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Abstract

Key message The outcome of the present study leads to the application of a spatially explicit rule-based expert system (RBES) algorithm aimed at automatically classifying forest areas according to the European Forest Types (EFT) system of nomenclature at pan-European scale level. With the RBES, the EFT system of nomenclature can be now easily implemented for objective, replicable, and automatic classification of field plots for forest inventories or spatial units (pixels or polygons) for thematic mapping. ContextForest Types classification systems are aimed at stratifying forest habitats. Since 2006, a common scheme for classifying European forests into 14 categories and 78 types (European Forest Types, EFT) exists. Aims This work presents an innovative method and automated classification system that, in an objective and replicable way, can accurately classify a given forest habitat according to the EFT system of nomenclature. MethodsA rule-based expert system (RBES) was adopted as a transparent approach after comparison with the well-known Random Forest (RF) classification system. The experiment was carried out based on the information acquired in the field in 2010 ICP level I plots in 17 European countries. The accuracy of the automated classification is evaluated by comparison with an independent classification of the ICP plots into EFT carried out during the BioSoil project field survey. Finally, the RBES automated classifier was tested also for a pixel-based classification of a pan-European distribution map of beech-dominated forests. ResultsThe RBES successfully classified 94% of the plots, against a 92% obtained with RF. When applied to the mapped domain, the accuracy obtained with the RBES for the beech forest map classification was equal to 95%. Conclusion The RBES algorithm successfully automatically classified field plots and map pixels on the basis of the EFT system of nomenclature. The EFT system of nomenclature can be now easily and objectively implemented in operative transnational European forest monitoring programs.

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... Another expert system on the European scale was developed by Giannetti et al. (2018) for the classification of European Forest ...
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Aim The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location Europe. Methods We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified dataset. Finally, we mapped the plot locations for each habitat. Results Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment.
... Alternatively, they can be defined quantitatively and the resulting forest classes be differentiated using formal assignment rules (e.g. classification trees) on the basis of chosen thresholds for relevant variables (e.g., Bebi et al. 2001;Bruciamacchie 2001;Giannetti et al. 2018). Quantitative assignment rules can be defined by expert knowledge, after preliminary inspection of the main axes of variation of potential variables (e.g., Aunós et al. 2007), but a more data-driven approach involves two steps: (1) to define homogeneous groups of forest plots using unsupervised classification methods (i.e., hierarchical or non-hierarchical clustering); ...
Article
Key message We present a novel approach to define pure- and mixed-forest typologies from the comparison of pairs of forest plots in terms of species identity, diameter, and height of their trees. Context Forest typologies are useful for many purposes, including forest mapping, assessing habitat quality, studying forest dynamics, or defining sustainable management strategies. Quantitative typologies meant for forestry applications normally focus on horizontal and vertical structure of forest plots as main classification criteria, with species composition often playing a secondary role. The selection of relevant variables is often idiosyncratic and influenced by a priori expectations of the forest types to be distinguished. Aims We present a general framework to define forest typologies where the dissimilarity between forest stands is assessed using coefficients that integrate the information of species composition with the univariate distribution of tree diameters or heights or the bivariate distribution of tree diameters and heights. Methods We illustrate our proposal with the classification of forest inventory plots in Catalonia (NE Spain), comparing the results obtained using the bivariate distribution of diameters and heights to those obtained using either tree heights or tree diameters only. Results The number of subtypes obtained using the tree diameter distribution for the calculation of dissimilarity was often the same as those obtained from the tree height distribution or to those using the bivariate distribution. However, classifications obtained using the three approaches were often different in terms of forest plot membership. Conclusion The proposed classification framework is particularly suited to define forest typologies from forest inventory data and allows taking advantage of the bivariate distribution of diameters and heights if both variables are measured. It can provide support to the development of typologies in situations where fine-scale variability of topographic, climatic, and legacy management factors leads to fine-scale variation in forest structure and composition, including uneven-aged and mixed stands.
... Inside the three areas, elevation ranges from 0 m above sea level (a.s.l.) to 1000 m a.s.l. Six out of the 14 European Forest Types are represented [70]. Within the regions composing the reference disturbance dataset, trees are cut with a rotation period of approximately 18-20 years. ...
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Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems.
... It could thus be beneficial for international efforts for harmonizing national forest inventories, initialized by the COST Action E43 (COST, 2006;McRoberts et al., 2008McRoberts et al., , 2012). At pan-European level it could, for instance, contribute to further developments in the ICP Forests, which is International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (JRC, 2011;Giannetti et al., 2018). More globally, it could assist the development of essential biodiversity variables from ALS (Pereira et al., 2013;Proença et al., 2017), and contribute to the use of remote sensing to inform policy-makers on progress towards sustainable development goals and biodiversity targets (O'Connor et al., 2015;Vihervaara et al., 2017). ...
Article
Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data-quadratic mean diameter QMD (), Gini coefficient GC (), basal area larger than mean BALM () and density of stems N ()-. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, GC and BALM were the most important variables in the identification of FSTs. Lower, medium and high values of GC and BALM characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using QMD and N. Then we used similar structural predictors derived from ALS-maximum height (Max), L-coefficient of variation (Lcv), L-skewness (Lskew), and percentage of penetration (cover),-and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions.
... En effet, les tableaux 95 de relevés 'princeps' ayant servi à la description des syntaxons sont souvent difficiles d'accès car généralement publiés depuis plusieurs décennies dans des revues éditées uniquement au format papier. L'utilisation de ces relevés implique un important travail de recherche bibliographique et de numérisation des données qui constituent des tâches très chronophages.Par conséquent, nous avons procédé à une expertise basée sur nos connaissances de terrain, se rapprochant des méthodes mises en oeuvre pour l'établissement d'autres SE en Europe, tels que le SE des forêts européennes développé parGiannetti et al. (2018) ou plus récemment le SE des habitats EUNIS proposé par Chytrý et al. (2020). Idéalement, les systèmes experts devraient être établis directement lors de la description phytosociologique des syntaxons, que ce soit directement par leurs auteurs ou bien à l'issue de synthèse comme celle proposée actuellement en France dans le cadre du PVF2 (par exemple, Delbosc et al. 2021b). ...
Thesis
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L’inventaire et la cartographie des habitats sont des processus essentiels pour la mise en œuvre des politiques de conservation de la nature. Les méthodes actuelles, basées sur des prospections de terrain, sont difficilement applicables sur de vastes territoires et jugées inadaptées à un suivi régulier et harmonisé des habitats. L’objectif de cette thèse est d’explorer des approches innovantes afin de faciliter l’inventaire et la cartographie des habitats sur de grands sites naturels, en prenant comme cas d’étude le site Natura 2000 ‘Estuaire de la Loire’. Un système expert a été développé pour l’identification de relevés phytosociologiques afin d’établir la typologie des habitats du site. Cette démarche a permis de rattacher de manière formelle 1843 relevés de végétation à 89 habitats EUNIS et 17 habitats d’intérêt communautaire. Des images satellites Sentinel-2 et des données aéroportées hyperspectrales et LiDAR ont été exploitées pour spatialiser les habitats du site par télédétection. Ces différentes données, aux caractéristiques complémentaires (résolutions spatiales, résolutions spectrales, répétitivité, 3D), ont permis de cartographier avec une très grande précision la majorité des habitats des 24 000 ha de l’estuaire de la Loire. L’application de ces nouvelles approches démontre l’intérêt d’associer les systèmes experts et la télédétection pour typifier et cartographier des habitats de façon rentable et reproductible favorisant une gestion concertée du site Natura 2000.
