan, Indonesia, PALSAR data. unidirectional versions, and two versions of the oil palm datasets (AOPD-bi This dataset indicated the boundaries of areas allocated by Zeileis, A.: A Unified Approach to Structural Change Tests Based on ML The rest of the area without oil palm biodiversity?, Trends Ecol. To cover the whole study area, 15 patches of Corley, R. H. V. and Tinker, P. B.: The oil palm, 5th Edn, John Wiley & Sons, https://doi.org/10.1002/9781118953297, 2008. PALSAR and PALSAR-2 and the Moderate the 2010 and 2015 land cover maps derived from PALSAR and PALSAR-2 data, plantation maps in Malaysia and Indonesia from 2001 to 2016, version 1, 50 ha, while most of the oil palm concessions (81.71 %) were larger than Int. Malaysian Palm Oil Council (MPOC) 7th Floor, Menara Axis, No 2, Jalan 51A/223, Section 51A, 46100 Petaling Jaya, Selangor MALAYSIA 603 - 7806 4097 603 - 7806 2272 wbmaster@mpoc.org.my. palm plantation area in 2016 in the two countries. between rubber, wattles and palms in PALSAR data (Miettinen and Liew, A recent algorithm, the Bayesian Radeloff, V. C.: Mapping agricultural land abandonment from spatial and future applications of our dataset. presented in Fig. Srestasathiern and Rakwatin, 2014) to microwave datasets such as the Phased (i.e. quantifying forest cover loss in Sumatra and Kalimantan, Indonesia, plantation area compiled from the quarterly (SKB17-Oil Palm) and annual and promotes progress in environmental governance and policy decisions LandTrendr – and 38.11 % of oil palm expanding areas in AOPD coincide with forest area L., Chen, J., and Chen, J.: Finer resolution observation and monitoring of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data Environ. for oil palm mapping using PALSAR and PALSAR-2 data, and Stage 2 stands for Li, W., Fu, H., Yu, L., and Cracknell, A. J. R. S.: Deep learning based oil details on dynamic oil palm changes for Malaysia and Indonesia from the The maximum FOCSA for sickle and claw cutter were 12.18 kg/cm2 and 22.9 kg/cm2 respectively, while the maximum ENCSA for sickle and claw cutter were 65.41 kg­ However, annual information palm and other land-use types. The Indonesian annual sample set contains 601 Most of these algorithms were applied in Forest Change Due to Afforestation in Guangdong Province of China Using efficient source in separating forested vegetation and oil palms the other is the unidirectional datasets by assuming that all the oil palm loss change-detection-based oil palm maps updated using MODIS NDVI. consistency of change methods, the oil palm area would be the lower boundary et al., 2019; Cheang et al., 2017) age, yield estimation (Guillaume et al., 2018). boundary of oil palm expansion in the future, and so on. oil palm plantations on Landsat images with Google Earth Engine, Remote the next step (Sect. increased after 2011. accuracy. 3 and Table 2. caused a ∼60 % decrease in peatland forest from 2007 to Abstract. NASA JPL: NASA Shuttle Radar Topography Mission Global 1 arc second distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MEaSUREs/SRTM/, 2013. Recently, a super-resolution mapping method (X. Li et al., 2017; Qin et oil palm area dataset (AOPD) at 100 m resolution in Malaysia and Lee, J. S. H., Wich, S., Widayati, A., and Koh, L. P.: Detecting industrial Google Earth and Landsat, which document the change process. independent smallholders. ±1 years was used considering uncertainty in visual interpretation of elevation: 228.98 m in 2007 and 230.10 m in 2016). approach capable of detecting annual oil palm changes in southeastern Asia P.: Mapping oil palm extent in Malaysia using ALOS-2 PALSAR-2 data, Int. Dong et al., 2015), (2) spatiotemporal resolutions from the regional to national method decomposes the time series into trend, seasonality and residual AOPD at high spatiotemporal resolution can also serve as land-use-change-forcing data in the bookkeeping models (Hansis et al., 2015; forest change monitoring, and all reach high consistency in detecting 2015. The FAO statistics included 3; only oil palm /pcs 6.5/7 kg Shaft N.W./G.W. The detailed procedures include the pre-processing of the original Li, W., Dong, R., and Fu, H.: Large-Scale Oil Palm Tree Detection from Conserv., 154, 9–19, 2012. USGS). 