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Remote Sensing Technology and Application  2022, Vol. 37 Issue (5): 1029-1042    DOI: 10.11873/j.issn.1004-0323.2022.5.1029
Application of Space Observation Technology in Oil Palm Research
Qiang Zhao1(),Le Yu2(),Yidi Xu2,Weijia Li3,Juepeng Zheng2,Haohuan Fu2,Hui Lu2,Yongguang Zhang1,Peng Gong4
1.School of Geography and Ocean Science,Nanjing University,Nanjing 210023,China
2.Department of Earth System Science,Tsinghua University,Beijing 100084,China
3.CUHK-SenseTime Joint Lab,The Chinese University of Hong Kong,Hong Kong,China
4.Department of Geography and Earth Sciences,The University of Hong Kong,Hong Kong,China
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Oil palm is a major economic crop and the area of land converted to oil palm cultivation in the tropics has expanded rapidly. Oil palm has become the world's largest source of vegetable oil and it provides tremendous regional economic benefits. However, the expansion of oil palm cultivation has led to the loss of forests, arable land, and peatland, which has caused severe ecological and environmental problems. Application of 3S (RS, GIS, GNSS) technology is useful for the collection, analysis, and management of spatial information, and is essential for both optimizations of the spatial distribution of land use and sustainable development. This paper analyzes the progress of 3S technology application in oil palm research on the basis of a literature review and scientometric analysis. The factors affecting the precision of oil palm mapping are also discussed. We established that papers describing 3S technology application in oil palm research are based primarily on the study of land cover change, and that scientific institutions and researchers in Malaysia, the United States, China, Indonesia, and the United Kingdom are the major contributors. Currently, the application of 3S technology in oil palm research includes oil palm mapping, oil palm land change monitoring, oil palm tree counting, tree age estimation, aboveground biomass and carbon storage estimation, suitability analysis, yield estimation, pest and disease monitoring, and plantation management. The accuracy of mapping is not correlated significantly with the year of publication of specific literature but is correlated with RS data sources and classification methods. The use of 3S technology in oil palm research is currently dominated by RS, which has been used in diverse fields of oil palm research. GIS technology is used mainly for oil palm land change mapping, suitability analysis, plantation management, and pest and disease monitoring, while GNSS is used largely as an additional tool in pest and disease monitoring and plantation management.

Key words:  Oil palm      3S technology      Scientometric analysis      Sustainable development     
Received:  30 July 2021      Published:  13 December 2022
ZTFLH:  TP79  
Corresponding Authors:  Le Yu     E-mail:;
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Qiang Zhao
Le Yu
Yidi Xu
Weijia Li
Juepeng Zheng
Haohuan Fu
Hui Lu
Yongguang Zhang
Peng Gong

Cite this article: 

Qiang Zhao,Le Yu,Yidi Xu,Weijia Li,Juepeng Zheng,Haohuan Fu,Hui Lu,Yongguang Zhang,Peng Gong. Application of Space Observation Technology in Oil Palm Research. Remote Sensing Technology and Application, 2022, 37(5): 1029-1042.

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Fig.1  The spatial distribution of oil palm researches in different periods
Fig.2  Annual statistics on the number of publications and citations of oil palm research papers
Fig.3  Keywords co-occurrence network
Fig.4  Co-citation network at the institutional level and the regiona level in oil palm research
Fig.5  The relationship between mapping accuracy and publication time
Fig.6  Frequency of use of different remote sensing data sources
Fig.7  Classification accuracy of different remote sensing data sets
Fig.8  Frequency and accuracy of different classification methods
Fig.9  The relationship between the number of classes and the accuracy of the classification system
1 Najib N E M, Kanniah K D, Cracknell A P, et al. Synergy of active and passive remote sensing data for effective mapping of oil palm plantation in Malaysia[J]. Forests,2020,11(8):858. DOI: .
