Landscapes Forest and Global Change - ESA - Escola Superior ...

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F.M. Rabenilalana et al. 2010. Multi-temporal analysis of forest landscape fragmentation in the North East of Madagascar 93 For this computation, a 3 by 3 kernel (majority filter) was used so that c equalled 9.This interval choice was linked to the importance of reflectance. A reflectance of 100% means that the surface reflects all solar energy in the atmosphere. This corresponds to a fragmentation index of 0. When the reflectance is 0%, the fragmentation index equals 1. 2.1.3 Change analysis Land cover maps from two different dates, 2004 and 2009, were used to make land cover predictions for 2010. Using Land Change Modeler (Eastman 2006), this was done in two major stages: the transition potential sub-model, which is developed in this paper, stage and the change prediction model stage. A transition sub-model consists of a single land cover transition or a group of transitions that are thought to have the same underlying driver variables. In the first stage, particular transitions of interest for the sub-model and the variables which are assumed to drive the transitions taking place were specified. Variables considered as drivers of deforestation were distance to rivers and streams, paths, villages and to topographic parameters. Using Cramer’s V or Phi statistic the strength, association or dependency between two variables (Agresti 2002) was analyzed. The closer V is to 0 when the smaller ie the association between the categorical variables X and Y. On the other hand, V being close to 1 is an indication of a strong association between two variables. Afterward, gain and loss of fragmentation (forests cover) area were calculated between two periods with the aim to determine the rate of fragmentation in the study area. 3. Results 3.1 Fragmentation Index The fragmentation was classified in two groups depending on the fragmentation index: a) a forest fragment was considered to have a low level of fragmentation when the index was between [0; 0.1[, b) a forest fragment was considered to have a high level of fragmentation when the index was within the interval [0.1; 1]. Table 1 shows the fragmentation index. In 2004, the index ranged from 0 to 0.875 while in 2009 the values were between 0 and 0.25. The reasons for this difference could be either a) the luminosity at the time when the images were taken was not the same or b) the forest fragments of 2004 have been converted into less fragments in 2009, which is not evident in the short period. In terms of patch numbers, there is a large difference between 2004 and 2009. 17 % of forests cover with low fragmentation in 2004 shows a high level of fragmentation or has been converted into other land uses in 2009. Table 1: Fragmentation index characteristics Period Total area Count Minimum Maximum Mean Standard deviation Low fragmented class Patches number 2004 30487 18918482 0 0.875 0.054 0.01 65535 5633 2009 22472 18918482 0 0.25 0.017 0.01 11231 6442 Fragmented class Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.) 2010, Instituto Politécnico de Bragança, Bragança, Portugal.
F.M. Rabenilalana et al. 2010. Multi-temporal analysis of forest landscape fragmentation in the North East of Madagascar 94 3.2 Fragmentation forest area In terms of forest area, a decrease (occurred during the period under review) was observed between the two periods. A deforestation rate of 5.25 % per year was calculated. Figure 1 explains that 46% of low fragmented forest was changed into the other land use classes in 2009. Approximately, most of the forested area lost has been converted into cropland and only 36% in highly fragmented forest. In fact, there has been a tendency of highlighting small-scale migratory farmers and "poverty" as the major cause of landscape change. In spite of increased fragmentation, the “gains and losses’ analysis showed that some fragments persisted. In reality, more or less 35% of the total forest area resists change. These values illustrate landscape changes at a very high rate. And if there is no action taken soon, large amounts of habitat and biodiversity values will be lost. Area (Ha) 35 000,00 30 000,00 25 000,00 20 000,00 15 000,00 10 000,00 5 000,00 0,00 2004 2009 Year Low fragmented Fragmented Figure 1: Forest area change from 2004 to 2009 3.2 Change analysis The impact of distance variables (distance to streams, rivers, paths, villages and topographic parameters) on landscape changes was calculated. Cramer’s V test revealed that a) the selected distance variables did not influence low fragmented forest patches because V equal 0; b) distance to path and topographic parameters did, however, impact high fragmented forest patches (see figure 2). V* 0,45 0,4 0,35 0,3 0,25 0,2 0,15 0,1 0,05 0 Village distance to fragmented forest Path distance to fragmented forest Stream & river distance to fragmented forest Topography to fragmented forest Variables distance Cramer's V* ( V significant) Figure 2: Test of driver variables Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.) 2010, Instituto Politécnico de Bragança, Bragança, Portugal.
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Landscapes Forest and Global Change
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The contributions to this volume ha
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Table of contents Foreword Preface
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Fine-scale mapping of High Nature V
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Characterization of a Maculinea alc
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Urban tree inventory and socio-econ
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xiii Foreword Twenty years ago, Dr.
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Section 1 Scaling in landscape anal
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V. Cortes et al. 2010. Environmenta
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D. S. de Ron et al. 2010. Habitat s
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B. Bożętka 2010. Recent relations
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S.F. Chen et al. 2010. A physiotope
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V.D. de Campos & S M. Carvalho 2010
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N.S. Evseeva & Z.N. Kvasnikova 2010
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N.S. Evseeva & Z.N. Kvasnikova 2010
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S. Frank et al. 2010. A regionally
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T.-C. Lin et al. 2010. Immediate ef
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G.M. Pastur et al. 2010. Indirect e
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E.W Saragih et al. 2010. Effects of
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L.R.P. Williamson et al. 2010. Anth
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N. Zurbriggen et al. 2010. Modeling
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Section 4 Biodiversity conservation
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Akbarzadeh et al. 2010. Ecotourism
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Akbarzadeh et al. 2010. Ecotourism
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C. Carvalho-Santos 2010. Fine-scale
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I. Catalano et al. 2010. Wood macro
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R.A. Diaz-Varela et al. 2010. Exten
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R.A. Diaz-Varela et al. 2010. Asses
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P.A.E. Diogo & J. Aranha 2010. GIS
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P.A.E. Diogo & J. Aranha 2010. GIS
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V. Etemad & N. Avani 2010. Investig
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A.E. Eycott et al. 2010. The impact
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E. Fernández-Núñez et al. 2010.
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N. Fracassi & D. Somma 2010. Partic
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J. Hartter et al. 2010. Fortresses
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M.B. Horta et al. 2010. Landscape S
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Sara Marques et al. 2010. Impact of
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M.G.C. Martins & J. Aranha 2010. El
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J.-F. Mas et al. 2010. Modeling lan
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R.S. Moro & T.K. Pereira 2010. Eval
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R.S. Moro & C.H Rocha 2010. A metho
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V. Núñez et al. 2010. Landscape i
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M.C.C. Rodrigues et al. 2010. Contr
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M.C. Silva et al. 2010. Using Busin
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M. Tsibulnikova 2010. Economic esti
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M.L. Leite & J.S.V. Filho2010. Anal
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J. Superson et al. 2010. The defore
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M.L Porto et al. 2010. Naturalness
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M. Rehor & V. Ondracek 2010. Applic
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J. Russell et al. 2010. Developing
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Section 7 Management and sustainabi
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Section 9 Symposia
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Network theory to conserve and reco
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Management and conservation of Medi
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IUFRO Unit 8.01.02 Landscape Ecolog
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