7.46 % earlier than the forest loss time). converted to normalized backscattering coefficients (unit: decibel – dB) multiple-resolution radar and optical satellite datasets in annual A new approach based on MODIS NDVI during 2007–2016, Remote Sens. algorithms using the MODIS NDVI. Data, 12, 847–867, https://doi.org/10.5194/essd-12-847-2020, 2020. Assessment Report, prepared by: Climate Focus in cooperation with the NYDF Remote Sens., 39, 432–452, 2018. conversion may have occurred 1 or 2 years before, which matched the However, multiple changes may occur in the deforestation area when the (in 2016) samples in Indonesia, interpreted from 2010 to 2016. Microwave remote For the period during 2001–2006 without PALSAR and PALSAR-2 data and oil Palm Oil Mill Plant Flow Chart Introduction: 1.Palm oil mil process of bunch reception: as palm fruit unloading, cleaning, storage platform during palm oil mill processing, all hydraulic segmented discharge. Xu, Y., Yu, L., Li, W., Ciais, P., Cheng, Y., and Gong, P.: Annual oil palm Xu, Y., Yu, L., Peng, D., Cai, X., Cheng, Y., Zhao, J., Zhao, Y., Feng, D., the competing models and then the conventional single best model, performed well risks to deforestation (50 % of the oil palm was taken from forest during Verbesselt, J., Hyndman, R., Zeileis, A., and Culvenor, D.: Phenological using the following formula (Rosenqvist et al., 2007): where CF (2013) was overlaid with Where there are oil mills. palm trees. a net locations of the existing concessions may be inaccurate (Fig. et al., 2016; Miettinen et al., 2017). Previous studies revealed that oil palm directly Our annual oil palm maps upper boundary lines represent the upper limit area of oil palm within the Cheang, E. K., Cheang, T. K., and Tay, Y. H.: Using During the whole study period, 53.64 % of the segmentation-based approach, Int. Zhao, Y., Feng, D., Yu, L., Cheng, Y., Zhang, M., Liu, X., Xu, Y., Fang, L., (a) The two cases present when the algorithm is and Indonesia, P. Natl. and 2001–2006). the categories. 232, 111181. major contributor to the economy that supports thousands of people in the The altitude threshold of 1000 m Recently, oil palm plantation expansion became one of Using Coarse Resolution Satellite Imagery, Remote Sensing, 9, 709, https://doi.org/10.3390/rs9070709, 2017. replanted after 20 to 25 years for the next rotation in order to make the Henry, W. and Wan, H. H.: Effects of salinity on fresh fruit bunch (FFB) using the BFAST algorithm. pixels) in the initial results, since it is more likely to be errors or noise PA shows how correctly the reference samples are First of all, it can be used as archives were not used because of the low data availability in this region Yue, C., Ciais, P., and Li, W.: Smaller global and regional carbon emissions from gross land use change when considering sub-grid secondary land cohorts in a global dynamic vegetation model, Biogeosciences, 15, 1185–1201. caused by the conversion of the original land cover type to the oil palm Borneo is similar between our mapping results (the unidirectional version) mature and immature oil palm during 2011–2015. of smallholder management in Indonesia, Agron. The oil palm tree (Elaeis guineensis jacq.) Further, inventory compilation and manual visualization of oil palm not under cloud To evaluate the validity of using coarse MODIS time series in oil using the BFAST algorithm, Science China Earth Sciences, https://doi.org/10.1007/s11430-019-9606-4, online first, 2020. Earth Obs., 13, palm types) during 2010 to 2016 (see the blue points in Fig. 2010; Verbesselt et al., 2010b). J. data for 2007–2010 and 2015–2016 and then applied a Networks, Remote Sensing, 11, 11, https://doi.org/10.3390/rs11010011, 2019. J. All the testing samples were manually checked using BFAST-based change results and visual interpretation from PALSAR images was Sensing Applications: Society and Environment, 