doi: 10.3390/f11080858
2 Xu Y, Yu L, Li W, et al. Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016[J]. Earth System Science Data,2020,12(2):847-867. DOI: .
doi: 10.5194/essd-12-847-2020
3 Li W, Dong R, Fu H, et al. Large-scale oil palm tree detection from high-resolution satellite images using two-stage convolutional neural networks[J]. Remote Sensing,2019,11(1):11. DOI: .
doi: 10.3390/rs11010011
4 Xu Y, Ciais P, Yu L, et al. Oil palm modelling in the global land-surface model ORCHIDEE-MICT[J]. Geoscientific Model Development,2021,14(7):4573-4592. DOI: .
doi: 10.5194/gmd-14-4573-2021
5 Cheng Y, Yu L, Xu Y, et al. Towards global oil palm plantation mapping using remote-sensing data[J]. International Journal of Remote Sensing,2018,39(18):5891-5906. DOI: .
doi: 10.1080/ 01431161.2018.1492182
6 Cheng Y, Yu L, Xu Y, et al. Mapping oil palm plantation expansion in malaysia over the past decade (2007-2016) Using ALOS-1/2 PALSAR-1/2 Data[J]. International Journal of Remote Sensing, 2019,40(19):7389-7408. DOI: 。
doi: 10.1080/01431161.2019.1580824
7 Sarzynski T, Giam X, Carrasco L, et al. Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia,using the Google Earth Engine[J]. Remote Sensing, 2020,12(7):1220. DOI: .
doi: 10.3390/rs12071220
8 Barnes A D, Jochum M, Mumme S, et al. Consequences of tropical land use for multitrophic biodiversity and ecosystem functioning[J]. Nature Communications,2014,5:5351. DOI: .
doi: 10.1038/ncomms6351
9 Busch J, Ferretti-Gallon K, Engelmann J, et al. Reductions in emissions from deforestation from Indonesia's Moratorium on new oil palm, timber, and logging concessions[J]. Proceedings of the National Academy of Sciences,2015,112(5):1328-1333. DOI: .
doi: 10.1073/pnas.1412514112
10 Glinskis E A, Gutierrez-Velez V H. Quantifying and understanding land cover changes by large and small oil palm expansion regimes in the Peruvian Amazon[J]. Land Use Policy,2019,80:95-106. DOI: .
doi: 10.1016/j.landusepol.2018.09.032
11 Ramdani F, Moffiet T, Hino M, et al. Local surface temperature change due to expansion of oil palm plantation in Indonesia[J]. Climatic Change,2014,123(2):189-200. DOI: .
doi: 10.1007/s10584-013-1045-4
12 Horton A J, Lazarus E D, Hales T C, et al. Can riparian forest buffers increase yields from oil palm plantations?[J]. Earths Future,2018,6(8):1082-1096. DOI: .
doi: 10.1029/2018ef 000874
13 Furumo R P, Aide T M. Characterizing commercial oil palm expansion in Latin America: Land use change and trade[J]. Environmental Research Letters,2017,12(2):024008. DOI: .
doi: 10.1088/ 1748-9326/aa5892
14 Englund O, Berndes G, Persson U M, et al. Oil palm for biodiesel in Brazil-Risks and opportunities[J]. Environmental Research Letters,2015,10(4):044002. DOI: .
doi: 10.1088/1748-9326/10/4/044002
15 De Petris S, Boccardo P, Borgogno-Mondino E, et al. Detection and characterization of oil palm plantations through MODIS EVI time series[J]. International Journal of Remote Sensing,2019,40(19):7297-7311. DOI: .
doi: 10.1080/01431161. 2019. 1584689
16 Chong K L, Kanniah K D, Pohl C, et al. A review of remote sensing applications for oil palm studies[J]. Geo-Spatial Information Science,2017,20(2):184-200. DOI: .
