scholarly journals Ecosystem services assessment and sensitivity analysis based on ANN model and spatial data: A case study in Miaodao Archipelago

2022 ◽  
Vol 135 ◽  
pp. 108511
Author(s):  
Liting Yin ◽  
Wei Zheng ◽  
Honghua Shi ◽  
Dewen Ding
Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 267
Author(s):  
Lydia Olander ◽  
Katie Warnell ◽  
Travis Warziniack ◽  
Zoe Ghali ◽  
Chris Miller ◽  
...  

A shared understanding of the benefits and tradeoffs to people from alternative land management strategies is critical to successful decision-making for managing public lands and fostering shared stewardship. This study describes an approach for identifying and monitoring the types of resource benefits and tradeoffs considered in National Forest planning in the United States under the 2012 Planning Rule and demonstrates the use of tools for conceptualizing the production of ecosystem services and benefits from alternative land management strategies. Efforts to apply these tools through workshops and engagement exercises provide opportunities to explore and highlight measures, indicators, and data sources for characterizing benefits and tradeoffs in collaborative environments involving interdisciplinary planning teams. Conceptual modeling tools are applied to a case study examining the social and economic benefits of recreation on the Ashley National Forest. The case study illustrates how these types of tools facilitate dialog for planning teams to discuss alternatives and key ecosystem service outcomes, create easy to interpret visuals that map details in plans, and provide a basis for selecting ecosystem service (socio-economic) metrics. These metrics can be used to enhance environmental impact analysis, and help satisfy the goals of the National Environmental Policy Act (NEPA), the 2012 Planning Rule, and shared stewardship initiatives. The systematic consideration of ecosystem services outcomes and metrics supported by this approach enhanced dialog between members of the Forest planning team, allowed for a more transparent process in identification of key linkages and outcomes, and identified impacts and outcomes that may not have been apparent to the sociologist who is lacking the resource specific expertise of these participants. As a result, the use of the Ecosystem Service Conceptual Model (ESCM) process may result in reduced time for internal reviews and greater comprehension of anticipated outcomes and impacts of proposed management in the plan revision Environmental Impact Statement amongst the planning team.


2021 ◽  
Vol 53 (4) ◽  
Author(s):  
J.-L. Gourdine ◽  
A. Fourcot ◽  
C. Lefloch ◽  
M. Naves ◽  
G. Alexandre

AbstractThe present study aims to assess (1) the ecosystem services (ES) provided by LFS and (2) the differential ES between local (Creole) and exotic breeds from pig, cattle and goat. The ES are defined as the benefits that humans derive from LFS. They were summarized in 12 ES indicators that cover services related to provisioning, ecological and socio-cultural aspects and territorial vitality. A total of 106 LFS units that covers the five agroecological zones of Guadeloupe were analysed. Functional typologies of LFS per species were created from surveys. The effect of breed on the ES indicators was tested. Results showed that the 40 pig LFS units were separated into 3 clusters that were differentiated in ES according to provisioning ES (cluster 1), cultural use and sale to the neighborhood (cluster 2) and pork self-consumption (cluster 3). The typology of the 57 farms with cattle distinguished 4 clusters with differences in ES provided in self-consumption (cluster1), ecological ES (cluster 2), socio-cultural ES for racing or draught oxen (cluster 3) and ES associated with territory vitality (cluster 4). The 66 goat LFS units were classified into 3 clusters different in ES concerning self-consumption (cluster 1), cultural aspects (cluster 2) and provisioning ES (cluster 3). Our study highlights that ES indicators are not breed dependent (P > 0.10) but rather livestock farming system dependent. The ES rely more on the rearing management than on the breed type, and up to now, there are no specifications in Guadeloupe to differentiate management between breeds.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.


2021 ◽  
Vol 49 ◽  
pp. 101286
Author(s):  
Erica Honeck ◽  
Louise Gallagher ◽  
Bertrand von Arx ◽  
Anthony Lehmann ◽  
Nicolas Wyler ◽  
...  

2021 ◽  
pp. 135481662098768
Author(s):  
Laura I Luna

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.


Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 703
Author(s):  
Astrid Vannoppen ◽  
Jeroen Degerickx ◽  
Anne Gobin

Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, trends in landscape attractiveness may be linked to certain policies, or the absence of policies over time. A spatial and temporal evaluation of landscape attractiveness is thus desirable for landscape policy development. In this paper, landscape attractiveness was spatially evaluated for Flanders (Belgium) using landscape indicators derived from geospatial data as a case study. Large local differences in landscape quality in (i) rural versus urban areas and (ii) between the seven agricultural regions in Flanders were found. This observed spatial variability in landscape attractiveness demonstrated that a localized approach, considering the geophysical characteristics of each individual region, would be required in the development of landscape policy to improve landscape quality in Flanders. Some trends in landscape attractiveness were related to agriculture in Flanders, e.g., a slight decrease in total agricultural area, decrease in dominance of grassland, maize and cereals, a decrease in crop diversity, sharp increase in the adoption of agri-environmental agreements (AEA) and a decrease in bare soil conditions in winter. The observed trends and spatial variation in landscape attractiveness can be used as a tool to support policy analysis, assess the potential effects of future policy plans, identify policy gaps and evaluate past landscape policy.


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