No abstract

No abstract

No abstract

Aim of the study: The objective of the study was the comparative assessment of various spatial resolutions of optical satellite imagery including Landsat-TM, ASTER, and Quickbird data to estimate the forest structure attributes of Hyrcanian forests, Golestan province, northern Iran.Material and methods: The 112 square plots with area of 0.09 ha were measured using a random cluster sampling method and then stand volume, basal area, and tree stem density were computed using measured data. After geometric and atmospheric corrections of images, the spectral attributes from original and different synthetic bands were extracted for modelling. The statistical modelling was performed using CART algorithm. Performance assessment of models was examined using the unused validation plots by RMSE and bias measures.Main Results: The results showed that model of Quickbird data for stand volume, basal area, and tree stem density had a better performance compared to ASTER and TM data. However, estimations by ASTER and TM imagery had slightly similar results for all three parameters.Research highlights: This study exposed that the high-resolution satellite data are more useful for forest structure attributes estimation in the Hyrcanian broadleaves forests compared with medium resolution images without consideration of images costs. However, regarding to be free of the most medium resolution data such as ASTER and TM, ETM+ or OLI images, these data can be used with slightly similar results.

Abstract:Modelling the spatial distribution of plants is one of the indirect methods for predicting the properties of plants and can be defined based on the relationship between the spatial distribution of vegetation and environmental variables. In this article, we introduce a new method for the spatial prediction of the dominant trees and species, through a combination of environmental and satellite data. Based on the basal area factor (BAF) frequency for each tree species in a total of 518 sample plots, the dominant tree species were determined for each plot. Also, topographical maps of primary and secondary properties were prepared using the digital elevation model (DEM). Categories of soil and the climate maps database of the Doctor Bahramnia Forestry Plan were extracted as well. After pre-processing and processing of spectral data, the pixel values at the sample locations in all the independent factors such as spectral and non-spectral data, were extracted. The modelling rates of tree and shrub species diversity using data mining algorithms of 80% of the sampling plots were taken. Assessment of model accuracy was conducted using 20% of samples and evaluation criteria. Random forest (RF), support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms were used for spatial distribution modelling of dominant species groups using environmental and spectral variables from 80% of the sample plots. Results showed physiographic factors, especially altitude in combination with soil and climate factors as the most important variables in the distribution of species, while the best model was created by the integration of physiographic factors (in combination with soil and climate) with an overall accuracy of 63.85%. In addition, the results of the comparison between the algorithms, showed that the RF algorithm was the most accurate in modelling the diversity.

No abstract

No abstract

No abstract