doi: 10.1080/ 10095020.2017.1337317
17 Asari N, Suratman M N, Jaafar J, et al. Modelling and mapping of Above Ground Biomass (AGB) of oil palm plantations in Malaysia using remotely-sensed data[J]. International Journal of Remote Sensing,2017,38(16):4741-4764. DOI: .
doi: 10.1080/ 01431161.2017.1325533
18 Cheng Y, Yu L, Cracknell A P, et al. Oil palm mapping using landsat and palsar: A case study in Malaysia[J]. International Journal of Remote Sensing,2016,37(22):5431-5442. DOI: .
doi: 10.1080/01431161.2016.1241448
19 Chemura A, van Duren I, van Leeuwen L M, et al. Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing cata: The case of Ejisu-Juaben district,Ghana[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,100:118-127. DOI: .
doi: 10.1016/j.isprsjprs. 2014.07.013
20 de Almeida A S, Guimaraes Vieira I C, Ferraz S F B, et al. Long-term assessment of oil palm expansion and landscape change in the Eastern Brazilian Amazon[J]. Land Use Policy, 2020:90. DOI: .
doi: 10.1016/j.landusepo1. 2019.104321
21 Vargas H, Camacho A, Arguello H, et al. Spectral unmixing approach in hyperspectral remote sensing: A tool for oil palm mapping[J]. TecnoLógicas,2019,22(45):131-145. DOI: .
doi: 10. 22430/22565337.1228
22 Abd Mubin N, Nadarajoo E, Shafri H Z M, et al. Young and mature oil palm tree detection and counting using convolutional neural network deep learning method[J]. International Journal of Remote Sensing,2019,40(19):7500-7515. DOI: .
doi: 10.1080/ 01431161.2019.1569282
23 Broich M, Hansen M C, Potapov P, et al. Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in sumatra and Kalimantan, Indonesia[J]. International Journal of Applied Earth Observation and Geoinformation,2011,13(2):277-291. DOI: .
doi: 10.1016/j.jag.2010.11.004
24 Shafri H Z M, Hamdan N, Saripan M I, et al. Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery[J].International Journal of Remote Sen-sing,2011,32(8):2095-2115. DOI: .
doi: 10.1080/0143116100 3662928
25 Zheng J, Fu H, Li W, et al. Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2020,167:154-177. DOI: .
doi: 10.1016/j.isprsjprs. 2020.07.002
26 Phua M H, Chong C W, Ahmad A H, et al. Understanding rat occurrences in oil palm plantation using high-resolution satellite image and GIS data[J]. Precision Agriculture, 2018,19 (1):42-54. DOI: .
doi: 10.1007/s11119-016-9496-z
27 Tajudin N S, Musa M H, Abu Seman I, et al. Predicting the variability of copper and zinc in leaf and soil of oil palm planted on a 12 Ha land using geospatial information system technology[J]. Jurnal Teknologi, 2015,77(24):113-118.
28 Phua M H, Chong C W, Ahmad A H, et al. Predicting rat occurrence in oil-palm plantation using GIS and geoeye data[J]. Environmental Engineering and Management Journal, 2016,15(11):2511-2518. DOI: .
doi: 10.30638/eemj. 2016.275
29 Ruslan S A, Muharam F M, Zulkafli Z, et al. Using satellite-measured relative humidity for prediction of metisa Plana's population in oil palm plantations: A comparative assessment of regression and artificial neural network models[J]. PLOS One,2019,14(10):e0223968.DOI: .
doi: 10.1371/journal.pone.0223968
30 Johaerudin, Nakagoshi N. GIS-based land suitability assessment for oil palm production in landak regency, West Kalimantan[J]. Hikobia, 2011,16 (1):21-31.
