Alpine forest biodiversity estimated from the space: testing the Spectral Variation Hypothesis comparing Landsat 8 and Sentinel 2 using a multi-temporal Rao Q index

Forests cover about 30 percent of the earth surface, they are the most biodiverse terrestrial ecosystems and they are at the base of many ecological processes and services. The loss of forest biodiversity makes in risk the benefits that the humans derived from theme. The assessment of biodiversity is therefore an important and essential goal to achieve, that however can result difficult, time consuming and expensive if estimated through field data. Through the remote sensing it is possible to estimate in a more objectively way the species diversity, using limited resources, covering broad surfaces with high quality and standardized data. One of the method to estimate biodiversity from remote sensing data is through the Spectral Variation Hypothesis (SVH) , which states that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of that area. The SVH has been tested using different indexes and measures; recently in literature, the Rao’s Q index, applied to remote sensing data has been theoretically tested as a new and innovative spectral variation measure. In this paper for the first time, the SVH through the Rao’s Q index has been tested with an NDVI time series derived from the Sentinel 2 (with a spatial resolution of 10m) and Landsat 8 satellites (spatial resolution of 30m) and correlated with data of species diversity (through Shannon’s H) collected in forest. The results showed that the Rao’s Q is a grateful spectral variation index. For both the sensors, the correlation with the field data had the same tendency as the NDVI trend, reaching the highest value of correlation (through the coefficient of determination R2) in June, when the NDVI was at its peak. In this case the correlation reached a value of R2=0.61 for the Sentinel 2 and of R2=0.45 for the Landsat 8, showing that the SVH is scale and sensor dependent. The SVH tested with optical images through the Rao’s Q index showed grateful and promising results in alpine forests and could lead to as much good results with other remote sensing data or in other ecosystems

Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Climate change has already led to a wide range of impacts on our society, the economy and the environment.According to future scenarios, mountain regions are highly vulnerable to climate impacts, including changes in the water cycle (e.g. rainfall extremes, melting of glaciers, river runoff), loss of biodiversity and ecosystems services, damages to local economy (drinking water supply, hydropower generation, agricultural suitability) and human safety (risks of natural hazards). This is due to their exposure to recent climate warming (e.g. temperature regime changes, thawing of permafrost) and the high degree of specialization of both natural and human systems (e.g. mountain species, valley population density, tourism-based economy). These characteristics call for the application of risk assessment methodologies able to describe the complex interactions among multiple hazards, biophysical and socio-economic systems, towards climate change adaptation.Current approaches used to assess climate change risks often address individual risks separately and do not fulfil a comprehensive representation of cumulative effects associated to different hazards (i.e. compound events). Moreover, pioneering multi-layer single risk assessment (i.e. overlapping of single-risk assessments addressing different hazards) is still widely used, causing misleading evaluations of multi-risk processes. This raises key questions about the distinctive features of multi-risk assessments and the available tools and methods to address them.Here we present a review of five cutting-edge modelling approaches (Bayesian networks, agent-based models, system dynamic models, event and fault trees, and hybrid models), exploring their potential applications for multi-risk assessment and climate change adaptation in mountain regions.The comparative analysis sheds light on advantages and limitations of each approach, providing a roadmap for methodological and technical implementation of multi-risk assessment according to distinguished criteria (e.g. spatial and temporal dynamics, uncertainty management, cross-sectoral assessment, adaptation measures integration, data required and level of complexity). The results show limited applications of the selected methodologies in addressing the climate and risks challenge in mountain environments. In particular, system dynamic and hybrid models demonstrate higher potential for further applications to represent climate change effects on multi-risk processes for an effective implementation of climate adaptation strategies

A Novel Data Fusion Technique for Snow Cover Retrieval

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the moderate-resolution imaging spectroradiometer (MODIS) data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well-known underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season

Spatial-Explicit Climate Change Vulnerability Assessments Based on Impact Chains. Findings from a Case Study in Burundi

