Estimation of Basal Area in West Oak Forests of Iran Using Remote Sensing Imagery

Abstract

The objective of this study is to evaluate the capability of satellite imagery for the estimation of basal area in Northern Zagros Forests. The data of the high resolution geometric (HRG) sensor of SPOT-5 satellite dated in July 2005 were used. Investigation of the quality of Satellite images shows that these images have no radiometric distortion. Overlaying of geocoded images with the digital topographic maps indicated that the images have high geometric precision. A number of 319 circular plots (0.1 ha) were established using systematic random method in the study area. All trees having diameter at breast height (DBH) (i.e. 1.3 m above ground) greater than 5 cm were callipered in each plot. Basal area in each plot was determined using field data. Main bands, artificial bands such as vegetation indices and principle component analysis (PCA) were studied. Digital numbers related to each plot were extracted from original and artificial bands. All plots were ordinated by major geographic aspects and the best fitted regression models were determined for both the study area without consideration of aspects and with consideration of major geographic aspects by multiple regression analysis (step wise regression). The results from regression analysis indicated that the square root of basal area without consideration of aspects has a high correlation with band B1 (r = –0.60). The consideration of aspects resulted in correlation of different indices with square root of basal area such that in northern forests, band B1 had higher correlation coefficient(r = –0.67) among other indices. In Eastern forests, the same band showed correlation of basal area with different correlation coefficient (r = –0.65). In southern and western forests, the square root of basal area had higher correlation (r = –0.68) with RVI. The use of the square root of basal area as a dependent variable in multivariate linear regression improved the results. The assessment of model validity indicated that the proposed models are properly valid

Tree Age as Adjustment Factor to Ndvi

“…The Soil Adjusted Vegetation Index (SAVI), proposed by (Huete, 1988), stands out amongst these variations for achieving, for the first time, to optimize the vegetation response in situations of low canopy cover, where the soil contributes to the final spectral response. Due to its pioneering and functionality, NDVI and SAVI are commonly applied in forest quantitative analyzes (Ponzoni and Shimabukuro, 1998;Xavier, 1998;Bolfe et al, 2012;Ghahramany et al, 2012;Mouissa and Fournier, 2013) and have also provided the basis for the development of many other VIs. Bolfe (2010) lists 26 VIs created since 1974 until 1998, but hundreds of VIs can potentially be formulated with different spectral bands combinations (Meng et al, 2007).…”

“…Forest stocks are traditionally estimated by forest inventories data (e.g. age and dbh) which sampling scheme usually covers less than 3% of the planted area (Trotter et al, 1997) and therefore lack the spatial character of alternative estimates, such as those using VIs from satellite images as independent variables (Ardo, 1992;Carreiras et al, 2006;Berra et al, 2012;Ghahramany et al, 2012). On the other hand, VIs have a reduced dynamic range and are not able to sense the continuing increase of biophysical parameters values throughout plants' development life cycle and saturate (Franklin, 1986;Danson and Curran, 1993).…”

“…For this, it is essential to have information about both quality and quantity of forest stands throughout all rotation cycle, which is traditionally made with aid of sampled data collected during forest inventories. Alternatively, satellite images have being used to obtain indirect estimates of forest biophysical parameters (Ardo, 1992;Carreiras et al, 2006;Ghahramany et al, 2012).…”

Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands

“…Values closer to 0 represent non-vegetated surfaces (Rouse et al, 1973). Several studies have used images to estimate variables, such as biomass (Martins et al, 2011), percentage of canopy cover (Carreiras et al, 2006), diameter at breast height (Linhares & Ponzoni, 2001), height (Accioly et al, 2002), and basal area (Ghahramany et al, 2012), among others.…”

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

“…Field surveys are the most accurate way to collect forest structural data [16,17], but require in-situ measurements that are generally limited to a small area (plot area). Consequently, they do not band depth analysis [62], narrow-band normalized difference water indices (NDWI) [31], or from MS data by mostly either employing original spectral bands or computing broad-band vegetation indices (VI) [63][64][65]. As an example, Schlerf et al [61] used HyMap data of highly managed and relatively homogenous Norway spruce (Picea abies) stands and reported significant linear relationships between forest leaf area index (LAI) and crown volume with a narrow-band perpendicular vegetation index (PVI).…”