31 Viera-Torres M, Sinde-Gonzalez I, Gil-Docampo M, et al. Generating the baseline in the early detection of bud rot and red ring disease in oil palms by geospatial Technologies[J]. Remote Sensing,2020,12(19):3229. DOI: .
doi: 10.3390/rs 12193229
32 Hoffmann C, Weise C, Koch T, et al. From UAS data acquisition to actionable Information-How an End-to-end solution helps oil palm plantation operators to perform a more sustainable plantation management[J]. ISPRS-International Archi-ves of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2016,XLI-B1:1113-1120. DOI: .
doi: 10.5194/isprsarchives-XLI-B1- 1113-2016
33 Chen C, Ibekwe-SanJuan F, Hou J, et al. The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis[J]. Journal of the American Society for Information Science and Technology,2010,61(7):1386-1409. DOI: .
doi: 10.1002/asi.21309
34 Gaveau D L, Sheil D, Husnayaen, et al. Rapid conversions and avoided deforestation: Examining four decades of industrial plantation expansion in Borneo[J]. Scientific Reports,2016,6:32017. DOI: .
doi: 10.1038/srep32017
35 Morel A C, Saatchi S S, Malhi Y, et al. Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALASAR data[J]. Forest Ecology and Management,2011,262(9):1786-1798. DOI: .
doi: 10. 1016/j.foreco.2011.07.008
36 Dong R, Li W, Fu H, et al. Oil palm plantation mapping from high-resolution remote sensing images using deep learning[J]. International Journal of Remote Sensing,2020,41(5):2022-2046. DOI: .
doi: 10.1080/01431161.2019.1681604
37 Razali S M, Marin A, Nuruddin A A, et al. Capability of Integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions[J]. Sensors, 2014,14(5):8259-8282. DOI: .
doi: 10.3390/s140508259
38 Santos C, Messina J P. Multi-sensor data fusion for modeling african palm in the ecuadorian Amazon[J]. Photogrammetric Engineering and Remote Sensing,2008,74(6):711-723. DOI: .
doi: 10.14358/pers.74.6.711
39 Dong X, Quegan S, Yumiko U, et al. Feasibility study of C- and L-band SAR time series data in tracking Indonesian plantation and natural forest cover changes[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(7):3692-3699. DOI: .
doi: 10.1109/jstars. 2015. 2400439
40 Shaharum N S N, Shafri H Z M, Ghani W A W A K, et al. Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms[J].Pertanika Journal of Science and Technology,2019,27:123-135.
41 Charters L J, Aplin P, Marston C G, et al. Peat swamp forest conservation withstands pervasive land conversion to oil palm plantation in North Selangor, Malaysia[J]. International Journal of Remote Sensing,2019,40(19):7409-7438. DOI: .
doi: 10.1080/01431161. 2019.1574996
42 Descals A, Szantoi Z, Meijaard E, et al. Oil palm (Elaeis guineensis) mapping with details: Smallholder versus Industrial plantations and their extent in Riau, Sumatra[J]. Remote Sensing, 2019,11(21):2590. DOI: .
doi: 10.3390/rs 11212590
43 Ahmed A A, Pradhan B, Sameen M I, et al. An optimized object-based analysis for vegetation mapping using integration of QuickBird and Sentinel-1 data[J]. Arabian Journal of Geosciences,2018,11(11):1-10. DOI: .
doi: 10.1007/s12517-018-3632-1
44 Salas-Gonzalez D M. Changes in the area cultivated with oil palm in the canton of osa, Puntarenas, 2014-2018 Period[J]. Revista Geografica de America Central,2020(65):93-120. DOI: .
doi: 10.15359/rgac.65-2.4
45 Hernandez-Rojas D A, Lopez-Barrera F, Bonilla-Moheno M, et al. Preliminary analysis of the land use dynamic associated with oil palm (Elaeis Guineensis) plantations in Mexico[J]. Agrociencia, 2018,52(6):875-893.
