Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data

Li, Zhe; Fox, Jefferson

doi:10.1016/j.apgeog.2011.06.018

Cited by 253 publications

(217 citation statements)

References 48 publications

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“…However, the substantial computation time and heuristic training process of machine learning classifiers make rubber tree growth mapping over large areas inefficient. Experiments conducted by Li and Fox [19] suggest that using approaches of neural networks and decision trees with spectral information and vegetation indices overestimated the number of rubber tree pixels. Additionally, these classifiers require training sites to contain sufficient both "presence" and "absence" information.…”

Section: Introductionmentioning

confidence: 99%

“…Li and Fox [28] improved rubber tree growth mapping using ASTER data by integrating Mahalanobis typicalities with a neural network model. Another successful application using Mahalanobis typicality approach was the mapping of rubber trees across the mainland Southeast Asia using the Mahalanobis typicality method with MODIS time-series NDVI and statistical data [19]. In this study we examined the potential of Mahalanobis typicalities for rubber tree growth distribution mapping using Landsat 5 TM imagery.…”

Section: Introductionmentioning

confidence: 99%

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Rubber Tree Distribution Mapping in Northeast Thailand

Li¹,

Fox²

2011

IJG

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In many parts of mainland Southeast Asia rubber plantations are expanding rapidly in areas where the crop was not historically found. Monitoring and mapping the distribution of rubber trees in the region is necessary for developing a better understanding of the consequences of land-cover and land-use change on carbon and water cycles. In this study, we conducted rubber tree growth mapping in Northeast Thailand using Landsat 5 TM data. A Mahalanobis typicality method was used to identify different age rubber trees. Landsat 5 TM 30 m non-thermal reflective bands, NDVI and tasseled cap transformation components were selected as the model input metrics. The validation was carried out using provincial level agricultural statistical data on the rubber tree growth area. At regional (Northeast Thailand) and provincial scales, the estimates of mature and middle-age rubber stands produced from 30 m Landsat 5 TM data compared well (high statistical significance) with the provincial rubber tree growth statistical data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rubber Tree Distribution Mapping in Northeast Thailand

Li¹,

Fox²

2011

IJG

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“…Natural rubber is one of the most important raw materials in industry, agriculture, defense, transportation, and daily life [5,6]. Demand for rubber is increasing with economic development; however, the regions where rubber trees are planted are limited due to the stringent environmental requirements for their growth [7].…”

Section: Introductionmentioning

confidence: 99%

“…Efforts by breeders led to many rubber tree cultivar clones being selected and planted in non-traditional planting areas, such as Chinese rubber plantations. These were established in Hainan and Yunnan Provinces, in areas as far north as 22 • N, while rubber plantations are typically located in latitudes that range from 10 • N to 10 • S [6,10]. Two new rubber tree cultivars, Yunyan 77-2 and Yunyan 77-4, were selected and confirmed as triploids which were largely planted in Yunnan, (12) Original clones PB, Prang Besar Rubber Estate, Malaysia; RRIC, Rubber Research Institute of Ceylon.…”

Section: Introductionmentioning

confidence: 99%

Male Parent Identification of Triploid Rubber Trees (Hevea brasiliensis) and the Mechanism of 2n Gametes Formation

Yao

Long

et al. 2016

Forests

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Eight triploids were screened among offspring of the rubber tree clone GT1 × different clones by flow cytometry and chromosome counting. Twenty-five simple sequence repeat (SSR) markers were screened to identify the origin of 2n gametes, to determine the male parents of these triploids, and to evaluate the mechanism of 2n gamete formation using band configurations and microsatellite DNA allele counting peak ratios (MAC-PR). The results showed that 2n gametes originated from the maternal rubber tree clone GT1, contributing the extra genome copy present in the triploids. It was confirmed that GT1 is able to produce a 2n megagametophyte spontaneously. Many male parents were shown to provide pollen for formation of triploid rubber trees, including clones RRIC 103, Yunyan 277-5, and three other clones. The second division restitution (SDR) was likely the main mechanism involved in formation of megagametophytes in GT1, as the rate of maternal heterozygosity restitution (HR) of all eight triploids varied from 27.78% to 75.00%, with a mean of 51.46%, and all 25 markers varied from 0% to 100%, with a mean of 51.69%. Elucidation of the origin and formation of 2n gametes will help optimize further sexual hybridization of polyploid rubber trees.

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“…Technologies with the ability to make this distinction would be vital resources for zero net deforestation targets, which value both the protection of native forests and the planting of new ones, and zero gross deforestation targets, which are particularly concerned with gross loss of forest area over time and broadly aim for no deforestation anywhere [30]. Remote sensing technologies that can map tree plantations separately from native forests will also contribute to more effective monitoring and a greater understanding of the impacts of land conversion due to growth in commercial agriculture [31,32], and critically inform carbon credit schemes such as the United Nation's Reducing Emissions from Deforestation and Forest Degradation (REDD) Programme [33,34].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data

Cited by 253 publications

References 48 publications

Rubber Tree Distribution Mapping in Northeast Thailand

Rubber Tree Distribution Mapping in Northeast Thailand

Male Parent Identification of Triploid Rubber Trees (Hevea brasiliensis) and the Mechanism of 2n Gametes Formation

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

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