Assessment of land cover changes and spatial drivers behind loss of permanent meadows in the lowlands of Italian Alps

Monteiro, A.; Fava, Francesco; Hiltbrunner, Erika; Marianna, G. Della; Bocchi, Stefano

doi:10.1016/j.landurbplan.2010.12.015

Cited by 70 publications

(41 citation statements)

References 44 publications

(62 reference statements)

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“…Dominant land cover changes in Eastern and East-Central Europe in the 1985-2000 period were farmland abandonment, afforestation, forest overutilisation and in some areas cropland expansion (Bičík et al 2001;Palang 2006;Kuemmerle et al 2007Kuemmerle et al , 2008Łowicki 2008;Lakes et al 2009;15 Main-Knorn et al 2009;Feranec et al 2010;Hatna and Bakker 2011;Baumann et al 2011), while in Western Europe urbanization, afforestation and in many areas farmland abandonment (Hietel et al 2005;Falcucci et al 2007;Feranec et al 2007Feranec et al , 2010Schneeberger et al 2007a,b;Hersperger et al 2009;Gimmi et al 2011;Monteiro et al 2011). We argue, that any unusual processes (e.g.…”

Section: Historical Legacy and Local Adaptation As Explanations For Umentioning

confidence: 79%

Drivers of grassland loss in Hungary during the post-socialist transformation (1987–1999)

et al. 2012

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The increase in the speed of land-cover change experienced worldwide is becoming a growing concern. Major socio-economic transitions, such as the breakdown of socialism in Europe, may lead to particularly high rates of landscape transformations. In this paper we examined the loss of semi-natural grasslands in Hungary between 1987 and 1999. We studied the relationship between 9 5 potential driving forces and the fate of grasslands using logistic GLMs.Grassland loss was found to be very high (1.31 % per year), which is far higher than either before or after this period. The most influential predictors of grassland loss were environmental and landscape characteristics (soil type, area of remnant grassland patches), and the socio-economic context (distance to paved road, and nearest settlement, human population density). Several 10 processes and relationships can only be understood from a historical perspective (e.g. large extent of afforestation, strong decrease of soil water table). Grassland loss during the study period emerged as a consequence of survival strategies of individual farmers seeking adaptation to the changing environmental and socio-economic conditions, and not urbanization and agricultural intensification which are the main underlying drivers for the ongoing landscape transformations in 15 most parts of the developed world.Though globalization increasingly influences local land use decisions , reconstructing and modelling recent landscape changes cannot be done without a proper understanding of local history and culture. Our analysis shows the importance of large-area yet high resolution landscape change research, which may reveal unexpected patterns of land cover change, undetected at coarser scales. 20Keywords: East-Central Europe, land-cover change, logistic GLMs, proximate and underlying driving forces LANDSCAPE ECOLOGY, 28: (5) pp. 789-803. (2013) 3

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Section: Historical Legacy and Local Adaptation As Explanations For Umentioning

confidence: 79%

Drivers of grassland loss in Hungary during the post-socialist transformation (1987–1999)

et al. 2012

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“…The resultant number of points with no change (i.e., coded as 0) was much larger than the number of points with change (i.e., coded as 1). Therefore, to obtain an equal number of samples of locations with change and no change, we ran another random sampling procedure on points coded as 0 (Cheng & Masser, 2003;Monteiro, Fava, Hiltbrunner, Della Marianna, & Bocchi, 2011). Consequently, the numbers of sample points used in the later logistical regression were 1106, 650, 968, and 1244 for the periods of 1972e1984, 1984e 1992, 1992e2000, and 2000e2010, respectively.…”

Section: Data Samplingmentioning

confidence: 99%

Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors?

2013

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a b s t r a c tUrban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972e1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.

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“…Regarding geo-physical variables, altitude and slope were chosen as they are the most common predictors of changes in land cover (Monteiro et al 2011;Texeira et al 2014;Geri et al 2009). They were calculated via ArcGis9.3, and based on digital elevation data from the m is very homogenous, as forests make up almost all of the land cover, rendering a more detailed categorization of higher altitudes unnecessary.…”

Section: Regression Analysismentioning

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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Assessment of land cover changes and spatial drivers behind loss of permanent meadows in the lowlands of Italian Alps

Cited by 70 publications

References 44 publications

Drivers of grassland loss in Hungary during the post-socialist transformation (1987–1999)

Drivers of grassland loss in Hungary during the post-socialist transformation (1987–1999)

Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors?

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

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