A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.
Within the framework of the program on Earth remote sensing from space, the hyperspectral camera NA-GS (scientific instrument "Hyperspectrometer") produced by NPO Lepton (Zelenograd, Moscow) will be installed on the Russian segment of the International Space Station (ISS) for experimental testing of the ground-space system for monitoring and forecasting natural and man-made disasters. The practical use of this system is associated with solving certain problems of thematic processing hyperspectral images that must meet certain quality criteria. In this paper, we propose a technique for determining the operational capabilities of NA-GS instrument based on statistical simulation modeling (SSM) data. The concept of the proposed SSM includes the ability to perform model experiments for a test polygon of complex shape, simulation of hyperspectral imaging of selected parts of the polygon with a specified accuracy, and taking into account the clouds and the zenith angle of the sun. The influence of external observation conditions on the quality of hyperspectral images is considered. Numerical experiments were carried out for selected test areas. The analysis of the results obtained confirms reliability of the proposed technique.
Aerospace images with a spatial resolution of less than 1 m are actively used by regional services to obtain and update information about various environmental objects. Considerable efforts are being devoted to the development of remote sensing methods for forest areas. The structure of the forest canopy depends on various parameters, most of which are determined by ground-based methods during forest management works. Remote sensing methods for assessing the structural parameters of forest stands are based on texture analysis of panchromatic and multispectral images. A statistical approach is often used to extract texture features. The basis of this approach is the description of the distributions characterizing the mutual arrangement of image pixels in grayscale. This paper compares the effectiveness of matrix based statistical methods for extracting textural features for solving the problem of classifying various natural and manmade objects, as well as structures of the forest canopy. We consider statistics of various orders based on estimates of the distributions of gray levels, as well as the mutual occurrence, frequency, difference and structuring of gray levels. The results of assessing the informativeness of statistical textural characteristics in determining various structures of the forest canopy are presented. Dependences of the classification results on the choice of distribution parameters are determined. For the quantitative validation of the results obtained, data from ground surveys and expert visual classification of very high resolution WorldView-2 images of the territories of Savvatyevkoe and Bronnitskoe forestries are used.
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