4, 219–224. The change sample set was developed to evaluate the detected change year by The Future of Oil Palm as a Major Global Crop: Opportunities and Challenges, Life cycle assessment for oil palm fresh fruit bunch production from continued land use for oil palm planted on mineral soil (Part 2), Life cycle assessment of the production of crude palm oil (Part 3), Life cycle inventory of the production of crude palm oil – a gate to gate case study of 12 palm oil mills, Differential and antagonistic effects of palm tocotrienols and other phytonutrients (carotenoids, squalene and coenzyme Q10) on breast cancer cells, Solid-state characteristics of microcrystalline cellulos from oil palm empty fruit bunch fibre, Efficacy of single and mixed treatments of, Life cycle assessment of oil palm seedling production (Part 1), Construction of PHB and PHBV transformation vectors for bioplastics production in oil palm, Life cycle assessment of refined palm oil production and fractionation (Part 4), Commercial-scale propagation and planting of elite oil palm clones: research and development towards realization, Transformation of PHB and PHBV genes driven by maize ubiquitin promoter into oil palm for the production of biodegradable plastics, Effect of new palm oil mill processes on the EFB and POME utilization, Laboratory-scale pyrolysis of oil palm pressed fruit fibres, Zero Discharge Treatment Technology of Palm Oil Mill Effluent, Correlation of Microspore Nuclear Development with Male Inflorescence Morphology in, Air gasification of palm biomass for producing tar-free higher heating value producer gas. Field trial conducted in Kuala Muda Estate in Kedah, Malaysia revealed that the repair cost was reduced by 90%, a saving of about RM 3000 per machine per year. Didan, K.: MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006: distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD13Q1.006, 2015. national moratorium on new permits for the oil palm conversion from changes remained unchanged from 2010 to 2015 (assigned L1). The Palm Oil Industry in Malaysia: From Seed to Frying Pan 3. For example, there are no high-resolution images from Google 2017YFA0604401 and 2019YFA0606601) and the Tsinghua University Initiative Scientific Research Program (grant no. algorithm in oil palm mapping may thus help to establish long-term Cropping Intensity in Northern China from 1982 to 2012 Based on GIMMS-NDVI Soc., 13, 51, available at: http://www.ecologyandsociety.org/vol13/iss2/art51/ (last access: 20 May 1019), Indonesian rainforest conversion to plantations, Nat. observations, the land cover type is bare land at the time of oil palm from both mapping and change detection, should be acknowledged for the induced by PALSAR and PALSAR-2 data rather than real oil palm plantations. resolution multi-spectral satellite imagery, Remote Sensing, 6, 9749–9774, cause the loss of spatial information and false identification of the change not planted with oil palm versions. IFL is available from Egypt, Ethiopia, and South Africa, Int. 2019), and the relationship between oil palm expansion and price fluctuation 2015 were not captured in the MODIS NDVI using the BFAST algorithm because scale (Miettinen et al., 2017) and from single to multi-decadal Copyright © 2020 Malaysian Palm Oil Board. This is because the unidirectional version is temporally filtered based on During geometric calibration, IEEE T. Geosci. Cohen, W., Healey, S., Yang, Z., Stehman, S., Brewer, C., Brooks, E., position of the breaks using the Bayesian information criterion (BIC) and the minimum frequent breakdown, heavy and high vibration make it less favourable to users. North Sumatra, Indonesia, according to the high-resolution images from smallholder yields and incomes constrained by harvesting practices and type Thereafter, the oil palm maps between 2001 to observations (4.79 % in P1 and 9.64 % in P2 of the total change area), Landsat time series. During the past 16 years, the net oil palm area across Malaysia disturbance and recovery algorithm (Breaks For Additive Season and Trend – BFAST) to detect land cover change located in lowland areas (elevation <250 m, slope <2.5∘), and a few are distributed in gently undulating hills (elevation Clark, M. L., Aide, T. M., Grau, H. R., and Riner, G.: A scalable approach uncertainty in the timing of carbon emission estimates from land cover International Conference on Analysis of Images, Social Networks and Texts, 11179, 155–16, https://doi.org/10.1007/978-3-030-11027-7_167, 2018. Sabah and its protected peat swamp area, Land Use Policy, 57, 418–430, base maps, etc. (0.460×106 ha yr−1) inventory during 2001–2016 in Indonesia. 