Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images

Egor Dmitriev,Anton Sokolov,Sergey Donskoy,Petr Melnik,Vladimir Kozoderov,G.M Tsibulsky,L.G Sverdlik,E.A Lupyan,A.K Sagatelian,V.I Kharuk,V.B Kashkin,M.V Noskov

doi:10.1051/e3sconf/20197501003

Abstract

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.

Highlights

IntroductionUsing State Forest Inventory (SFI) materials in medium- and long-term planning document development will help make informed decisions on forest management
The State Forest Inventory (SFI) is the key element of forest inventory work
Last forest inventory maps being vectorized with current changes, digital base material is created for all the targets

Summary

Introduction

Using SFI materials in medium- and long-term planning document development will help make informed decisions on forest management. A network of permanent inventory plots of constant radius is created to determine quantitative and qualitative characteristics of forests and systematic monitoring of their health. The materials of the forest inventory, updated for forest growth course and according to Earth remote sensing data, are used to build a network of permanent inventory plots. Last forest inventory maps being vectorized with current changes, digital base material is created for all the targets. After making the current changes from the forest inventory maps, the digital base materials are updated according to Earth remote sensing data. Remote inventory methods make it possible to significantly increase the SFI efficiency, since currently large forest areas of the Russian Federation are not wired with an inventory plot network. Ground-based methods for forest inventory are considered timeconsuming and expensive

The method of remote inventory automation

Numerical experiments and discussion

Water Artificial Soils Pine Birch Aspen Herbs RCM CVM

Findings

Conclusion