| [1] |
Team PIC. 2024. Rubber Cultivation in India www.pmfias.com/rubber-plant/ (accessed on 1 February 2025) |
| [2] |
Negash YT, Sriplod T, Hassan AM. 2021. A causal sustainable natural rubber development framework using a hierarchical structure with linguistic preferences in Thailand. |
| [3] |
Statista. 2023. Natural rubber production statistics www.statista.com/topics/3268/rubber/ (accessed on 1 February 2025) |
| [4] |
Warren-Thomas E, Nelson L, Juthong W, Bumrungsri S, Brattström O, et al. 2019. Rubber agroforestry in Thailand provides some biodiversity benefits without reducing yields. |
| [5] |
Public Debt Management Office T. 2024. Kingdom of Thailand Sustainability-Linked Financing Framework. Bangkok, Thailand: Public Debt Management Office www.pdmo.go.th/pdmomedia/documents/2024/Oct/PDMO%20SLB%20Framework_Final.pdf |
| [6] |
Chen B, Dong J, Thi Thu Hien T, Yun T, Kou W, et al. 2025. A full time series imagery and full cycle monitoring (FTSI-FCM) algorithm for tracking rubber plantation dynamics in the Vietnam from 1986 to 2022. |
| [7] |
Dong J, Xiao X, Chen B, Torbick N, Jin C, et al. 2013. Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. |
| [8] |
Kou W, Xiao X, Dong J, Gan S, Zhai D, et al. 2015. Mapping deciduous rubber plantation areas and stand ages with PALSAR and landsat images. |
| [9] |
Gao S, Liu X, Bo Y, Shi Z, Zhou H. 2019. Rubber identification based on blended high spatio-temporal resolution optical remote sensing data: a case study in Xishuangbanna. |
| [10] |
Xiao CW, Li P, Feng ZM. 2019. Monitoring annual dynamics of mature rubber plantations in Xishuangbanna during 1987-2018 using Landsat time series data: a multiple normalization approach. |
| [11] |
Ye S, Rogan J, Sangermano F. 2018. Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach. |
| [12] |
Zhai D, Dong J, Cadisch G, Wang M, Kou W, et al. 2018. Comparison of pixel- and object-based approaches in phenology-based rubber plantation mapping in fragmented landscapes. |
| [13] |
Liu J, Chen Z, Zhao Z. 2025. Assessing the accuracy of forest above-ground biomass and carbon storage estimation by meta-analysis based close-range remote sensing. |
| [14] |
Li Z, Fox JM. 2012. Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data. |
| [15] |
Fan H, Fu X, Zhang Z, Wu Q. 2015. Phenology-based vegetation index differencing for mapping of rubber plantations using landsat OLI data. |
| [16] |
Wang Y, Hollingsworth PM, Zhai D, West CD, Green JMH, et al. 2023. High-resolution maps show that rubber causes substantial deforestation. |
| [17] |
Hoang NT, Potapov P, Olofsson P, Kanemoto K. 2025. Accuracy of rubber-related deforestation maps. |
| [18] |
Sheil D, Descals A, Meijaard E, Gaveau D. 2025. Rubber planting and deforestation. |
| [19] |
Barrett F, McRoberts RE, Tomppo E, Cienciala E, Waser LT. 2016. A questionnaire-based review of the operational use of remotely sensed data by national forest inventories. |
| [20] |
Nguyen TH, Jones S, Soto-Berelov M, Haywood A, Hislop S. 2020. Landsat time-series for estimating forest aboveground biomass and its dynamics across space and time: a review. |
| [21] |
Zhao PP, Lu DS, Wang GX, Liu LJ, Li DQ, et al. 2016. Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data. |
| [22] |
Urbazaev M, Thiel C, Cremer F, Dubayah R, Migliavacca M, et al. 2018. Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. |
| [23] |
Santoro M, Beaudoin A, Beer C, Cartus O, Fransson JES, et al. 2015. Forest growing stock volume of the northern hemisphere: spatially explicit estimates for 2010 derived from Envisat ASAR. |
| [24] |
Coops NC, Tompalski P, Goodbody TRH, Queinnec M, Luther JE, et al. 2021. Modelling lidar-derived estimates of forest attributes over space and time: a review of approaches and future trends. |
| [25] |
Cai Y, Zhu P, Li X, Liu X, Chen Y, et al. 2025. Dynamics of China's forest carbon storage: the first 30 m annual aboveground biomass mapping from 1985 to 2023. |
| [26] |
Chen J, Li X, Huang Z, Xuan J, Chen C, et al. 2024. Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms: a case study of Masson pine (Pinus massoniana). |
| [27] |
Francini S, Schelhaas MJ, Vangi E, Lerink B, Nabuurs GJ, et al. 2024. Forest species mapping and area proportion estimation combining Sentinel-2 harmonic predictors and national forest inventory data. |
| [28] |
Li DQ, Lu DS, Wu YZ, Luo KF. 2022. Retrieval of eucalyptus planting history and stand age using random localization segmentation and continuous land-cover classification based on Landsat time-series data. |
| [29] |
Shang R, Lin XD, Chen JM, Liang YJ, Fang KY, et al. 2025. China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022. |
| [30] |
Huang C, Goward SN, Masek JG, Thomas N, Zhu Z, et al. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. |