Climate change vulnerability assessments are an essential instrument to identify regions most vulnerable to adverse impacts of climate change and to determine appropriate adaptation measures. Vulnerability assessments directly support countries in developing adaptation plans and in identifying possible measures to reduce adverse consequences of changing climate conditions. Against this background, this paper describes a vulnerability assessment using an integrated and participatory approach that builds on standardized working steps of previously developed ‘Vulnerability Sourcebook’ guidelines. The backbone of this approach is impact chains as a conceptual model of cause–effect relationships as well as a structured selection of indicators according to the three main components of vulnerability, namely exposure, sensitivity and adaptive capacity. We illustrate our approach by reporting the results of a vulnerability assessment conducted in Burundi focusing on climate change impacts on water and soil resources. Our work covers two analysis scales: a national assessment with the aim to identify climate change ‘hotspot regions’ through vulnerability mapping; and a local assessment aiming at identifying local-specific drivers of vulnerability and appropriate adaptation measures. Referring to this vulnerability assessment in Burundi, we discuss the potentials and constraints of the approach. We stress the need to involve stakeholders in every step of the assessment and to communicate limitations and uncertainties of the applied methods, indicators and maps in order to increase the comprehension of the approach and the acceptance of the results by different stakeholders. The study proved the practical usability of the approach at the national level by the selection of three particularly vulnerable areas. The results at a local scale supported the identification of adaption measures through intensive engagement of local rural populations

Monitoring mountains in a changing world: new horizons for the Global Network for Observations and Information on Mountain Environments (GEO-GNOME)

Mountains are globally distributed environments that provide significant societal benefits, a function that is increasingly compromised by climatic change, environmental stress, political and socioeconomic transformations, and unsustainable use of natural resources. Gaps in our understanding of these processes and their interactions limit our capacity to inform decisions, where both generalities of mountain regions (eg climate processes) and specificities (eg context-specific manifestations of climate risks) matter. The Global Network for Observations and Information on Mountain Environments (GEO-GNOME), a Group on Earth Observations initiative, aims to fill these gaps through accessible Earth Observation (EO) as well as in-situ data and information on global change drivers, conditions, and trends. A workshop convened by the Mountain Research Initiative (MRI) revised GEO-GNOME's work plan, galvanizing a network that promotes relevant monitoring of global change in mountains and is responsive to the integrated knowledge needs of policy, research, and management

Supporting agri-food projects to implement climate change adaptation through the interactive online tool ‘CRISP’

Introduction International agri-food system programmes are increasingly seeking to mainstream climate action across their portfolios. A range of methods and tools exists, but there is no “ready-to-use” tool that allows a cost- and time-effective climate risk assessment for specific agri-food systems and the development of adaptation hypotheses. The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), with Eurac Research and the Alliance of Bioversity International and the International Centre for Tropical Agriculture (CIAT), set out to provide an easy-to-use tool that considers the specific characteristics of agri-food systems under a changing climate. Objectives The Climate Risk Planning & Managing Tool for development programmes in agri-food systems (CRISP) is a web-based tool for projects planners and implementers in the agri-food sector. It allows them to identify starting points for climate risk management and develop adaptation hypotheses to backstop their intervention’s design – in a quick and easy way. Methodology Using the impact chain methodology as a framework, we undertook a literature search to identify relevant climate risks in the context of selected agro-ecological systems across five regions. We organised the findings into an extensive knowledge database. We then co-designed a tool with potential users that would allow the database to be queried in different ways depending on the user needs. Findings Potential users of the tool see promise in using it to improve their programming in the agri-food sector. They suggest expanding the knowledge database to include more agro-ecological systems, value chain concepts and national policy-related data. Significance of the work for policy and practice The CRISP tool will help users to identify starting points for climate risk management. The tool provides science-based evidence and linkages to complementary tools and approaches to implement climate actions. It will assist practitioners in the agri-food sector to develop adaptation hypotheses to help guide the project from the planning phase onward

Modelling the consequences of land-use change on landscape pattern and biodiversity

