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Estimating Mixed Broadleaves Forest Stand Volume Using Dsm Extracted from Digital Aerial Images : Volume Xxxix-b8, Issue 1 (30/07/2012)

By Sohrabi, H.

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Book Id: WPLBN0004016999
Format Type: PDF Article :
File Size: Pages 4
Reproduction Date: 2015

Title: Estimating Mixed Broadleaves Forest Stand Volume Using Dsm Extracted from Digital Aerial Images : Volume Xxxix-b8, Issue 1 (30/07/2012)  
Author: Sohrabi, H.
Volume: Vol. XXXIX-B8, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


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Sohrabi, H. (2012). Estimating Mixed Broadleaves Forest Stand Volume Using Dsm Extracted from Digital Aerial Images : Volume Xxxix-b8, Issue 1 (30/07/2012). Retrieved from

Description: Dept. of Forestry, Natural resources and Marine Science Faculty, Tarbiat Modares University, Iran. In mixed old growth broadleaves of Hyrcanian forests, it is difficult to estimate stand volume at plot level by remotely sensed data while LiDar data is absent. In this paper, a new approach has been proposed and tested for estimating stand forest volume. The approach is based on this idea that forest volume can be estimated by variation of trees height at plots. In the other word, the more the height variation in plot, the more the stand volume would be expected. For testing this idea, 120 circular 0.1 ha sample plots with systematic random design has been collected in Tonekaon forest located in Hyrcanian zone.

Digital surface model (DSM) measure the height values of the first surface on the ground including terrain features, trees, building etc, which provides a topographic model of the earth's surface. The DSMs have been extracted automatically from aerial UltraCamD images so that ground pixel size for extracted DSM varied from 1 to 10 m size by 1m span. DSMs were checked manually for probable errors. Corresponded to ground samples, standard deviation and range of DSM pixels have been calculated. For modeling, non-linear regression method was used.

The results showed that standard deviation of plot pixels with 5 m resolution was the most appropriate data for modeling. Relative bias and RMSE of estimation was 5.8 and 49.8 percent, respectively.

Comparing to other approaches for estimating stand volume based on passive remote sensing data in mixed broadleaves forests, these results are more encouraging. One big problem in this method occurs when trees canopy cover is totally closed. In this situation, the standard deviation of height is low while stand volume is high. In future studies, applying forest stratification could be studied.



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