46 Li W, Fu D, Su F, et al. Spatial-temporal evolution and analysis of the driving force of oil palm patterns in Malaysia from 2000 to 2018[J]. ISPRS International Journal of Geo-Information,2020,9(4):280. DOI: .
doi: 10.3390/ijgi9040280
47 Nourqolipour R, Shariff A R B M, Balasundram S K, et al. A GIS-based model to analyze the spatial and temporal development of oil palm land use in Kuala Langat District, Malaysia[J]. Environmental Earth Sciences,2015,73(4):1687-1700. DOI: .
doi: 10.1007/s12665-014-3521-1
48 Nourqolipour R, Shariff A R B M, Ahmad N B, et al. Multi-Objective-based modeling for land use change analysis in the South West of Selangor, Malaysia[J]. Environmental Earth Sciences,2015,74(5):4133-4143. DOI: .
doi: 10.1007/s12665-015-4486-4
49 Dong T, Zhang J, Gao S, et al. Single-tree detection in high-resolution remote-sensing images based on a cascade neural network[J]. ISPRS International Journal of Geo-Information, 2018,7 (9). DOI: .
doi: 10.3390/ijgi 7090367
50 Li W, Fu H, Yu L, et al. Deep learning based oil palm tree detection and counting for high-resolution remote sensing images[J]. Remote Sensing,2017,9(1):22. DOI: .
doi: 10.3390/rs9010022
51 Santoso H, Tani H, Wang X F, et al. A simple method for detection and counting of oil palm trees using high-resolution multispectral satellite imagery[J]. International Journal of Remote Sensing,2016,37(21):5122-5134. DOI: .
doi: 10.1080/01431161.2016.1226527
52 Fawcett D, Azlan B, Hill T C, et al. Unmanned Aerial Vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis Guineensis) canopy segmentation and height estimation[J]. International Journal of Remote Sensing,2019,40(19):7538-7560. DOI: .
doi: 10.1080/01431161.2019. 1591651
53 Hamsa C S, Kanniah K D, Muharam F M, et al. Textural measures for estimating oil palm age[J]. International Journal of Remote Sensing,2019,40(19):7516-7537. DOI: .
doi: 10.1080/ 01431161.2018.1530813
54 Avtar R, Ishii R, Kobayashi H, et al. Efficiency of multi-frequency, multi-polarized SAR data to monitor growth stages of oil palm plants in Sarawak, Malaysia[C]∥2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013.
55 Tan K P, Kanniah K D, Cracknell A P, et al. Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in Southern Peninsular Malaysia[J]. International Journal of Remote Sensing,2013,34(20):7424-7446. DOI: .
doi: 10. 1080/ 01431161.2013.822601
56 McMorrow J. Linear regression modelling for the estimation of oil palm age from Landsat TM[J]. International Journal of Remote Sensing,2001,22(12):2243-2264. DOI: .
doi: 10.1080/01431160117188
57 Margono B A, Potapov P V, Turubanova S, et al. Primary forest cover loss in Indonesia over 2000-2012[J].Nature Clima-te Change,2014,4(8):730-735. DOI: .
doi: 10.1038/nclimate 2277
58 Cracknell A P, Kanniah K D, Tan K P, et al. Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth Imagery[J].International Journal of Remo-te Sensing,2013,34(20):7400-7423. DOI: .
doi: 10.1080/01431161. 2013.820367
59 Nunes M H, Ewers R M, Turner E C, et al. Mapping aboveground carbon in oil palm plantations using LiDAR: A comparison of tree-centric versus area-based approaches[J]. Remote Sensing,2017,9(8):816. DOI: .
doi: 10.3390/rs9080816
60 Thenkabail P S, Stucky N, Griscom B W, et al. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived Savannas using IKONOS data[J]. International Journal of Remote Sensing,2004,25(23):5447-5472. DOI: .
doi: 10.1080/0143116041 2331291279
61 Foody G M, Cutler M E, McMorrow J, et al. Mapping the biomass of bornean tropical rain forest from remotely sensed data[J]. Global Ecology and Biogeography,2001,10(4):379-387. DOI: .
