Remote Sensing
Tayebe Managhebi; Akram JafarAghaee
Abstract
Forest biomass is one of the most important parameters in the ecosystem changes assessment and global carbon cycle modelling. In the other hand, the forest height is an effective parameter in the allometric equations which are used for biomass estimation. In this research the effect of two physical factors, ...
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Forest biomass is one of the most important parameters in the ecosystem changes assessment and global carbon cycle modelling. In the other hand, the forest height is an effective parameter in the allometric equations which are used for biomass estimation. In this research the effect of two physical factors, forest height and forest density, will be evaluated in the applicability of the four common inversion algorithms for forest height estimation based on the Polarimetric Interferometry SAR (PolInSAR) technique. The applicability of the digital elevation model (DEM) differencing, volume coherence amplitude, hybrid and three-stage methods is studied for different forest height and forest density by using simulated polarimetric interferometric SAR data in L-band. The experimental results of the forest height estimation in simulated data with a density of 100 to 900 trees per hectare and a height of 10 to 18 meters show that the results of the hybrid method show high sensivity to changes in both height and density. The root mean square of error was 5.8, 5.6, 3.2 and 4 m for data with variable height and 11.6, 6.7, 5.8 and 5.3 m for data with different densities, respectively.
Remote Sensing
Tayebe Managhebi; Mohammadreza Mobasheri
Abstract
The leaf water content is a specific index for the assessment of the physiological status of the plant based on the water content of the vegetation. This research provides an appropriate model based on the reflectance spectra between 400 and 2500 nm to estimate the leaf water content. We examined 53 ...
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The leaf water content is a specific index for the assessment of the physiological status of the plant based on the water content of the vegetation. This research provides an appropriate model based on the reflectance spectra between 400 and 2500 nm to estimate the leaf water content. We examined 53 different species of the well-known Leaf Optical Properties Experiment and a total of 263 spectral curves were employed in a supervised modelling procedure. In doing so, three different linear models were proposed based on the two different indices and their combination. The first index refers to the ratio of reflectance value in two wavelengths and the second one is the ratio of the derivative of the spectral curve slop in two wavelengths. The experimental results indicate the dependence between the water absorption bands and leaf water content. Finally determination of coefficient for hybrid linear model, which is used both indices, resulted to be 87 percent, indicating a very good fit. Also, the 0.06 relative root mean square error represents the aceptable accuracy in the wáter content modelling.
