scholarly journals A common typology for ecosystem characteristics and ecosystem condition variables

One Ecosystem ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Bálint Czúcz ◽  
Heather Keith ◽  
Amanda Driver ◽  
Bethanna Jackson ◽  
Emily Nicholson ◽  
...  
Keyword(s):  
Hierarchical Classification ◽  
Ancillary Data ◽  
Practical Applications ◽  
Ecological Concepts ◽  
Quantitative Metrics ◽  
Ecosystem Condition ◽  
Reporting Structure ◽  
Ecosystem Accounting ◽  
Biotic Characteristics ◽  
Landscape Level

The UN System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA) aims at regular and standardised stocktaking on the extent of ecosystems, their condition and the services they provide to society. Recording the condition of ecosystems is one of the most complex pieces in this exercise, needing to be supported by robust and consistent guidelines. SEEA EEA defines the condition of an ecosystem as its overall quality, measured in terms of quantitative metrics describing both abiotic and biotic characteristics. The main objective of this paper is to propose a simple universal classification (typology) for these ecosystem condition characteristics and metrics, based on long standing ecological concepts and traditions. The proposed SEEA EEA Ecosystem Condition Typology (SEEA ECT) is a hierarchical classification consisting of six classes grouped into three main groups (abiotic, biotic and landscape-level ecosystem characteristics). In order to facilitate practical applications, SEEA ECT is cross-linked to the most relevant existing typologies for ecosystem characteristics currently used for other purposes. To ensure clarity and practicality, we identified potential overlaps between classes and also identified the most important groups of ‘ancillary data’ that should not be considered as ecosystem condition characteristics. We consider that this new typology for ecosystem condition will create a meaningful reporting structure for ecosystem condition accounts, thus facilitating its standardisation and broad application.

scholarly journals Development of a classification algorithm for efficient handling of multiple classes in sorting systems based on hyperspectral imaging

2018 ◽  
Author(s):  
Rosalba Calvini ◽  
Giorgia Orlandi ◽  
Giorgia Foca ◽  
Alessandro Ulrici
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Classification Tree ◽  
Hierarchical Classification ◽  
Plastic Waste ◽  
Classification Model ◽  
Qualitative Assessment ◽  
Practical Applications ◽  
Relevant Variables ◽  
Foreign Materials

When dealing with practical applications of hyperspectral imaging, the development of efficient, fast and flexible classification algorithms is of the utmost importance. Indeed, the optimal classification method should be able, in a reasonable time, to maximise the separation between the classes of interest and, at the same time, to correctly reject possible outlier samples. To this aim, a new extension of Partial Least Squares Discriminant Analysis (PLS-DA), namely Soft PLS-DA, has been implemented. The basic engine of Soft PLS-DA is the same as PLS-DA, but class assignment is subjected to some additional criteria which allow samples not belonging to the target classes to be identified and rejected. The proposed approach was tested on a real case study of plastic waste sorting based on near infrared hyperspectral imaging. Household plastic waste objects made of the six recyclable plastic polymers commonly used for packaging were collected and imaged using a hyperspectral camera mounted on an industrial sorting system. In addition, paper and not recyclable plastics were also considered as potential foreign materials that are commonly found in plastic waste. For classification purposes, the Soft PLS-DA algorithm was integrated into a hierarchical classification tree for the discrimination of the different plastic polymers. Furthermore, Soft PLS-DA was also coupled with sparse-based variable selection to identify the relevant variables involved in the classification and to speed up the sorting process. The tree- structured classification model was successfully validated both on a test set of representative spectra of each material for a quantitative evaluation, and at the pixel level on a set of hyperspectral images for a qualitative assessment.

scholarly journals A conceptual framework and practical structure for implementing ecosystem condition accounts

One Ecosystem ◽  
2020 ◽  
Vol 5 ◽  
Author(s):  
Heather Keith ◽  
Bálint Czúcz ◽  
Bethanna Jackson ◽  
Amanda Driver ◽  
Emily Nicholson ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Stage Structure ◽  
Accounting System ◽  
Ecosystem Integrity ◽  
Common Definition ◽  
Ecosystem Condition ◽  
Core Elements ◽  
Instrumental Values ◽  
Ecosystem Accounting ◽  
And Function

