Integrated modelling approaches to analysis of climate change impacts on forests and forest management

Aaheim, Asbjørn; Chaturvedi, Rajiv Kumar; Sagadevan, Anitha

doi:10.1007/s11027-010-9254-x

Cited by 31 publications

(12 citation statements)

References 50 publications

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“…On the other hand, forests can help mitigating climate change, e.g. acting as carbon sinks (Birdsey and Pan 2015), supporting biofuels or preventing soil erosion and desertification (Aaheim et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Implications of future bioclimatic shifts on Portuguese forests

et al. 2016

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As climate is an important driver of vegetation distribution, climate change represents an important challenge to forestry. We (1) identify prevailing bioclimatic conditions for 49 relevant forest species in Portugal and (2) assess future shifts under climate change scenarios. We compute two bioclimatic indices (aridity and thermicity) and a new composite index, at *1 km spatial resolution, and overlap with the species' current ranges. Locations are based on a digital inventory, while climate parameters for both recent-past and future climates (2041-2060), under RCP4.5 and RCP8.5, are provided by a multi-model ensemble of climate simulations. Results for future scenarios highlight an overall warming and drying trend. Supramediterranean and mesomediterranean climates will be significantly reduced, while thermomediterranean climates will dramatically increase, from their almost absence in current conditions to an area coverage of *54 % in 2041-2060 for RCP8.5. There is also a clear shift from hyper-humid and humid to sub-humid and from the latter to semi-arid climates (area coverage of *13 % in 2041-2060 for RCP8.5). Lower thermomediterranean subhumid to semi-arid zones will cover the southern half of Portugal. These projections identify the most vulnerable (e.g. Betula pubescens, Quercus pyrenaica and Castanea sativa) and the most adapted (e.g. Quercus suber, Q. rotundifolia, Ceratonia siliqua, Pinus pinea, Quercus coccifera) species in future climates. Current bioclimatic zones associated with Eucalyptus globulus and Pinus pinaster, economically relevant species, will be moderately reduced and relocated. Possible adaptation measures are discussed to improve forest resilience to climate change, while maintaining its economic and environmental benefits.

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Section: Introductionmentioning

confidence: 99%

Implications of future bioclimatic shifts on Portuguese forests

et al. 2016

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“…The Gitelsen Green Index (GI) 13 and corresponding Red Index (RI) have been evaluated for the estimation of total canopy chlorophyll concentration. The two indices are expressed by the following relationship:…”

Section: Spectral Vegetation Indexmentioning

confidence: 99%

“…Spatially explicit LAI data sets are required to parameterize vegetation-atmosphere interactions in climate and land surface models, and temporally consistent LAI time series are crucial for monitoring the seasonal and inter-annual variations in the amount of forest vegetation. Remote sensing of forest biophysical variables such as LAI is further complicated by the contribution of understory vegetation, litter, soil, bark as well as plant and relief shadow, all of which influence the radiometric signal 13 . These stress indices also have their shortcomings and can not groundlessly be adopted for any satellite sensor.…”

Section: Leaf Area Index (Lai)mentioning

confidence: 99%

Remote sensing of climate changes effects on forest biophysical variables

2014

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Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Forest vegetation phenology constitutes an efficient bio-indicator of climate and anthropogenic changes impacts and a key parameter for understanding and modeling vegetation-climate interactions. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of forest biophysical parameters like as Normalized Difference Vegetation Index (NDVIs) and Leaf Aria Index (LAI), which requires time-series data with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). This paper will quantify this impact over a forest ecosystem Cernica-Branesti placed in the North-Eastern part of Bucharest town, Romania, with NDVI and LAI parameters extracted from MODIS Terra and NOAA AVHRR satellite images in synergy with meteorological data over 2000-2013 periods. For investigated test area, considerable NDVI and LAI decline have been observed during heat wave and drought events of 2003, 2007 and 2012 years. Under water stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics.

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“…Even when a linear relationship between NDVI/LAI is often assumed, the relationship is not always linear since the vegetation indices approach a saturation level asymptotically for LAI ranging from 2 to 6, depending on the type of vegetation cover, and environmental conditions [11]. However, by assuming a non-linear relationship, the LAI estimates from NDVI are then highly dependent upon certain factors such as canopy geometry, leaf and soil optical properties, sun position and cloud coverage.…”

Section: Leaf Area Index (Lai)mentioning

confidence: 99%

Vegetation cover quality assessment through MODIS time series satellite data in an urban region

et al. 2013

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To preserve urban vegetation land cover quality and mitigate its degradation is an important task for urban planning and environmental management of Bucharest metropolitan area in Romania. Since vegetation land cover dynamics directly affect the urban landscape characteristics and air quality, remote sensing represents an effective tool for vegetation land cover quality assessment at regional scale. In particular, the use of satellite-based vegetation indices, like the NDVI (Normalized Difference Vegetation Index), can provide important information when evaluating Urban Vegetation Cover Quality (UVCQ) patterns in urban areas, which represents one of the most sensitive landscape components to urban environmental degradation. This paper proposes an approach for the regional-scale assessment of UVCQ by means of an NDVI-based (functional) indicator using freely available time series MODIS Terra/Aqua (Moderate Resolution Imaging Spectroradiometer) satellite data. As a case study, Bucharest metropolitan area landscape experiencing climate and anthropogenic changes, increasing human pressure and high vulnerability to degradation was chosen. As UVCQ indicator, the NDVI-based vegetation cover classification was produced by means of unsupervised multivariate statistical techniques and compared with spatio-temporal changes during 2002-2012 period, statistical indicators, and field data related to land cover management observed in the study area. Results demonstrate that the obtained remotely sensed vegetation land cover characterization can be effectively considered as a proxy of the UVCQ status of the examined area. Due to the large availability over time and low cost of satellite images, the proposed approach can be applied to wider urban/periurban regions, to monitor vegetation quality and indirectly control vegetation land degradation.

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Integrated modelling approaches to analysis of climate change impacts on forests and forest management

Cited by 31 publications

References 50 publications

Implications of future bioclimatic shifts on Portuguese forests

Implications of future bioclimatic shifts on Portuguese forests

Remote sensing of climate changes effects on forest biophysical variables

Vegetation cover quality assessment through MODIS time series satellite data in an urban region

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