Rubber Tree Distribution Mapping in Northeast Thailand

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International Journal of Geosciences

ISSN Print: 2156-8359
ISSN Online: 2156-8367
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"Rubber Tree Distribution Mapping in Northeast Thailand"
written by Zhe Li, Jefferson M. Fox,
published by International Journal of Geosciences, Vol.2 No.4, 2011

has been cited by the following article(s):

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[1]	Semi-Automatic Classification of Rotating Crops in Northern Thailand by Using Temporal LANDSAT Images
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[2]	Synergistic Use of Sentinel-1 and Sentinel-2 to Map Natural Forest and Acacia Plantation and Stand Ages in North-Central Vietnam
	2021

[3]	Application of Optical Remote Sensing in Rubber Plantations: A Systematic Review
	2021

[4]	Latest 30-m map of mature rubber plantations in Mainland Southeast Asia and Yunnan province of China: Spatial patterns and geographical characteristics
	2021

[5]	Biomass supply chain for power generation in southern part of Thailand
	2020

[6]	Using machine learning algorithm and landsat time series to identify establishment year of para rubber plantations: A case study in Thalang district, Phuket Island …
	2020

[7]	Sentinel-2 red-edge spectral indices (RESI) suitability for mapping rubber boom in Luang Namtha Province, northern Lao PDR
	2020

[8]	A renormalized modified normalized burn ratio (RMNBR) index for detecting mature rubber plantations with Landsat-8 OLI in Xishuangbanna, China
	2019

[9]	Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification
	2019

[10]	Simulating land use for protecting food crop areas in northeast Thailand using GIS and Dyna-CLUE
	2019

[11]	Is the phenology-based algorithm for mapping deciduous rubber plantations applicable in an emerging region of northern Laos?
	2019

[12]	Automatic Classification of Pararubber Trees in Thailand from LANDSAT-8 Images using Neural Networks Method
	2019

[13]	Rubber production in Continental Southeast Asia: its potentialities and limitations
	2019

[14]	Mapping rubber stand ages in Luangnamtha district (Northern Laos) using NDVI and LSWI from Landsat images
	2019

[15]	The role of physiography and land suitability in the distribution of rubber plantation in Tripura
	2019

[16]	The viability of remote sensing for extracting rubber smallholding information: A case study in Malaysia
	2018

[17]	Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach
	ISPRS Journal of Photogrammetry and Remote Sensing, 2018

[18]	The household economics of rubber intercropping during the immature period in Northeast Thailand
	Journal of Sustainable Forestry, 2018

[19]	Expansion dynamics of deciduous rubber plantations in Xishuangbanna, China during 2000–2010
	GIScience & Remote Sensing, 2018

[20]	Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel-and object-based tree growth model and annual Landsat time series
	ISPRS Journal of Photogrammetry and Remote Sensing, 2018

[21]	Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987–2015
	Remote Sensing, 2018

[22]	Climate change impact assessment on the potential rubber cultivating area in the Greater Mekong Subregion
	Environmental Research Letters, 2018

[23]	An Evaluation of EO-1 Hyperion Data for Estimating Age of Rubber Plantation
	2017

[24]	COMBINATION OF ALOS PALSAR AND LANDSAT 5 IMAGERY FOR RUBBER TREE MAPPING
	2017

[25]	Extracting distribution and expansion of rubber plantations from Landsat imagery using the C5. 0 decision tree method
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[26]	Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery
	The Egyptian Journal of Remote Sensing and Space Science, 2017

[27]	Mapping rubber trees based on phenological analysis of Landsat time series datasets
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[28]	A Hybrid Approach to Improve Classification Accuracy of Mapping Perennial Crops in Bảo Lâm District, Lâm Đồng Province
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[29]	Monitoring and mapping of the extent of industrial forests in Malaysia
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[30]	Spatiotemporal changes of global rubber production during 1961-2013
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[31]	Estimating Leaf Area Index of Rubber Tree Plantation Using Worldview-2 Imagery
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[32]	INFLUENCE OF SEEDLING ESTABLISHMENT TECHNIQUES AND IRRIGATION SYSTEMS ON GROWTH, WATER USE EFFICIENCY AND NUTRIENT …
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[33]	Policy-driven rubber plantation and its driving factors: a case of smallholders in northeast Thailand
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[34]	Advancing Ecohealth in Southeast Asia and China: Lessons from the Field Building Leadership Initiative
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[35]	Pushing the Limits: The Pattern and Dynamics of Rubber Monoculture Expansion in Xishuangbanna, SW China
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[36]	STAND AGE MODEL FOR MAPPING SPATIAL DISTRIBUTION OF RUBBER TREE USING REMOTELY SENSED DATA IN KEDAH, MALAYSIA
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[37]	Review of remote sensing monitoring and socioeconomic and environmental impacts of rubber plantation expansion in Laos in the geoeconomic context
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[38]	地缘经济背景下的老挝橡胶林地扩张监测及其影响研究综述
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[39]	Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images
	International Journal of Applied Earth Observation and Geoinformation, 2016