Ecosystem condition is a fundamental component in the ecosystem accounting framework as part of the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA). Here, we develop a conceptual framework and present a practical structure for implementing ecosystem condition accounts to contribute to the revision process of the SEEA EEA, focussing on six core elements: (1) developing a common definition of ecosystem condition, (2) establishing a conceptual framing for ecosystem condition, (3) portraying the role of condition within the SEEA EEA accounting system, (4) deriving an inclusive multi-purpose approach, (5) describing the components of condition accounts and (6) developing a three-stage structure for reporting accounts. We develop a conceptual framework for an inclusive condition account, building on an ecological understanding of ecosystems upon which definitions, concepts, classifications and reporting structures were based. The framework encompasses the dual perspectives of first, the interdependencies of ecosystem composition, structure and function in maintaining ecosystem integrity and second, the capacity of ecosystems to supply services as benefits for humans. The following components of ecosystem condition accounts are recommended to provide comprehensive, consistent, repeatable and transparent accounts: (1) intrinsic and instrumental values, together with ecocentric and anthropocentric worldviews; (2) a formal typology or classification of characteristics, variables and indicators, based on selection criteria; (3) a reference condition used both to compare past, current and future levels of indicators of condition and as a basis for aggregation of indicators; and (4) a three-stage approach to compiling accounts with increasing levels of information and complexity that are appropriate for different purposes and applications. The recommended broad and inclusive scope of ecosystem condition and the demonstrated practical methods for implementation of accounts will enhance the ecosystem accounting framework and thus support a wider range of current and potential applications and users.

scholarly journals Hierarchical classification system of Germany’s ecosystems as basis for an ecosystem accounting – methods and first results

One Ecosystem ◽  
2020 ◽  
Vol 5 ◽  
Author(s):  
Karsten Grunewald ◽  
Burkhard Schweppe-Kraft ◽  
Ralf-Uwe Syrbe ◽  
Sophie Meier ◽  
Tobias Krüger ◽  
...  
Keyword(s):  
Land Use ◽  
Classification System ◽  
Hierarchical Classification ◽  
Data Sources ◽  
Accounting System ◽  
Federal States ◽  
German Federal States ◽  
First Results ◽  
Ecosystem Accounting ◽  
Accounting Changes

Information on changes in the area of different ecosystems is needed in order to establish an accounting system for ecosystem conditions and services. Currently, there are no comprehensive field mappings for the German federal states that obey a uniform mapping system. To create a nationwide “ecosystem accounting”, it is necessary to develop a uniform system of ecosystem classifications that can consistently deal with diverse nationwide data sources on the extent and condition of ecosystems, some of which use their own forms of classification. Against this background, we present a concrete proposal on how to combine and blend GIS land-use and ecosystem data that is compatible with EU-wide approaches with other regularly collected data sources, for example, from sample-based surveys, so as to generate a complete, updatable picture of the state of Germany’s ecosystems. The area shares of ecosystem types (ETs) can be shown in maps. Allocation tables with different classes or levels (layers) enable an ecosystem extent accounting, which are used to help draw up balances (area balance, status balance, service balance) and can be further detailed, depending on the task at hand. First results and trends of areal changes of main and sub-ecosystem types in Germany, based on the proposed classification system, are presented and discussed. However, the brevity of the considered timeframe (the three periods 2012-2015-2018) does not yet allow us to pinpoint trends or migratory movements, as these may be masked by methodological changes in the classification of land use and land cover. Nonetheless, the presented system for accounting changes in ecosystem areas should be continued and developed in the future in order to create a useful tool for biodiversity monitoring in Germany.

scholarly journals Applying the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework at catchment scale to develop ecosystem extent and condition accounts

One Ecosystem ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Catherine Farrell ◽  
Lisa Coleman ◽  
Mary Kelly-Quinn ◽  
Carl Obst ◽  
Mark Eigenraam ◽  
...  
Keyword(s):  
Environmental Economic ◽  
Urban Areas ◽  
National Level ◽  
Remote Sensing Data ◽  
Habitats Directive ◽  
Future Research ◽  
Catchment Scale ◽  
Ancillary Data ◽  
Ecosystem Accounting ◽  
The Eu

