Effect of climate data uncertainty on ecological land classification: a case study from Argentina

2019 ◽  
Vol 78 (3) ◽  
pp. 261-270
Author(s):  
MR Derguy ◽  
AA Drozd ◽  
S Martinuzzi ◽  
JL Frangi ◽  
MF Arturi
Keyword(s):  
Data Uncertainty ◽  
Climate Data ◽  
Land Classification ◽  
Ecological Land Classification

The Common Denominator of Land Classification in Canada: an Ecological Approach to Mapping

1980 ◽  
Vol 56 (1) ◽  
pp. 19-20 ◽  
Author(s):  
J. S. Rowe
Keyword(s):  
Ecological Approach ◽  
Common Denominator ◽  
Land Classification ◽  
Ecological Land Classification ◽  
Common Basis ◽  
The Common

The cores and boundaries of land units are located by reference to relationships between climate, landform and biota in ecological land classification. This appeal to relationships, rather than to climate, or to geomorphology, or to soils, or to vegetation alone, provides the common basis for land classification.

Regional biogeographical model of vegetation zones in doctoral programme Regional Biography in Olomouc (Case study for Norway spruce)

2020 ◽  
Author(s):  
Ivo Machar ◽  
Marián Halás ◽  
Zdeněk Opršal
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Climate Data ◽  
Ecological Data ◽  
The Czech Republic ◽  
Climate Conditions ◽  
Vegetation Zones ◽  
Landscape Research

Regional climate changes impacts induce vegetation zones shift to higher altitudes in temperate landscape. This paper deals with applying of regional biogeography model of climate conditions for vegetation zones in Czechia to doctoral programme Regional Geography in Palacky University Olomouc. The model is based on general knowledge of landscape vegetation zonation. Climate data for model come from predicted validated climate database under RCP8.5 scenario since 2100. Ecological data are included in the Biogeography Register database (geobiocoenological data related to landscape for cadastral areas of the Czech Republic). Mathematical principles of modelling are based on set of software solutions with GIS. Students use the model in the frame of the course “Special Approaches to Landscape Research” not only for regional scenarios climate change impacts in landscape scale, but also for assessment of climate conditions for growing capability of agricultural crops or forest trees under climate change on regional level.

Development of Rainfall Intensity-Duration-Frequency Curves Based on Dynamically Downscaled Climate Data: Arizona Case Study

2021 ◽  
Vol 26 (5) ◽  
pp. 05021005
Author(s):  
Amin Mohebbi ◽  
Simin Akbariyeh ◽  
Montasir Maruf ◽  
Ziyan Wu ◽  
Juan Carlos Acuna ◽  
...  
Keyword(s):  
Rainfall Intensity ◽  
Climate Data ◽  
Intensity Duration Frequency ◽  
Frequency Curves

THE FOREST HABITAT TYPES IN SOUTHEASTERN MANITOBA AND THEIR APPLICATION TO FOREST MANAGEMENT

1964 ◽  
Vol 42 (10) ◽  
pp. 1417-1444 ◽  
Author(s):  
D. Mueller-Dombois
Keyword(s):  
Soil Moisture ◽  
Forest Management ◽  
Sand Dunes ◽  
Land Classification ◽  
Habitat Types ◽  
Forest Habitat ◽  
Regional Environment ◽  
Nutrient Regime ◽  
Ecological Land Classification ◽  
Silvicultural Practices

A forest ecological land classification in southeastern Manitoba resulted in the description of 14 forest habitat types, including three subtypes. These are based on silviculturally significant differences of soil moisture and nutrient regime, which are interpreted through tangible features of the three ecosystem components: vegetation, soil, and landform. The types encompass the regional environment from the driest habitats on sand dunes to the wettest in low moor bogs and from the nutritionally poorest on siliceous sandy podzols to the richest on alluvial bottomlands.The classification is to serve as a basic framework for silvicultural practices in the area. Aspects of application to current forest management are discussed.

scholarly journals Inclusive Design of Earth Observation Decision Support Systems for Environmental Governance: A Case Study of Lake Nokoué