In den nächsten zehn bis zwanzig Jahren wird in Deutschland mit einem flächenhaften Brachfallen von landwirtschaftlichen Flächen auf marginalen Standorten gerechnet. Die Auswirkungen von solchen Landnutzungsänderungen auf die Landschaft sind vielfältig und wenig untersucht. Vor diesem Hintergrund diskutiert und präsentiert die vorliegenden Arbeit Möglichkeiten zur modellgestützten Darstellung der Auswirkungen von Landnutzungsänderungen auf das Landschaftsmuster (1) und die Biodiversität (2).Zur Darstellung von Landnutzungsänderungen auf das Landschaftsmuster (Aufgabe 1) wurde das Modell PAGE (Pattern Generator) entwickelt. PAGE disaggregiert vorhanden Landnutzungsszenarien auf höheren Ebenen (z.B. Landkreisebene) bis auf den einzelnen Standort und stellt die Ergebnisse in Form von digitalen Landnutzungskarten zur Verfügung. Die Disaggregierung erfolgt nach deterministischen Regeln und basiert auf einer von der naturräumlichen Ausstattung abhängigen Eignungsbewertung für die landwirtschaftliche Nutzung. Um den eher konzeptionell geprägtem Gegenstand Biodiversität quantitativ beschreiben zu können (Aufgabe 2) wurde das modellgestützte Indikatorsystem BAT (Biodiversity Assessment Tool) entwickelt. In BAT werden drei Aspekte von Biodiversität beleuchtet: Komposition, Struktur und Funktion. Biodiversität wird dabei überwiegend auf der Lebensraumebene (Biotopeben) betrachtet. Sieben ausgewählte Indikatoren kombinieren Methoden zur Bestimmungen von Landschaftsmaßen mit ökologisch-funktionellen Ansätzen. Als Indikatoren wurden gewählt: Anteil naturnaher Flächen, Vielfalt naturnaher Biotoptypen, Flächendichte, durchschnittliche Flächengröße, Vernetzung naturnaher Flächen, Hemerobie und Anteil ungestörter Flächene. Als Repräsentant für die Arteneben wird zusätzlich der Anteil an potentiellen Storchhabitaten ermittelt. Alle Indikatoren werden auf Rasterbasis für definierte Analyseumgebungen mit einheitlichem Flächenbezug mit Hilfe der Moving Window Methode berechnet. Durch eine Koppelung der beiden Modelle können die Auswirkungen von konkreten, ökonomisch begründeten Landnutzungsszenarien dargestellt werden. Zusätzlich werden mit Hilfe von Szenarienbündeln, die Landnutzungsänderungen von 0 100% berücksichtigen, die Reaktion der Biodiversität als Funktion der Intensität einer Landnutzungsänderungen ( Response Funktionen ) erforscht. In einer Fallstudie in drei Landkreisen in Brandenburg und dem Bundesland Brandenburg zeigte sich ein räumlich und funktionell sehr differenzierte Bild. Als Reaktion auf eine angenommen Teilliberalisierung der Agrarmärkte fallen bis zu 50% aller Ackerflächen, vor allem in Niederungsgebieten und auf den trockenen, armen Standorten aus der Nutzung. Unter der Annahmen von naturnahe Grünlandtypen als Folgenutzung profitieren diese Bereiche von einer Erhöhung des Anteils naturnaher Biotope, einer Erhöhung der Biotopvernetzung und der Entstehung von großflächigen naturnahen Biotopkomplexen. Zur Informationsverdichtung werden die Ergebnisse von der Rasterebene (25m) auf Gemeinde-, Landkreis- und Bundeslandebene aggregiert und mit Hilfe einer hirarchischen Cluster Biodiverstitätklassen ausgewiesen. Insgesamt hat sich das hier präsentierte indikatorgestützte, räumlich explizite Verfahren auf Biotopebene sehr als Methode zum Monitoring von Biodiversität in verschiedenen Zeit- und Raumebene bewährt und kann für landschaftsökologische Fragestellungen empfohlen werden.In the upcoming decades Germany is expected to face a partial abandonment of agricultural areas as a consequence of decreasing subsidies and market prices for agricultural products. The impacts of such land use changes are manifold and less investigated. This thesis discusses and presents methods, how the impact of land-use changes on landscape pattern (1) and biodiversity (2) can be assessed by landscape models. For task (1) the model PAGE (Pattern Generator) was developed. PAGE disaggregates scenarios about future land use on higher levels (e.g. district level) and produces digital land-use maps of future landscapes. PAGE uses deterministic rules which are based on a suitability assessment of the landscape for agricultural use. Task (2) is complex to solve, since biodiversity is more a concept than a quantitative value. The Biodiversity Assessment Tool (BAT) uses an indicator based approach, which assess biodiversity mainly on the ecosystem level considering three aspects of biodiversity: composition, structure and function. The seven selected indicators combine the technology of landscape metrics with an ecological and functional approach. The indicators are: fraction, richness, patch density, mean patch size, and connectivity of natural and semi-natural ecosystems as well as the degree of internal (hemeroby) and external disturbances. The species level is represented by a calculation of the fraction of potential habitats for the white stork (ciconia ciconia). All indicators are calculated raster-based with a moving window analysis neighbourhood. By coupling both models the impacts of scenario based land-use changes on biodiversity can be assessed. A broadened approach which considers sets of scenarios with land-use changes form 0-100% allows to investigate the general relationship between biodiversity and land-use by means of so called response functions. A case study in the state of Brandenburg showed a very heterogeneous picture in the response to land-use changes. Up to 50% of all arable fields fall abandoned as a consequence of a partly liberation of the European agro-markets. Abandonment appeared mainly in wet lowland areas and on poor and sandy soils. If semi-natural grasslands would be the successor of arable land biodiversity in most of these areas will increase, namely the fraction, the connectivity and the patch size of semi-natural areas. For a aggregation of information the results were aggregated from the raster size (25m) to the community, district and state level and classified into biodiversity classes by hierarchical cluster process. The indicator based approach to assess biodiversity on the ecosystem level has proofed to be very suitable for the monitoring of biodiversity in a broad range of scales in time and space, particularly in investigations of landscapes and land-use

