Nonindigenous Aquatic Species

Online resource center, maintained by U.S.G.S., provides information, data, links about exotic plants, invertebrates, vertebrates, diseases and parasites. Central repository contains accurate and spatially referenced biogeographic accounts of alien aquatic species. Search for species by state, drainage area, citation in texts; find fact sheets, maps showing occurrence in the U.S. Or, for each taxon, review list of exotic species, find scientific, common name, photo, status; link to facts and distribution map. Educational levels: General public, High school

Geoinformatic methodologies and quantitative tools for detecting hotspots and for multicriteria ranking and prioritization: application on biodiversity monitoring and conservation

Chi ha la responsabilità di gestire un’area protetta non solo deve essere consapevole dei problemi ambientali dell’area ma dovrebbe anche avere a disposizione dati aggiornati e appropriati strumenti metodologici per esaminare accuratamente ogni singolo problema. In effetti, il decisore ambientale deve organizzare in anticipo le fasi necessarie a fronteggiare le prevedibili variazioni che subirà la pressione antropica sulle aree protette. L’obiettivo principale della Tesi è di natura metodologica e riguarda il confronto tra differenti metodi statistici multivariati utili per l’individuazione di punti critici nello spazio e per l’ordinamento degli “oggetti ambientali” di studio e quindi per l’individuazione delle priorità di intervento ambientale. L’obiettivo ambientale generale è la conservazione del patrimonio di biodiversità. L’individuazione, tramite strumenti statistici multivariati, degli habitat aventi priorità ecologica è solamente il primo fondamentale passo per raggiungere tale obiettivo. L’informazione ecologica, integrata nel contesto antropico, è un successivo essenziale passo per effettuare valutazioni ambientali e per pianificare correttamente le azioni volte alla conservazione. Un’ampia serie di dati ed informazioni è stata necessaria per raggiungere questi obiettivi di gestione ambientale. I dati ecologici sono forniti dal Ministero dell’Ambiente Italiano e provengono al Progetto “Carta della Natura” del Paese. I dati demografici sono invece forniti dall’Istituto Italiano di Statistica (ISTAT). I dati si riferiscono a due aree geografiche italiane: la Val Baganza (Parma) e l’Oltrepò Pavese e Appennino Ligure-Emiliano. L’analisi è stata condotta a due differenti livelli spaziali: ecologico-naturalistico (l’habitat) e amministrativo (il Comune). Corrispondentemente, i risultati più significativi ottenuti sono: 1. Livello habitat: il confronto tra due metodi di ordinamento e determinazione delle priorità, il metodo del Vettore Ideale e quello della Preminenza, tramite l’utilizzo di importanti metriche ecologiche come il Valore Ecologico (E.V.) e la Sensibilità Ecologica (E.S.), fornisce dei risultati non direttamente comparabili. Il Vettore Ideale, non essendo un procedimento basato sulla ranghizzazione dei valori originali, sembra essere preferibile nel caso di paesaggi molto eterogenei in senso spaziale. Invece, il metodo della Preminenza probabilmente è da preferire in paesaggi ecologici aventi un basso grado di eterogeneità intesa nel senso di differenze non troppo grandi nel E.V. ed E.S. degli habitat. 2. Livello comunale: Al fine di prendere delle decisioni gestionali ed essendo gli habitat solo delle suddivisioni naturalistiche di un dato territorio, è necessario spostare l’attenzione sulle corrispondenti unità amministrative territoriali (i Comuni). Da questo punto di vista, l’introduzione della demografia risulta essere un elemento centrale oltre che di novità nelle analisi ecologico-ambientali. In effetti, l’analisi demografica rende il risultato di cui al punto 1 molto più realistico introducendo altre dimensioni (la pressione antropica attuale e le sue tendenze) che permettono l’individuazione di aree ecologicamente fragili. Inoltre, tale approccio individua chiaramente le responsabilità ambientali di ogni singolo ente territoriale nei riguardi della difesa della biodiversità. In effetti un ordinamento dei Comuni sulla base delle caratteristiche ambientali e demografiche, chiarisce le responsabilità gestionali di ognuno di essi. Un’applicazione concreta di questa necessaria quanto utile integrazione di dati ecologici e demografici viene discussa progettando una Rete Ecologica (E.N.). La Rete cosi ottenuta infatti presenta come elemento di novità il fatto di non essere “statica” bensì “dinamica” nel senso che la sua pianificazione tiene in considerazione il trend di pressione antropica al fine di individuare i probabili punti di futura fragilità e quindi di più critica gestione.Who has the responsibility to manage a conservation zone, not only must be aware of environmental problems but should have at his disposal updated databases and appropriate methodological instruments to examine carefully each individual case. In effect he has to arrange, in advance, the necessary steps to withstand the foreseeable variations in the trends of human pressure on conservation zones. The essential objective of this Thesis is methodological that is to compare different multivariate statistical methods useful for environmental hotspot detection and for environmental prioritization and ranking. The general environmental goal is the conservation of the biodiversity patrimony. The individuation, through multidimensional statistical tools, of habitats having top ecological priority, is only the first basic step to accomplish this aim. Ecological information integrated in the human context is an essential further step to make environmental evaluations and to plan correct conservation actions. A wide series of data and information has been necessary to accomplish environmental management tasks. Ecological data are provided by the Italian Ministry of the Environment and they refer to the Map of Italian Nature Project database. The demographic data derives from the Italian Institute of Statistics (ISTAT). The data utilized regards two Italian areas: Baganza Valley and Oltrepò Pavese and Ligurian-Emilian Apennine. The analysis has been carried out at two different spatial/scale levels: ecological-naturalistic (habitat level) and administrative (Commune level). Correspondingly, the main obtained results are: 1. Habitat level: comparing two ranking and prioritization methods, Ideal Vector and Salience, through important ecological metrics like Ecological Value (E.V.) and Ecological Sensitivity (E.S.), gives results not directly comparable. Being not based on a ranking process, Ideal Vector method seems to be used preferentially in landscapes characterized by high spatial heterogeneity. On the contrary, Salience method is probably to be preferred in ecological landscapes characterized by a low degree of heterogeneity in terms of not large differences concerning habitat E.V. and E.S.. 2. Commune level: Being habitat only a naturalistic partition of a given territory, it is necessary, for management decisions, to move towards the corresponding administrative units (Communes). From this point of view, the introduction of demography is an essential element of novelty in environmental analysis. In effect, demographic analysis makes the goal at point 1 more realistic introducing other dimensions (actual human pressure and its trend) which allows the individuation of environmentally fragile areas. Furthermore this approach individuates clearly the environmental responsibility of each administrative body for what concerns the biodiversity conservation. In effect communes’ ranking, according to environmental/demographic features, clarify the responsibilities of each administrative body. A concrete application of this necessary and useful integration of ecological and demographic data has been developed in designing an Ecological Network (E.N.).The obtained E.N. has the novelty to be not “static” but “dynamic” that is the network planning take into account the demographic pressure trends in the individuation of the probable future fragile points