... Three other plots are installed in deciduous broadleaved forest: Fagus sylvatica L. in Wijnendale (WIJ) and Hoeilaart (HOE) and a mixture of Quercus robur L. and F. sylvatica L. in Gontrode (GON). According to the World Reference Base for Soil Resources [22][23][24], the soil of WIJ is classified as an Umbrisol, GON as a Planosol and HOE as a Retisol, formerly classified as Albeluvisol. The associated qualifiers are listed in Table A1, as well as the parent material of the soils. ...
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Monitoring volumetric soil water content (θv) is the key for assessing water availability and nutrient fluxes. This study evaluated the empirical accuracy of θv measurements using standard and in situ calibrated frequency domain reflectometers (FDR) with gravimetric water content and bulk density measurements of 1512 samples gathered from 15 profiles across 5 ICP Forests level II intensive monitoring plots. The predicted θv, calibrated with standard functions, predominantly underestimated the real water content. The measurement error exceeded the threshold of 0.03 m3 m−3 in 93% of all soil layers. Layer specific calibration removed bias and reduced the overall prediction error with a factor up to 2.8. A simple linear regression often provided the best calibration model; temperature correction was helpful in specific cases. To adequately remove bias in our study plots, a calibration dataset of up to 24 monthly observations was required for topsoils (whereas 12 observations sufficed for subsoils). Based on estimated precision errors, 3 sensors per soil layer proved to be sufficient, while up to 16 sensors are needed to meet the required accuracy in organic topsoils. Validating FDR sensor outputs using in situ gravimetric measurements is essential for quality control and assurance of long term θv monitoring and for improving site specific instrumentalization.
... Besides, there are some limitations of forest classifications based on syntaxonomy (EEA 2006), mainly because it does not reflect the characteristics of forest ecosystems that are primary determinants of the indicators used for sustainable management of European forest types (EFTs). In this view, a functional classification could link phytosociological classes of woody vegetation with EFTs, providing a step toward effective indicators of sustainable forest management (Barbati et al. 2014, Giannetti et al. 2018) also for Natura 2000 habitat types (Bonari et al. 2021). ...
Article
Vegetation is often classified through phytosociology, which defines floristically and ecologically coherent units identified by diagnostic species. Since species- and community-environment relations are regulated by plant functional traits, it is likely that phytosociology has a strong functional underpinning, although the past and current phytosociology does not explicitly tackle this issue. Here we provide an analysis of functional traits of 221 woody species from Northern Italy, diagnostic of 21 European woody vegetation classes (including alien dominated ones). We assessed whether the functional space occupied by selected species corresponds to the physiognomy and ecology of the vegetation they represent, and whether this could help to evaluate major threats, such as invasion by alien species. For each species we collected from Authors' datasets leaf trait data (leaf area, specific leaf area, leaf dry matter content, carbon to nitrogen ratio, leaf nitrogen content) and whole plant (plant height, seed mass) traits, and calculated Competitor, Stress-tolerant, Ruderal (CSR) scores. We identified the multidimensional functional trait space of diagnostic species and clustered classes according to their scores in the functional space, to check whether their physiognomy was coherently represented and mirrored in the CSR space. Lastly, we tested for differences between native and neophyte species and their overlap with classes. Diagnostic species mirrored the global spectrum of plant form and function, and classes showed a functional pattern coherent with their physiognomy and ecology. Evergreen dominated classes showed a similar convergence toward conservative characteristics and the stress-tolerant strategy, as opposed to deciduous forest classes that showed a tendency toward the competitive strategy. None of the classes showed a marked ruderal strategy, thus abiotic stress and biotic competition are the main ecological drivers affecting woody vegetation. Neophyte woody species exhibited relatively more competitive strategies compared to natives, and their invasion could be facilitated in resource-limited or mildly disturbed environments, should climate warming or increased nutrient availability occur. We demonstrated that plant traits and CSR strategies of woody diagnostic species reliably indicate the structure and functions of the phytosociological classes they represent, opening the way to the development of a "functional phytosociology".
... The species composition of a tree complex, appropriate for a particular type of forest habitat, is related with the ecological selection and shaping of the tree complex. This selection is justified due to the potential economic value of the wood material with a specific volume increment and the shaped quality of the tree complex in a particular habitat [12,13]. ...
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This article starts a series of articles on dependences between the conditions of the growth of trees in forests and the technical aspects and directions of using the raw material obtained from these trees. This is a key feature for wood purchasers because it determines the efficiency of production and directly affects the final financial result of their activity. Wood represents an environmentally sustainable and renewable material, which is a widely available raw material on the market and must meet specific quality and strength requirements. These parameters indicate the utility values of wood and the possibilities of its use. One of the factors influencing the properties of wood is the type of the forest habitat it comes from. In order to determine this influence, tests were carried out to show how tree growth conditions affected changes in the density and strength of raw wood. The assumption (hypothesis) about the correlation between the static bending strength of Scots pine (Pinus sylvestris L.) wood and the forest habitat was verified on four forest types, i.e., fresh coniferous forest (FCF), fresh mixed coniferous forest (FMCF), fresh mixed forest (FMF) and fresh forest (FF). The properties depend largely on the wood structure, its origin on the cross section and the length of the stems. The raw material selected for the study came from Scots pine trees growing in forests in central Poland. The study confirmed the influence of the habitat on changes in the density and strength of pinewood. There was a correlation between the habitat FMCF and the quality parameters of the raw material, which reflected the wood structure r = 0.775; p < 0.05.
... Six out of the 14 European Forest Types [44,45] are represented in the study area. ...
Article
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Large-scale forest monitoring benefits greatly from change detection analysis based on remote sensing data because it enables characterizing forest dynamics of disturbance and recovery by detecting both gradual and abrupt changes on Earth’s surface. In this study, two of the main disturbances occurring in Mediterranean forests, harvesting operations and forest fires, were analyzed through the analysis of Landsat Times Series images in a case study in Central Italy (Tuscany region). Disturbances were characterized based on their distinct temporal behaviors before and after the event: a period of 20 years (1999–2018) was used to extract and analyze at pixel level spectral trajectories for each disturbance and produce descriptive temporal trends of the phenomena. Recovery metrics were used to characterize both short- (5 years) and long-term aspects of recovery for harvested and burned areas. Spectral, recovery, and trend analysis metrics were then used with the Random Forest classifier to differentiate between the two disturbance classes and to investigate their potential as predictors. Among spectral bands, the Landsat SWIR 1 band proved the best to detect areas interested by harvesting, while forest fires were better detected by the SWIR 2 band; among spectral indices, the NBR scored as the best for both classes. On average, harvested areas recovered faster in both short- and long-term aspects and showed less variability in the magnitude of the disturbance event and recovery rate over time. This tendency is confirmed by the results of the classifier, which obtained an overall accuracy of 98.6%, and identified the mean of the post-disturbance values of the trend as the best predictor to differentiate between disturbances.
