Littérature scientifique sur le sujet « Forest systems, climate change, forest modelling, spatial analysis »

Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.

This work introduces a methodology for assessing near-future fire weather pattern changes based on the Canadian Fire Weather Index system components (Fire Weather Index (FWI), Initial Spread Index (ISI), Fire Severity Rating (FSR)), applied in touristic areas in Greece. Four series of daily raster-based datasets for the fire seasons (May–October), concerning a historic (2006 to 2015) and a future climatology period (2036–2045), were created for the areas under consideration, based on high-resolution climate modelling with the Representative Concentration Pathway (RCP), PCR 4.5 and RCP 8.5 scenarios. The climate model data were obtained from the European Coordinated Downscaling Experiment (EURO-CORDEX) climate database and consisted of atmospheric variables as required by the FWI system, at 12.5 km spatial resolution. The final datasets of the abovementioned variables used for the study were processed at 5 km spatial resolution for the domain of interest after applying regridding based on the nearest neighbour interpolating process. Geographic Information Systems (GIS) spatial operations, including spatial statistics and zonal analyses, were applied on the series of the derived daily raster maps in order to provide a number of output thematic layers. Moreover, historic FWI percentile values, which were estimated for Greece in the frame of a past research study of the Environmental Research Laboratory (EREL), were used as reference data for further evaluation of future fire weather changes. The straightforward methodology for the assessment of the evolution of spatial and temporal distribution of Fire weather Danger due to climate change presented herewith is an essential tool for enhancing the knowledge for the decision support process for forest fire prevention, planning and management policies in areas where the fire risk both in terms of fire hazard likelihood and expected impact is quite important due to human presence and cultural prestige, such as archaeological and tourist protected areas.

Research Highlights: Validating modelling approach which combines global framework conditions in the form of climate and policy scenarios with the use of forest decision support system to assess climate change impacts on the sustainability of forest management. Background and Objectives: Forests and forestry have been confirmed to be sensitive to climate. On the other hand, human efforts to mitigate climate change influence forests and forest management. To facilitate the evaluation of future sustainability of forest management, decision support systems are applied. Our aims are to: (1) Adopt and validate decision support tool to incorporate climate change and its mitigation impacts on forest growth, global timber demands and prices for simulating future trends of forest ecosystem services in Lithuania, (2) determine the magnitude and spatial patterns of climate change effects on Lithuanian forests and forest management in the future, supposing that current forestry practices are continued. Materials and Methods: Upgraded version of Lithuanian forestry simulator Kupolis was used to model the development of all forests in the country until 2120 under management conditions of three climate change scenarios. Selected stand-level forest and forest management characteristics were aggregated to the level of regional branches of the State Forest Enterprise and analyzed for the spatial and temporal patterns of climate change effects. Results: Increased forest growth under a warmer future climate resulted in larger tree dimensions, volumes of growing stock, naturally dying trees, harvested assortments, and also higher profits from forestry activities. Negative impacts were detected for the share of broadleaved tree species in the standing volume and the tree species diversity. Climate change effects resulted in spatially clustered patterns—increasing stand productivity, and amounts of harvested timber were concentrated in the regions with dominating coniferous species, while the same areas were exposed to negative dynamics of biodiversity-related forest attributes. Current forest characteristics explained 70% or more of the variance of climate change effects on key forest and forest management attributes. Conclusions: Using forest decision support systems, climate change scenarios and considering the balance of delivered ecosystem services is suggested as a methodological framework for validating forest management alternatives aiming for more adaptiveness in Lithuanian forestry.

Abstract This study attempts to understand the climatic resilience of two forest types of central India—that is, Tectona grandis (Teak) forest of Satpura Tiger Reserve and Shorea robusta (Sal) forest of Kanha Tiger Reserve—using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) extracted from MODIS, and climate variable data sets at highest spatial and temporal scales. Teak and Sal forests within the core area of the selected tiger reserves represent the least anthropogenic disturbances, and therefore, the observed changes in NDVI and EVI over the past 16 years could be analyzed in the context of climate change. The correlation analysis between climatic variables (minimum temperature, maximum temperature, mean temperature, and total annual rainfall) and forest response indicators (NDVI/EVI) at seasonal and annual scales revealed that Teak and Sal forests are more sensitive to change in past temperature as compared with rainfall. Also, the changes in NDVI and EVI of Sal forest are correlated more to minimum temperature, and that of Teak forest to maximum temperature. The analysis of sapling girth class of Sal and Teak further revealed that Sal as compared with Teak is more affected because of the changing climate variables of the recent past. The findings of the study will help manage forests more efficiently in the context of changing climate.

