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Open AccessArticle

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

by 1,*,†, 2,*,†, 3, 2, 1 and 4
1
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
2
Institute of Forest Resource and Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
3
Survey & Planning Institute of State Forestry Administration, Beijing 100714, China
4
School of Natural Resources, West Virginia University, Morgantown, WV 26506, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Sangram Ganguly, Compton Tucker, Parth Sarathi Roy and Prasad S. Thenkabail
Remote Sens. 2016, 8(2), 125; https://doi.org/10.3390/rs8020125
Received: 20 October 2015 / Revised: 14 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination ( R a d j 2 ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( R a d j 2 > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images. View Full-Text
Keywords: uneven-aged forest management; forest structural diversity; spectral and textural measures; Pearson’s correlation analysis; all subsets multiple linear regression uneven-aged forest management; forest structural diversity; spectral and textural measures; Pearson’s correlation analysis; all subsets multiple linear regression
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MDPI and ACS Style

Meng, J.; Li, S.; Wang, W.; Liu, Q.; Xie, S.; Ma, W. Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images. Remote Sens. 2016, 8, 125. https://doi.org/10.3390/rs8020125

AMA Style

Meng J, Li S, Wang W, Liu Q, Xie S, Ma W. Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images. Remote Sensing. 2016; 8(2):125. https://doi.org/10.3390/rs8020125

Chicago/Turabian Style

Meng, Jinghui; Li, Shiming; Wang, Wei; Liu, Qingwang; Xie, Shiqin; Ma, Wu. 2016. "Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images" Remote Sens. 8, no. 2: 125. https://doi.org/10.3390/rs8020125

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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