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当前位置: 首页 > 文章 > Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices 林业研究(英文版) 2020,31 (2)
Position: Home > Articles > Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices Journal of Forestry Research 2020,31 (2)

Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices

作  者：
Nouri Abolfazl;Bahman Kiani;Mohammad Hossein Hakimi;Mohammad Hossein Mokhtar
单  位：
Department of Forest Sciences, Faculty of Natural Resources, Yazd University, Yazd, Islamic Republic of Iran;Department of Desert Management, Faculty of Natural Resources, Yazd University, Yazd, Islamic Republic of Ira;Department of Desert Management, Faculty of Natural Resources, Yazd University, Yazd, Islamic Republic of Iran
关键词：
Basal area;Canopy cover;Density;Landsat-8;Modeling;TREE CANOPY COVER;LEAF-AREA INDEX;ABOVEGROUND BIOMASS;TM IMAGERY;LANDSAT 8;REGRESSION;RE
摘  要：
Remote sensing is an important tool for studying and modeling forest stands. Vegetation indices obtained from Landsat-8 remotely sensed data were used to estimate the forest parameters in the western mountains of Iran. Thirty-four sample points were selected on the map of Bayangan County, Kermanshah province, Iran. At each point, a cluster of five rectangular plots of 8100 m(2) and 200 m apart was established. Some clusters were primarily sampled to determine the variance of the forest parameters. The coefficient of variation was used as a criterion for sample allocation. Stand density, canopy cover and basal area per hectare were calculated for each plot. Vegetation indices were extracted from the Landsat-8 images for each plot. Simple linear and nonlinear regressions were conducted to develop the models. The models were validated using an independent data set. Stability of model parameters was statistically evaluated. The results showed that Normalized difference vegetation index (NDVI) and Transformed normalized difference vegetation index (TNDVI) followed by Simple ratio vegetation index (SRVI) were the best predictors, explaining up to 91% of the variations with high precision. For NDVI, Soil adjusted vegetation index 2 (SAVI2) and SRVI, the cubic model was more appropriate than the linear model for predicting the forest parameters. For this model, the R-square value increased while NRMSE decreased significantly. For Infrared percentage vegetation index (IPVI), the quadratic model was better, but, for other vegetation indices, nonlinear models were not superior to linear ones. Finally, it can be concluded that Landsat-8 imagery is suitable for predicting forest parameters in the oak forests of western Iran. Of course, large plots must be selected, and pre-classification is necessary to gain accurate and precise estimations.

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