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The EuroVegChecklist (EVC) is currently considered to be the basis for the phytosociological classification of the European vegetation, and it is therefore important that it is continually updated, improved and refined. This article revises the nomenclature of three Mediterranean grassland-related classes and some of their contents down to the rank of association, namely Helianthemetea guttati, Poetea bulbosae and Stipo giganteae-Agrostietea castellanae, on the basis of the fourth edition of the International Code of Phytosociological Nomenclature (ICPN). The results show that the correct author citations for the first two classes are as follows: Tuberarietea guttatae Braun-Blanquet 1973, and Poetea bulbosae Rivas Goday et Rivas-Martínez ex Navarro Andrés et Valle Gutiérrez 1984. Instead, the name Stipo giganteae-Agrostietea castellanae is invalid and it is replaced by the new class name Agrostietea castellanae. In addition, we designate the neotype of the association Corynephoro articulati-Helianthemetum guttati (Corynephoro divaricati-Tuberarietum guttatae nom. corr. nov. et mut. nov.), the lectotype of the Poo bulbosae-Trifolietum subterranei, and mutate the names Helianthemetalia guttati and Helianthemion guttati to Tuberarietalia guttatae and Tuberarion guttatae, respectively, as well as two names of associations with a name-giving taxon belonging to the genus Tuberaria. [ABSTRACT FROM AUTHOR]

Changes in Vegetation Indices (VIs) over time can describe vegetation phenology; however, it is not known which phenological features contribute most to land cover classification. Feature selection could potentially solve this problem. In this study, phenological feature importance and selection were evaluated by using two-year Sentinel-2 (S-2) data and single-date PlanetScope (PS) to classify a 50 km2 native podocarp forest in New Zealand. The study area was classified into nine classes. Single-date PS and S-2 data were fused to a base image with the same spatial resolution as PS and 8 bands containing spectral data from S-2; this image was used to produce 30 Vegetation Indices (VIs). Phenological features – amplitude (AMP) and phase (PH) were extracted from these VIs using time-series S-2 only, and harmonic analysis in Google Earth Engine. For accurately classifying forests and identifying the most important features, three classification scenarios (fused bands & VIs, fused bands & phenological features, fused bands & VIs & phenological features) were developed using a Neural Network. Variable Selection Using Random Forest (VSURF) was applied on these scenarios to evaluate the impact of feature selection. Results indicate that VSURF could reduce the time needed for the classification while maintaining a comparable level of accuracy. Phenological features improved accuracy from 90% to 94%, driven mostly by Red-Edge Triangulated Vegetation Index-AMP&PH, Normalised Near-Infrared-PH, Greenness Index-PH, Water Body Index-PH, Normalised Difference Vegetation Index-PH, Normalised Green-PH, Red-Edge Normalised Difference Vegetation Index-PH, Leaf Chlorophyll Content-AMP, and Simple Near-Infrared and Blue Ratio-PH. These features reflect changes in the structure, biochemical, and physiological characteristics of the canopy. A lack of ground-based measurements precluded an evaluation of the accuracy of these phenological aspects and an explanation of their distinctive contribution to the model. Overall, the findings show that specific phenological features can improve the classification of New Zealand's indigenous podocarp forests. [ABSTRACT FROM AUTHOR]

Vegetation mapping from remote sensing data has proven useful for monitoring ecosystems at local, regional and global scales. Generally based on supervised classification methods, ecosystem mapping needs representative and consistent labelling. Such completeness is often difficult to achieve and requires the exclusion of minority species poorly represented in the studied scene in the training base. This exclusion leads to wrong predictions in the resulting map. In this study, the use of a posteriori classification rejection methods to limit the errors associated with minority species was evaluated in three different mapping scenarios: classification according to vegetation layers, prediction of genera from various vegetation types from low vegetation to trees and mapping of habitat (assemblages of species). For this purpose, several supervised classification methods based on Support Vector Machines (SVM), Random Forests (RF) and Regularized Logistic Regression (RLR) algorithms were first applied to hyperspectral images covering the reflective domain. On these classifications, the usual evaluation methods (confusion matrix and its derivatives calculated on an independent test set composed of the majority species) showed performances similar to those of the state-of-the-art. However, the introduction of a new score taking into account minority species demonstrated the need to include them in the evaluation process to provide robust performance quantification representing map effectiveness. Three a posteriori rejection methods, based on simple thresholding, K-means and SVM algorithms, were well suited to monitor minority species. The performance gain depended on the mapping scenario, the machine learning model and the rejection method. An increase in performance with the inclusion of minority species of up to 12% could be observed through the new score. These methods also detected a similar proportion of prediction errors associated with predominant species [ABSTRACT FROM AUTHOR]

NATO's current security doctrine needs to change. It needs to enhance its political will and institutional capacity to manage climate change threats, both within the alliance itself and within the area most vulnerable to its southern flank: the Middle East. NATO will need to evolve into a CATO, a "Climate Alliance Treaty Organization," that deals with the security implications of potential tipping points and develops policies in response. A case study as to how NATO can assume this role can be seen in how the organization dealt with environmental issues in Iraq, in the aftermath of war with the Islamic State. [ABSTRACT FROM AUTHOR]

A phytosociological analysis of the Iberian Quercus faginea forests was carried out. For this purpose, 291 relevés were studied. A multivariate analysis consisting of a numerical classification based on the floristic composition of the relevés, was performed. The coherence and explanatory value of the current syntaxa are reviewed. As a result, the diversity of Iberian gall oak forests is resolved into 15 associations characterised according to their floristic, biogeographical, bioclimatic and synecological data supported by the statistical analyses. Thirteen have been described previously, and two new associations are proposed: Glandoro diffusae-Quercetum fagineae and Helianthemo mollis-Quercetum fagineae. A new combination, a lectotypification and a nomenclatural correction are also proposed. The Ios2 (ombrothermic index of the warmest bimonth of the summer quarter), Itc (compensated thermicity index) and Ic (continentality index) indexes proved to be fundamental to explain the syntaxonomic differentiation of the forests in the study. We consider that the differentiation and precision in the definition of clear syntaxonomic entities is vital to prioritize objectives in the conservation of these singular forests, and to ensure their adequate management in the context of the Habitats Directive. [ABSTRACT FROM AUTHOR]

Vegetation is crucial for wetland ecosystems. Human activities and climate changes are increasingly threatening wetland ecosystems. Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands. This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images, combining super-resolution techniques and a novel self-constructing graph attention neural network (SGA-Net) algorithm. The SGA-Net algorithm includes a decoding layer (SCE-Net) to precisely fine marsh vegetation classification in Honghe National Nature Reserve, Northeast China. The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network (SRCNN) obtained higher accuracy with a peak signal-to-noise ratio (PSNR) of 28.87 and structural similarity (SSIM) of 0.76 in spatial quality and root mean squared error (RMSE) of 0.11 and R2 of 0.63 in spectral quality. The improvement of classification accuracy (MIoU) by enhanced super-resolution generative adversarial network (ESRGAN) (6.19%) was greater than that of SRCNN (4.33%) and super-resolution generative adversarial network (SRGAN) (3.64%). In most classification schemes, the SGA-Net outperformed DeepLabV3 + and SegFormer algorithms for marsh vegetation and achieved the highest F1-score (78.47%). This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping. [ABSTRACT FROM AUTHOR]