Using Worldview-2 satellite imagery to detect indicators of high species diversity in grasslands

The high small-scale diversity of plant species in semi-natural grasslands can be seen as a function of environmental conditions and land use history. This study explores the potential of using Worldview-2 spectral imagery and accessible GIS data to identify a set of vegetation characteristics known to influence biodiversity in semi-natural grasslands. Field sampling was done in 52 grassland sites, with presence and frequency of plant species and vegetation structural composition recorded in 4 m x 4 m plots. Plant species data were used to calculate overall species richness, grassland specialist richness, grassland generalist richness and Ellenberg indicator values for reaction (R), nutrients (N), soil moisture (M) and light (L). Generalized Additive models (GAM) were constructed to explain observed vegetation variables, predicted by mean values and standard deviations of WordView-2 satellite spectral reflectance and GIS data of grassland habitat area, soil type and land use history. The study was carried out on two spatial scales: 4m x 4m plots and grassland sites (0.25 ha - 14 ha). The results show that high resolution satellite imagery has potential of characterizing species diversity indirectly by the habitat productivity and heterogeneity. Grassland habitats with high small-scale species diversity had relatively low spectral heterogeneity. It was difficult to measure species diversity on a fine spatial scale using only remote sensing variables. Grassland management history is a very good predictor of species composition and diversity, especially for specialized grassland species. Ellenberg values for soil moisture (M) and nutrients (N) were successfully modelled using remote sensing data. In grasslands where the species diversity is largely driven by environmental gradients like nutrients or soil moisture, ecological indicators can be used as an alternative to species diversity to assess habitat quality