doi: 10.1046/j.1466-822X.2001.00248.x
62 Fan Y, Roupsard O, Bernoux M, et al. A sub-canopy structure for simulating oil palm in the community land model (Clm-Palm): Phenology, allocation and yield[J]. Geoscientific Model Development, 2015,8 (11):3785-3800. DOI: .
doi: 10.5194/gmd-8-3785-2015
63 Khamis A, Ismail Z, Haron K, et al. Nonlinear growth models for modeling oil palm yield growth[J]. Journal of Mathematics and Statistics,2005,1(3):225-232. DOI: .
doi: 10.3844/jmssp.2005.225.232
64 Balasundram S K, Memarian H, Khosla R, et al. Estimating oil palm yields using vegetation indices derived from QuickBird[J]. Life Science Journal, 2013,10(4):851-860.
65 Azuan N H, Khairunniza-Bejo S, Abdullah A F, et al. Analysis of changes in oil palm canopy architecture from basal stem rot using terrestrial laser scanner[J]. Plant Disease, 2019,103 (12):3218-3225. DOI: .
doi: 10.1094/ pdis-10-18-1721-re
66 Shafri H Z M, Anuar M I, Seman I A, et al. Spectral discrimination of healthy and ganoderma-infected oil palms from hyperspectral data[J]. International Journal of Remote Sensing, 2011,32(22):7111-7129. DOI: .
doi: 10.1080/01431161. 2010. 519003
67 Liaghat S, Ehsani R, Mansor S, et al. Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms[J]. International Journal of Remote Sensing, 2014,35(10):3427-3439. DOI: .
doi: 10.1080/01431161.2014.903353
68 Liaghat S, Mansor S, Ehsani R, et al. Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm[J]. Computers and Electronics in Agriculture, 2014,101:48-54. DOI: .
doi: 10.1016/j.compag. 2013.12.012
69 Santoso H, Tani H, Wang X, et al. Random forest classification model of basal stem rot disease caused by Ganoderma boninense in oil palm plantations[J]. International Journal of Remote Sensing,2017,38(16):4683-4699. DOI: .
doi: 10.1080/0143 1161.2017.1331474
70 Santoso H, Tani H, Wang X F, et al. Classifying the severity of basal stem rot disease in oil palm plantations using Worldview-3 imagery and machine learning algorithms[J]. International Journal of Remote Sensing, 2019,40(19):7624-7646. DOI: .
doi: 10.1080/ 01431161.2018.1541368
71 Izzuddin M A, Hamzah A, Nisfariza M N, et al. Analysis of multispectral imagery from Unmanned Aerial Vehicle (UAV) using object-based image analysis for detection of ganoderma disease in oil palm[J]. Journal of Oil Palm Research,2020,32(3):497-508. DOI: .
doi: 10.21894/jopr.2020.0035
72 Santoso H, Gunawan T, Jatmiko R H, et al. Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery[J].Precision Agriculture,2010,12(2):233-248. DOI: .
doi: 10.1007/s11119-010-9172-7
73 Samseemoung G, Jayasuriya H P W, Soni P, et al. Oil palm pest infestation monitoring and evaluation by helicopter-moun-ted, low altitude remote sensing platform[J]. Journal of Applied Remote Sensing,2011,5(1):053540. DOI: .
doi: 10.1117/1.3609843
74 Husin N A, Khairunniza-Bejo S, Abdullah A F, et al. Application of ground-based LiDAR for analysing oil palm canopy properties on the occurrence of Basal Stem Rot (BSR) Disease[J]. Scientific Reports, 2020,10(1):1-16. DOI: .
doi: 10.1038/s41598-020-62275-6
75 Rakib M R M, Bong C F J, Khairulmazmi A, et al. Association of copper and zinc levels in oil palm (Elaeis Guineensis) to the spatial distribution of ganoderma species in the plantations on peat[J]. Journal of Phytopathology, 2017,165(4):276-282. DOI: .
doi: 10.1111/ jph.12559
76 Pirker J, Mosnier A, Kraxner F, et al. What are the limits to oil palm expansion?[J]. Global Environmental Change, 2016,40:73-81. DOI: .