Forests store approximately as much carbon as is in the atmosphere, with potential to take in or release carbon rapidly based on growth, climate change and human disturbance. Above-ground biomass (AGB) is the largest carbon pool in most forest systems, and the quickest to change following disturbance. Quantifying AGB on a global scale and being able to reliably map how it is changing, is therefore required for tackling climate change by targeting and monitoring policies. AGB can be mapped using remote sensing and machine learning methods, but such maps have high uncertainties, and simply subtracting one from another does not give a reliable indication of changes. To improve the quantification of AGB changes it is necessary to add advanced statistical methodology to existing machine learning and remote sensing methods. This review discusses the areas in which techniques used in statistical research could positively impact AGB quantification. Nine global or continental AGB maps, and a further eight local AGB maps, were investigated in detail to understand the limitations of techniques currently used. It was found that both modelling and validation of maps lacked spatial consideration. Spatial cross validation or other sampling methods, which specifically account for the spatial nature of this data, are important to introduce into AGB map validation. Modelling techniques which capture the spatial nature should also be used. For example, spatial random effects can be included in various forms of hierarchical statistical models. These can be estimated using frequentist or Bayesian inference. Strategies including hierarchical modelling, Bayesian inference, and simulation methods can also be applied to improve uncertainty estimation. Additionally, if these uncertainties are visualised using pixelation or contour maps this could improve interpretation. Improved uncertainty, which is commonly between 30% and 40%, is in addition needed to produce accurate change maps which will benefit policy decisions, policy implementation, and our understanding of the carbon cycle.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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SUMMARYSpatial simulation may be used to model the potential effects of current biodiversity approaches on future habitat modification under differing climate change scenarios. To illustrate the approach, spatial simulation models, including landscape-level forest dynamics, were developed for a semi-natural grassland of conservation concern in a southern Italian protected area, which was exposed to woody vegetation encroachment. A forest landscape dynamics simulator (LANDIS-II) under conditions of climate change, current fire and alternative management regimes was used to develop scenario maps. Landscape pattern metrics provided data on fragmentation and habitat quality degradation, and quantified the spatial spread of different tree species within grassland habitats. The models indicated that approximately one-third of the grassland area would be impacted by loss, fragmentation and degradation in the next 150 years. Differing forest management regimes appear to influence the type of encroaching species and the density of encroaching vegetation. Habitat modifications are likely to affect species distribution and interactions, as well as local ecosystem functioning, leading to changes in estimated conservation value. A site-scale conservation strategy based on feasible integrated fire and forest management options is proposed, considering the debate on the effectiveness of protected areas for the conservation of ecosystem services in a changing climate. This needs to be tested through further modelling and scenario analysis, which would benefit from the enhancement of current modelling capabilities of LANDIS-II and from combination with remote sensing technologies, to provide early signals of environmental shifts both within and outside protected areas.

Adaptation to climate change requires prediction of its impacts, especially on ecosystems. In this work we simulated the change in bird species richness in the boreal forest of Quebec, Canada, under climate change scenarios. To do so, we first analyzed which geographical and bioclimatic variables were the strongest predictors for the spatial distribution of the current resident bird species. Based on canonical redundancy analysis and analysis of variance, we found that annual temperature range, average temperature of the cold season, seasonality of precipitation, precipitation in the wettest season, elevation, and local percentage of wet area had the strongest influence on the species’ distributions. We used these variables with Random Forests, Multivariate Adaptive Regression Splines and Maximum Entropy models to explain spatial variations in species abundance. Future species distributions were calculated by replacing present climatic variables with projections under different climate change pathways. Subsequently, maps of species richness change were produced. The results showed a northward expansion of areas of highest species richness towards the center of the province. Species are also likely to appear near James Bay and Ungava Bay, where rapid climate change is expected.

