An Internet-based decision support tool for non-industrial private forest landowners

Kirilenko, Andrei; Chivoiu, Bogdan; Crick, Julie; Ross‐Davis, Amy L.; Schaaf, Kenli A.; Shao, Guofan; Singhania, Vikas

doi:10.1016/j.envsoft.2006.11.001

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…This reveals an effort to eliminate the factor of subjectivity during the procedure. Other relevant DSSs proposed by Rasinmaki et al (2009) and Kirilenko et al (2007) have developed XML applications and propose a web-based DSS to provide decision support to managers and landowners in the field of sustainable forest management. There is also web-based DSS tool developed and adopted by the Texas Forest Service of the USA called 'Timberland Decision Support System' (TDSS/2014 ) which is a decision tool for short-range forest owners and other stakeholders to invest in timber trade management and growing stock.…”

Section: Discussionmentioning

confidence: 99%

A DSS for the identification of forest land types by the Greek Forest Service

Athanasiadis

Andreopoulou

2015

IJSAMI

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The issue of land use and identification of land types is of great interest for the Greek Forest Service in order to avoid legal conflicts between the State and private landowners. The main decision problem is the interpretation of the law and the required objectivity during the process. This paper present a hypertext-oriented decision support system, based on hyperlinks and questionnaires, for the identification of forest land types. The interface was designed based on HTML programming language using the software Dreamweaver™ as several Java scripts were also employed to set the rules and treaties that hyperlinks must fulfil. Its purpose is to propose a methodology to achieve more objective and reliable decisions. Forest services and foresters-decision makers could use such a decision tool aiming at sustainable management in land use, as it can also assist in achieving an efficient administration which is a prerequisite for sustainable governance.

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Section: Discussionmentioning

confidence: 99%

A DSS for the identification of forest land types by the Greek Forest Service

Athanasiadis

Andreopoulou

2015

IJSAMI

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“…As common ground, the majority of these S-DSS has been developed to tackle site-specific forest management issues such as the need of combining silvicultural and harvesting operations or carry out regeneration planning in protection forests [26]. However, it should be noted that these systems are mostly conceived in the shape of software developed for professional use to be applied to a specific geographic area for which they have been specifically designed, in addition, few online tools are designed for private and non-professional forest owners in a user-friendly environment [27]. Web technologies can help building platform-independent distributed computation facilitating the exchange of complex information [28].…”

Section: Forest Dss Systemsmentioning

confidence: 99%

A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale

Marano

Langella

Basile

et al. 2019

Forests

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Forests are part of a complex landscape mosaic and play a crucial role for people living both in rural and urbanized spaces. Recent progresses in modelling and Decision Support System (DSS) applied to the forestry sector promise to improve public participative forest management and decision-making in planning and conservation issues. However, most DSS are not open-source systems, being in many cases software designed for site-specific applications in forest ecosystems. Furthermore, some of these systems often miss challenging the integration of other land uses within the landscape matrix, which is a key issue in modern forestry planning aiming at linking recent developments in open-source Spatial-DSS systems to sectorial forest knowledge. This paper aims at demonstrating that a new type of S-DSS, developed within the Life+ project SOILCONSWEB over an open-source Geospatial Cyber-Infrastructure (GCI) platform, can provide a strategic web-based operational tool for forest resources management and multi-purpose planning. In order to perform simulation modelling, all accessible via the Web, the GCI platform supports acquisition and processing of both static and dynamic data (e.g., spatial distribution of soil and forest types, growing stock and yield), data visualization and computer on-the-fly applications. The DSS forestry tool has been applied to a forest area of 5,574 ha in the southern Apennines of Peninsular Italy, and it has been designed to address forest knowledge and management providing operational support to private forest owners and decision-makers involved in management of forest landscape at different levels. Such a geospatial S-DSS tool for supporting integrated forest knowledge at landscape represents a promising tool to implement sustainable forest management and planning. Results and output of the platform will be shown through a short selection of practical case studies.

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“…Those (Solomon et al 2007). At local and regional scales, ecosystem models have been used to forecast changes in natural resources for improved management (Hood et al 2007, Kirilenko et al 2007). Ecological models have also played a role in forecasting the timing and intensity of infectious diseases and in the development of control strategies .…”

Section: The Need For Ecological Forecastingmentioning

confidence: 99%

Ecological forecasting and data assimilation in a data-rich era

Luo

Ogle

Tucker

et al. 2011

Ecological Applications

259

235

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Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.

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An Internet-based decision support tool for non-industrial private forest landowners

Cited by 17 publications

References 7 publications

A DSS for the identification of forest land types by the Greek Forest Service

A DSS for the identification of forest land types by the Greek Forest Service

A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale

Ecological forecasting and data assimilation in a data-rich era

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