... The study stand, almost flat (slope <2%) and with 0.1 Ha of extension, is dominated by oak (Quercus robur), although some individuals of other deciduous species such as chestnut tree (Castanea sativa), maple (Acer pseudoplatanus) or deciduous Prunus sp. are also present. The stand is representative of the Atlantic deciduous forests spread over west Europe including, according to Giannetti et al. (2018), the European Forest Types categories 4 (acidophilus oak and oak-birch forests), 5 (mesophilic deciduous forests) and 8 (thermophiles deciduous forests). The agricultural use of the site ceased 50 years ago and then the forest started to develop, being nowadays the canopy closure of the stand approximately 80%. ...
Article
Litterfall constitutes one of the main vectors for mercury (Hg) transfer to forested ecosystems, so we studied the deposition of Hg through senescent vegetation (oak leaves, twigs and miscellaneous) in a deciduous forest plot of Southwest Europe dominated by Quercus robur in 2015 and 2016. Total Hg concentrations increased in the following order: bole wood (1.4 μg kg⁻¹) < bark (8.3 μg kg⁻¹) < twigs (12.2 μg kg⁻¹) < miscellaneous (36.0 μg kg⁻¹) < oak leaves (39.3 μg kg⁻¹) < mineral soil (42.4 μg kg⁻¹) < Oi horizons (48.7 μg kg⁻¹) < Oe + Oa horizons (71.6 μg kg⁻¹). Mercury accumulation rates in oak leaves during the growing season were 0.15–0.18 μg kg⁻¹ day⁻¹. Mercury deposition fluxes were 26 and 21 μg m⁻² yr⁻¹ for 2015 and 2016, respectively, with oak leaves being the fraction that contributed the most. Mercury determination in litterfall sorted biomass fractions lead to a more accurate estimation of the total annual Hg deposition fluxes through litterfall. Higher Hg content was obtained for organic horizons (average of 60.2 μg kg⁻¹) than for mineral soil (mean of 42.4 μg kg⁻¹), but the soil Hg pool was higher in the latter. The results confirmed the necessity of taking into account the Hg pool in the deeper mineral soil layers as they accumulate substantial quantities of Hg associated to organic C and Al compounds, preventing its mobilization to other compartments of the terrestrial ecosystems.
... The area is located at an altitude ranging between 500 and 900 m a.s.l. (Barbati et al., 2014;Giannetti et al., 2017) are represented. ...
Article
In the EU 2020 biodiversity strategy, maintaining and enhancing forest biodiversity is essential. Forest managers and technicians should include biodiversity monitoring as support for sustainible forest management and conservation issues, through the adoption of forest biodiversity indices. The present study investigates the potential of a new type of Structure from Motion (SfM) photogrammetry derived variables for modelling forest structure indicies, which do not require the availability of a digital terrain model (DTM) such as those obtainable from Airborne Laser Scanning (ALS) surveys. The DTM-independent variables were calculated using raw 3D UAV photogrammetric data for modeling eight forest structure indices which are commonly used for forest biodiversity monitoring, namely: basal area (G); quadratic mean diameter (DBH mean); the standard deviation of Diameter at Breast Height (DBH σ); DBH Gini coefficient (Gini); the standard deviation of tree heights (H σ); dominant tree height (H dom); Lorey's height (H l); and growing stock volume (V). The study included two mixed temperate forests areas with a different type of management, with one area, left unmanaged for the past 50 years while the other being actively managed. A total of 30 field sample plots were measured in the unmanaged forest, and 50 field plots were measured in the actively managed forest. The accuracy of UAV DTM-independent predictions was compared with a benchmark approach based on traditional explanatory variables calculated from ALS data. Finally, DTM-independent variables were used to produce wall-to-wall maps of the forest structure indices in the two test areas and to estimate the mean value and its uncertainty according to a model-assisted regression estimators. DTM-independent variables led to similar predictive accuracy in terms of root mean square error compared to ALS in both study areas for the eight structure indices (DTM-independent average RMSE % = 20.5 and ALS average RMSE % = 19.8). Moreover, we found that the model-assisted estimation, with both DTM-independet and ALS, obtained lower standar errors (SE) compared to the one obtained by model-based estimation using only field plots. Relative efficiency coefficient (RE) revealed that ALS-based estimates were, on average, more efficient (average RE ALS = 3.7) than DTM-independent, (average RE DTM-independent = 3.3). However, the RE for the DTM-independent models was consistently larger than the one from the ALS models for the DBH-related variables (i.e. G, DBH mean , and DBH σ) and for V. This highlights the potential of DTM-independent variables, which not only can be used virtually on any forests (i.e., no need of a DTM), but also can produce as precise estimates as those from ALS data for key forest structural variables and substantially improve the efficiency of forest inventories.
... Study area 3 is in the northern pre-Appennine area; the elevation ranges between 35 and 930 m a.s.l., and it is dominated by Quercus pubescens, Quercus cerris, and Castanea sativa. Six out of the 14 European Forest Types (Giannetti et al. 2018) are represented in the study area. More information about the orography and growing stock volume amount of Tuscany forests can be found in Chirici et al. (2020a). ...
Article
Key message This work analyses the rate of recovery of the spectral signal from clearcut areas of coppice Mediterranean forests using Landsat Time Series (LTS). The analysis revealed a more rapid rate of spectral signal recovery than what was found in previous investigations in boreal and temperate forests. ContextThe rate of post-disturbance vegetation recovery is an important component of forest dynamics.AimsIn this study, we analyze the recovery of the spectral signal from forest clearcut areas in Mediterranean conditions when the coppice system of forest management is applied.Methods We used LTS surface reflectance data (1999–2015).We generated an annual reference database of clearcuts using visual interpretation and local forest inventory data, and then derived the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) spectral trajectories for these clearcuts. From these spectral trajectories, we calculated the Years to Recovery or Y2R, the number of years it takes for a pixel to return to within a specified threshold (i.e., 70%, 80%, 90%, 100%) of its pre-disturbance value. Spectral recovery rates were then corroborated using measures of canopy height derived from airborne laser scanning (ALS) data.ResultsThe coppice system is associated with rapid recovery rates when compared to rates of recovery from seeds or seedlings in temperate and boreal forest conditions. We found that the Y2R derived from the spectral trajectories of post-clearcut NBR and NDVI provided similar characterizations of rapid recovery for the coppice system of forest management applied in our study area. The ALS measures of canopy height indicated that the Y2R metric accurately captured the rapid regeneration of coppice systems.Conclusion The rapid rate of spectral recovery associated with the coppice system is 2–4 years, which contrasts with values reported in boreal and temperate forest environments, where spectral recovery was attained in approximately 10 years. NBR is an effective index for assessing rapid recovery in this forest system.
... Moreover, some authors have reported that areas characterized by very complex topographic features (i.e. from flat terrain to mountains up to 2000 m a.s.l.) affect the spectral signature and the data saturation values of forest aboveground biomass and growing stock volume (Lu et al., 2012Foody et al., 2003;Nichol and Sarker, 2011). However, the saturation effect was reported in the literature even when ALS data were used (Nilsson et al., 2017;Giannetti et al., 2018aGiannetti et al., ,2018bLefsky et al., 2005). ...
Article
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy. For a study area of more than 48,657 km 2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model. Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R 2 = 0.69 and RMSE % = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.
Article
The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.