SUMMARYThe present paper provides a geographically scaled analysis of adaptation to climate change using adoption of agricultural systems observed across Africa. Usingc. 9000 farm surveys, spatial logit models were applied to explain observed agricultural system choices by climate variables after accounting for soils, geography and other household characteristics. The results reveal that strong neighbourhood effects exist and a spatial re-sampling and bootstrapping approach can remove them. The crops-only system is adopted most frequently in the lowland humid forest, lowland sub-humid, mid-elevation sub-humid Agro-Ecological Zones (AEZs) and in the highlands in the east and in southern Africa. Integrated farming is favoured in the lowland dry savannah, moist savannah and semi-arid zones in West Africa and eastern coastal zones. A livestock-only system is favoured most in the mid/high-elevation moist savannahs located in southern Africa. Under a hot and dry Canadian Climate Centre (CCC) scenario, the crops-only system should move out from the currently favoured regions of humid zones in the lowlands towards the mid-/high elevations. It declines by more than 5% in the lowland savannahs. Integrated farming should increase across all the AEZs by as much as 5%, but less so in the deserts or in the humid forest zones in the mid-/high elevations. A livestock-only system should increase by 2–5% in the lowland semi-arid, dry savannah and moist savannah zones in the lowlands. Adaptation measures should be carefully scaled, up or down, considering geographic and ecological differentials as well as household characteristics, as proposed in the present study.

<p><strong>Abstract.</strong> Trentino is an Italian alpine region (about 6200&amp;thinsp;km²) with a forest coverage exceeding 60% of its whole surface. In the past, forest landscape has changed dramatically, especially in periods of forest over-exploitation.</p><p>Previous studies in some Trentino sub-regions (Val di Fassa, Paneveggio) have identified these changes and the current trend of forest growth at the expenses of open areas, such as pastures and grasslands, due to the abandonment of rural areas. This phenomenon leads to the rapid Alpine landscape change and profoundly affects the ecological features of mountain ecosystems. To be able to monitor and to take future actions about this trend it is fundamental to know in detail the historical situation of the progressive changes on the land use that occurred over Trentino.</p><p>The work aims to comprehensively reconstruct the forest cover of whole Trentino at high resolution (5&amp;thinsp;m&amp;thinsp;&amp;times;&amp;thinsp;5&amp;thinsp;m pixels) using a series of maps spanning a long period, consisting in historical maps, aerial images, remote sensed information and historical archives. The datasets were archived, processed and analyzed using the Free and Open Source Software (FOSS) GIS GRASS and QGIS. Historical maps include Atlas Tyrolensis (dated 1770), Theresianischer Kataster (dated 1859) and Italian Kingdom Forest Map (IKFM) of 1936. The aerial imagery dataset includes aerial images taken in 1954, which have been orthorectified during this research, and orthophotos available for years 1973, 1994, 2000, 2006, 2010 and 2016. Remote sensed information includes Landsat and recent Lidar data, while historical archives consist mostly in Forest Management Plans available since around 1950.</p><p>The versatility of the wide variety of modules supplied from the FOSS GRASS and QGIS enabled to perform a diverse set of analysis and pre-processing (e.g.:orthorectification) on a heterogeneous dataset of input images. We will focus on the different strategies and methodologies implemented in the FOSS GIS used to process the various types of geographic data, challenges for the future of the research and the fundamental role of the FOSS systems in this process.</p><p>Quantifying forest change in the time-span of our dataset can be used to perform further analysis on ecosystem services, such as protection from soil erosion, and on modification of biome diversity and to create future change scenarios.</p>

