NDLI: Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

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Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

Content Provider	Scilit
Author	Li, Guiying Lu, Dengsheng Moran, Emilio Hetrick, Scott
Copyright Year	2011
Description	Journal: International Journal of Remote Sensing This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.
Related Links	http://europepmc.org/articles/pmc3285540?pdf=render https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3285540/pdf
Ending Page	8230
Page Count	24
Starting Page	8207
ISSN	01431161
e-ISSN	13665901
DOI	10.1080/01431161.2010.532831
Journal	International Journal of Remote Sensing
Issue Number	23
Volume Number	32
Language	English
Publisher	Informa UK Limited
Publisher Date	2011-08-09
Access Restriction	Open
Subject Keyword	Journal: International Journal of Remote Sensing Remote Sensing Vegetation Index Classification Algorithm Different Classification Object-based Classification Land-cover Classification Landsat Thematic Mapper Imagery Classification Tree Analysis Cta Different Classification Algorithm Maximum Likelihood Classification Moist Tropical Region Overall Classification Accuracy Oca Textural Image Land-cover Classification Accuracy Artificial Neural Network
Content Type	Text
Subject	Earth and Planetary Sciences

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