Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region

Peterson, Lara K.; Bergen, Kathleen M.; Brown, Daniel G.; Ващук, Л. Н.; Blam, Y.

doi:10.1016/j.foreco.2008.10.037

Cited by 60 publications

(36 citation statements)

References 32 publications

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“…Even though CA models face challenges to incorporate human decision making, they have been nevertheless found them useful for modeling ecological aspects of LULC [13][14][15]68]. While CA Markov models have been more commonly used to evaluate the impact of protection on a forested landscape by projecting forest cover changes in the future [109,110], we used this model to generate a hypothetical scenario of our present observable landscape without the effect of a policy intervention. We found this modeling approach useful and transferable to other areas where policy evaluation using traditional methods are not possible.…”

Section: Discussionmentioning

confidence: 99%

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

2012

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Establishment of protected areas (PA) has been one of the leading tools in biodiversity conservation. Globally, these kinds of conservation interventions have given rise to an increase in PAs as well as the need to empirically evaluate the impact of these PAs on forest cover. Few of these empirical evaluations have been geared towards comparison of pre and post policy intervention landscapes. This paper provides a method to empirically evaluate such pre and post policy interventions by using a cellular automata-Markov model. This method is tested using remotely sensed data of Bannerghatta National park (BNP) and its surrounding, which have experienced various national level policy interventions (Indian National Forest Policy of 1988) and rapid land cover change between 1973 and 2007. The model constructs a hypothetical land cover scenario of BNP and its surroundings (1999 and 2007) in the absence of any policy intervention, when in reality there has been a significant potential policy intervention effect. The models predicted a decline in native forest cover and an increase in non forest cover post 1992 whereas the actual observed landscape experienced the reverse trend where after an initial decline from 1973 to 1992, the forest cover in BNP is towards recovery post 1992. Furthermore, the models show a higher deforestation and lower reforestation than the observed deforestation and reforestation patterns for BNP post 1992. Our results not only show the implication of national level policy changes on forest cover but also show the usefulness of our method in evaluating such conservation efforts.

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Section: Discussionmentioning

confidence: 99%

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

2012

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“…Regression analysis and other statistical modelling approaches are commonly used in determining the relationships between land cover dynamics and environmental, socioeconomic, and spatial variables (Peterson et al 2009). However, the observed changes in forest cover are not sufficient to validate such a model because only correlation without causality can be detected through regression analysis.…”

Section: Methodsmentioning

confidence: 99%

Characteristics and drivers of forest cover change in the post-socialist era in Croatia: evidence from a mixed-methods approach

2016

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Extensive forests in Croatia represent an important biological and economic resource in Europe. They are characterized by heterogeneity in forest management practices dating back to the socialist planned economy of the pre-1991 era. In this study we investigated the difference in rates of deforestation and reforestation in private and state owned forests during the post-socialist period, and the causal drivers of change. The selected region of Northern Croatia is characterised by a high percentage of privately owned forests with minimal national monitoring and control. We used a mixed-methods approach which combines remote sensing, statistical modelling and a household-based questionnaire survey to assess the rates of forest cover change and factors influencing those changes. The results show that predominantly privately owned forests in Northern Croatia have recorded a net forest loss of 1.8% during the 1991-2011 period, while Croatia overall is characterised by a 10% forest cover increase in predominantly state-owned forests. Main factors influencing forest cover changes in private forests are slope, altitude, education structure, population age and population density. The results also show that the deforestation in private forests is weakening overall, mostly due to the continuation of the de-agrarization and de-ruralisation processes which began during socialism.

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“…Viana and Aranha (2010) applied LCM for the modeling and evaluation of landscape spatial patterns. CA_MARKOV has been used to analyze spatial patterns for many applications, such as to analyze: land use patterns in towns and villages (Sang et al, 2011), neighborhood effects on urbanization (Tong et al, 2012), urban growth (Mitsova et al, 2011), variation among forest management areas (Peterson et al, 2009), and the impacts of land use (XianBin et al, 2009).…”

Section: Allocation Of Changementioning

confidence: 99%

Comparison of simulation models in terms of quantity and allocation of land change

Olmedo

Pontius

Paegelow

et al. 2015

Environmental Modelling & Software

120

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-François Mas. Comparison of simulation models in terms of quantity and allocation of land change. Environmental Modelling and Software, Elsevier, 2015, pp.214-221. 10.1016/j.envsoft.2015 Comparison of simulation models in terms of quantity and allocation of land change a b s t r a c tOur article illustrates how to compare the outputs from models that simulate transitions among categories through time. We illustrate the concepts by comparing two land change models: Land Change Modeler and Cellular Automata Markov. We show how the modeling options influence the quantity and allocation of simulated transitions, and how to compare output maps from pairs of model runs with respect to a reference map of transitions during the validation interval. We recommend that the first step is to assess the quantity of each transition and to determine the cause of the variation in quantity among model runs. The second step is to assess the allocation of transitions and to determine the cause of the variation in allocation among model runs. The separation of quantity and allocation of the transitions is a helpful approach to communicate how models work and to describe pattern validation.

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Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region

Cited by 60 publications

References 32 publications

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

Characteristics and drivers of forest cover change in the post-socialist era in Croatia: evidence from a mixed-methods approach

Comparison of simulation models in terms of quantity and allocation of land change

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