stand volume and regression and landsat 8

Stand Volume And Regression And Landsat 8 Free Public Full
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Stand Volume And Regression And Landsat 8 Download Citation Copy

Abdolrassoul Salman Mahiny Gorgan University of Agricultural Sciences and Natural Resources Download full-text PDF Read full-text Download full-text PDF Read full-text Download citation Copy link Link copied Read full-text Download citation Copy link Link copied Citations (45) References (24) Figures (12) Abstract and Figures Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources.

In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETM) reflectance values.

We used multivariate regression technique to predict stand volume and tree density.

The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R 2 43; root mean square error (RMSE) 97.4 m3 ha1) than other ETM bands and vegetation indices.

In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R 2 73.4; RMSE 170.13 ha1) than other combinations of ETM bands and vegetation indices.

Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETM reflectance values and the utility of transformed bands in modelling stand volume and tree density.

Based on the results of this study, we conclude that ETM data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area.

Forest managers could use ETM data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions.

Distribution of the stand structure in the study area depicted by ( a ) stand volume histogram for field plots and ( b ) tree density histogram for field plots..

R 2, adjusted R 2, mean square error and Mallows Cp computed for all combinations in estimation stand volume.

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Stand Volume And Regression And Landsat 8 Free Public Full

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YAGHMAEE and A. S.

MAHINY College of Forestry, Gorgan University of Agricultural Sciences Natural Resources, Gorgan, 49138-15739, Iran College of Basic Sciences, Gorgan University of Agricultural Science Natural Resources, Gorgan, Iran College of Environmental Sciences, Gorgan University of Agricultural Science Natural Resources, Gorgan, Iran ( Received 24 February 2008; in final form 21 September 2008 ) Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources.

In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETM ) reflectance values.

The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R 2 43; root mean square error (RMSE) 97.4 m 3 ha - 1 ) than other ETM bands and vegetation indices.

In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R 2 73.4; RMSE 170.13 ha - 1 ) than other combinations of ETM bands and vegetation indices.

Introduction Fore sts are vita l ecosy stem ty pes acro ss the planet and play an impor tant ro le in the glob al carb on cycle.

Forest cove r also heavily in flue nces nut rient and hydro logi cal cycles.

Up-t o-dat e inform atio n on fore st cover and how it chan ges thro ugh time is of great imp ortanc e to succ essf ul and su stai nabl e mana geme nt of these vi tal ecosy stem typ es.

The temp erate dec idu ous forest s in norther n Iran have bee n under heavy press ure from indus tria l harve ster s and local peo ple.

Email: shataeeyahoo.com International Journal of Remote Sensing ISSN 0143-1161 printISSN 1366-590 1 online 2010 Taylor Francis DOI: 10.108001431160903140811 International Journal of Remote Sensing Vol.

No. 11, 10 June 2010, 29592975 Downloaded By: Mohammadi, Jahangir At: 11:21 27 September 2010.

Clear and st raigh tfo rward protoco ls also re move ambi gui ties when im pleme nti ng such plan s (Khorr ami 2004 ).

Norm ally, a larg e amoun t of work, time and cost are needed to gather up-to -date inform ation on various aspect s of fore st cove r such as aver age stan d volu me, tree densi ty and forest grow th (Aver y and Burk har t 1994, Kilp elan en and To kola 1999 ).