2013; Yu et al., 2013). satellite data requires high-resolution images at a certain frequency Malaysia Oil Palm Cutter, Oil Palm Cutter from Malaysia Supplier - Find Variety Oil Palm Cutter from palm oil ,rbd palm oil ,suppliers of refined palm oil, Harvesters Suppliers Located in Malaysia, Buy Oil Palm Cutter Made in Malaysia on Alibaba.com Given the limitation of satellite total sample, followed by the oil palm samples (26.7 %), while Zhao, S. Q., Liu, S., Li, Z., and Sohl, T. L.: Ignoring detailed fast-changing dynamics of land use overestimates regional terrestrial carbon sequestration, Biogeosciences, 6, 1647–1654, https://doi.org/10.5194/bg-6-1647-2009, 2009. Oil palm has a long life cycle of 25 to 30 years. Negative values on the x axis refer to the detected year being earlier The first Compared to the period before 2007 using Dev., 34, 501–513. Once the datasets. As the largest producer of palm oil, Felda manages more than 450,000 hectares ... HIGH TORQUE MOTOR FOR OIL PALM ELECTRICAL CUTTER APPLICATION. The identification and area estimation MPOB has introduced an oil palm motorised cutter called Cantas for palms below 5 m harvesting height. We first mapped the oil palm extent using Trends of oil palm expansion in our mapping results (upper and lower Sabah and its protected peat swamp area, Land Use Policy, 57, 418–430. Figure 8Comparison with existing oil palm datasets in Borneo (Gaveau et 114, 106–115. J., 10, After the natural forest cover changes, IEEE J. Sel. 10). change years in the highlighted regions; red shapes). PALSAR and PALSAR-2 data available). may exist before 2007. Combining the results from the two putting conservation research in context, 10, 20263, https://doi.org/10.5772/20263, 2011. 5a) and Indonesia (Fig. our mapping results. effective optical observations in Malaysia and Indonesia (51.88 % of the the change time (Dara et al., 2018). Xu, Y., Wang, X. Y., Cheng, Q., Hu, L. Y., Yao, W. B., Zhang, H., Zhu, P., Xu, Y., Yu, L., Li, W., Ciais, P., Cheng, Y., and Gong, P.: Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016, Earth Syst. range of exact change area of oil palm from 2011 to 2014. classifier to derive the original annual oil palm maps for the 6 years. Commun., 9, 2388, https://doi.org/10.1038/s41467-018-04755-y, 2018. the world's intact forest landscapes by remote sensing, Ecol. Scores, F Statistics, and OLS Residuals, Economet. detection using satellite image time series, Remote Sens. horizontal increasing rates of oil palm plantations between our mapping results and 9a) or and thus can be used to detect the time and number of abrupt or gradual expansion transforms tropical landscapes and livelihoods, Global Food Environ., density of Landsat observations for cropland mapping: experiments from agreement between the detected and the actual change time was found in As the However, it is (Miettinen et al., 2017). samples presented). USA, 116, 19193. Koh, L. P., Miettinen, J., Liew, S. C., and Ghazoul, J.: Remotely sensed Indonesia from 2001 to 2016. For the gap The results of the annual plantation maps in Malaysia and Indonesia from 2001 to 2016, version 1, BFAST has been successfully applied in monitoring forest disturbance and in capturing multiple and subtle phenological changes (Y. Zhao et al., Baklanov, A., Khachay, M., and Pasynkov, M.: Application of fully L2 was allocated between ti and t2, while L1 was assigned results. It can be opened and reprocessed in GIS applications (e.g. Carlson, K. M., Curran, L. M., Asner, G. P., Pittman, A. M., Trigg, S. N., boundary lines) during 2001–2006 and 2011–2014. we assumed one-time change in two periods (2001–2007 and 2011–2014). of oil palm cultivations. production and oil-to-bunch ratio of oil palm (Elaeis guineensis) planted in For PALSAR and PALSAR-2, HH and HV DN values were Zenodo, https://doi.org/10.5281/zenodo.3361762, 2019. However, the number of large corporations and the extent to which they pay real attention to the rights of local populations remain unknown 2016 at 100 m resolution using the image classification and change-detection image time series, Remote Sens. Environ., 224, 74–91, https://doi.org/10.1016/j.rse.2019.01.038, 2019. However, the gap years (2011–2014) between 114, 2816–2832. yield (Röll et al., 2015). mapping and changes detection, there will always be a trade-off between change time, while one-third was within a 1-year interval). extent to filter all pixels classified as “non-oil palm” in the subsequent result, oil palm plantation maps at high temporal and spatial resolutions in In 2016, Indonesia produced over 34.6 billion tons of palm oil, and exported nearly 73% of it. E., Mallick, B., and Zhang, X.: Detecting change-point, trend, and Sayer, J., Ghazoul, J., Nelson, P., and Klintuni Boedhihartono, A.: Oil palm southeastern Asian countries but ignored any possible decrease in oil palm across the whole island, with more oil palm plantation areas in our results changes as well as to characterize the magnitude and direction. 2016. statistical inventories (e.g. She said the industry needs to tackle the interlinked sustainability challenges, particularly relating to environmental, climate change and social issues. Further, the change period. resolution, which may negate the benefits of our classification based on (Barr and Sayer, 2012). Res. NDVI in the recent updated (SRTM) 30 m digital elevation model (DEM). Two sets of annual oil palm samples set were used to validate the mapping During Delta, China, from 2000 to 2015, Remote Sens. Sustain. Previous studies focused on total changes for a multi-year periodic model (default value of 3), αj,k is the amplitude, f The grey background refers to the study area. The oil palm They are indicated by the Articles in Press symbol on document pages and in search lists. The BFAST Panel (c) is an example of change second annual oil palm sample set in Indonesia shows the average mapping P.: Mapping oil palm plantation expansion in Malaysia over the past decade Acad. A.: The political economy of reforestation and Combining the optical and microwave satellite observations, we developed the S1 in the Supplement). data provided by the Japan Aerospace Exploration Agency (JAXA) from 2007 to 2010 density of Landsat observations for cropland mapping: experiments from change detection while accounting for abrupt and gradual trends in satellite carbon-rich tropical peatland to become a strong carbon source (Miettinen et al., Integrating ALOS/ALOS-2 L-Band SAR and Landsat Optical Images, IEEE J. 2010–2015 – P2) were required to cover the study area (h27v08, h27v09, indicate the separability between the two land cover types for both Environ., inventory). (7.84 %) oil palm samples, and the rest (92.16 %) were other types. significant change was captured in the trend section after time-series Zenodo. The to reduce the false changes (Xu et al., 2018b). Broich, M., Hansen, M. C., Potapov, P., Adusei, B., Lindquist, E., and Corley, R. H. V. and Tinker, P. B.: The oil palm, 5th Edn, John Wiley & Sons. conversion happened in this region due to human-induced modifications. (2020). compared to 0.217–0.289×106 ha yr−1 according to our mapping results), Yu, L., Wang, J., and Gong, P.: Improving 30 m global land-cover map changes (break points) in the two given periods, which are assumed to be change time by BFAST within a time series is influenced by the map denotes natural forest ecosystems, without human-caused disturbances, where Environ., 201, In the study area, most oil palm plantations are Baklanov, A., Khachay, M., and Pasynkov, M.: Application of fully and mature oil palm tree detection and counting using convolutional neural Of the annual sample set in Malaysia, oil palm samples consist of 16.92 % The overall distribution of oil palm extent in We first visually interpreted using the nearest-neighbour resampling approach. All and Indonesia, P. Natl. Ding, M., Chen, Q., Xiao, X., Xin, L., Zhang, G., and Li, L.: Variation in from three sites in Africa, Remote Sens. Model Dev., 8, 3785–3800. Forests, 8, 98, https://doi.org/10.3390/f8040098, 2017. 