To the methods for actualization of main stands parameters of hardwood tree species of Ukraine

Continuous forest inventory, as one of the methods of forest management of the country, in contrast to the basic inventory, has a number of advantages, which primarily provide the opportunity to obtain the most complete and reliable information about the current state of the forest, as it provides annual updates of main stands parameters of forest found on Ukraine. Hardwood tree species (oak, ash, beech, hornbeam, etc.) occupy a special place among all that grow in Ukraine and occupy almost 44 % of the forest area covered with forest vegetation. According to the latest state forest inventory as of 01.01.2011, hardwood tree species are dominated by oak stands - 62.6 % of the area of all hardwood tree species, forest beech - 20.2 %, hornbeam - 3.2 % and ash ordinary, forming mainly mixed stands with oak. A systematic approach to the effective solution of the problem of continuous forest inventory requires the development of objective methods and mathematical models for updating the main stands parameters of forests. In Ukraine, for actualization main stands parameters, two methods have been developed to forecast their growth. The first is based on modeling the percentage of current increment by average height and wood stock, the second - on the developed dynamic site index curves and yield tables for modal stands. The aim of the work is to improve the methodological approaches to modeling the growth prognosis of the main stands parameters by the second method. To achieve these goals used the method of nonlinear regression using IBM SPSS Statistics. As a result of the conducted researches it was offered to model a new unified ratio of the stands parameter a year ahead to the same stands parameter now multiplied by the age of the stand to model the growth prognosis for all stands parameters. This made it possible during the simulation to describe the changes in growth by the main stands parameters with almost absolute accuracy (the coefficient of determination of the obtained models is 1.0). The equation obtained for growth prognosis has the same form for stands of all tree species, of different origin, composition and site index classes.

Integration of statistical forest reflectance model and Sentinel-2 MSI images into a continuous forest inventory system

Spectral signatures of forest stands in Sentinel-2 MSI spectral bands are simulated with the statistical forest reflectance model SFRM, and compared to the spectral signatures measured in spectral images at ten study sites in Estonia. As an overall measure of the agreement between simulated and measured spectral signatures is used the total error which is calculated as the sum of relative errors over spectral bands B2 to B11 of Sentinel-2. The distribution of the total error has strongly positive skewness at all study sites and all types of forests (broadleaf, pine and spruce forests). The right tile of the distribution is low. The stands of high value of the total error far right in the tail of the distribution may have some errors in their inventory data, or the inventory data are outdated. Pertinent stands should have priority in their in situ checking process. The model SFRM is a simple and reliable tool for the validity checking of forest inventory data, using routinely collected forest inventory data and operational satellite information of moderate spatial resolution. The model is simple and computationally efficient. Preparing input data for the model is a simple query in the forest inventory database. The suggested procedure can be incorporated into automated systems of continuous forest inventory.

INVENTÁRIO DE CARBONO EM FRAGMENTO DE FLORESTA OMBRÓFILA MISTA POR DETECÇÃO REMOTA

O trabalho teve como objetivo inventariar o carbono de um fragmento de Floresta Ombrófila Mista utilizando dados provenientes de sensores de média resolução espacial. Uma cena dos sensores ASTER, LISS e TM foi empregada na obtenção dos dados radiométricos (espectrais), e os dados de biomassa e carbono (biofísicos) foram oriundos de parcelas de inventário florestal contínuo em São João do Triunfo, PR. A metodologia consistiu em estabelecer a relação empírica entre esses conjuntos de dados por meio de equações lineares de regressão. À exceção do sensor TM, que apresentou resultado insatisfatório, o uso dos dados oriundos dos sensores LISS e ASTER foi adequado para se inventariar o carbono florestal por detecção remota, com erros inferiores aos estabelecidos nas campanhas de inventários tradicionais (α < 0,05).Palavras-chave: Estoque de carbono; sensoriamento remoto; ASTER; TM; LISS. AbstractCarbon inventory in a fragment of Mixed Ombrophylous Forest by remote sensing. The research aims to make inventory of carbon of a fragment of Araucaria Forest using data from medium spatial resolution sensors. Satellite data from ASTER, TM and LISS were used to obtain the radiometric data. The above ground biomass and carbon data (biophysical data) were derived from the continuous forest inventory located in São João do Triunfo, PR. The methodology consisted of establishing the empirical relationship between spectral and biophysical data sets using linear regression. Except for the TM data, which showed unsatisfactory results, the use of ASTER and LISS satellite data was suited to forest carbon inventory by remote sensing, with errors lower than those set in traditional inventory campaigns (α < 0,05).Keywords: Carbon stock; remote sensing; ASTER; TM; LISS.

Forest Vegetation Simulator Ingrowth Tool: Incorporating Ingrowth Tree Lists into Forest Vegetation Simulator Growth Projections

This article describes the Forest Vegetation Simulator Ingrowth Tool (FVS_IT), software developed in the Python language 5 and tested using the Northeast variant of FVS (FVS-NE). This tool incorporates specified ingrowth tree lists, stored in secondary tree list files, into FVS projections. It functions by retrieving information from an FVS keyword file, which is then modified to project data in a stepwise manner using user-defined time intervals. Between each time step in a simulation, FVS_IT incorporates ingrowth into projections by appending ingrowth tree records to projected tree lists and compiles a new tree list for the next time step. Outputs include both appended tree lists and stand summaries from FVS so that users can conduct further analyses. The FVS_IT application is useful when assessing and calibrating FVS using continuous forest inventory or permanent sample plots where periodic remeasurements include ingrowth trees.