Ecosystem accounting is a tool to integrate nature into decision-making in a more structured way. Applying the use of nationally available datasets at catchment scale and following the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework, we present results from a catchment case study in Ireland, highlighting findings specifically in relation to the development of ecosystem extent and condition accounts. In the absence of a national ecosystem map, CORINE landcover mapping formed the basic data for extent and type of ecosystems, distinguishing woodlands and forest, peatland and heathland, grasslands and cropland and urban areas, with limited coverage of linear freshwater rivers, hedgerows and coastal ecosystems. Additional remote sensing data provided higher resolution at catchment scale, while limited site-level survey data were available. Condition data gathered for reporting under the EU Water Framework Directive were available at sub-basin level for surface waterbodies. Data were available at national level for habitats reported for the EU under the Habitats Directive (59 habitats reported), covering ~ 25% of the study area. Data for ecosystem types outside of these reporting frameworks were in the form of ancillary data only, providing information on pressures, threats and intensity of use. Our findings in Ireland reflect work across the European region, highlighting the role of data gathering and stakeholder engagement. We outline some of the data gaps to provide information for future research and alignment of data for the purpose of NCA, both at catchment and national scale.

scholarly journals Imaging Correlography Using Ptychography

2019 ◽  
Vol 9 (20) ◽  
pp. 4377
Author(s):  
Li ◽  
Wen ◽  
Song ◽  
Jiang ◽  
Zhang ◽  
...  
Keyword(s):  
Phase Retrieval ◽  
Imaging Technique ◽  
Perceptual Quality ◽  
Imaging Method ◽  
Long Distance ◽  
Practical Applications ◽  
Input Noise ◽  
Optimization Framework ◽  
Quantitative Metrics

Imaging correlography, an effective method for long-distance imaging, recovers an object using only the knowledge of the Fourier modulus, without needing phase information. It is not sensitive to atmospheric turbulence or optical imperfections. However, the unreliability of traditional phase retrieval algorithms in imaging correlography has hindered their development. In this work, we join imaging correlography and ptychography together to overcome such obstacles. Instead of detecting the whole object, the object is measured part-by-part with a probe moving in a ptychographic way. A flexible optimization framework is proposed to reconstruct the object rapidly and reliably within a few iterations. In addition, novel image space denoising regularization is plugged into the loss function to reduce the effects of input noise and improve the perceptual quality of the recovered image. Experiments demonstrate that four-fold resolution gains are achievable for the proposed imaging method. We can obtain satisfactory results for both visual and quantitative metrics with one-sixth of the measurements in the conventional imaging correlography. Therefore, the proposed imaging technique is more suitable for long-range practical applications.

scholarly journals A review of ecosystem condition accounts: lessons learned and options for further development

One Ecosystem ◽  
2020 ◽  
Vol 5 ◽  
Author(s):  
Joachim Maes ◽  
Amanda Driver ◽  
Bálint Czúcz ◽  
Heather Keith ◽  
Bethanna Jackson ◽  
...  
Keyword(s):  
Reference Conditions ◽  
Composite Index ◽  
Condition Index ◽  
Lessons Learned ◽  
Reference Levels ◽  
Standard Practice ◽  
Ecosystem Condition ◽  
Ecosystem Accounting ◽  
Standard Set ◽  
Almost All

Ecosystem condition accounts are part of the System of Environmental-Economic Accounting – Experimental Ecosystem Accounting (SEEA EEA). An ecosystem condition account contains aggregated statistical information about the overall abiotic and biotic quality of an ecosystem at a policy relevant spatial scale. This article reviews 23 publicly-accessible reports undertaken or commissioned by government agencies, academic and non-government organisations that discuss or present an ecosystem condition account. This analysis revealed that ecosystem condition is usually reported for one or more ecosystem types, but there is little consistency in the terminology used to define ecosystem types. All case studies report variables or indicators that measure specific ecosystem characteristics in order to make inferences about the overall condition of ecosystems. All studies included biotic indicators and almost all studies included species-based indicators in the condition account. The thematic aggregation of indicators into a single composite index (or in a few composite sub-indices) is not a standard practice, but applied in about half of the studies. The definition and use of a reference condition or reference levels for specific indicators against which the reported condition can be evaluated is not a standard practice, but was applied in about half of the studies. Based on this analysis, we suggest the revision of the SEEA EEA to propose a globally-consistent typology of ecosystem types; to recommend a list of ecosystem condition indicators according to an agreed classification; to provide further guidance on aggregation methods and on the development of an ecosystem condition index that can be used to compare ecosystem condition across ecosystem types and across different accounting areas; to provide further guidance on how best to set reference levels and reference conditions against which the past, current and future ecosystem condition can be assessed; and to propose a standard set of statistical tables for reporting the condition account.

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