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

What do we know about multidimensional poverty in China: its dynamics, causes, and implications for sustainability

Poverty is a primary obstacle to achieving sustainable development. Therefore, exploring the spatiotemporal dynamics and causes of poverty is of great significance to the sustainable poverty reduction of the “post poverty alleviation era” in China. This paper used the multisource big data of 2022 counties in China from 2000 to 2015 to establish a comprehensive evaluation framework to explore the multidimensional poverty situation in China. The results showed the following findings: There is an obvious spatiotemporal heterogeneity of multidimensional poverty, showing a typical stair-like gradient from high in the west to low in the east, with the poverty level in state-designated poverty counties higher and intensifying over time. The spatial differentiation of multidimensional poverty is contributed to by multiple factors, in which the geographical condition has a stronger impact on state-designated poverty counties, while natural endowment and human resources have a stronger effect on non-state-designated poverty counties. These things considered, the regional poverty causes were relatively stable before 2015, but the poverty spatial agglomeration of some regions in the Northwest, Northeast, and Yangtze River Economic Belt has undergone significant changes after 2015. These findings can help policymakers better target plans to eliminate various types of poverty in different regions

Aliens, Aircraft, and Accuracies: Surveying for Understory Invasive Plants Using Unmanned Aerial Systems

Invasive (alien) plants are introduced species that can cause harm to native ecosystems, industries, or human health. Managing invasive species requires knowing where they are, and early detection of new populations increases the likelihood of local eradication. Unmanned aerial systems (UAS) are an emerging remote sensing technology that can capture very high spatial resolution imagery, are easily deployed, and may offer a more efficient alternative to extensive ground surveys to locate invasive plants. Imagery collected with UAS has been used to map invasive plants in open canopy habitats, but has yet to be tested for mapping invasive plants in forest understories. My aim was to explore the feasibility of UAS as an understory invasion monitoring tool, including tests of season, sensor type, and image classification method for reliable invasive detection. I collected imagery from a 21-hectare mixed and deciduous New Hampshire forest during spring and fall periods of phenology mismatch between native vegetation and two focal invasive plants, Berberis thunbergii (Japanese barberry) and Rosa multiflora (multiflora rose). I achieved up to 82% classification accuracy by grouping B. thunbergii and R. multiflora as an Invasive class. There were no significant differences in invasive detectability between sensors or classification methods, but spring imagery yielded the highest accuracies overall. Simpler pixel-based classifications are sufficient for achieving over 70% classification accuracy, though object-based segmentation can improve accuracy. UAS are promising technology with potential to reduce and target invasive plant ground surveys for temperate forest management

Forage supply of West African rangelands : Towards a better understanding of ecosystem services by application of hyperspectral remote sensing

Grazing is the predominant type of land use in savanna regions all over the world. Although large savanna areas in Africa are still grazed by wild herbivores, the West African Sudanian savanna region mainly comprises rangeland ecosystems, providing the important ecosystem service of forage supply for domestic livestock. However, these dryland rangelands are threatened by global change, including a predicted in-crease in climatic aridity and variability as well as land degradation caused by overgrazing. In this context, the international research project WASCAL (West African Science Service Centre on Climate Change and Adapted Land Use) was initiated to investigate the effects of climatic change in this region and to develop effective adaptation and mitigation measures. This cumulative dissertation aims at providing a methodology for a regular knowledge-driven monitoring of forage resources in West Africa. Due to the vast and remote nature of Sudanian savannas, remote sensing technologies are required to achieve this goal. Hence, as a first step, it was necessary to test whether hyperspectral near-surface remote sensing offers the means to model and estimate the two most important aspects of forage supply, i.e. forage quantity (green biomass) and quality (metabolisable energy) (Chapter 2.1). Evidence was provided that partial least squares regression was able to generate robust and transferable forage models. In a second step, direct and indirect drivers of forage supply on the plot and site level were identified by using path modelling within the well-defined concept of social-ecological systems (Chapter 2.2). Results indicate that the provisioning ecosystem service of forage supply is mainly driven by land use, while climatic aridity exerts foremost indirect control by determining the way people use their environment. Building on these findings, upscaling of models was tested to generate maps of forage quality and quantity from satellite images (Chapter 2.3). Here, two different available data sources, i.e. multi- and hyperspectral satellites, were compared to serve the overall objective to install a regular forage monitoring system. In conclusion, preliminary forage maps could be created from both systems. An independent validation would be a research desiderate for future studies. Moreover, both systems feature certain shortcomings that might only be overcome by future satellite missions