doi: 10.1016/j.gloenvcha.2016.06. 007
77 Ogunkunle A O. Soil in land suitability evaluation-An exa-mple with oil palm in Nigeria[J]. Soil Use and Management, 1993,9(1):35-40.DOI: .
doi: 10.1111/j.1475-2743.1993.tb 00925.x
78 Xin Y, Sun L, Hansen M, et al. Biophysical and socioeconomic drivers of oil palm expansion in Indonesia[J]. Environmental Research Letters,2020,16(3):034048. DOI: .
doi: 10. 1088/1748-9326/abce83
79 Olaniyi A O, Ajiboye A J, Abdullah A M, et al. Agricultural land use suitability assessment in Malaysia[J]. Bulgarian Journal of Agricultural Science, 2015,21 (3):560-572.
80 Pohl C, Kanniah K D, Loong C K, et al. Monitoring oil palm plantations in Malaysia[C]∥2016 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE, 2016.
81 Shafri H Z M, Ismail M H, Razi M K M, et al. Application of LiDAR and optical data for oil palm plantation management in Malaysia[C]∥LiDAR Remote Sensing for Environmental Monitoring XIII. BELLINGHAM, WA: SPIE-INT SOC OPTICAL ENGINEERING, 2012.
82 Yu L, Liang L, Wang J, et al. Meta-discoveries from a synthesis of satellite-based land-cover mapping research[J]. International Journal of Remote Sensing, 2014,35(13):4573-4588. DOI: .
doi: 10.1080/01431161. 2014.930206
83 Ming Dongping, Wang Qun, Yang Jianyu,et al. Spatial scale of remote sensing image and selection of optimal spatial resolution[J]. Journal of Remote Sensing,2008,12(4):529-537
83 明冬萍, 王群, 杨建宇. 遥感影像空间尺度特性与最佳空间分辨率选择. 遥感学报,2008,12(4):529-537
84 Rakwatin P, Longépé N, Isoguchi O, et al. Using multiscale texture information from ALOS PALSAR to map tropical forest[J].International Journal of Remote Sensing,2012,33(24):7727-7746. DOI: .
doi: 10.1080/ 01431161.2012.701349
85 Yusoff N M, Muharam F M, Khairunniza-Bejo S, et al. Towards the use of remote-sensing data for monitoring of abandoned oil palm lands in Malaysia: A semi-automatic approach[J]. International Journal of Remote Sensing,2017,38(2):432-449. DOI: .
doi: 10.1080/01431161.2016.1266111
86 Koh L P, Miettinen J, Liew S C, et al. Remotely sensed evidence of tropical peatland conversion to oil palm[J]. Proceedings of the National Academy of Sciences of the United States of America,2011,108(12):5127-5132. DOI: .
doi: 10.1073/pnas. 1018776108
87 Li L, Dong J, Tenku S N, et al. Mapping oil palm plantations in cameroon using PALSAR 50 m orthorectified mosaic images[J]. Remote Sensing,2015,7(2):1206-1224. DOI: .
doi: 10.3390/rs70201206
88 Carlson K M, Curran L M, Asner G P, et al. Carbon emissions from forest conversion by Kalimantan oil palm plantations[J]. Nature Climate Change,2012,3(3):283-287. DOI: .
doi: 10. 1038/nclimate1702
89 Yuan Ming. Research and implementation of FPGA-based object detection accelerator for remote sensing[D]. Wuxi: Jiangnan University, 2020
89 袁鸣. 基于FPGA的遥感目标检测加速器的研究与实现[D]. 无锡: 江南大学, 2020.
90 Nooni I K, Duker A A, Van Duren I, et al. Support vector machine to map oil palm in a heterogeneous environment[J]. International Journal of Remote Sensing, 2014,35 (13):4778-4794. DOI: .
doi: 10.1080/01431161.2014. 930201
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