Classification of tropical forest classes from Landsat TM data.

International Journal of Remote Sensing ◽

10.1080/01431169608948777 ◽

1996 ◽

Vol 17 (12) ◽

pp. 2353-2367 ◽

Author(s):

G. M. FOODY ◽

R. A. HILL

Keyword(s):

Tropical Forest ◽

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Identifying terrestrial carbon sinks: Classification of successional stages in regenerating tropical forest from Landsat TM data

Remote Sensing of Environment ◽

10.1016/s0034-4257(95)00196-4 ◽

1996 ◽

Vol 55 (3) ◽

pp. 205-216 ◽

Author(s):

Giles M. Foody ◽

Gintautas Palubinskas ◽

Richard M. Lucas ◽

Paul J. Curran ◽

Miroslav Honzak

Keyword(s):

Tropical Forest ◽

Carbon Sinks ◽

Terrestrial Carbon ◽

Successional Stages

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Image segmentation for humid tropical forest classification in Landsat TM data

International Journal of Remote Sensing ◽

10.1080/014311699213082 ◽

1999 ◽

Vol 20 (5) ◽

pp. 1039-1044 ◽

Author(s):

R. A. Hill

Keyword(s):

Image Segmentation ◽

Tropical Forest ◽

Forest Classification ◽

Humid Tropical Forest

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Classification of Megathyrsus maximus Accessions Grown in The Colombian Dry Tropical Forest by Nutritional Assessment during contrasting seasons

10.21203/rs.3.rs-125473/v2 ◽

2021 ◽

Keyword(s):

Tropical Forest ◽

Nutritional Assessment ◽

Dry Tropical Forest

Abstract The authors have requested that this preprint be withdrawn due to erroneous posting.

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Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2012.03.011 ◽

2012 ◽

Vol 70 ◽

pp. 66-77 ◽

Author(s):

M.E.J. Cutler ◽

D.S. Boyd ◽

G.M. Foody ◽

A. Vetrivel

Keyword(s):

Tropical Forest ◽

Forest Biomass ◽

Image Texture ◽

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An evaluation of fuzzy and texture-based classification approaches for mapping regenerating tropical forest classes from Landsat-TM data

International Journal of Remote Sensing ◽

10.1080/01431169508954437 ◽

1995 ◽

Vol 16 (4) ◽

pp. 747-759 ◽

Author(s):

G. PALUBINSKAS ◽

R. M. LUCAS ◽

G. M. FOODY ◽

P. J. CURRAN

Keyword(s):

Tropical Forest ◽

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Comparison of back propagation and binary diamond neural networks in the classification of a Landsat TM image

Computers & Geosciences ◽

10.1016/s0098-3004(96)00037-4 ◽

1996 ◽

Vol 22 (9) ◽

pp. 995-1001 ◽

Author(s):

Shane D. Murnion

Keyword(s):

Neural Networks ◽

Back Propagation ◽

Landsat Tm Image ◽

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Estimation of forest damage in Mediterranean areas by fuzzy classification of Landsat TM images

10.1117/12.373086 ◽

1999 ◽

Author(s):

Fabio Maselli ◽

Lorenzo Bottai ◽

Arturo Oradini

Keyword(s):

Fuzzy Classification ◽

Forest Damage ◽

Mediterranean Areas

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Segmentation of LANDSAT TM image using the Markov random field model toward a category classification of higher accuracy

10.1117/12.578405 ◽

2005 ◽

Author(s):

Shuji Kawaguchi ◽

Kensuke Yamazaki

Keyword(s):

Random Field ◽

Markov Random Field ◽

Field Model ◽

Random Field Model ◽

Markov Random Field Model ◽

Landsat Tm Image ◽

Tm Image ◽

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Estimation of tropical forest biomass using Landsat TM imagery and permanent plot data in Xishuangbanna, China

International Journal of Remote Sensing ◽

10.1080/01431161.2010.507677 ◽

2011 ◽

Vol 32 (20) ◽

pp. 5741-5756 ◽

Author(s):

Cunjian Yang ◽

He Huang ◽

Siyuan Wang

Keyword(s):

Tropical Forest ◽

Forest Biomass ◽

Permanent Plot ◽

Landsat Tm Imagery

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The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/36.868888 ◽

2000 ◽

Vol 38 (5) ◽

pp. 2310-2321 ◽

Author(s):

M. Simard ◽

S.S. Saatchi ◽

G. De Grandi

Keyword(s):

Decision Tree ◽

Tropical Forest ◽

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