[40]	1961-2013 年全球橡胶生产时空演变特征
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[41]	Mapping rubber tree plantations using a Landsat-based phenological algorithm in Xishuangbanna, southwest China
	Remote Sensing Letters, 2015

[42]	Estimating Aboveground Biomass of Rubber Tree Using Remote Sensing in Phuket Province, Thailand
	Journal of Medical and Bioengineering Vol, 2015

[43]	Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images
	Remote Sensing, 2015

[44]	Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series
	Remote Sensing of Environment, 2015

[45]	Mapping Rubber Tree Stand Age using Pléiades Satellite Imagery: A Case Study in Talang District, Phuket, Thailand
	Engineering Journal, 2015

[46]	中老缅泰交界地区土地利用变化信息挖掘与国别对比
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[47]	Índices de vegetação em seringais, outras culturas arbóreas e remanescentes florestais no noroeste do Estado de São Paulo.
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[48]	SPATIO-TEMPORAL ANALYSIS OF RUBBER AREA AND ITS ASSOCIATION WITH SOIL AND TOPOGRAPHY IN KANYAKUMARI DISTRICT
	Rubber Science, 2014

[49]	Initial results of the spatial distribution of rubber trees in Peninsular Malaysia using remotely sensed data for biomass estimate
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[50]	Object-oriented classification of rubber plantations from Landsat satellite imagery
	Third International Conference on Agro-geoinformatics (Agro-geoinformatics 2014), 2014

[51]	中南半岛森林覆被变化研究进展
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[52]	Progress of research on forest land cover change in the Indo-China Peninsula
	PROGRESS IN GEOGRAPHY, 2014

[53]	Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery
	Remote Sensing of Environment, 2013

[54]	RUBBER TREE EXPANSION IN FOREST RESERVE AND PADDY FIELD ACROSS THE GREATER MEKONG SUB-REGION, NORTHEAST THAILAND BASED ON REMOTELY SENSED IMAGERY
	C MONGKOLSAWAT, W PUTKLANG - negistda.kku.ac.th, 2012

[55]	Assessing Thaichote satellite data in support of mapping rubber tree plantation in Northeast Thailand
	2012

[56]	An Application of THEOS Data to Rubber Plantation Areas in Mukdahan Province, Northeast Thailand
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[57]	Transformative Interventions Towards Socio-Economic and Environmental Sustainability of Smallholder Rubber


[58]	2 Data Preparation

[1]	Rubber expansion and age-class mapping in the state of Tripura (India) 1990–2021 using multi-year and multi-sensor data Environmental Monitoring and Assessment, 2023 DOI:10.1007/s10661-023-10942-2

[2]	Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows Remote Sensing, 2023 DOI:10.3390/rs15051228

[3]	Rubber expansion and age-class mapping in the state of Tripura (India) 1990–2021 using multi-year and multi-sensor data Environmental Monitoring and Assessment, 2023 DOI:10.1007/s10661-023-10942-2

[4]	Transnational evidence for socio-economic factors affecting income and plantation expansion into natural habitats in smallholder rubber Resources, Conservation & Recycling Advances, 2023 DOI:10.1016/j.rcradv.2023.200161

[5]	Bare Land Referenced Algorithm from Hyper-Temporal Data (BRAH) for Land Use and Land Cover Age Estimation Land, 2023 DOI:10.3390/land12071387

[6]	Transnational evidence for socio-economic factors affecting income and plantation expansion into natural habitats in smallholder rubber Resources, Conservation & Recycling Advances, 2023 DOI:10.1016/j.rcradv.2023.200161

[7]	Synergistic Use of Sentinel-1 and Sentinel-2 to Map Natural Forest and Acacia Plantation and Stand Ages in North-Central Vietnam Remote Sensing, 2021 DOI:10.3390/rs13020185

[8]	Application of Optical Remote Sensing in Rubber Plantations: A Systematic Review Remote Sensing, 2021 DOI:10.3390/rs13030429

[9]	Latest 30-m map of mature rubber plantations in Mainland Southeast Asia and Yunnan province of China: Spatial patterns and geographical characteristics Progress in Physical Geography: Earth and Environment, 2021 DOI:10.1177/0309133320983746

[10]	Using machine learning algorithm and landsat time series to identify establishment year of para rubber plantations: a case study in Thalang district, Phuket Island, Thailand International Journal of Remote Sensing, 2020 DOI:10.1080/01431161.2020.1799450

