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
|