Improvement of tropical vegetation mapping using a remote sensing technique: A case of Khao Yai National Park, Thailand

Trisurat, Yongyut; Eiumnoh, Apisit; Murai, Shunji; Hussain, M. Z.; Shrestha, R.

doi:10.1080/01431160050021277

Cited by 43 publications

(17 citation statements)

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“…Classifying tropical vegetation types using remote sensing is difficult because of the complexity of tropical vegetation (Trisurat et al 2000, Krishnaswamy et al 2004, and high accuracy is not always achievable. The overall accuracies achieved in the current study from rule-based and MLC approaches were better than those reported from other tropical forest studies (Trisurat et al 2000, Yang et al 2001.…”

Section: Discussionmentioning

confidence: 99%

Classifying tropical deciduous vegetation: a comparison of multiple approaches in Popa Mountain Park, Myanmar

Htun

Mizoue

Yoshida

2011

International Journal of Remote Sensing

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Although several studies have reported that rule-based methods are better than other image classification methods, no study has quantified their performance for tropical deciduous vegetation classification. We compared rule-based and maximum likelihood classification (MLC) approaches in classifying tropical deciduous vegetation in Popa Mountain Park, Myanmar. Classification was primarily based on Thematic Mapper (TM) bands of multi-season Landsat images, normalized difference vegetation indices (NDVIs), NDVI differences, mean NDVI and elevation (advanced spaceborne thermal emission and reflection radiometer digital elevation model (Aster DEM)). We used two main approaches for classification, a single-step approach in which all vegetation types were classified in one procedure, and a two-step approach in which forest and non-forest were discriminated first and then forest was classified into additional classes. Each of those approaches was conducted with and without elevation under the rule-based and MLC approaches, yielding eight separate methods. The two-step approaches generated more accurate results and all classifications improved markedly when elevation was included. The rule-based two-step with elevation approach produced the best overall accuracy and reliability.

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Section: Discussionmentioning

confidence: 99%

Classifying tropical deciduous vegetation: a comparison of multiple approaches in Popa Mountain Park, Myanmar

Htun

Mizoue

Yoshida

2011

International Journal of Remote Sensing

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“…In Amazonia, much attention has focused on the latter to address the impact of deforestation and the re-growth of secondary land covers. As a result, there has also been much effort to distinguish between 'natural' and anthropogenic forest types, such as agroforestry systems (Brondizio et al 1996;Brondizio 2005), and forest regrowth (Achard et al 2002) or initial, intermediate, and advanced secondary forests (Trisurat et al 2000, Brondizio 2005, Lu 2005). These various ecological and anthropogenic forest classes are difficult to distinguish spectrally, often having a direct negative impact on accuracy results.…”

Section: Introductionmentioning

confidence: 98%

A step-wise land-cover classification of the tropical forests of the Southern Yucatán, Mexico

Schmook

Dickson

Sangermano

et al. 2011

International Journal of Remote Sensing

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“…Unsurprisingly, it is one of the most commonly used classification methods in remote sensing studies of tropical forests (Trisurat et al 2000, Pedroni 2003, Thenkabail et al 2004). The k-nn method has been tested in the analysis of tropical vegetation only in papers I, II and IV, but is employed widely and also in operative use in satellite-imagebased forest inventories (Tomppo 1996, Nilsson 1997, Tomppo et al 1999, Gjertsen et al 2000, Franco-Lopez et al 2001, Tomppo et al 2001, Reese et al 2003, McInerney et al 2005, Koukal et al 2007, McRoberts et al 2007) and in land cover and non-forest/forest classifications (Franco-Lopez et al 2001, Haapanen et al 2004 in the boreal and temperate zone.…”

Section: Prediction Methodsmentioning

confidence: 99%

Remote sensing of floristic patterns in the lowland rain forest landscape

Thessler¹

2008

Diss. For.

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Land use and conservation planning urgently need information on floristic variation over large rain forest areas. Floristic variation can not be inventoried in every location and of all the flora, thus inventory is limited in sample sites of a group(s) of indicator species and modelled to predict the floristic composition of non-inventoried sites using spatially continuous information on the environment. Modelling is, however, practicable only if the dimensions of species data can be drastically reduced to a surrogate of floristic composition. The aim was to explore whether remote sensing can be applied to study and map the spatial variation of surrogates in lowland old-growth rain forest.I studied three surrogates: 1) number of species in ecological categories, 2) vegetation / forest type classification, and 3) species composition, summarized as the scores of three ordination axes. The understorey Melastomataceae and pteridophytes, and tree and palm species were used as indicator species. Landsat TM or ETM+ -satellite images and the SRTM digital elevation model were used as a proxy of environmental variation. The prediction methods included a k nearest neighbour method and linear discriminant analysis. The study areas were located in eastern Ecuador, in north-eastern Peru and northern Costa Rica.The main finding was that floristic patterns in lowland rain forest, expressed as vegetation classes, ordination axis scores or the number of species in ecological categories, can be predicted on the basis of remotely sensed data and field observations. The accuracy of the predictions depended on feature selection and weighting and on spatial resolution. The k-nn method proved to be a promising method in predicting floristic variation when it was expressed as a continuous variable, such as ordination axis scores or number of species. It also performed better than linear discriminant analysis in distinguishing forest classes using satellite image data.

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Improvement of tropical vegetation mapping using a remote sensing technique: A case of Khao Yai National Park, Thailand

Cited by 43 publications

References 0 publications

Classifying tropical deciduous vegetation: a comparison of multiple approaches in Popa Mountain Park, Myanmar

Classifying tropical deciduous vegetation: a comparison of multiple approaches in Popa Mountain Park, Myanmar

A step-wise land-cover classification of the tropical forests of the Southern Yucatán, Mexico

Remote sensing of floristic patterns in the lowland rain forest landscape

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