| [31] |
Kennedy RE, Yang Z, Cohen WB. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. |
| [32] |
Verbesselt J, Hyndman R, Newnham G, Culvenor D. 2010. Detecting trend and seasonal changes in satellite image time series. |
| [33] |
Zhu Z, Woodcock CE. 2014. Continuous change detection and classification of land cover using all available Landsat data. |
| [34] |
Braun A. 2014. Climate variability and rubber production in Southern Thailand. International Journal of Agricultural Technology 10:1−12 |
| [35] |
Tongkaemkaew U, Patanothai A. 2013. Expansion of rubber plantations in Northeast Thailand: income and living status of full-time hired labors. Khon Kaen Agriculture Journal 41:497−506 |
| [36] |
Arora A. 2024. Top-10 rubber producing countries in the world 2024 https://eaclify.com/top-10-rubber-producing-countries-in-the-world-2024/ |
| [37] |
Zhou T, Shi P, Jia G, Dai Y, Zhao X, et al. 2015. Age-dependent forest carbon sink: estimation via inverse modeling. |
| [38] |
Warren-Thomas E, Dolman PM, Edwards DP. 2015. Increasing demand for natural rubber necessitates a robust sustainability initiative to mitigate impacts on tropical biodiversity. |
| [39] |
Huang IY, James K, Thamthanakoon N, Pinitjitsamut P, Rattanamanee N, et al. 2022. Economic outcomes of rubber-based agroforestry systems: a systematic review and narrative synthesis. |
| [40] |
Britannica. 1998. Rubber—tapping, coagulation, processing www.britannica.com/science/rubber-chemical-compound/Tapping-and-coagulation (accessed on 1 July 2024) |
| [41] |
Xu H, Chen Y, Chen B, Mansaray LR, Lu D, et al. 2025. A new framework for mapping rubber plantations through the combination of semiautomatic sample migration, dynamic phenology, and change detection variables based on time series Landsat images. |
| [42] |
Lan G, Chen B, Yang C, Sun R, Wu Z, et al. 2022. Main drivers of plant diversity patterns of rubber plantations in the Greater Mekong Subregion. |
| [43] |
Haralick RM, Shanmugam K, Dinstein IH. 1973. Textural features for image classification. |
| [44] |
Rouse JW, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the great plains with ERTS. Proc. Third Earth Resources Technology Satellite-1 Symposium, Washington DC, USA, 10–14 December 1973. Washington DC: NASA Special Publication. pp. 309–317 https://ntrs.nasa.gov/citations/19740022614 |
| [45] |
Zhang XY, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, et al. 2003. Monitoring vegetation phenology using MODIS. |
| [46] |
Xu H. 2007. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. |
| [47] |
Escuin S, Navarro R, Fernández P. 2007. Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. |
| [48] |
Didan K, Munoz AB, Solano R, Huete A. 2015. MODIS vegetation index user's guide (MOD13 series). Tucson, AZ: University of Arizona: Vegetation Index and Phenology Lab https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf (accessed on 1 February 2025) |
| [49] |
Farr TG, Rosen PA, Caro E, Crippen R, Duren R, et al. 2007. The shuttle radar topography mission. |
| [50] |
Foody GM. 2002. Status of land cover classification accuracy assessment. |
| [51] |
Dice LR. 1945. Measures of the amount of ecologic association between species. |
| [52] |
Chen B, Xiao X, Wu Z, Yun T, Kou W, et al. 2018. Identifying establishment year and pre-conversion land cover of rubber plantations on Hainan Island, China using Landsat data during 1987-2015. |
| [53] |
Petsri S, Chidthaisong A, Pumijumnong N, Wachrinrat C. 2013. Greenhouse gas emissions and carbon stock changes in rubber tree plantations in Thailand from 1990 to 2004. |
| [54] |
Nizami SM. 2012. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. |
| [55] |
Ratnasingam J, Thiruselvam K, Ioras F. 2016. The potential of rubber and acacia plantations for forest carbon stocks in Malaysia. |
| [56] |
Tiko JM, Ndjadi SS, Obandza-Ayessa JL, Mweru JPM, Michel B, et al. 2025. Carbon sequestration potential in rubber plantations: a complementary approach to tropical forest conservation strategies, a review. |
| [57] |
Friedlingstein P, O'Sullivan M, Jones MW, Andrew RM, Hauck J, et al. 2025. Global carbon budget 2024. |
| [58] |
Blagodatsky S, Xu J, Cadisch G. 2016. Carbon balance of rubber (Hevea brasiliensis) plantations: a review of uncertainties at plot, landscape and production level. |
| [59] |
UNFCCC. 2022. Thailand's article 6 initial report https://unfccc.int/sites/default/files/resource/202312071650---Article%206.2%20Initial%20Report_Thailand_V1.0.pdf |
| [60] |
Balestra M, Marselis S, Sankey TT, Cabo C, Liang X, et al. 2024. LiDAR data fusion to improve forest attribute estimates: a review. |
| [61] |
Hu T, Sun X, Su Y, Guan H, Sun Q, et al. 2020. Development and performance evaluation of a very low-cost UAV-lidar system for forestry applications. |