[11]	Sentinel-2 red-edge spectral indices (RESI) suitability for mapping rubber boom in Luang Namtha Province, northern Lao PDR International Journal of Applied Earth Observation and Geoinformation, 2020 DOI:10.1016/j.jag.2020.102176

[12]	Simulating land use for protecting food crop areas in northeast Thailand using GIS and Dyna-CLUE Journal of Geographical Sciences, 2019 DOI:10.1007/s11442-019-1629-7

[13]	Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification Remote Sensing, 2019 DOI:10.3390/rs11070831

[14]	Automatic Classification of Pararubber Trees in Thailand from LANDSAT-8 Images using Neural Networks Method 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2019 DOI:10.1109/ICEAST.2019.8802606

[15]	Is the phenology-based algorithm for mapping deciduous rubber plantations applicable in an emerging region of northern Laos? Advances in Space Research, 2019 DOI:10.1016/j.asr.2019.09.022

[16]	A renormalized modified normalized burn ratio (RMNBR) index for detecting mature rubber plantations with Landsat-8 OLI in Xishuangbanna, China Remote Sensing Letters, 2019 DOI:10.1080/2150704X.2018.1541106

[17]	The viability of remote sensing for extracting rubber smallholding information: A case study in Malaysia The Egyptian Journal of Remote Sensing and Space Science, 2018 DOI:10.1016/j.ejrs.2018.05.001

[18]	Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach ISPRS Journal of Photogrammetry and Remote Sensing, 2018 DOI:10.1016/j.isprsjprs.2018.01.002

[19]	Expansion dynamics of deciduous rubber plantations in Xishuangbanna, China during 2000–2010 GIScience & Remote Sensing, 2018 DOI:10.1080/15481603.2018.1466441

[20]	The household economics of rubber intercropping during the immature period in Northeast Thailand Journal of Sustainable Forestry, 2018 DOI:10.1080/10549811.2018.1486716

[21]	Climate change impact assessment on the potential rubber cultivating area in the Greater Mekong Subregion Environmental Research Letters, 2018 DOI:10.1088/1748-9326/aad1d1

[22]	Stand age estimation of rubber ( Hevea brasiliensis ) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series ISPRS Journal of Photogrammetry and Remote Sensing, 2018 DOI:10.1016/j.isprsjprs.2018.07.003

[23]	Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987–2015 Remote Sensing, 2018 DOI:10.3390/rs10081240

[24]	Mapping rubber trees based on phenological analysis of Landsat time series data-sets Geocarto International, 2017 DOI:10.1080/10106049.2017.1289559

[25]	Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery The Egyptian Journal of Remote Sensing and Space Science, 2017 DOI:10.1016/j.ejrs.2017.01.004

[26]	Policy-driven rubber plantation and its driving factors: a case of smallholders in northeast Thailand International Journal of Sustainable Development & World Ecology, 2017 DOI:10.1080/13504509.2016.1143410

[27]	Extracting distribution and expansion of rubber plantations from Landsat imagery using the C5.0 decision tree method Journal of Applied Remote Sensing, 2017 DOI:10.1117/1.JRS.11.026011

[28]	Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images International Journal of Applied Earth Observation and Geoinformation, 2016 DOI:10.1016/j.jag.2016.03.011

[29]	Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images International Journal of Applied Earth Observation and Geoinformation, 2016 DOI:10.1016/j.jag.2016.03.011

[30]	Pushing the Limits: The Pattern and Dynamics of Rubber Monoculture Expansion in Xishuangbanna, SW China PLOS ONE, 2016 DOI:10.1371/journal.pone.0150062

[31]	Estimating Leaf Area Index of Rubber Tree Plantation Using Worldview-2 Imagery Journal of Life Sciences and Technologies, 2016 DOI:10.18178/jolst.4.1.1-6

[32]	Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images Remote Sensing, 2015 DOI:10.3390/rs70101048

[33]	Mapping rubber tree plantations using a Landsat-based phenological algorithm in Xishuangbanna, southwest China Remote Sensing Letters, 2015 DOI:10.1080/2150704X.2014.996678

[34]	Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series Remote Sensing of Environment, 2015 DOI:10.1016/j.rse.2015.03.001

[35]	Estimating Aboveground Biomass of Rubber Tree Using Remote Sensing in Phuket Province, Thailand Journal of Medical and Bioengineering, 2015 DOI:10.12720/jomb.4.6.451-456

[36]	Object-oriented classification of rubber plantations from Landsat satellite imagery 2014 The Third International Conference on Agro-Geoinformatics, 2014 DOI:10.1109/Agro-Geoinformatics.2014.6910635

[37]	Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery Remote Sensing of Environment, 2013 DOI:10.1016/j.rse.2013.03.014

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	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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