Vegetation patterns of are the result of environmental drivers operating across a range of ecological and evolutionary scale. Those vegetation patterns at global and continental scales underpin the existence of large-scale functional entities called biomes. Traditionally, terrestrial biomes are recognised and distinguished by way of physiognomic traits thought to reflect the action of selective filters such as climate or disturbance (fire, grazing). This approach is well suited to recognising zonal biomes; it fails, however, to account for the role of other drivers such as soil and hydrology, potentially leading to over simplified representations of the biome patterns in ecologically complex regions such as Western Australia. It is, therefore, of vital importance to consider these patterns and processes and recognise the azonal biomes as well. Using a previously published physiognomic map of the vegetation of Western Australia, we adopted a bottom-up approach to translate the vegetation classification categories into a new biome classification of Western Australia that would more accurately account for the zonal and azonal structures recorded across the landscape. Five continental zonal biomes (Australian Eucalyptus Savanna, Australian Hummock Grassland, Mulga Shrubland, Australian Temperate Woodland, Australian Oceanic Temperate Forest) and one relict biome (Australian Vine Thicket) were identified. We further distinguished 34 types of azonal biomes, including three new ones: argillobiome, duplo-pedobiome and metallobiome. Predictive modelling (using Classification and Regression Trees) was used to define the potential extent of zonal biomes, with the model returning an overall accuracy of 90%. This study shows that a bottom-up approach based on vegetation maps can be used as an effective approximation for defining a robust classification of biome patterns. The predicted zonal biomes correspond well with existing traditional biogeographic classifications, yet some residual climatic discordance warrants further study. [ABSTRACT FROM AUTHOR]

One class (Sphacelarietea cirrosae), one order (Sphacelarietalia cirrosae), two alliances (Phyllophoro crispae-Codion vermilarae, Gongolarion barbatae) and five associations (Phyllophoro crispae-Codietum vermilarae, Nereietum filiformisae, Rhodochorton purpureum-Apoglossetum ruscifolii, Gongolario barbatae-Sphacelarietum cirrosae, Ericarietum bosphoricae) of macroalgal communities within the North Caucasian shelf of the Black Sea oligosaprobic sublittoral vegetation are described for the first time, using the Braun-Blanquet approach. Detrended correspondence analysis corroborated the results of the syntaxonomic analysis and revealed two main factors influencing the floristic composition of the investigated communities: depth with associated light level and intensity of water movement. The diagnoses of described syntaxa are given. A comparison of Mediterranean and Black Sea syntaxa is made, and the specificity of the vegetation of the North Caucasian shelf of the Black Sea is shown. [ABSTRACT FROM AUTHOR]

The Global Change Observation Mission-Climate (GCOM-C), launched in 2017, has suitable bands matching the photochemical reflectance index (PRI) definition. It also has the bands for the normalized difference vegetation index (NDVI). The PRI has a unique capability to detect plant stress caused by excessive light and drought. However, no moderate-resolution satellites had suitable bands for the PRI, requiring two narrow bands in green light in the definition. In this study, we conducted the early validation study of PRI and NDVI derived from the GCOM-C satellite and demonstrated those accuracies and characteristics in Mongolian grassland. The Mongolian Steppes (dry grasslands) are widely distributed on the plateau and therefore suitable for satellite validation. It is particularly suitable for the PRI validation because Mongolian grasslands have water stress due to the small amount of precipitation in summer. Therefore, we carried out field campaigns at three study sites in Mongolia. In this study, we found the seasonal pattern of PRI suggesting the potential to detect the water stress of vegetation, which is essential information for informed management of the grasslands. However, the correlation between the satellite-derived PRI and the in-situ PRI was negative because of the dependence of GCOM-C PRI on the soil moisture at sparse vegetation. For the accuracy assessment of PRI, which depends on rapidly changing light and soil moisture in a day, more exact synchronization of in-situ and satellite observation is required. On the other hand, we found that the NDVI derived from GCOM-C was highly accurate: The correlation coefficient (R) between the satellite-derived NDVI and the in-situ NDVI was 0.988 (RMSE=0.052). GCOM-C NDVI has enough similarities with MODIS NDVI in terms of accuracy, spatial resolution, and frequency. For example, we demonstrated that GCOM-C NDVI could detect the phenology with the same or better accuracy than MODIS NDVI. We also demonstrated their difference: the soil moisture dependence in sparse vegetation. The less dependency of GCOM-C NDVI on the soil moisture leads to a better classification of vegetation and non-vegetation in the sparse grassland than MODIS NDVI. [ABSTRACT FROM AUTHOR]

This paper investigates the invasion of a reach of the Touws River by the deciduous alien tree Tamarix ramosissima Ledeb. (Pink Tamarisk, Saltcedar). The objectives of the paper were to investigate the utility of a delta-normalised difference vegetation index (dNDVI) in evaluating the status, pattern and process of invasion, and to evaluate the implications of the invasion for riverscape morphodynamics and physical habitat structure and function. The spectral classification developed had an overall accuracy of 59.89%, omission error of 28.25%, and commission error of 11.86%. The classification of vegetation was used to quantify the aerial extent and pattern of invasion (currently at least 65.50% of the valley floor vegetation cover). Field observations and existing knowledge of seed dispersal mechanisms and germination constraints of the invasive tree were compared with those of the indigenous sweet thorn Vachellia karroo, to provide insight into invasion patterns and processes within the reach. The linear channel-flanking distribution of the invasion has the potential to alter reach hydrology and morphodynamics, which are key building blocks of physical habitat structure and function. The spectral classification approach demonstrated is relatively simple, has a strong biophysical basis in that it exploits a known, predictable and observable seasonal vegetation response that produces a clear spectral signature, and can be applied to long reaches of rivers impacted by alien vegetation invasions. [ABSTRACT FROM AUTHOR]

Mountain areas are very often considered to be biodiversity hotspots. Rocks are one of the most unique habitats and serve as a suitable biotope for many endemic and specialized plant species. Here we present the results of the vegetation studies conducted in central and eastern Pamir-Alai and the south-western Tian Shan mountains in 2013‒2019. Based on 334 phytosociological relevés we propose a synopsis of the rock communities of the montane and colline zones of eastern Middle Asia. As a result of our research, we distinguish 17 communities defined into five groups of petrophytic vegetation within the Caricion koshewnikowii alliance. We describe 11 new associations: Feruletum bucharicae, Scrophularietum glabellae, Asperulo albiflorae–Kafirniganietum hissaricae, Scutellarietum immaculatae, Campanuletum eugeniae, Silenetum kuschakewiczii, Rosularietum platyphyllae, Spireaetum pilosae, Silenetum lithophilae, Campanuletum albertii, and Rhinactinidietum limoniifoliae. Distinctiveness of environmental conditions together with the species composition of the phytocoenoses within the montane and colline belts of eastern Middle and Central Asia make the investigated vegetation among those with the highest specific diversity and richness of endemisms in the whole area. The main factors that determine the species composition of the investigated vegetation seem to be phytogeography, altitude, bedrock type, and exposition. Supplemental data for this article is available online at https://doi.org/10.1080/11263504.2021.1922531. [ABSTRACT FROM AUTHOR]