Technical Report
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This document includes additional maps, tables, and figures on tree crown condition and damage causes that are not included in the print version of the ICP Forests 2018 Technical Report. For detailed information on ICP Forests, please visit the programme website at http://icp-forests.net
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The development of laser scanning technologies has gradually modified methods for forest mensuration and inventory. The main objective of this study is to assess the potential of integrating ALS and TLS data in a complex mixed Mediterranean forest for assessing a set of five single-tree attributes: tree position (TP), stem diameter at breast height (DBH), tree height (TH), crown base height (CBH) and crown projection area radii (CPAR). Four different point clouds were used: from ZEB1, a hand-held mobile laser scanner (HMLS), and from FARO ® FOCUS 3D, a static terrestrial laser scanner (TLS), both alone or in combination with ALS. The precision of single-tree predictions, in terms of bias and root mean square error, was evaluated against data recorded manually in the field with traditional instruments. We found that: (i) TLS and HMLS have excellent comparable performances for the estimation of TP, DBH and CPAR; (ii) TH was correctly assessed by TLS, while the accuracy by HMLS was lower; (iii) CBH was the most difficult attribute to be reliably assessed and (iv) the integration with ALS increased the performance of the assessment of TH and CPAR with both HMLS and TLS.
Book
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“Ecosystem Assessment and Fuzzy Systems Management” is the edited outcome of the 3rd International Conference on Ecosystem Assessment Management (ICEAM) and the Workshop on the Construction of an Early Warning Platform for Eco-tourism (WCEWPE) in Hainan on May 5-12, 2013, Haikou, China. The 3rd ICEAM and the WCEWPE, built on the success of previous conferences, are major Symposiums for scientists, engineers and logistic management researchers presenting their the latest achievements, developments and applications in all areas of Ecosystem Assessment Management, Early Warning Platform for Eco-tourism and fuzziology. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists. The book, containing 47 papers, is divided into five parts: “Ecosystem Assessment, Management and Information”; “Intelligent Algorithm, Fuzzy Optimization and Engineering Application”; “Spatial Data Analysis and Intelligent Information Processing”; “Tourism Culture, Development and Planning” and “Application of Operations Research and Fuzzy Systems”.
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Picea omorika (Pančić) Purk., the Serbian spruce, is a living fossil tree restricted to a small area at the boundary of Serbia and Bosnia Herzegovina. It grows in cool temperate mixed forests on mountain slopes but also withstands poorly aerated soils. Poor regeneration, fire impact in the 19th century and forest tree competition together with climate warming in recent years has left the Serbian spruce with the status of endangered species. Its prominent columnar habit and silvery sheen, together with tolerance to pollution, make it a valuable tree for urban landscapes.
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The European forest types — Categories and types for sustainable forest management reporting and policy presents the findings of a study carried out by an international consortium of experts aimed at providing the Ministerial Conference on the Protection of Forests in Europe (MCPFE) with an user‑friendly forest types classification. The primary goal of the scheme is to improve the MCPFE reporting on sustainable forest management (SFM) in Europe, with special regard to forest type based SFM indicators.
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This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops.
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Fuel flammability is defined as the relative ease with which a fuel will ignite and burn with a flame. Different forest types have different intrinsic “baseline” flammabilities, depending on the susceptibility to burn of the main tree species that build up the forest cover. In this perspective, we apply a forest-type based approach to classify and map the flammability level of the forests in Italy. Baseline forest type-based flammability values provided at European level have been assigned to a national forest types map, derived from Corine Land Cover 2006. Flammability values have been then further calibrated against the Burned Area Selection Ratio, a measure of the actual incidence of wildfires among different forest types. Accordingly, a calibrated flammability index was derived by simple linear regression, so that predicted flammability reflects more accurately the observed fire incidence among different forest types in Italy. Findings show that the flammability of forest types ranges from low to high: on average, the 17% of the forest cover has medium to high flammability, with wide regional variability. Notably, in four Regions (Sardegna, Sicilia, Puglia, Calabria) over one third of total forest area has medium to high flammability. Large scale mapping of forest flammability is crucial to define long-term priorities among forest areas as to how to distribute fire management effort. Areas falling into medium to high flammability classes should be given priority for implementing fuel management treatments in order to mitigate fire hazard from the stand to the landscape scale.
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A vegetation classification approach is needed that can describe the diversity of terrestrial ecosystems and their transformations over large time frames, span the full range of spatial and geographic scales across the globe, and provide knowledge of reference conditions and current states of ecosystems required to make decisions about conservation and resource management. We summarize the scientific basis for EcoVeg, a physiognomic-floristic-ecological classification approach that applies to existing vegetation, both cultural (planted and dominated by human processes) and natural (spontaneously formed and dominated by nonhuman ecological processes). The classification is based on a set of vegetation criteria, including physiognomy (growth forms, structure) and floristics (compositional similarity and characteristic species combinations), in conjunction with ecological characteristics, including site factors, disturbance, bioclimate, and biogeography. For natural vegetation, the rationale for the upper levels (formation types) is based on the relation between global-scale vegetation patterns and macroclimate, hydrology, and substrate. The rationale for the middle levels is based on scaling from regional formations (divisions) to regional floristic-physiognomic types (macrogroup and group) that respond to meso-scale biogeographic, climatic, disturbance, and site factors. Finally, the lower levels (alliance and association) are defined by detailed floristic composition that responds to local to regional topo-edaphic and disturbance gradients. For cultural vegetation, the rationale is similar, but types are based on distinctive vegetation physiognomy and floristics that reflect human activities. The hierarchy provides a structure that organizes regional/continental vegetation patterns in the context of global patterns. A formal nomenclature is provided, along with a descriptive template that provides the differentiating criteria for each type at all levels of the hierarchy. Formation types have been described for the globe; divisions and macrogroups for North America, Latin America and Africa; groups, alliances and associations for the United States, parts of Canada, Latin America and, in partnership with other classifications that share these levels, many other parts of the globe.
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QuestionsHow many vegetation classes and alliances are described in European countries? What are the main factors driving the number of these vegetation types? What would be the estimated number of vegetation types in so far under-explored regions outside Europe? LocationTwenty-three countries and regions in Europe. Fourteen regions outside Europe. Methods We compiled lists of vegetation types (phytosociological classes as broader units and alliances as narrower ones) described in European countries with a well-developed vegetation classification. The delimitation and nomenclature of classes and alliances were standardized according to a unified classification system for Europe. Generalized linear models were used to test the influence of floristic richness, area, sampling effort, altitudinal range and geological diversity on the number of vegetation types. Best predictors were used to estimate the number of classes and alliances in selected regions over the world. ResultsIn agreement with broadly recognized diversity patterns, we found the highest numbers of vegetation types in countries with a mediterranean climate. The numbers of classes and alliances per country did not depend on the country size, while country's floristic richness was identified as the main explanatory variable, accounting for up to 67% of explained deviance. The number of alliances increased in countries with broad altitudinal range and large geological diversity, but these variables did not influence the number of classes. Accordingly, estimates of the number of alliances for other (mostly non-European) regions were adjusted when including altitudinal range as an additional predictor. Conclusions At broad scales, the number of vegetation types can be predicted from floristic richness, which indirectly accounts for the effect of area, and from environmental heterogeneity. Furthermore, this overview demonstrates that the number of phytosociological alliances reflects biogeographic patterns in Europe better than the number of classes. We also estimate, for the first time, the number of vegetation types that may be expected in so far poorly surveyed regions worldwide, using procedures of vegetation classification analogous to those used in European phytosociology.
