Rubber Tree Distribution Mapping in Northeast Thailand

Li, Zhe; Fox, Jefferson

doi:10.4236/ijg.2011.24060

Cited by 37 publications

(15 citation statements)

References 33 publications

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“…Previous studies can be generally divided into three groups based on sensor types: optical sensors, microwave or radar sensors, and integration of optical and radar sensors. A few studies used images from optical sensors (MODIS, Landsat and ASTER) and calculated image statistics and classified images to map rubber plantations in Southeast Asia [7,10,11]. As cloud cover occurs frequently in the moist tropical areas, high temporal resolution image data (e.g., MODIS) make it possible to obtain some cloud-free observations.…”

Section: Introductionmentioning

confidence: 99%

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

et al. 2015

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Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by integration of PALSAR 50-m mosaic images and multi-temporal Landsat TM/ETM+ images. The L-band PALSAR 50-m mosaic images were used to map forests (including both natural forests and rubber trees) and non-forests. For those PALSAR-based forest pixels, we analyzed the multi-temporal Landsat TM/ETM+ images from 2000 to 2009. We first studied phenological signatures of deciduous rubber plantations (defoliation and foliation) and natural forests through analysis of surface reflectance, Normal Difference OPEN ACCESSRemote Sens. 2015, 7 1049Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) and generated a map of rubber plantations in 2009. We then analyzed phenological signatures of rubber plantations with different stand ages and generated a map, in 2009, of rubber plantation stand ages (≤5, 6-10, >10 years-old) based on multi-temporal Landsat images. The resultant maps clearly illustrated how rubber plantations have expanded into the mountains in the study area over the years. The results in this study demonstrate the potential of integrating microwave (e.g., PALSAR) and optical remote sensing in the characterization of rubber plantations and their expansion over time.

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Section: Introductionmentioning

confidence: 99%

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

et al. 2015

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“…A number of studies have used remote sensing data to map rubber plantations [4,5]. These studies mostly focuses on the use of spectral signatures with cluster analysis and traditional classifiers, or the temporal signals of optical images to identify and delineate rubber plantations [6,7].…”

Section: Introductionmentioning

confidence: 99%

Object-oriented classification of rubber plantations from Landsat satellite imagery

Dai

Luo

Fang

et al. 2014

2014 the Third International Conference on Agro-Geoinformatics

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Due to increasing global demand for natural rubber products, rubber (Hevea brasiliensis) plantation expansion has occurred in many regions where it was originally considered unsuitable. However, accurate maps of rubber plantations are not available, which substantially constrain our understanding of the environmental and socio-economic impacts of rubber plantation expansion. In this study, the rubber plantations was accurate mapped from Landsat satellite imagery based on object-oriented classification method in Yangjiang State Farm in Hainan Island in 2010. The results show that: (1) The rubber plantation area in Yangjiang State Farm was estimated at 5866 hm 2 in 2010, which was slightly higher than the stand inventory data (5190 hm 2 ) in 2009. (2) The resulting rubber plantation map has a high accuracy according to the confusion matrix by using the ground truth ROIs. The overall accuracy is 90% and the kappa coefficient is 0.9. It showed that objectoriented classification method is suitable for mapping rubber plantation from Landsat satellite imagery.

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“…Previous studies; Yusoff and Muharam (2015), Fan et al (2015), Senf et al (2013), Dong et al (2013), Kou et al (2015) and Li & Fox (2011 focused specifically on distinguishing rubber trees against either deciduous forest or evergreen forest depending on the region where the studies were conducted. However, for a country such as Malaysia, instead of evergreen trees that are spectrally similar in properties with rubber trees, cropland such as oil palm trees also exhibit this property which can cause misclassification of those three types of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Mapping rubber trees based on phenological analysis of Landsat time series data-sets

Razak

Shariff

Ahmad

et al. 2017

Geocarto International

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This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized Difference Vegetation Index (NDVI) to verify the three important phenological metrics of rubber trees; defoliation, foliation and their growing stages. A decade (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) ago, Landsat time series NDVIs were used to study the strength of relationship between rubber trees, evergreen trees and oil palm trees. Two important results that could discriminate these three types of vegetation were found; firstly, a weak relationship of NDVIs between rubber trees and evergreen trees during the defoliation period (r 2 = 0.1358) and secondly between rubber trees and oil palm trees during the growing period (r 2 = 0.2029). This analysis was verified using Support Vector Machine to map the distribution of the three types of vegetation. An accurate mapping strategy of rubber trees was successfully formulated.

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Rubber Tree Distribution Mapping in Northeast Thailand

Cited by 37 publications

References 33 publications

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

Object-oriented classification of rubber plantations from Landsat satellite imagery

Mapping rubber trees based on phenological analysis of Landsat time series data-sets

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