Peatland vegetation is dominant among azonal vegetation types within the central zone of Lake Baikal eastern coast. In order to classify the phytosociological diversity of peatland vegetation, the supervised k-means clustering method was applied. According to the main concept of the method, the unidentified dataset from this study was subjected to the classification among identified units (a priori groups) from the north-eastern area of Lake Baikal. The unidentified dataset is composed of 55 relevés, which were collected in the central zone of the Lake Baikal eastern coast. The identified a priori groups comprise 12 associations distinguished according to the Braun-Blanquet approach. Those associations are based on classification of 138 relevés, which were taken in the course of studies of the north-eastern part of Lake Baikal, such as Svyatoi Nos Peninsula and Barguzinskiy mountain range. As a result, we obtained the assignment of unidentified relevés among those a priori groups. The supervised k-means clustering performed well in assignment of unidentified relevés to a priori groups and proved to be a useful tool for the classification and syntaxonomical positioning of unidentified peatland vegetation. The peatland vegetation was classified into classes: Oxycocco-Sphagnetea and Scheuchzerio-Caricetea nigrae. [ABSTRACT FROM AUTHOR]

This report presents the second of two parts of a bioclimatic classification of the vegetation of the United States. Herein, we present our data for 631 weather stations with a temperate macrobioclimate, in which 65 alliances and 28 isobioclimates were identified. [ABSTRACT FROM AUTHOR]

Wetland vegetation classification often treated the saltmarsh as a single type of land-cover (LCT). Mapping the dynamic and spatially complex coastal zones using optical remote sensing is still challenging. This study firstly analyzed the spectral properties of target objects generated by Landsat 8 (OLI), formulated new spectral indices and then proposes a rule-based approach to mapping five vegetated (saltmarsh, seagrass, mangrove, non-mangrove forest, and agricultural land) and three non-vegetated (wet sand, saltpan, and built-up areas) LCT in the study area, that is, large coasts located in the south-eastern coasts of Bangladesh. The thresholds of spectral indices were selected from the newly introduced spectral indices over the method development site (Bakkhali estuary). The rule-based LCT classification process followed a set of cascade rules of image thresholding and masking, based on a hierarchical tree in order to generate detailed thematic maps of saltmarsh land-cover. Overall accuracy (OA) and Kappa coefficient (K) of rule-based approach were 84.6% and 0.821, respectively. The reliability and robustness of the approach was tested over two independent external validation test sites: Karnaphuli river estuary and Teknaf peninsula and consistent accuracy results achieved: OA = 81.7% (K = 0.787) and OA = 84.6% (K = 0.821) respectively. [ABSTRACT FROM AUTHOR]

Afghanistan's annual opium survey relies upon time-consuming human interpretation of satellite images to map the area of potential poppy cultivation for statistical sample design. Deep Convolutional Neural Networks (CNNs) have shown ground-breaking performance for image classification tasks by encoding local contextual information, in some cases outperforming trained analysts. In this study, we investigate the development of a CNN to automate the classification of agriculture from medium-resolution satellite imagery as an alternative to manual interpretation. The residual network (ResNet50) CNN architecture was trained and validated for delineating the agricultural area using labelled multi-seasonal Disaster Monitoring Constellation (DMC) satellite imagery (32 m) of Helmand and Kandahar provinces. The effect of input image chip size, training sampling strategy, elevation data, and multi-seasonal imagery were investigated. The best-performing single-year classification used an input chip size of 33 × 33 pixels, a targeted sampling strategy and transfer learning, resulting in high overall accuracy (94%). The inclusion of elevation data marginally lowered performance (93%). Multi-seasonal classification achieved an overall accuracy of 89% using the previous two years' data. Only 25% of the target year's training samples were necessary to update the model to achieve >94% overall accuracy. A data-driven approach to automate agricultural mask production using CNNs is proposed to reduce the burden of human interpretation. The ability to continually update CNN models with new data has the potential to significantly improve automatic classification of vegetation across years. [ABSTRACT FROM AUTHOR]

Melittio-Quercion frainetto and Melittio-Quercion confertae are two alliances, which were proposed about simultaneously 40 years ago through two different papers regarding southern Italy and southern Greece, respectively. The aims of this paper are to establish valid name, nomenclatural type and distribution range of these two alliances and to verify whether they display a sufficient degree of syntaxonomic autonomy when compared to the ecologically and geographically neighbouring alliances. A data matrix composed of 43 frequency columns summing 1242 phytosociological relevés x 625 taxa was prepared and statistically analysed. The results suggested that the thermophilous Quercus cerris and Q. frainetto oak woods of the southern Apennines and the southern Balkans deserve to be classified in two separated alliances. The application of the principle IV of the ICPN established that the correct name of the alliance is Melittio albidae-Quercion frainetto Barbéro, Bonin, Gamisans et Quézel in Bonin et Gamisans 1976 and that it was typified in southern Italy. Accordingly, the new name Geranio asphodeloidis-Quercion frainetto is here proposed for southern Greece in substitution of Melittio-Quercion confertae (nom. illeg.). The syntaxonomic proposal advanced in this paper brings significant modifications in the current version of the checklist of the European syntaxa (Eurovegchecklist). [ABSTRACT FROM AUTHOR]

The existence of a consistent inverse relationship between vegetation greenness and surface temperature has been the premise of many environmental studies. However, some authors have found that the nature of this relationship varies depending on the spatial and temporal scales of analysis as well as vegetation type. Therefore, this research aims to contribute to the understanding of this matter by evaluating the annual and intra-seasonal relationship between Leaf Area Index (LAI) and Land Surface Temperature (LST) in Central Mexico using monthly anomalies of Moderate Resolution Imaging Spectroradiometer (MODIS) data collected from 2002 to 2017. LAI is proposed as an alternative index to overcome the shortcomings of other widely used indicators of vegetation greenness (e.g. Normalized Difference Vegetation Index and Enhanced Vegetation Index). The presence/absence of any relationship is investigated through the notion of Granger causality, while the sign and strength of the relationship is estimated by means of an Impulse-Response (IR) function. Unlike traditional regression and correlation analysis, the Granger causality approach enables the examination of lagged effects of one variable over the other based on past values of both variables. IR coefficients, which have been rarely used in the related literature, help to model the over-time response of a variable with the change of another variable. The overall results indicate that, at any temporal scale, Granger causality from LST to LAI occurs more consistently than causality in the opposite direction. At the annual scale, the nature of the relationship is primarily inverse in both directions and usually weaker from LAI to LST. At the seasonal scale, the occurrence of LST to LAI causality is higher in spring (it occurs in about 40% of the evaluated pixels) and lower in winter (10%) among all forest types. The effect of LST on LAI is predominantly inverse (median coefficient 1 month after impulse −0.043) and particularly strong in deciduous broadleaf forest during summer. On the other hand, the effect of LAI on LST is mainly direct in autumn and inverse in the remaining seasons, except for evergreen needleleaf forest where the effect is inverse only in summer. The highest presence (23%) and strength (−0.039) of LAI to LST causality occur in spring over deciduous broadleaf forest. Based on these results, caution has to be exercised when assuming a consistent strong inverse relationship between vegetation greenness and surface temperature, which seems to be the general consensus in much of the literature that makes use of these two variables to study an environmental phenomenon. [ABSTRACT FROM AUTHOR]