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Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground at - tributes of vegetation to each pixel in a digital landscape map. The gradient nearest neighbor method integrates vegeta - tion measurements from regional grids of field plots, mapped environmental data, and Landsat Thematic Mapper (TM) imagery. In the Oregon coastal province, species gradients were most strongly associated with regional climate and geographic location, whereas variation in forest structure was best explained by Landsat TM variables. At the regional level, mapped predictions represented the range of variability in the sample data, and predicted area by vegetation type closely matched sample-based estimates. At the site level, mapped predictions maintained the covariance structure among multiple response variables. Prediction accuracy for tree species occurrence and several measures of vegetation structure and composition was good to moderate. Vegetation maps produced with the gradient nearest neighbor method are appropriately used for regional-level planning, policy analysis, and research, not to guide local management deci - sions.
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We analysed the effects of patch configuration and site history on vascular plants in ancient forests of the Eastern Po Plain, documented back to 1740. Despite their reduced size, all the forests are part of Natura 2000 Network and significantly contribute to the maintenance of a threatened habitat and support biological diversity of the Continental biogeographic region. The presence of some functional ecological plant species groups was correlated with patch configuration and age. Habitat quality, in terms of suitability for forest species, was found to be important in explaining the presence of species of high conservation value, but patch age (as an indicator for habitat quality) played a major role too. For core forest species, patch area is a redundant variable in explaining species richness relative to habitat quality and patch age and the extinction of specialists seems to occur mainly in a deterministic way. Even small forest fragments can be very important for maintaining plant species diversity, at least if they are of high habitat quality and if the forest management is appropriate. However, to achieve a long term conservation, management plans should also aim at an improvement of the anthropogenic matrix surrounding forest remnants.
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An attempt to obtain a consistent spatial model of natural potential vegetation (NPV) for the mainland Portuguese territory is reported. Spatial modeling procedures performed in a Geographic Information System (GIS) environment, aimed to operationalize phytosociological expert-knowledge about the putative distribution of potential zonal forest communities dominant in the Portuguese continental territories. The paradigm for NPV assumed was that of Rivas-Martinez (1976) and Rivas-Martinez et al. (1999), which presupposes, for a given territory, a univocal correspondence between a uniform combination of bioclimatic stage and lithology, given a biogeographical context, and a unique successional sequence leading to a single climax community (i.e.a vegetation series (VS)). Information issued from both literature and a team of phytosociologists possessing detailed knowledge about Portuguese vegetation, namely about forests and its serial vegetation, was acknowledged as a starting point for the construction of such a habitat-vegetation correspondence model. First, a bioclimatic map concerning the "Worldwide Bioclimatic Classification System" (WBCS) of Rivas-Martinez (1981-2004), obtained by multivariate geostatistical interpolation issuing from the work of Mesquita (2005), was set. Several partial matrices, one for each biogeographical Province, combined such habitat statements to VS. Initial incoherence due to vagueness of statements led to an important amount of both superimposition of VS and habitat gaps in the matrices. Further rearrangement of the table according to known field distribution of VS by experts allowed setting an approximate univocal correspondence VS-habitat. Finally, an intersection of bioclimatic, lithology and biogeographic maps yielded over a thousand habitat combinations to be associated each to a single VS through implementation of the matrices as a set of rules. Again, inconsistencies were solved likewise, but this time by direct observation of the map by experts. Keeping of phytosociological consistency and fidelity to information on actual vegetation field distribution was always mandatory during the process.
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Following selection of the 13 biodiversity variables that were evaluated as both important and feasible for assessment by NFIs and grouping them into essential features, additional information was solicited regarding the degree to which the 13 variables are currently assessed by NFIs. The objective was to evaluate the prospects for harmonized estimates of biodiversity indicators based on these variables. The prospects varied considerably depending on the particular variable and essential feature. The evaluations produced positive harmonization possibilities for forest categories and the tree height and diameter variables associated with forest structure. For forest age, possibilities were constrained by lack of common reference definitions. However, possibilities for construction of a common reference definition and bridges to compensate for the differences in estimates resulting from using national and reference definitions were deemed positive. Prospects for regeneration, ground vegetation, and naturalness were less positive because of variability in definitions, assessment methods, measurement thresholds and other factors. Thus, efforts at harmonization for these essential features were constrained to a few variables or a few countries with similar NFI features.
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In order to map the spatial distribution of twenty tree species groups over Europe at 1km × 1km resolution, the ICP-Forest Level-I plot data were extended with the National Forest Inventory (NFI) plot data of eighteen countries. The NFI grids have a much smaller spacing than the ICP grid. In areas with NFI plot data, the proportions of the land area covered by the tree species were mapped by compositional kriging. Outside these areas, these proportions were mapped with a multinomial multiple logistic regression model. A soil map, a biogeographical map and bioindicators derived from temperature and precipitation data were used as predictors. Both methods ensure that the predicted proportions are in the interval [0,1] and sum to 1. The regression predictions were iteratively scaled to the National Forest Inventory statistics and the Forest map of Europe. The predicted proportions for the twenty tree species were validated by the Bhattacharryya distance between predicted and observed proportions at 230 plot data separated from the calibration data. Besides, the map with the predicted dominant species was validated by computing the error matrix. The median Bhattacharryya distance in the subarea with NFI plot data was 1.712, whereas in the subarea with ICP-Level-I data, this was 2.131. The scaling did not significantly decrease the Bhattacharryya distance. The estimated overall accuracy of this map was 43%. In areas with NFI plot data, overall accuracy was 57%, outside these areas 33%. This gain was mainly attributable to the much denser plot data, less to the prediction method. KeywordsLogistic regression–Kriging–Map validation–Bhattacharryya distance–Confusion matrix–Overall accuracy
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Sampling design has long been a key methodology issue of vegetation science. There are still unsolved problems, amongst which the influence of the positioning (spatial arrangement) of plots remains a basic one. Random sampling is considered the only statistically valid option, along with its amendment by stratifying. However, huge amounts of plot-sample data originated from using subjective plot positioning, involving a great deal of the author’s judgement, experience and bias. Field studies examining the role of subjectivity within a larger area (a landscape) are lacking. In the presented case-study, datasets sampled using computer-based geographically stratified random (R) and field-based subjective (S) designs are compared. In 1999–2000, 120 R and 244 S plots were spread across some 270 km2. Their properties are compared regarding the differences in vegetation variability by ordination, and diversity by rarefaction. Although the differences are not conspicuous, both datasets partly contain different vegetation types and the S dataset contains at least some species-richer plots. The number of R plots had been clearly underrated while the expected species diversity values were nearly reached by the S. More R than S plots would probably be needed to reach the expected diversity maximum.
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We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950-2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledge-based methods and inclusion of additional co-variates, particularly layers obtained through remote sensing.