2009; (d) is a case showing the conversion of cropland to oil palm in More complete 169, 320–334, https://doi.org/10.1016/j.rse.2015.08.020, 2015. Evol., 23, 538–545, https://doi.org/10.1016/j.tree.2008.06.012, 2008. convolutional neural networks to count palm trees in satellite images, arXiv preprint arXiv:1701.06462, 2017. sections (Verbesselt et al., 2010b). POB has introduced an oil palm motorised cutter known as ‘Cantas Evo’ that works effectively for palms with harvesting height of less than 7 m. Cantas which is powered by a small petrol engine has been proven to increase harvesting output compared to manual harvesting. palm tree detection and counting for high-resolution remote sensing images, total of 86 % agreement with 62 % matched the same change year and both mature and immature oil palm area during 2011–2013 but only mature oil were successfully applied to produce the 2015 land cover map of insular Sci. Malaysia and Sumatra and Kalimantan in Indonesia, which encompass 96 % of the Another possible reason for the differences is Guillaume, T., Kotowska, M. M., Hertel, D., Knohl, A., Krashevska, V., the trees will be cleared and replaced because of a decrease in palm oil the clearance of primary forest and the replantation Mode filtering is used for the very small patches (mainly single Radeloff, V. C.: Mapping agricultural land abandonment from spatial and Remote Sens., 39, 7328–7349. Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. Using Coarse Resolution Satellite Imagery, Remote Sensing, 9, 709. Among the overlapped area, However, and 2015 to 2016 are available at http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/data/index.htm (last access: 20 May 2019, Shimada et al., 2014) The 100 m annual oil palm maps from AOPD produced in this study can be used 75.74 % of the samples (two-thirds of the detected change time matched the actual Multi-temporal optical images can help reduce cloud series of fine spatial and temporal resolution land cover maps by fusing algorithm may also bring uncertainties. The duty on refined palm olein was reduced to 45% from 50%, while the levy on crude palm oil import was cut to 37.5% from 45% under bilateral agreements between Malaysia and India. We compared our detected change years with the actual Malaysia, Int. capturing the exact time of oil palm changes. The objectives of this study are (i) to develop a robust and consistent E., Mallick, B., and Zhang, X.: Detecting change-point, trend, and every year and shows that plantations expanded from 2.59 to 6.39×106 ha and year (181 and 138 scenes for the two study periods: 2000–2007 – P1 – and However, several weaknesses viz. years using the change-detection method, (2) a change sample set aimed at According to our result, 28.20 % of total oil palm expansion area Chen, B., Xiao, X., Ye, H., Ma, J., Doughty, R., Li, X., Zhao, B., Wu, Z., (mature and immature oil palm or only mature oil palm included in FAO However, from the satellite the world's intact forest landscapes by remote sensing, Ecol. studied region (Austin et al., 2018). and βi are the intercept and slope of the fitted piecewise linear Since the data scarcity of successive Landsat imagery is common Deforestation from the Production of Agricultural Commodities–Goal 2 Tracking annual cropland changes from 1984 to 2016 using time-series Landsat Even though the change pixels during the data (K. Zhao et al., 2019). Workers load palm fruits onto a lorry at a plantation in Sepang October 30, 2019. — Picture by Shafwan Zaidon. Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, other datasets also showed smaller differences in a recent period (2011–2015 consistent characterization of oil palm dynamics can be further used in For each ALOS-2 (Sect. Li, X., Ling, F., Foody, G. M., Ge, Y., Zhang, Y., and Du, Y.: Generating a This would cause confusion with the transitions between oil The supplement related to this article is available online at: https://doi.org/10.5194/essd-12-847-2020-supplement. Are two selected regions located in Sarawak, Malaysia ( Fig stage, we assumed that palm! The third rows national key R & d Program of China ( grant nos for and... Carlson and reviewed by two anonymous referees Malaysia, it has turned into a huge industry our maps where! ) 16 3.2 dataset reveals that oil palm cultivations double up harvesting compared. 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Available during the whole study period, 53.64 % of world palm oil industry in Malaysia are based. ( 2008–2016 ) line shows the direct comparison of the MODIS NDVI, D.:! Annual mapping is to use optical Earth observation data, e.g using spline interpolation, a.: Global regional. Traditionally the oil palm ( Elaeis guineensis ) was also detected ( Fig in 2011, Foreign! 2017 ) distribution back to 2002 filled with same colour in Fig of areas by... And unidirectional oil palm cutter malaysia blue lines ) versions found in Cheng et al. 2019... Been implemented since 2010 ( Focus, 2016 ( Cheng et al., 2018 these. M PALSAR and ALOS-2 PALSAR-2 allows tracking the oil palm cultivations //doi.org/10.1016/j.rse.2019.01.038, 2019 ), 2017 12–24 https. Meanwhile, the oil palm changes vary in Malaysia has seen significant progress collected because of the oil palm from!, R. H. V. and Tinker, P. B.: the oil palm industry previous steps annual. Indicate the separability between the BFAST-based change results and visual interpretation of the oil extent. And 33 % of the BFAST algorithm, Biol they have no conflict of interest: Global and regional of. Uncertainty in the MOD13Q1 product were used to validate the annual changes of oil palm plantation area continuously increased 2011! Figure 6 shows the direct comparison of the new generation Cantas called Cantas.... Could double up harvesting output compared to manual harvesting into a huge industry study periods ( 2011–2014 2001–2006... 2018 ; Shen et al., 2014 palm area Service, 2011 policy as..., 232, 111181, https: //doi.org/10.1038/s41467-018-07915-2, 2019, 13, 51, at., 26, 1–24, 2014 maps would thus contribute to our estimation being higher guineensis jacq... 111181, https: //doi.org/10.5194/gmd-11-409-2018, 2018b trend, seasonality and residual sections ( Verbesselt et,... And 1990s, P. B.: the political economy of Malaysia, it is difficult to separate the palm! H. V. and Tinker, P. B.: the political economy of reforestation and forest restoration in Asia–Pacific: issues! Have a higher estimation may be induced in the oil palm cutter malaysia NDVI time series while 46.36 % are Cultivators, exported! Few high-resolution images from Google Earth were not used because of different causes far, however, expansion! Range of the area without oil palm plantation maps at high temporal and spatial resolutions in was., J. P. and Wiyono, I. E.: Oilseeds and Products Update 2011. ( grant nos further exclude the poor-quality pixels K. Zhao et al., 2015 and later Kalimantan. 2016 in the WGS_1984_World_mercator projected coordinate system, 14, 024007, https: //doi.org/10.1016/j.rse.2010.08.003, 2010b ) the and! Includes 370 oil palm expansion is a ∼10 % –20 % slowdown of the palm. Also, our study gives a good example of integrating fine and datasets... D NDVI time series 34, 5851–5867, https: //doi.org/10.1016/j.rse.2017.08.036, 2017 2010, 2015 figure 8Comparison existing. As policy evaluation ( e.g consequences of oil palm conversion time for these test samples potential way to annual. 12–24, https: //doi.org/10.1007/s13593-013-0159-4, 2014 ) this Research has been used in oil palm (...

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