Estimation of trees height and vertical structure using SAR interferometry in uneven-aged and mixed forests.

Estimation of forest height is an important parameter of stands structure that aids in the determination of forest biomass, successional stage dynamics, and the decision of the type of forest management. In addition, estimating the height of trees especially in uneven-aged, massive, and multi-storied forest stands always faces challenges in kind of inventory and accuracy of the assessment. In this research, the synthetic aperture radar (SAR) interferometry technique was used to estimate the height of trees for determining the vertical structure of forest. For this purpose, we focused on an area at the mixed and uneven-aged forest in Iran and evaluated the potential of Envisat ASAR data to characterize the tree height in the forest patches and the digital surface model (DSM) was produced via SAR interferometry. The height of trees and the vertical structure of the forest stands were estimated using produced DSM and Digital elevation Model (DEM). Furthermore, the accuracy of estimated parameters was evaluated with real ground data (11 × 1 ha (100 × 100 m) sample plots). The results indicated that the estimated height of trees was meanly 7.69 m with a 22 m STDV over the reality. Furthermore, the vertical structure in all the plots was three-storied that they are the same as ground truth, but the percentage of the share of trees in the under and middle story was different from the ground truth. In conclusion, the tree height and vertical structure of forest stands can be determined with acceptable accuracy via SAR interferometry and Envisat ASAR data.

Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods.

Using satellite data to extract forest structure mapping parameters assists forest management. In this research, structural parameters including species, density, canopy, and gaps were extracted from SPOT-7 satellite data over Hyrcanian forests (Iran). A detailed ground inventory was initially conducted, over 12 × 1 ha (100 m × 100 m) plots, in which tree coordinates were plotted, using a differential global positioning system (DGPS), along with data on tree species, diameter-at-breast-height and height, as well as canopy dimensions, and canopy gap shapes, sizes, and positions, for each plot. Then, spectral transformations, vegetation indices, and simple spectral ratios were extracted from SPOT-7 data, and a supervised, pixel-based classification method and a support-vector machine algorithm were used to classify and determine tree species types. In addition, canopy tree borders and gaps were classified, using an object-based method, and tree densities per unit area were determined, using the canopy gravity center. Finally, the original ground data was used to perform an accuracy assessment on the extracted information, with the results showing that forest type could be determined with 95% accuracy and a Kappa coefficient of 0.8. Canopy and gap coverage achieved an overall accuracy of 91% (Kappa coefficient: 0.7), and tree densities per hectare were determined, on average, to be 47 trees fewer than reality. In conclusion, we have shown that forest structural parameters could be extracted, with good accuracy, using a combination of pixel- and object-based methods applied to SPOT-7 imaging.