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

The future dynamics of forest species and ecosystems depends on the effects of climate change and their resilience and adaptive potential are highly related to forest management strategies. The main expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequency, intensity and duration of extreme events. The purpose of this doctoral thesis is to provide information to support forest management strategies potentially useful to mitigate the effects of climate change to Italian forests. Among all the forest tree species occurring across the Italian peninsula, 19 were considered as the most important for their economic, ecological and aesthetic value. The ecological niche of species was firstly described on the bases of climate requirements and compared with existing scientific literature and expert knowledge in Italy. Then the described niches were projected into the future by means of a species distribution modelling approach to derive insight of the forecasted impact of climate change on Italian forests and to derive implication for future forest management strategies. To model the climatic requirements, interpolated climate data of average annual temperatures and precipitation (1km) were used and 6 different Global Circulation Models (GCMs) were employed to describe future climate condition and in addition to a local Regional Climate Model (RCM). Future climate data were referred to unique emission scenario (the intermediate RCP 4.5) for 2050s. Results showed a substantial shift in knowledge with only 46% of the observations falling within the potential joint temperature and precipitation limits as defined by expert knowledge. Moreover, the similarity between current observed and potential limits differ from species to species with broad leaves, in general, more frequently distributed within their potential climatic limits than conifers. Paying attention to future climate conditions the analysis showed strong differences between the different climate models; the RCM demonstrated to be a more variable scenario than GCMs. The Apennines strip will probably be affected by strong and important changes as well as the sub-alpine zone. However, no sensible variations in the extension of the forest area have been predicted. The analyses also indicated that forest suitability is going to remain almost unchanged in mountain areas, while in valleys or flood and plains areas is likely to decrease. Moreover, the model establishes a possible strong negative impact of climate change at the level of pure woods compared to mixed woods, characterized by greater species richness and therefore a higher level of biodiversity. Finally, pure softwood stands (e.g. Pinus, Abies) may be more affected by the impacts of global warming than hardwoods (e.g. Fagus, Quercus). According to the provided results and scenarios, specific silvicultural practises should be applied to increase the species richness and favouring hardwoods currently growing as dominates species under conifers canopy. Increased thinning frequency and intensity and a reduced rotation period may contribute to increasing the natural regeneration, gene flow and (eventually) support species migration.

The conservation of Earth's forest ecosystems is one of the great environmental challenges facing humanity in the 21st century. All of Earth's ecosystems now face the spectre of the accelerated greenhouse effect and rates of change in climatic regimes that have hitherto been unknown. In addition, multiple use forestry – where forests are managed to provide for both a supply of wood and the conservation of biodiversity – can change the floristic composition and vegetation structure of forests with significant implications for wildlife habitat. Wildlife, fire and future climate: a forest ecosystem analysis explores these themes through a landscape-wide study of refugia and future climate in the tall, wet forests of the Central Highlands of Victoria. It represents a model case study for the kind of integrated investigation needed throughout the world in order to deal with the potential response of terrestrial ecological systems to global change. The analyses presented in this book represent one of the few ecosystem studies ever undertaken that has attempted such a complex synthesis of fire, wildlife, vegetation, and climate. Wildlife, fire and future climate: a forest ecosystem analysis is written by an experienced team of leading world experts in fire ecology, modelling, terrain and climate analysis, vegetation and wildlife habitat. Their collaboration on this book represents a unique and exemplary, multi-disciplinary venture.

AbstractThis chapter examines from systems and livelihood perspectives, with Nemoral and Boreal forest zones of the Global North and Sweden as examples, how forestry may meet current and future sustainability challenges both as a traditional resource base and with respect to other ecosystem services. Previous and current forest policy/governance is briefly described against the background that Swedish forestry is based both on huge holdings by few industrial owners as well as on a multitude of small individual, often family owned, forest estates. Successful delivery against environmental objectives will require careful balancing of interests and the active participation of local forest owners. Cumulative effects of old and new societal demands on forestry and their impact on local livelihoods poses in this respect a systemic risk as economic and social sustainability often gets limited consideration. There is a need for a more synoptic and systemic analysis of how forestry is affected by multiple, partly contradictory, demands from an increasing array of stakeholders, in order to enable a move towards a biobased economy. Stakeholder-based group modelling is a potentially powerful analytic and conflict reducing approach that could help improve forestry’s contribution to the acute need to handle the climate change and current sustainability challenges.