Agroecological models have the potential to contribute to both the fight against climate change and a shift away from the dominant food system. In this article, I discuss the challenges that ecological farmers in Canada are facing in terms of scaling out agricultural systems that will help to reduce greenhouse gas emissions and sequester carbon. I draw on Gramscian theories to argue that alliance-building is required in order to advance a counter-hegemonic agroecology in Canada, with those alliances going beyond narrowly conceived class-based interests. I suggest that the challenges farmers are facing highlight the need for a just transition in agriculture, and that the social transformation that this would entail means that proponents of agroecology must consider the positionalities of environmentalists, scholars, farmers and farm workers, and Indigenous peoples across the country. [ABSTRACT FROM AUTHOR]

The aim was to study the relationships among forest communities dominated by Pinus nigra s.l. in the Apennine-Balkan biogeographical province. The opportunity to deepen this argument was offered by Pinus nigra natural communities present in the central Apennines (Gran Sasso d'Italia). The data collected on the vegetation were elaborated with the phytosociological method, although in the choice of the higher order syntaxa, it was considered appropriate to broaden the approach to different tectonic, palaeobotanical, palaeogeographic and biogeographic analysis factors, to define the classes and reference orders. This led to the recognition that the black pine forests of the study area, together with those of the central-southern limestone Apennines, should be included in the class Erico-Pinetea Horvat 1959. For the territory investigated, we describe the new association Junipero hemisphaericae–Pinetum nigrae and the new order Junipero communis–Pinetalia nigrae ordo nov. hoc loco, of which the southern Balkan alliance Fraxino orni-Pinion nigrae Em 1978 is considered the alliance typus. In this context, the new alliance Junipero hemisphaericae–Pinion nigrae (typus: Junipero hemisphaericae–Pinetum nigrae) represents the central-southern Apennine vicariant. The new order Junipero communis-Pinetalia nigrae represents the vicariant of the order Erico-Pinetalia Horvat 1959 in the central-southern Apennine-Balkan mountain ranges. [ABSTRACT FROM AUTHOR]

Due to the instantaneous field-of-view (IFOV) of the sensor and diversity of land cover types, some pixels, usually named mixed pixels, contain more than one land cover type. Soft classification can predict the portion of each land cover type in mixed pixels in the absence of spatial distribution. The spatial distribution information in mixed pixels can be solved by super resolution mapping (SRM). Typically, SRM involves two steps: soft class value estimation, which is similar to the image super resolution of image restoration, and land cover allocation. A new SRM approach utilizes a deep image prior (DIP) strategy combined with a super resolution convolutional neural network (SRCNN) to estimate fine resolution fraction images for each land cover type; then, a simple and efficient classifier is used to allocate subpixel land cover types under the constraint of the generated fine fraction images. The proposed approach can use prior information of input images to update network parameters and no longer require training data. Experiments on three different cases demonstrate that the subpixel classification accuracy of the proposed DIP-based SRM approach is significantly better than the three conventional SRM approaches and a transfer learning-based neural network SRM approach. In addition, the DIP-SRM approach performs very robustly about small-area objects within multiple land cover types and significantly reduces soft classification uncertainty. The results of this paper provide an extension for utilizing SRCNN to address SRM issues in hyperspectral images. [ABSTRACT FROM AUTHOR]

Although regional wetland mapping studies have mostly relied on optical sensors, synthetic aperture radar (SAR) sensors are being increasingly applied. The aim of this study is to analyse the ability of the Phased Array type L-band Synthetic Aperture Radar on board of the Advanced Land Observing Satellite (ALOS/PALSAR-1) data to identify, delineate and monitor wetlands, and to evaluate the importance of scene selection in a highly unpredictable wetland. Three SAR scene sets (Year A, Year B and Inter-annual) were built for this purpose, considering the intra-annual and inter-annual hydrologic variability and the phenologic variability of the studied coastal wetland. Seven land cover types were defined, including three permanently flooded wetland classes, three temporarily flooded wetland classes and one non-wetland class. An object-based unsupervised classification approach was applied on each multi-temporal set. The obtained clusters were characterized by a temporal signature and assigned to the seven land cover types using a decision tree with user-defined thresholds. The accuracy assessment of each product was performed using a set of 258 data sites, including field collected data and data retrieved from Landsat 8 Operational Land Imager (OLI) imagery acquired during the dates of the field campaign. The Year B set showed the best accuracy (83.4% overall, 75% Kappa coefficient (κ)) and the lowest omission and commission mean errors (16.6% and 16.1% respectively). The classes that were best differentiated are permanently flooded wetlands (PFW) and non-wetlands (NW) in all sets. [ABSTRACT FROM AUTHOR]

The Centaureo-Portenschlagiellion describes the cliff vegetation of the Southeastern Adriatic. This habitat type harbours many endemic taxa and has a high value for biodiversity conservation. Notwithstanding its importance, knowledge of the syntaxonomy of this alliance is still poor. This article aims at revising the synchorological, coenological and floristic relationships of the associations of the Centaureo-Portenschlagiellion. The revision is based on a data set of 103 relevés of Mediterranean xerothermic cliffs from Croatia, Bosnia i Herzegovina and Montenegro. The relevés were clustered by using the Flexible beta method. An indicator species analysis was used to identify the diagnostic taxa of the main clusters of relevés and non-metric multidimensional scaling ordination was undertaken to visualize the floristic relationships among them. Results revealed that only seven associations belong to the Centaureo-Portenschlagiellion, whereas the others already described in phytosociological literature were invalidly described or should be treated as syntaxonomic synonyms of the previous ones. Two associations (Inulo-Centaureetum cuspidatae and Portenschlagiello-Campanuletum portenschlagianae) were reduced to the rank of subassociations of the Moltkio-Inuletum verbascifoliae. Two other subassociations were described for the first time. Data provided with this revision may be considered as essential base-line information that should aid in evaluating the state of this vegetation type in the future. [ABSTRACT FROM AUTHOR]

Nothing will shape urban life in this century more than carbon—efforts to abolish it, and the consequences of its pollution. Critical urban studies must put the climate emergency at the very core of the discipline. This paper suggests four methodological injunctions to this end: (1) a field-wide development of carbon literacy along the lines of how all critical urbanists understand capital and inequalities; (2) research that links technical low-carbon urban projects to urban spaces' core political conflicts; (3) both a recuperation of historical cases of democratizing, massive built environment intervention, and an engagement with the cutting-edge technologies of green urbanism, each in service of producing egalitarian visions of climate-friendly urban spaces; finally, (4) I argue that critical urbanists must join the fight, forging new alliances within and beyond universities to prevent eco-apartheid, and articulate a no-carbon, radically democratic alternative. [ABSTRACT FROM AUTHOR]

