Potential for the use of GIS and spatial analysis techniques as tools for monitoring changes in forest productivity and nutrition, a New Zealand example

Payn, T. W.; Hill, R. B.; Höck, Barbara; Skinner, M. F.; Thorn, A. J.; Rijkse, W. C.

doi:10.1016/s0378-1127(99)00041-9

Cited by 25 publications

(12 citation statements)

References 2 publications

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“…Isaac (1949) produced the only known map of SI for coastal areas of Washington and Oregon, based on field reconnaissance and elevation information. Many of the recent spatial predictions of SI found in the literature have been created by relating multiple environmental variables in a geographic information system through techniques of regression tree analysis, multiple regression, or geostatistics (Höck et al 1993;Iverson et al 1997;Payn et al 1999;Chen et al 2002;McKenney and Pedlar 2003;Gustafson et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Predicting site index with a physiologically based growth model across Oregon, USA

Swenson¹,

Waring²,

Fan³

et al. 2005

Can. J. For. Res.

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With expanded interests in sustaining productivity under changing climate, management, and disturbance regimes, we sought a means of mapping the potential productivity of forests across the state of Oregon in the Pacific Northwest, USA. We chose the mapping tool 3-PG, a simplified physiologically based process model that can be driven with monthly averaged climatic data (DAYMET) and estimates of soil fertility based on soil nitrogen content. Maximum periodic mean increment (MAI, m3·ha1·year1), a measure of the forest's productive potential, was generated by the 3-PG spatial model and mapped at 1-km2 resolution for the most widely distributed tree species, Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Maximum MAI is linearly correlated with yield table site indices and therefore comparable with field-derived estimates of site indices obtained from measurement of tree heights and ages at 5263 federal forest survey points. The model predicted 100-year site index (SI) reasonably well (R2 = 0.55; RMSE = 9.1), considering the difference in spatial resolution between the modeled (1 km2) and field-measured SI (<0.1 ha) and that field plots were offset for confidentiality by 13 km. We created a map of the differences between modeled and field-measured SI and found that the 3000 points within ±6 m error were relatively evenly distributed across Oregon. Improving the accuracy in modeling and mapping forest productivity using 3-PG will likely require refinements in soil surveys, the quality of climatic data, the location of field plots, and the model functions and species parameters.

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Section: Introductionmentioning

confidence: 99%

Predicting site index with a physiologically based growth model across Oregon, USA

Swenson¹,

Waring²,

Fan³

et al. 2005

Can. J. For. Res.

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“…Soil sampling would occur as described for regular monitoring but foliar sampling would be more frequent. Foliar analysis has a role in monitoring changes with time and managing fertilizer applications ,,. Measurement of leaf area index could also be a supplementary measure.…”

Section: Principlesmentioning

confidence: 99%

“…Efficacious soil sampling strategies need to be used. This will require consideration of spatial variability and use of geographic information systems and special analysis techniques to accurately map where the samples are taken and to design optimum sampling schemes ,. Agricultural studies suggest that on virgin sites where fertilizer has not been applied, 20–30 cores per bulked sample are suitable for total nitrogen; carbon; pH; exchange capacity; and exchangeable and extractable potassium, magnesium, phosphorus, and sulfur .…”

Section: Principlesmentioning

confidence: 99%

Principles of nutrient management for sustainable forest bioenergy production

Mead¹,

Smith

2012

WIREs Energy & Environment

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Nutrient management is an important part of ensuring sustainable forest production. Essential concepts for managing site nutrients are built on the knowledge of types of soil, tree nutrient demands, and how these are impacted by silviculture, harvesting, and other management practices. Managers must clearly define forest management objectives, and how bioenergy production or carbon dioxide‐offset objectives require special consideration. Managers must examine how silviculture, harvesting, and other management practices might affect nutrient pools and availability. Soil fertility management practices are designed in response to these evaluation steps. Fertility management alternatives may include altering management practices that affect the distribution of harvested tree branches, foliage, and tops, as well as addition of fertilizers, nitrogen‐fixing plants, or wood ash. Planting on sites with pre‐existing nutrient deficiencies may require adding nutrients to obtain merchantable tree growth over the short and long term. Nutrient management principles may also be required to minimize off‐site impacts of management practices, for example, on water quality or climate change. Managers must determine what nutrients are needed on forested sites, amendment rates, and timing, and take into account the relationships among nutrients, soil, climate, and plant processes throughout the rotation. Soil and foliar analysis and the use of soil and plant bioassay tools can assist in these evaluations. Sustainable management practices require that nutrient control strategies evaluate economic and energy balances, and if necessary, modify management practices, including nutrient amendments, to ensure that they are sustainable. Examples of forest nutrient management practices are described. This article is categorized under: Bioenergy > Systems and Infrastructure Energy and Development > Science and Materials Energy and Development > Economics and Policy Energy and Development > Climate and Environment

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“…The application of geostatistical techniques to point-data GIS layers enables, in one hand, to move from a punctual representation of forest attributes to a continuous representation as interpolated surfaces and, in another hand, to obtain a measure of uncertainty attached to the prediction of unsampled sites (Vanclay, 1994;Goovaerts, 1997;Santos et al, 2003;Pelissari et al, 2015). Coupled GIS and geostatistical methodologies have been successfully used to predict several forest variables, namely for forest productivity mapping (Hock et al, 1993;Payn et al, 1999;Bognola et al, 2009). At national or regional level studies, accurate kriging results are easily obtained as a wide range of biophysical conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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-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...

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Potential for the use of GIS and spatial analysis techniques as tools for monitoring changes in forest productivity and nutrition, a New Zealand example

Cited by 25 publications

References 2 publications

Predicting site index with a physiologically based growth model across Oregon, USA

Predicting site index with a physiologically based growth model across Oregon, USA

Principles of nutrient management for sustainable forest bioenergy production

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

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