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The history of digital soil mapping and modeling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyze, integrate, and visualize soil and environmental datasets. DSMM studies are diverse with specialized, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational DSMM used for routine mapping over large soil regions. Research-focused DSMM contrasts with need-driven DSMM and agency-operated soil surveys. Since there is no universal equation or digital soil prediction model that fits all regions and purposes the proposed strategy is to characterize recent DSMM approaches to provide recommendations for future needs at local, national and global scales. Such needs are not solely soil-centered, but consider broader issues such as land and water quality, carbon cycling and global climate change, sustainable land management, and more. A literature review was conducted to review 90 DSMM publications from two high-impact international soil science journals — Geoderma and Soil Science Society of America Journal. A selective approach was used to identify published studies that cover the multi-factorial DSMM space. The following criteria were used (i) soil properties, (ii) sampling setup, (iii) soil geographic region, (iv) spatial scale, (v) distribution of soil observations, (vi) incorporation of legacy/historic data, (vii) methods/model type, (viii) environmental covariates, (ix) quantitative and pedological knowledge, and (x) assessment method. Strengths and weaknesses of current DSMM, their potential to be operationalized in soil mapping/modeling programs, research gaps, and future trends are discussed. Modeling of soils in 3D space and through time will require synergistic strategies to converge environmental landscape data and denser soil datasets. There are needs for more sophisticated technologies to measure soil properties and processes at fine resolution and with accuracy. Although there are numerous quantitative models rooted in factorial models that predict soil properties with accuracy in select geographic regions they lack consistency in terms of environmental input data, soil properties, quantitative methods, and evaluation strategies. DSMM requires merging of quantitative, geographic and pedological expertise and all should be ideally in balance.
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In recent years, the conservation of tropical forests has received worldwide publicity whereas effective forest management, particularly for timber extraction, has attracted little attention and gained some notoriety. The overall aim of the present paper was to examine how environmental micro-variation in the Chiquibul Forest Reserve of Belize can influence species distribution and thereby inform management strategy. The paper deals first with the background to forest management in Belize, then considers the methodology used in the present study and fin~~ly assesses the preliminary results. The specific objectives are: (1) to assess the effects of changing scale on the variability of selected individual soil properties in forest plots within the same vegetation class; and (2) to examine the variation in soil properties and tree species distribution, and to integrate environmental and ecological data over a range of scales. BACKGROUND Whereas the global and regional distribution of tropical forests is broadly governed by climatic and altitudinal variation, individual forest tracts need to consider a range of other, locally important factors to explain species distribution and change. With very high species diversity, tropical forests present a major challenge in the attempt to unravel controlling factors in distribution and growth (Swaine et aI. 1987). Research that attempts to explain diversity has looked at species distribution according to a range of factors, with a general recognition that soil fertility plays a significant if ill­ defined role (Swaine 1996).
Conference Paper
The aim of this paper is the development of an algorithm, based on expert knowledge, for the Land cover classification of an Italian Grassland Area. To accomplish this task, a dataset composed by 4 Worldiew2 (WV-2) images, at 2 m of spatial resolution, has been considered. Despite their poor spectral resolution, Very High spatial Resolution (VHR) data allow the identification of individual objects by means of the information in the relationship between adjacent pixels, including texture and shape. For this reason a Geographic Object-Based Image Analysis (GEOBIA) approach consisting in a rule set based on the elicitation of expert rules concerning phenology, spatial features and agricultural practices in conjugation with prior spectral knowledge, has been used The study area, of almost 500 kmq, is located in Southern Italy (Puglia Region) within the Natura 2000 ?Alta Murgia? site (SCI/SPA IT9120007, according to Habitat Directive 92/43 and Bird Directive 147/2009), partly designated as a National Park as from 2004. Semi-natural dry grasslands cover almost 24% of the total area of the site which represent one of the most important areas for the conservation of this kind of ecosystems in Europe.
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A new colour vegetation map for Western Australia has been developed at a level of detail appropriate for publishing at the 1:3,000,000 scale. This memoir accompanies the map, which is based on the statewide mapping carried out by John Beard. The published maps and reports have been valuable resources for biologists and planners for decades, and this information is now available as a spatial dataset, making it more accessible for a broader range of uses. This memoir explains how the data were derived and describes the units of vegetation shown on the map. The 1:3,000,000-scale vegetation map shows the distribution of 75 major categories of natural plant cover in Western Australia as they would have been at the time of European settlement, with 50 major vegetation types, five categories of bare and poorly-vegetated ground and 20 vegetation mosaics (combinations of vegetation types). This classification was carried out in 1996-97 and is influenced by the original framework developed by Beard for the earlier 1:3,000,000 map. Vegetation types range from tall forests of karri (Eucalyptus diversicolor) in the high rainfall zone of the South West; through to the forests and woodlands dominated by eucalyptus, melaleuca, allocasuarina or acacia in the medium to low rainfall areas; the sclerophyllous shrublands of the South West; the saltbush, bluebush and samphire shrublands that occur on saline soils throughout the state; and the various grasslands of Triodia (spinifex) in central parts and Sorghum, Astrebla and Chrysopogon in northern parts of the state. These grasslands often have emergent tree cover of over 10% with density and are described as steppe or savanna. The map has been generated from a comprehensive, spatial database of the vegetation of the state, based mainly on the work of JS Beard carried out from 1964 to 1981. Almost 900 vegetation associations, derived from the 1:250,000-scale maps are included in the database. These were grouped into vegetation types based on physiognomy, floristics and, in some cases, ecological and regional attributes, and largely followed the framework developed by Beard. This work was carried out prior to the development of the National Vegetation Information System (NVIS; ESCAVI 2003). A subsequent reclassification of system associations (an intersection of Beard's vegetation associations with his vegetation systems) was described in six levels according to the NVIS, but is not described in this publication. The structure, composition, and general distribution of each of the vegetation types are described in this memoir. The memoir also includes details about the development and current delineation of the bioregions known as the IBRA (Interim Biogeographic Regionalisation of Australia). The description of vegetation types are referenced to these regions. The six most extensive vegetation types are: • low woodland, open low and sparse woodland dominated by Acacia aneura (mulga), which covers over 36 million ha and extends over eight IBRA regions; • a mosaic of open tree steppe and open shrub-steppe that covers over 25.5 million ha and dominates three desert IBRA regions; • shrub-steppe of hummock grassland dominated by Triodia spp. with scattered shrubs of Acacia spp. and Grevillea spp., mapped over nearly 25 million ha; • scrub, open scrub and sparse shrub of Acacia spp., Melaleuca spp. and other species, mapped widely over the state and that covers over 15 million ha; • the woodlands of the Avon Wheatbelt Bioregion, Coolgardie Bioregion, riverine areas and the Northern Kimberley Bioregion that cover nearly 13.5 million ha. These six vegetation types account for 55% of the state. The six most restricted vegetation types identified by the classification process are: • freshwater lakes, which total nearly 22,700 ha; • samphire with scattered trees or low trees, mapped over nearly 39,000 ha; • pockets of low forest of acacia, Rottnest cypress, coastal moort or mixed tropical forest (rain forest), which cover 55,600 ha; • sedgelands of the South West (59,000 ha); • thickets with medium open woodland or scattered trees (62,600 ha); • low woodland or open low woodlands over bluebush and/or saltbush (87,700 ha). Some of these could be incorporated into other units in future classifications. The 1:3,000,000-scale map gives a general impression of the vegetation of the state, as well as aspects of the geology, geomorphology and climate patterns. The vegetation database that underpins this map is currently being used for a wide range of research and planning purposes, including the on-going development of the terrestrial nature conservation reserve system. It is not ideal for this latter purpose as the scale of the mapping at 1:250,000 is relatively broad. More detailed vegetation datasets will be required to undertake more effective land use planning as conserving Western Australia's unique and fragile native vegetation communities becomes more challenging into the future.
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Forests provide a wide range of ecosystem services from which people benefit, and upon which all life depends. However, any rational decision related to the maintenance and enhancement of the multiple functions provided by the forests needs to be based on objective, reliable information. As such, forest monitoring and assessment are rapidly evolving as new information needs arise or new techniques and tools become available. Global change issues and utilities from ecosystem management are distinctively to be considered, so that forest inventory and mapping are broadening their scope towards multipurpose resources surveys. Recent changes in forest management perspective have promoted the consideration of forests as complex adaptive systems, thereby highlighting the need to account that such approaches actually work: forest monitoring and assessment are then expected to address and fully incorporate this perspective at global scale, seeking to support planning and management decisions that are evidence-based. This contribution provides selected considerations on the above mentioned issues, in the form of a commented discussion with examples from the literature produced in the last decade.
Article
Mangroves are difficult to map accurately from satellite data by means of parametric classification because of their spectral similarity to other coastal vegetation despite their habitat being inside coastal waters. This study aims to improve the mapping accuracy through incorporation of such spatial knowledge about mangroves in the Waitemata Harbor of Auckland, New Zealand, from SPOT data. The spatial knowledge was combined with spectral knowledge in the mapping. Supervised classification was found to map stunted and lush mangroves at an accuracy of, respectively, 46.7 percent and 68.3 percent. These accuracy levels rose, respectively, to 83.3 percent and 96.7 percent after the spatial knowledge was sequentially incorporated into the mapping. A similar accuracy level was achieved from knowledge-based spatial reasoning. If integrated simultaneously with spectral knowledge, spatial knowledge did not improve the accuracy noticeably because of difficulty in gaining quality spectral knowledge. It is concluded that knowledge-based, post-classification processing considerably improves the accuracy of mapping mangroves over parametric classification.
Article
Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped continuously over large areas. Nearest-neighbor imputation maps, maps developed from multivariate imputation of field plots, have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances can result in species being mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and imputing field plots based on their similarity across multiple spatial environmental variates, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We used this approach to map 13 million ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). Using both independent state forest and, more extensive, ecoregion validation data sets, we compared the imputation map to field inventory data, based on the dissimilarity of tree community composition and the rank order correlation of tree species abundance. Average Bray–Curtis dissimilarity between the imputation map and field plots was 0.32 and 0.12, for the state forest and ecoregion validation data sets, respectfully. Average Spearman rank order correlation was 0.81 and 0.93 for the state forest and ecoregion validation data sets, respectfully. Our analyses suggest that this approach to imputation can realistically capture regional variation in forest composition. We expect the imputation map will be valuable for several regional forest studies and that the approach could be successfully used in other regions.
Article
Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map. Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors. We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped. We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps. Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.
Article
Climate change is predicted to impact river systems in the southeastern USA through alterations of temperature, patterns of precipitation and hydrology. Future climate scenarios for the southeastern USA predict (1) surface water temperatures will warm in concert with air temperature, (2) storm flows will increase and base flows will decrease, and (3) the annual pattern of synchronization between hydroperiod and water temperature will be altered. These alterations are expected to disturb floodplain plant communities making them more vulnerable to establishment of invasive species. The primary objectives of this study are to evaluate whether native and invasive riparian plant assemblages respond differently to alterations of climate and land use. To study the response of riparian wetlands to watershed and climate alterations, we utilized an existing natural experiment imbedded in gradients of temperature and hydrology found among dammed and undammed rivers. We evaluated a suite of environmental variables related to water temperature, hydrology, watershed disturbance, and edaphic conditions to identify the strongest predictors of native and invasive species abundances. We found that native species abundance is strongly influenced by climate-driven variables such as temperature and hydrology, while invasive species abundance is more strongly influenced by site-specific factors such as land use and soil nutrient availability. The patterns of synchronization between plant phenology, annual hydrographs, and annual water temperature cycles may be key factors sustaining the viability of native riparian plant communities. Our results demonstrate the need to understand the interactions between climate, land use, and nutrient management in maintaining the diversity of riparian plant communities. Future climate change is likely to result in diminished competitiveness of native plant species, while the competitiveness of invasive species will increase due to anthropogenic watershed disturbance and accelerated nutrient and sediment export.
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Random resampling of the initial data set and geographically stratified resampling resulted in similar classifications. By contrast, classifications of the resampled data sets that were based on habitat stratifications (2–5) differed from each other and from the initial data set. Stratification 2 resulted in classifications that strongly reflected environmental factors with a coarse grain of spatial heterogeneity (e.g. macroclimate), whereas stratification 5 resulted in classifications emphasizing fine-grained factors (e.g. soil nutrient status). Stratification 3 led to the most deviating results, possibly due to the subjective nature of the traditional phytosociological classifications.
Article
Since 2003 the MCPFE-Forest Europe process has adopted a set of Pan-European Indicators that has become a policy instrument to monitor, evaluate and report progress towards sustainable forest management (SFM). Two new experimental tools have been introduced in the framework of the «State of Forests and Sustainable Forest Management in Europe 2011»: (i) pilot reporting by 14 classes of European Forest Types (EFTs) for a selection of quantitative SFM indicators; (ii) key parameters for monitoring progress for all quantitative indicators.
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Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
Article
Classification is an important problem in data mining. This paper focuses on a method of optimizing classifiers of neural network by Genetic Algorithm based on principle of gene reconfiguration, and implement classification by training the weight. The paper uses shift reverse logic crossover operation and the improved genetic algorithm The article using the typical method for optimizing BP neural network weight is BP algorithm, which has such disadvantages as slow practice speed and easy for running into local minimum. The article uses genetic algorithm based on gene reconfiguration to largely resolve the problem. Genetic algorithm optimizes neural network, mainly including neural network structure evolvement and neural network connection weight evolvement. The article firstly uses Simple Genetic Algorithm (SGA) for network structure evolvement and then adopts genetic algorithm based on gene reconfiguration for network weight practice. Experiment results show that Improved Genetic Algorithm (IGA) improve classifying veracity.
Article
Conventional soil mapping is limited in its capabilities in that it presents a summary of the soil surveyor's conceptual view of soil variation. As such, the method conveys little regarding what is known about the variation of individual soil properties, or the quantitative nature of their variation. We developed a new method for soil mapping, based on the concepts employed in the PROSPECTOR mineral exploration system, which builds on existing soil surveyor knowledge to construct quantitative statements about individual soil properties via the development of a network of rules. These rules operate within a system of Bayesian inference to assign the varying probability of occurrence of a soil property of interest within an area, given evidence that relates to it in a known way. Permissible evidence includes the range of attributes normally used by a soil surveyor, such as landform, vegetation, land use, or parent material, and can also include remotely sensed digital data. Evidence is weighted according to the uncertainty associated with it, and combined to produce a single estimate of probability of a given attribute. The relationship between the evidence and prediction is stated explicitly at each stage of the procedure and is thus repeatable in a consistent manner. The system has the advantage that while it does not discard the evidence and knowledge used in conventional soil survey, it produces quantitative estimates of the distribution of soil properties, which can be used for a wide range of applications. The data produced is amenable to storage in geographic information systems and related data bases. As such, it can be updated or enhanced as new information or knowledge becomes available.
Article
An important first step in marine spatial planning and ecosystem-based management efforts is the creation of benthic habitat maps that allow scientists and managers to understand the distribution of living and non-living resources on the seafloor. However, the location of boundaries between and composition of habitats is highly dependent on the approach taken to integrate abiotic and biotic information. The purpose of this study was to test “top-down” and “bottom-up” approaches for integrating physical and biological data derived from commonly used sub-tidal benthic mapping tools to create a habitat map compatible with the US Coastal and Marine Ecological Classification Standard (CMECS). We found that a top-down framework, where we tested for differences in macrofauna assemblages among side scan sonar facies, defined two broad-scale and general habitats. Using the bottom-up approach, where patterns in abiotic and biotic variables were examined with multivariate statistics (BEST, LINKTREE, ANOSIM, SIMPER), we generated seven biotopes based on the macrofauna abundance, percent sand, water depth, and backscatter standard deviation that corresponded well to, but provided more fine-scale detail than the top-down habitats. We were able to use the statistical relationship between abiotic variables and macrofauna assemblages in the LINKTREE to predict the spatial distribution of assemblages over ∼50% of the study area. We created a local catalogue of biotopes specific to our study area that contributes to the CMECS library. In addition, we were able to fully map CMECS Geoform, Surface Geology, and Biotic Cover Components. This mapping effort represented real progress toward reconciling the “data density mismatch” between physical and biological mapping methods, and it provided further evidence that using a bottom-up methodology preserves species–environment relationships.
Article
Questions: What is the relationship between alpine vegetation patterns and climate? And how do alpine vegetation patterns respond to climate changes?Location: Tibetan Plateau, southwestern China. The total area is 2500000 km2 with an average altitude over 4000 m.Methods: The geographic distribution of vegetation types on the Tibetan Plateau was simulated based on climatology using a small set of plant functional types (PFTs) embedded in the biogeochemistry-biography model BIOME4. The paleoclimate for the early Holocene was used to explore the possibility of simulating past vegetation patterns. Changes in vegetation patterns were simulated assuming continuous exponential increase in atmospheric CO concentration, based on a transient ocean-atmosphere simulation including sulfate aerosol effects during the 21st century.Results: Forest, shrub steppe, alpine steppe and alpine meadow extended while no desert vegetation developed under the warmer and humid climate of the early Holocene. In the future climate scenario, the simulated tree line is farther north in most sectors than at present. There are also major northward shifts of alpine meadows and a reduction in shrub-dominated montane steppe. The boundary between montane desert and alpine desert will be farther to the south than today. The area of alpine desert would decrease, that of montane desert would increase.Conclusions: The outline of changes in vegetation distribution was captured with the simulation. Increased CO2 concentration would potentially lead to big changes in alpine ecosystems.
Article
Phytosociology is a subdiscipline of plant ecology that describes the co-occurrence of plant species in communities. Gradient analysis and classification are its complementary tools. Various peculiarities and anachronisms of Central European phytosociology conceal its similarity with Anglo-American approaches. Phytosociology deserves to be updated as a part of modern vegetation science that can build on a vast heritage of high-quality data and the tools to store and analyse them in ways that go beyond syntaxonomy. By providing a context to more specialized pure and applied research, it has a crucial role to play in understanding community structure, ecosystem functioning and biological evolution.
Article
Question: While it is well known that species richness depends on plot size, it is not generally recognised that the same must be true for constancy. Accordingly, many authors use varying plot sizes when classifying vegetation based on the comparison of constancies between groups of plots. We ask whether the constancy-area relationship follows a general rule, how strong the effect of plot sizes is on constancies, and if it is possible to correct constancies for area.Location: For empirical evaluation, we use data from plant communities in the Czech Republic, Sweden and Russia.Methods: To assess the potential influence of differences in plot size on constancies, we develop a mathematical model. Then, we use series of nested plot species richness data from a wide range of community types (herbaceous and forest) to determine the parameters of the derived function and to test how much the shape of the constancy-area relationship depends on taxa or vegetation types.Results: Generally, the constancy-area relationship can be described by C (A)=1−(1−C0)(A/A0)^d, with C being constancy, A area, C0 known constancy on a specific area A0, and d a damping parameter accounting for spatial autocorrelation. As predicted by this function, constancies in plant communities always varied from values near 0% to near 100% if plot sizes were changed sufficiently. For the studied vegetation types, a two- to fourfold increase in plot size resulted in a change of conventional constancy classes, i.e. an increase of constancy by 20% or more.Conclusions: Vegetation classification, which largely relies on constancy values, irrespective of whether traditional or modern fidelity definitions are used, is strongly prone to distorting scale effects when relevés of different plot sizes are combined in studies. The constancy-area functions presented allow an approximate transformation of constancies to other plot sizes but are flawed by idiosyncrasies in taxa and vegetation types. Thus, we conclude that the best solution for future surveys is to apply uniform plot sizes within a few a priori delimited formations and to determine diagnostic species only within these formations. Finally, we suggest that more detailed analyses of constancy-area relationships can contribute to a better understanding of species-area relationships because the latter are the summation of the first for all species.
Article
The first objective of this paper is to define a new measure of fidelity of a species to a vegetation unit, called u. The value of u is derived from the approximation of the binomial or the hypergeometric distribution by the normal distribution. It is shown that the properties of u meet the requirements for a fidelity measure in vegetation science, i.e. (1) to reflect differences of a species’relative frequency inside a certain vegetation unit and its relative frequency in the remainder of the data set; (2) to increase with increasing size of the data set. Additionally (3), u has the property to be dependent on the proportion of the vegetation unit's size to the size of the whole data set.The second objective is to present a method of how to use the value of u for finding species groups in large data bases and for defining vegetation units. A species group is defined by possession of species that show the highest value of u among all species in the data set with regard to the vegetation unit defined by this species group. The vegetation unit is defined as comprising all relevés that include a minimum number of the species in the species group. This minimum number is derived statistically in such a way that fewer relevés always belong to a species group than would be expected if the differential species were distributed randomly among the relevés. An iterative algorithm is described for detecting species groups in data bases. Starting with an initial species group, species composition of this group and the vegetation unit defined by this group are mutually optimized. With this algorithm species groups are formed in a data set independently of each other. Subsequently, these species groups can be combined in such a way that they are suited to define commonly known syntaxa a posteriori.
Article
The paper gives an overview of applying fuzzy measures and relevant nonlinear integrals in data mining, discussed in five application areas: set function identification, nonlinear multiregression, nonlinear classification, networks, and fuzzy data analysis. In these areas, fuzzy measures allow us to describe interactions among feature attributes towards a certain target (objective attribute), while nonlinear integrals serve as aggregation tools to combine information from feature attributes. Values of fuzzy measures in these applications are unknown and are optimally determined via a soft computing technique based on given data.
Article
Separate logistic regression models were developed to predict the distribution and large‐scale spatial patterns of dominant graminoid species and communities in alpine grasslands. The models are driven by four bioclimatic parameters: degree‐days of growing season (basis 0 °C), a moisture index for July, potential direct solar radiation for March, and a continentality index. Geology and slope angle were used as a surrogate for nutrient availability and soil water capacity. The bioclimatic parameters were derived from monthly mean temperature, precipitation, cloudiness and potential direct solar radiation. The environmental parameters were interpolated using a digital elevation model with a resolution of 50 m. The vegetation data for model calibration originate from field surveys and literature. An independent test data set with samples from three different climatic zones was used to test the model. The degree of coincidence between simulated and observed patterns was similar for species and communities, but the κ‐values for communities were generally higher (κ= 0.539) than for species (mean individual κ= 0.201). Information on land use was detected as a major factor that could significantly improve both the species and the community model. Nevertheless, the climatic factors used to drive the model explained a major part of the observed patterns.