Drought Impact on Phenology and Green Biomass Production of Alpine Mountain Forest—Case Study of South Tyrol 2001–2012 Inspected with MODIS Time Series

Ecological balance and biodiversity of the alpine forest is endangered by global and local climatic extremes. It spurs a need for comprehensive forest monitoring, including in depth analyses of drought impact on the alpine woodland ecosystems. Addressing an arising knowledge gap, we identified and analyzed 2002–2012 aridity related responses within the alpine mountain forest of South Tyrol. The study exploited a S-mode PCA (Principal Component Analysis) based synergy between meteorological conditions rendered by the scPDSI (self-calibrated Palmer Drought Severity Index) and forest status approximated through MODIS (Moderate Resolution Imaging Spectroradiometer) derived NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index based on MODIS band 7) time series. Besides characterizing predominant forest temporal response to drought, we identified corresponding spatial footprints of drought impact, as well as examined aridity-related changes in forest phenology and biomass production. The latter was further evaluated in relation to forest type, elevation, aspect and slope. Recognized meteorological conditions highlighted: prolonged 2003–2007 mild to extreme drought, and overall regional drying tendencies. Arising remotely sensed forest responses accounted on localized decline in foliage water content and/or photosynthetic activity, but also indicated regions where forest condition improved despite the meteorological stress. Perceived variability in the forest response to drought conditions was governed by geographic location, species structure, elevation and exposition, and featured complexity of the alpine forest ecosystem. Among the inspected biophysical factors elevation had the strongest influence on forest phenology and green biomass production under meteorological stress conditions. Stands growing above 1400 m a.s.l. demonstrated initial increase in annual biomass growth at the beginning of the dry spell in 2003. Conversely, woodlands at lower altitudes comprising considerable share of hardwood species were more prone to biomass decline in 2003, but experienced an overall upturn in biomass production during the following years of the dry spell. Aspect showed moderate effect on drought-related phenology and green biomass production responses. Diverse forest ecosystem responses identified in this study were in line with known local and regional analyses, but also shed some new light on drought induced alternation of forest status