Islands crucially contribute to the Mediterranean Basin's high floristic diversity, which, however, is at risk facing climate and land-use changes. Besides the identification of highly diverse areas, the knowledge about factors favouring diversity is of great importance. We analysed plant species diversity and composition related to environmental factors over varied vegetation units on a former Italian prison island in the northwest of Sardinia. Due to a long history of land use with grazing and later abandonment the nowadays protected island features a semi-natural landscape and can serve as an example for strongly anthropogenic altered insular ecosystems. Floristic composition, soil properties, microclimate and ungulate abundance were assessed. Relationships of vegetation composition and diversity with abiotic variables were examined by Canonical Correspondence Analysis, which indicated the importance of air temperature, soil moisture, slope gradient and C/N ratio for floristic differentiation. Most important abiotic factors for plant species richness were relative air humidity and soil moisture, while floristic diversity was mainly determined by air temperature and pH. Furthermore, observation data pointed to an adverse influence of ungulate abundance for plant species diversity. Regarding nature conservation, grazing intensity thus must be critically taken into account, especially for sensitive vegetation units like the coastal garrigue. [ABSTRACT FROM AUTHOR]

The study anticipated to understand sand encroachment evolution through analysis of sand contribution across space and time using remote sensing in Laâyoune-Tarfaya basin, Morocco, over the period from 1987 to 2011. The assessment based on supervised classifications of Landsat imagery orthorectified data, using Maximum Likelihood (ML), Minimum Distance (MD) and Support Vector Machine (SVM) classifiers. In order to ameliorate the information, principal components analysis (PCA) and co-occurrence measurement algorithm were used for choosing bands and data transformation. Images differencing was applied on image pairs derived from classification to analyze sand encroachment evolution. All classifiers present enhanced performances, and revealed that area covered by sand was increased by 7%, 4.66% and 4.59% for ML, MD and SVM, respectively. Consequently, images differencing results confirmed that sand material increasing arise not only from coastal area contribution but also mostly from erosion of complicated sand dunes exist in the middle part of the studied area. Evaluating of the presented phenomenon dimensions and its consequences are extremely important to increase the local authorities awareness and mainly for avoiding or minimizing the consequences of the future sand dunes threats. [ABSTRACT FROM AUTHOR]

Tropical peat swamp forest is rapidly being converted into industrial-scale oil palm agriculture across South-east Asia. This has global and local environmental implications including habitat loss and carbon release. Monitoring oil palm expansion or land-use/land-cover on peatlands is feasible using remote sensing technology, however, there are few examples of assessments of different oil palm management systems. Three peatland land cover types were assessed and mapped using vegetation indices and supervised classifications of Landsat-8 level-2 imagery: peat swamp forest, large-scale oil palm plantation and smallholding. Firstly, we calculated and compared differences in vegetation indices (NDVI, EVI and SAVI) values between each of the land covers. Next, we mapped land cover using three classification methods – Support Vector Machine, Random Trees and Maximum Likelihood – with a single date and multi-temporal datasets. These were applied to the vegetation indices and also a three band Landsat image using band 2 (blue band), band 4 (red band) and band 5 (near-infrared band) i.e. false colour composite. The vegetation index assessment found significant differences in the NDVI, EVI and SAVI between peat swamp forest, large-scale plantation and smallholding. The NDVI in peat swamp forest was highest. In contrast, the EVI and SAVI in the peat swamp forest were the lowest. While the NDVI, EVI and SAVI in the large-scale plantations were greater than the smallholdings. Irrespective of landscape type, there were strong positive correlations between NDVI and EVI, NDVI and SAVI, and EVI and SAVI. For the land cover mapping, the best classifier was Random Trees with the multi-temporal Landsat bands 543 datasets with a mapping accuracy of 95.54% and a Kappa coefficient of 0.933. This study demonstrated that both vegetation indices and land cover mapping can provide planners and decision-makers with tools to monitor natural and production landscapes, managed under different systems in the tropics. [ABSTRACT FROM AUTHOR]

The information on the topography of the seabed included in data from airborne lidar bathymetry can be used for the detection of changes occurring at the bottom of the basin and for the detection of objects deposited on it. Processing of full waveform data enables obtaining data on the water surface and the identification of the underwater situation. The classification process based on the Random Forest (RF) algorithm is presented using data from lidar bathymetry. The classification was performed in two independent approaches using input vector consisting of 16 features. In the first approach, the entire point cloud was classified, in the second the point cloud did not contain points of the water surface. In the classification, traits based on the full waveform and resulting from point cloud geometry were used. The quantitative efficiency of classification was verified through error matrices. The obtained efficiency (100% water surface, 99.9% seabed and 60% objects) of the point classification of the objects enables the possibility for using the RF algorithm for detection of objects on the seabed. In comparison to Support Vector Machines, the RF algorithm has better results in the detection of points on the objects in point cloud with water surface. [ABSTRACT FROM AUTHOR]

This study presents the results of a floristic vegetational study on Robinia pseudoacacia neoformation forests in the peri-Adriatic sector of central Italy. This has allowed the characterization of these coenoses at the ecological, biogeographic, syntaxonomic and landscape levels. These currently represent the southernmost syntaxa of the Robinietea class described for the Italian peninsula, and the first syntaxonomic contribution of this class in Europe for the Mediterranean biogeographical region. We propose here the new alliance Lauro nobilis–Robinion pseudoacaciae of the order Chelidonio–Robinietalia pseudoacaciae and class Robinietea, with two new associations: Melisso altissimae–Robinietum pseudoacaciae and Rubio peregrinae–Robinietum pseudoacaciae. The new alliance Lauro nobilis–Robinion pseudoacaciae (typus: Melisso altissimae–Robinietum pseudoacaciae) brings together neoformation forests and pre-forest dominated by R. pseudoacacia in those territories with a Mediterranean macroclimate of the peri-Adriatic sector of central Italy. The optimum is found for the alluvial plain and low-slope morphologies, on soils that are moist and rich in organic matter and in areas with anthropic disturbance. On the basis of comparisons with the European context, the alliance Bryonio–Robinion described for the temperate territories of northern Italy is here validated. [ABSTRACT FROM AUTHOR]

In this country report, I offer a resident-outsider's perspective on the recent history and current landscape of Canadian feminist geography. I highlight the institutional framework that showcases Canadian feminist geography: the Suzanne Mackenzie Memorial Lectures, the biennial events put on at the Canadian Association of Geographers meeting with the support of the Canadian Women and Geography Study Group/Groupe d'étude sur les femmes et la géographie (CWAG). I discuss recent community-building efforts, including the Great Lakes Feminist Geography Collective, and scholarly workshops, and point to the creative outputs that have emerged from these collective workspaces. I point to a variety of Canadian feminist geographers who have laid the groundwork for the diverse field that exists today, as well as some who are re-making the field through the use of other ontological and methodological frameworks. I conclude with a commentary on the importance of community- and alliance-building, especially in the face of challenges like structural injustice, generational transition, and even physical distance. [ABSTRACT FROM AUTHOR]

Research into hyperlink interaction patterns has been particularly interested in whether they integrate the online space or segregate it into "echo chambers." Concentrating on contentious politics in national settings, the existing studies have mainly examined the relationships between domestic actors, mostly bloggers. This study seeks to expand the focus by including several actor types, allowing their connective actions to reach beyond national borders, and employing a comparative approach that contrasts high- with low-contentious contexts. Analyzing climate change hyperlink networks originating in the US and Switzerland, the results show that their transnational dimension plays a crucial role in polarizing the discourse, regardless of the specific political context. We find similar patterns that segregate climate advocates from skeptics and lead to distinct transnational relationships within the camps. The results demonstrate that countermovement actors in particular are able to forge strong transnational alliances. [ABSTRACT FROM AUTHOR]

This report presents the first of two parts of a bioclimatic classification of the vegetation of the United States. Using a geographical information system, 987 weather stations were located along a longitudinal macrotransect from the shores of the Atlantic to Pacific on four maps: Map of the Physiographic Divisions of the Conterminous US, US Potential Natural Vegetation Map, US Ecoregion Map, and Terrestrial Ecosystems-Isobioclimates Map of the Conterminous United States. Based on these maps, bibliographic resources and field data, we deduced the potential natural vegetation (PNV) of each weather station; then, we assigned the different PNV types to alliance or association levels using the US National Vegetation Classification (USNVC). In a next step, USNVC groups were related with similar level phytosociological syntaxa described in the study area. The bioclimatic distribution of the USNVC units defined was then interpreted using the bioclimatic classification proposed in successive approximations by S. Rivas-Martínez. The distribution of USNVC alliances was mainly linked to the macrobioclimates (Mediterranean, Temperate, and Tropical) of the longitudinal gradient examined, though some edaphic factors induced the appearance of specialized plant groups. Herein, we present our data for the Mediterranean macrobioclimate, in which 53 alliances and 28 isobioclimates were identified. [ABSTRACT FROM AUTHOR]

The Sentinel-1 satellites provide the formerly unprecedented combination of high spatial and temporal resolution of dual polarization synthetic aperture radar data. The availability of dense time series enables the derivation and analysis of temporally filtered annual backscatter signals. The study concentrates on the use of Sentinel-1 seasonal backscatter signatures for forest area estimation and forest type classification. A classification method based on time series similarity measures is introduced and tested in three test areas covered by various forest types including broadleaf temperate, boreal and montane forests. The results are compared with two European-wide Copernicus high resolution layers, namely forest type and tree cover density (TCD). The correspondence of forest/non-forest maps and TCD is high in all test areas, with overall accuracies for forest/non-forest classification between 86% and 91% and Pearson correlation coefficients for TCD between 0.68 and 0.74. The forest type classification (non-forest, coniferous and broadleaf forest classes) provides best results in temperate forests with an overall accuracy of 85%; in boreal forest, the accuracy decreases to only 65%. Generally, the method provides reliable results for forest area estimation, including regions where methods based on static parameters are often problematic (mountainous areas), and it enables forest type classification in temperate forests. [ABSTRACT FROM AUTHOR]

The research deals with three scrub communities/formations in the southern Balkans, maquis, pseudomaquis and shibljak, and their changes along the main (macro)ecological gradient. We performed DCA analysis and, since the first axis correlates with climatic data, the projection of relevés on this axis was accepted as proxy for the (macro)ecological gradient, and the species turnover along this gradient was elaborated. Linear regression was used to test the turnover of life forms, chorotypes and the life strategy of species (syntaxonomical affiliation). It was found that life forms, except hemicryptophytes and nanophanerophytes, do not change significantly along the gradient change; most chorotypes change significantly; among species, life strategies (syntaxonomical affiliation), the proportion of species of deciduous and evergreen forests and scrub, as well as species of dry grassland, change significantly. The classification established three clusters, representing three formations and we tested differences among them. It was established that stenomediterranean and Eurasian species and species of perennial grasslands differentiate all three clusters and some groups only two of them. We found that discontinuous formations can be established along a continuous gradient. Communities were also classified within the present synsystematic system. [ABSTRACT FROM AUTHOR]

In land cover mapping, the complexity of landscapes is fitted into classes that may limit the recognition of natural variability. In this study, we tested the power of land cover classes (defined on the CORINE land cover classification scheme, a standardized legend set by EU for land cover inventory) to separate different vascular plant assemblages in forest ecosystems. In order to separately identify the role of different sources of inconsistency between land cover classes and species composition, we compared three different inventory processes, based on (i) dominant tree species as observed in the field, (ii) visual interpretation of remotely sensed images and (iii) semi-automatic supervised classification of satellite images. Our results underline that classifying forest ecosystems on the basis of their canopy species produces an over-simplification of habitat variability. Consequently, land cover maps based on non-specialized classification schemes should not be regarded as good proxies for plant biodiversity. If land cover maps are intended to describe and manage landscapes and their associated biodiversity, it is necessary to improve their capacity to represent the complexity of ecosystems. [ABSTRACT FROM AUTHOR]

Mixed agro-silvo-pastoral landscapes are typically of significant natural, economic and cultural value. The montado (wood pasture) dominated systems of southern Portugal are a prime example, and experience high rates of land cover change. Tracking these changes is of importance for landscape level conservation and management. Whilst satellite remote sensing is the most realistic approach at large spatial scales, heterogeneous landscapes can present challenges to the accurate classification of land cover and its change. In this investigation we demonstrate a novel approach to the investigation of land cover change over a 25 year period (1984-2009) in an area of importance for the conservation of the Iberian lynx, Black vulture, and other threatened biodiversity in south-east Alentejo, Portugal. We apply a Tasselled Cap Transformation (TCT) to Landsat imagery from these two years, and then Change Vector Analysis (CVA) on the transformed data to highlight areas of vegetation gain and loss during the intervening period. Using a 2009 land cover classification, and a set of rules based on these vegetation changes, we then predict the land change over the 25 year period focussing on predominant classes of vegetation physiognomy. The results are discussed in terms of probable drivers, as well as implications for biodiversity and other landscape values. [ABSTRACT FROM AUTHOR]

Vegetation classification is a fundamental task in several applications such as forest management, remote sensing-based crop monitoring, and mitigation of plant diseases, digital prototyping of plants, and plant phenotyping, among others. We propose a novel supervoxel-based methodology to accurately detect vegetation from small-footprint full-waveform airborne laser scanning data in urban and mountainous scenes. Mathematically, the full-waveform decomposition and fitting model based on multiple kernels is presented to generate high-density 3D point clouds and their relevant attributes. The homogeneous supervoxels are then generated by using an enhanced probability density clustering (PDC) algorithm. For each supervoxel, we employ latent Dirichlet allocation to obtain supervoxels features through generalisation of geometric and full-waveform features of point clouds. The Support Vector Machine (SVM) and ensemble classifier random forest (RF) are used to classify these supervoxels into vegetation and non-vegetation. Our experiments on urban and mountainous scenes demonstrate that our approach achieves an overall accuracy of 98.27% and 96.47% respectively by RF classifier and achieves an overall accuracy of 98.16% and 97.67% on the same data sets by SVM classifier. By integrating full-waveform information and more meaningful generalised features, our method outperforms state-of-the-art methods at preserving a trade-off between missing alarm rate and false alarm rate. [ABSTRACT FROM PUBLISHER]

New syntaxa of different hierarchical levels are here described in order to update the “Syntaxonomic checklist of the Italian classes, orders and alliances (Vegetation Prodrome of Italy)”, promoted in 2012 by the “Ministry of Environment, Land and Sea Protection” in collaboration with the “Italian Society of Botany”. The new alliances, suballiances and associations belong to theSalici purpureae-Populetea nigraeandQuerco roboris-Fagetea sylvaticaeclasses. [ABSTRACT FROM PUBLISHER]

Many birds have songs with variable structure and sequences. In earlier studies, we characterized some features from the song structures of California Thrashers (Toxostoma redivivum). In the Thrashers, there were a large number of phrase types, dozens to hundreds and the songs that were sequences of these many phrases were not random, but show some structure referred to as syntax. For example, a syntactic rule might be that phrase type A can be followed by phrase type B, but not type C. We, along with others, have assumed that syntax is an important feature of songs. This paper describes an experimental attempt to determine that syntax is important to California Thrashers by recording the reaction of territorial thrashers to playbacks of other thrasher songs, some of which obeyed the syntax rules we had discovered while others violated those rules. We also obtained video recordings of their behaviour near the playback speakers. We observed differences in the reactions to the birds that heard these two types of playbacks. Resident males reacted to either playback type, but more strongly when the original order was preserved. We observed difference in their behavioural response to correct or altered syntax. This indicates that the syntax of their songs is perceived in territorial defence by the birds. [ABSTRACT FROM PUBLISHER]

An overview of the coastal vegetation of Site of Community Importance “Zone Umide della Capitanata” (Puglia Region) is here presented. Original relevés were performed in different locations of the site. On the basis of the in-field observations (142 original relevés) and of literature data, 44 plant communities have been defined, belonging to the following classes:Lemnetea minoris,Ruppietea maritimae, Potametea pectinati,Phragmito-Magnocaricetea, Cakiletea maritimae,Ammophiletea,Helichryso-Crucianelletea maritimae,Juncetea maritimi,Sarcocornietea fruticosae,Saginetea maritimae,Thero-Suaedetea,Tuberarietea guattatae,Nerio-Tamaricetea. For each vegetation unit, the distribution area at local and regional level and the relation to habitat types of the 92/43 EEC Directive are provided. [ABSTRACT FROM PUBLISHER]

This study investigates the utility of an off-the-shelf, consumer-grade unmanned aerial vehicle (UAV) for invasive species mapping in a lacustrine fringe environment. Specifically, this work sought to determine whether such a UAV would be capable of creating accurate maps of the extent of patches of an invasive plant, yellow flag iris (Iris pseudacorusL.), more efficiently than could be accomplished by a traditional field survey, which is often considered in the literature to provide the most accurate maps. The study was conducted at two lakes in the central interior of British Columbia. The UAV used in this study was a DJI Phantom 3 Professional that can acquire images using the built-in 12.4 MP digital camera. This UAV was selected because it is representative of the type of aerial platform that is easily accessible to invasive plant managers in terms of cost, ease of use, and lack of legal restrictions. Three methods of mapping the yellow flag iris were compared: (1) field survey, (2) manual interpretation of the raw UAV-acquired imagery and the orthoimage created from these data, and (3) pixel-based classification of the orthoimage created from the UAV imagery using a random forest classifier. The results revealed that, at both lakes considered, manual interpretation of the UAV-acquired imagery produced the most accurate maps of yellow flag iris infestation, with a false-positive and false-negative classification rate of less than 1%. [ABSTRACT FROM PUBLISHER]

The technique of unmanned aerial vehicle (UAV) offers great advantages in vegetation classification because of its high spatiotemporal resolutions and low cost and risk in field measurements. This study explores the potential of using hyperspectral images acquired from UAV to classify juvenile trees, and the feasibility of applying different spectral parameters to classify vegetation. In experimental wooded plots in Hebei province (38.703644°N, 115.400848°E), China, hyperspectral images were obtained from UAV in high spatial and spectral resolutions. The images were corrected for radiative and geometric distortions, and noise was removed. Ten principal components (PCs) and 32 hyperspectral vegetation indices (HVIs) were extracted. Different combinations of spectral parameters (i.e. 9 HVIs, 12 HVIs, 9 HVIs and 10 PCs, and 12 HVIs and 10 PCs) were applied to maximum likelihood classifications to identify the optimal combinations for vegetation classification. The overall accuracy was 67.5% using 9 HVIs, 71.6% using 12 HVIs, 92.5% using 9 HVIs and 10 PCs, 95.7% using12 HVIs and 10 PCs. The spectral parameter combination of 10 PCs and 12 HVIs was the most powerful for vegetation classification, with an overall accuracy of up to 95.7%. Classification accuracy using other combinations of spectral parameters requires further study. [ABSTRACT FROM PUBLISHER]

The characterization of vegetation is a very important ecological task, especially in sensitive mountain areas, as alpine regions often respond to small short-term variations of abiotic and biotic components as well as long-term global changes. Spatial techniques, such as imaging spectroscopy, allow for detailed classification of different syntaxonomic categories of vegetation and their status. Based on the Airborne Prism Experiment (APEX) and simulated Environmental Mapping and Analysis Program (EnMAP) data, this study focused on subalpine and alpine vegetation mapping in the eastern part of the Polish Karkonosze National Park (KPN). The spatial resolution of APEX (3.12 m) enabled the classification of 21 vegetation communities, which was generalized into eight vegetation types. These types were identified on scaled-up APEX data, as both 252 bands from most of the spectral range and a spectrally reduced dataset of 30 minimum noise fraction (MNF) transforms, and compared to the simulated (30 m spatial resolution) EnMAP data using test areas extracted from the field survey derived reference non-forest vegetation map. After preprocessing, a pixel purity index (PPI) was calculated using the MNF image and then the training and validation pixels were selected with Support Vector Machine classification of vegetation communities carried out using different kernel functions: linear, polynomial, radial basis function, and sigmoid. The classification accuracy was obtained for 21 base classes, and the best result was achieved by using the linear function and 252 bands (overall accuracy (OA) of 74.39%). The next step was to classify the eight generalized vegetation types, and the OA for the APEX data reached 90.72% while EnMAP reached 78.25%. The results show the potential use of APEX and EnMAP imagery in mapping subalpine and alpine vegetation on a community and vegetation-type scales, within a diverse ecosystem such as the Karkonosze National Park. [ABSTRACT FROM AUTHOR]

Based on the diagnostic morphological characters of theArundotaxa, and after both field and herbarium observation, three species (A.micrantha,A.pliniiandA.donax) were found along the eastern Adriatic coast. Altogether, 50 phytocoenological relevés fromArundostands were collected in Croatia, Montenegro and Albania between 2010 and 2013. Three associations with a predominance ofArundospecies were determined:Arundinetum plinianae,Arundinetum micranthaeandArundini donacis–Convolvuletum sepium. Among them,Arundinetum micranthaeis described and proposed as a new association, belonging to theArtemisietea vulgarisclass. It occurs frequently along the coastal belt between the cities of Split and Dubrovnik in South Croatia. The syntaxonomy of the associations is discussed and comparison with similar communities from Italy is made. The vegetation and floristic structure of the associations is discussed as a potential basis for biodiversity conservation programmes and for evaluating the state of these ecosystems in the future. [ABSTRACT FROM PUBLISHER]

Surface waterbodies in arid and semi-arid environments are threatened by both natural and anthropogenic pressures. Mapping the distribution of surface waterbodies is crucial for managing their dwindling quantities and quality. In this study, a fast and reliable method of water extraction has been introduced. A remote-sensing index called the simple water index (SWI) was formulated to differentiate waterbodies from vegetation class automatically, and to differentiate waterbodies from shadows or built-up areas (water-like features). Its performance was compared with the automated water extraction index (AWEI) and the modified normalized difference water index (MNDWI) on Landsat 8 Operational Land Imager (OLI) image of South Africa. The robustness of the algorithm was tested on images in Madagascar and the Democratic Republic of Congo (DRC) with different biomes. The overall accuracies and kappa coefficient (κ) were used to compare the performance of each index. The McNemar test was performed to assess the significance of the output map and the validation data set. The SWI showed the highest overall accuracy of 91.9% (κ = 0.83), whereas the AWEI and MNDWI yielded overall accuracies of 83.8% (κ = 0.65) and 78.4% (κ = 0.53), respectively. The McNemar test showed that there was no significant difference between the SWI map (p = 0.248), whereas both AWEI and MNDWI maps were significantly different from the validation data set atp = 0.041 andp = 0.013, respectively. The SWI approach reduces the thresholding problem by 50% over the conventional MNDWI and AWEI. It is expected that the SWI will also be useful for the accurate quantification of waterbodies for large areas. [ABSTRACT FROM AUTHOR]

The new allianceCerastio tomentosi-Globularion meridionalisand the new associationHelianthemo alpestris-Globularietum meridionalisare here proposed as a contribute to the prodrome of Italian vegetation. The new syntaxa are related to the chamaephytic high-mountain vegetation and they are characterized by a wide presence of endemics, southern European orophytes, mountain-Mediterranean, and amphi-Adriatic species. The proposal aims to differentiate, on a structural basis, the high-mountain vegetation of the Apennines at higher syntaxonomical levels. [ABSTRACT FROM AUTHOR]

In the course of the project “Syntaxonomic checklist of the Italian classes, orders and alliances (Vegetation Prodrome of Italy)”, promoted in 2012 by the Italian “Ministry of Environment, Land and Sea Protection” in collaboration with the “Italian Society of Botany”, it arises that some syntaxa are not validly published according to the International Code of Phytosociological Nomenclature rules. For this reason, these syntaxa names are validated in this paper. Furthermore, other new syntaxa of different hierarchical levels are here described for the first time. Therefore, one new order, two new suborders, six new alliances, two new suballiances and three new associations are here described. These new syntaxa belong to the following classes:Juncetea maritimi,Stellarietea mediae,Trifolio medii–Geranietea sanguinei,Alnetea glutinosaeandSalici purpureae–Populetea nigrae.Finally, the syntaxonomic arrangement ofAlnion incanaeis here discussed. [ABSTRACT FROM AUTHOR]

As relict traces of the Tertiary period,Quercus canariensisWilld. and the forests it forms comprise one of the most interesting woodlands, which prevails in southern Iberia with very particular edaphoclimatic envelopes. Combining phytosociological methodology with hierarchical cluster analyses, we aim to update the syntaxonomic scheme of the forests related to this species and the complex matrixes it forms with the hybrid speciesQuercus marianicaC. Vicioso. We propose one new association from Portugal, in a total of four types of Mirbeck's oak woodlands, in Southern Iberia. We conclude the necessity to maintain high standards on taxonomic and geobotanical surveys, with major biogeographic knowledge; it is indissociable from an accurate analysis and comprehension on the functional ecology and structure of these woodlands, regarding its dynamics. Therefore, their high conservation value, related to its relict status, supports the integration of these forests in Habitat 9240 (Annex B-I from Council Directive 92/43/EEC) in order to ensure its preservation. [ABSTRACT FROM AUTHOR]

This paper describes the vegetation of the Carson Desert (Nevada, USA) based on a phytosociological analysis of its major plant associations, as determined by the Braun-Blanquet method. Diagnostic tables, climatic, edaphic, and biogeographical data were used to establish floristic affinities among identified plant communities and to interpret their distributions in zonal gradients. Their syntaxonomic positions in the classesAllenrolfeetea occidentalisandArtemisio tridentatae–Juniperetea osteospermae, and in the new classSarcobatetea vermiculatiwere also established. Three associations (Atriplici confertifoliae–Sarcobatetum baileyi,Atriplici canescentis–Psorothamnetum polydenii, andSuaedo moquinii–Sarcobatetum vermiculati) are described here for the first time along with the orderSarcobatetalia vermiculatiand the allianceSarcobation vermiculati. [ABSTRACT FROM AUTHOR]

The Vegetation Prodrome of Italy was promoted in 2012 by the Italian “Ministry of Environment, Land and Sea Protection”, in collaboration with the “Italian Society of Botany”, to provide a comprehensive and systematic catalogue and description of Italian plant communities. The Prodrome that is presented in this paper is the first full organic synthesis of the vegetation of Italy at the alliance syntaxonomic level. It fulfils several needs, the main one being a unified and comprehensive national framework that may make an important contribution to the definition of the European Vegetation Prodrome. Syntaxonomy, as well as taxonomy, is sometimes based on considerations that may in part diverge: several authors tend to favour models that are divisive or aggregative to a greater or lesser extent in terms of flora, biogeography and ecology. These different points of view stimulate the scientific debate and allow the adoption of a framework that is more widely supported. The Prodrome includes 75 classes, 2 subclasses, 175 orders, 6 suborders and 393 alliances. The classes were grouped into nine broad categories according to structural, physiognomic and synecological elements rather than to syntaxonomic criteria. The rank, full valid name, any synonymies and incorrect names are provided for eachsyntaxon. The short declaration highlights the physiognomy, synecology, syndynamics and distribution of the plant communities that belong to thesyntaxon. The Prodrome of the Italian Vegetation is linked to the European Strategy for Biodiversity, the European Habitats Directive and the European Working Groups related to the ecosystems and their services. In addition to basic applications, the Prodrome can be used as a framework for scientific research related to the investigation of the relationships between plant communities and the environmental factors that influence their composition and distribution. [ABSTRACT FROM PUBLISHER]

During recent years, many studies have been undertaken to investigate how spectral characteristics of forests can provide information on spatial patterns of tree species diversity (TSD). Important advances have been made, and significant relationships between TSD and remotely sensed indicators of net primary productivity and environmental heterogeneity have been reported. However, the season-dependence of these relationships has not yet been fully investigated, and the influence of phenology remains poorly understood. In this study, we aim to assess how the relationships between remote sensing indicators and TSD depend on the season of the year. TSD measures, including species richness, Shannon’s diversity and Simpson’s diversity, were determined for 82 field plots in the Afromontane cloud forests of Taita Hills, Kenya. A time series of 15 Landsat images were used to calculate a set of spectral and heterogeneity metrics. The relationship between remote-sensing metrics and TSD measures was analysed by simple and multivariate regression analysis. We conclude that the relationships between remote-sensing metrics and TSD are season-dependent. Hence, it is demonstrated the date of image acquisition is an important aspect to be considered in biodiversity studies. Given that the dependence of the relationships is closely linked to climate seasonality defining vegetation phenology, the relationships may also vary according to geographical conditions. [ABSTRACT FROM AUTHOR]