Environmental determinants of the ecology and distribution of Acacia tortilis under arid conditions in Qatar

PhD ThesisScrub or woodland communities dominated by Acacia tortilis form one of the few tree-dominated natural ecosystems in the hyper-arid climate of Qatar, making it a very important tree species that provides an essential habitat both for native animals and domestic livestock. However, the conservation and sustainable management of this tree has so far been neglected and it is now severely impacted by overgrazing and wood fuel collection. This research investigates the main environmental, ecological and management factors affecting the growth and distribution of Acacia tortilis in Qatar, including the factors affecting its regeneration. It also aims to guide the implementation of conservation programmes and development of a strategy to forestall deforestation and prevent the extinction of Acacia tortilis in Qatar. Initially, field survey, remote sensing and GIS techniques, together with univariate and multivariate statistical modelling techniques, were used to explore environmental influences on distribution of A. tortilis in Qatar at a national scale. Different vegetation indices (VIs), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), were derived for a time series of Landsat TM/ETM+ images for 1998 and 2010 and tested using ground-truth data to explore the temporal dynamics of Acacia-dominated ecosystems which indicated substantial reduction in vegetation greenness in 2010 than 1998. The initial approach had limited success due to difficulties of identifying Acacia tortilis communities accurately on satellite images due to the sparsity of tree cover and indicates the limitations of using remote sensing methods for tracing vegetation dynamics in Qatar and similar arid and hyper arid environments. The multinomial logistic regression model has a superior ability to predict Acacia distribution and is a suitable method in the prediction of the occurrence of different vegetation types. Phytogeographical investigations of the environmental and biotic factors that control the distribution of the Acacia tortilis at a local scale, in both areas protected and unprotected from human land use impacts, demonstrate that topographic factors and their control on soil and water conditions are fundamental determinants. The distinctive topography of Qatar has resulted in a heterogeneous soil landscape with extreme contrasts of chemical and physical soil conditions within and between depressions and more elevated positions in soil toposequences. Depressional land forms are more suitable for the Acacia tree growth than the surrounding higher ground because ENVIRONMENTAL DETERMINANTS OF ACACIA TORTILIS IN QATAR II depression soils have greater soil water content, soil depth, organic carbon and available phosphorus contents. Conversely, the absence of Acacia trees in summit areas is related to severe limitations for tree growth, including negligible soil water content and shallow soil depth caused by impeding bedrock or cemented horizons resulting in drought stress, as well as large contents of gypsum and/or CaCO3 in soils. The slope-controlled movement of eroded soil material, water and plant debris, and the localised leaching of soluble salts, are suggested to be important processes that lead to improved soil quality and better tree growth in depressions. The regeneration of Acacia tortilis through seedling establishment is perhaps surprisingly shown to be greater in the unprotected than in protected areas. This is attributed to the importance of ingestion by large mammals (mainly domestic herbivores) on the germination and recruitment of Acacia seedlings. The greater frequency of Acacia saplings in depressions within the unprotected areas is, however, also attributed to the presence of greater amounts of soil water, soil depth, available phosphorus, and organic carbon. Although the action of browsing may be regarded as positive, most anthropogenic impacts were shown to have negative effects on the condition and distribution of Acacia tortilis. The results proved that the impacts of cutting and browsing were severe in the unprotected sites, despite the evidence of more active regeneration. It is concluded that there is an urgent need to review the provision and management of protected habitats for Acacia tortilis in Qatar. It is suggested that cutting for domestic use should be restricted; that conservation efforts should be concentrated in depressions that favour tree growth; and that the livestock numbers should be limited to enable seedling establishment without excessive browsing.“Qatar University”, for sponsoring my Ph.D. researc

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones