Developing an Index for Forest Productivity Mapping - A Case Study for Maritime Pine Production Regulation in Portugal

Abstract

-Productivity is very dependent on the environmental and biotic factors present at the site where the forest species of interest is present. Forest site productivity is usually assessed using empirical models applied to inventory data providing discrete predictions. While the use of GIS-based models enables building a site productivity distribution map. Therefore, the aim of this study was to derive a productivity index using multivariate statistics and coupled GIS-geostatistics to obtain a forest productivity map. To that end, a study area vastly covered by naturally regenerated forests of maritime pine in central Portugal was used. First, a productivity index (PI) was built based on Factorial Correspondence Analysis (FCA) by incorporating a classical site index for the species and region (Sh25 -height index model) and GIS-derived environmental variables (slope and aspect). After, the PI map was obtained by multi-Gaussian kriging and used as a GIS layer to evaluate maritime pine areas by productivity class (e.g., low, intermediate and high). In the end, the area control method was applied to assess the size and the number of compartments to establish by productivity class. The management compartments of equal productivity were digitized as GIS layer and organized in a temporal progression of stands' age regularly available for cutting each year during a 50-year schedule. The methodological approach developed in this study proved that can be used to build forest productivity maps which are crucial tools to support forest production regulation.Keywords: Site Productivity Classes; Area Control Method; Forest Management Compartments. DESENVOLVIMENTO DE UM ÍNDICE PARA O MAPEAMENTO DA PRODUTIVIDADE FLORESTAL -UM ESTUDO DE CASO PARA A REGULAÇÃO DA PRODUÇÃO DO PINHEIRO BRAVORESUMO -A produtividade florestal depende dos fatores ambientais e bióticos da estação onde a espécie florestal se encontra. A produtividade da estação florestal é geralmente avaliada com modelos empíricos aplicados a dados de inventário fornecendo previsões discretas. Enquanto o uso de modelos num SIG permite a construção de mapas de distribuição de produtividade da estação. Assim, o objetivo deste estudo foi desenvolver um índice de produtividade utilizando estatística multivariada e geoestatística acopladas ao SIG para obter um mapa de produtividade florestal. Para o efeito, selecionou-se uma área de estudo com floresta de regeneração natural de pinheiro bravo no centro de Portugal. Primeiramente, o índice de produtividade (PI) foi construído usando a Análise Fatorial das Correspondências (FCA) com um índice de qualidade de estação para a espécie e região (Sh25-modelo de índice de altura) e variáveis ambientais (declive e exposição). Depois, o mapa PI foi obtido por Krigagem multi-Gaussiana e usado como camada SIG para avaliar as áreas da espécie por classe de produtividade (p.e., baixa, intermedia e alta). No final, o método de controle da área foi aplicado para determinar o tamanho e o número de parcelas a serem estabelecidas por class...

Species Ecological Envelopes under Climate Change Scenarios: A Case Study for the Main Two Wood-Production Forest Species in Portugal

“…However, different methodological approaches were used for each one of these regions to map forest species' productive potential (e.g., using ecological-cultural characteristics and/or edaphic-climatic characteristics, or bioclimatic indices or productivity estimation), thus no methodological consistency exists considering all the country. The species' potential area mapping has also been essayed in several other studies [23][24][25]. However, none of these methods have been validated and/or their accuracy evaluated.…”

Modelling Maritime Pine (Pinus pinaster Aiton) Spatial Distribution and Productivity in Portugal: Tools for Forest Management

“…The number of compartments is defined by the harvesting age, and the size of each compartment considering the site class productivity aiming to ensure a constant wood production in each year. Fourth, the silvicultural schedule is applied to each compartment, supported by the species' potential distribution map, and will allow after harvesting the decision of whether to regenerate or to convert to another species or land use [9,26]. If the decision is to regenerate the Maritime pine stand, then measures to ensure natural regeneration sustainability should be considered.…”

“…Nowadays, access to big spatial data on climate and other environmental variables has fostered the use of powerful techniques from artificial intelligence and spatial statistics, such as machine learning (ML) and geostatistical modeling, which, coupled with geographic information systems (GIS) allow for the construction of simulated maps for the species' habitat suitability and productivity under the impacts of climate change [1][2][3][4][5][6][7][8][9][10][11]. Indeed, statistical modelling techniques such as classical regression (CR), generalized linear models (GLM), algorithmic modelling based on machine learning (ML), e.g., Bayesian networks (BNs), maximum entropy (MaxENT), and classification and regression trees (CART) have become increasingly popular [12].…”

“…Regarding forest species' productive potential mapping, several methodological approaches have been essayed in various studies [6][7][8][9][10][11]. In Portugal, forest species' productive potential maps for present and future climate change scenarios are available, for each one of the seven forest management regions the country, as divided in [13].…”

“…Finally, when the compartment is harvested (e.g., clear-cutting), a break in the fuel load is observed [26]. It is important to stress that application of the area control method requires previous knowledge of the area under productivity regulation [9,26]. Thus, the availability of cartographic support on species' current and potential distributions and productivity is key as a supporting tool for integrated landscape planning, species' afforestation, and sustainable forest management [26].…”