2021 ◽  
Vol 3 ◽  
Author(s):  
Ufuoma Ovienmhada ◽  
Fohla Mouftaou ◽  
Danielle Wood
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Earth Observation ◽  
Inclusive Design ◽  
Observation Data ◽  
Climate Data ◽  
Technology Design ◽  
Data Services

Earth Observation (EO) data can enhance understanding of human-environmental systems for the creation of climate data services, or Decision Support Systems (DSS), to improve monitoring, prediction and mitigation of climate harm. However, EO data is not always incorporated into the workflow for decision-makers for a multitude of reasons including awareness, accessibility and collaboration models. The purpose of this study is to demonstrate a collaborative model that addresses historical power imbalances between communities. This paper highlights a case study of a climate harm mitigation DSS collaboration between the Space Enabled Research Group at the MIT Media Lab and Green Keeper Africa (GKA), an enterprise located in Benin. GKA addresses the management of an invasive plant species that threatens ecosystem health and economic activities on Lake Nokoué. They do this through a social entrepreneurship business model that aims to advance both economic empowerment and environmental health. In demonstrating a Space Enabled-GKA collaboration model that advances GKA's business aims, this study first considers several popular service and technology design methods and offer critiques of each method in terms of their ability to address inclusivity in complex systems. These critiques lead to the selection of the Systems Architecture Framework (SAF) as the technology design method for the case study. In the remainder of the paper, the SAF is applied to the case study to demonstrate how the framework coproduces knowledge that would inform a DSS with Earth Observation data. The paper offers several practical considerations and values related to epistemology, data collection, prioritization and methodology for performing inclusive design of climate data services.

scholarly journals Experiences with virtual schools on “Climate data for impact assessments”

2021 ◽  
Author(s):  
Janette Bessembinder ◽  
Judith Klostermann ◽  
Rutger Dankers ◽  
Vladimir Djurdjevic ◽  
Tomas Halenka
Keyword(s):  
Summer School ◽  
Climate Modeling ◽  
Virtual Schools ◽  
Virtual School ◽  
Climate Data ◽  
Climate Services ◽  
Impact Modelling ◽  
Set Up ◽  
Impact Models

<p>The provision of climate services to users is a fast developing field. In support of this development, the IS-ENES3 project, funded within the EC Horizon2020 program, organized three schools on “Climate data for impact assessments” in 2020 and 2021. In an Autumn school, a Spring school and a Summer school, climate scientists and impact scientists were brought together. An important aim of the schools was to enhance interaction between Vulnerability-Impact-Adaptation (VIA) researchers, climate services providers and climate researchers. Another aim was to provide an overview of information on climate modeling, climate data, impact modelling and climate services based on the work of the IS-ENE3 project.</p><p>In the first three weeks a series of lectures was given, covering topics such as climate data and modelling, impact models, portals for accessing and processing climate data, setting-up impact assessments, and communication of results to stakeholders. In the last three weeks the participants worked in small groups of one climate scientist with one impact scientist on a case study under the guidance of the course lecturers. Impact and climate researchers were combined on purpose to let them experience how they could help each other.</p><p>Originally the schools were planned to take place on-site (e.g. in Prague) during one week; however, due to COVID-19 the schools had to be transformed to virtual schools with two weekly sessions during six weeks. Although the virtual set-up had some disadvantages (e.g. less possibilities for networking), there were also some advantages (e.g. the possibility to record the lectures and make them available to a broader audience; more time to explore and work with climate data in between the sessions, no CO<sub>2</sub> emissions for travelling). During this presentation we will present the set-up of the schools and the conversion to a virtual school. We will focus on the lessons learnt and the evaluation of the virtual schools by the participants and give some recommendations for similar schools and how to link the climate and VIA research communities .</p>

scholarly journals ¿Podemos estimar el impacto de las transformaciones del paisaje sobre la ecodiversidad?

FACENA ◽  
2008 ◽  
Vol 21 ◽  
pp. 37
Author(s):  
Juan José Neiff ◽  
Marcelo Rolón ◽  
Sylvina L. Casco
Keyword(s):  
Yerba Mate ◽  
Land Classification ◽  
Ecological Land Classification ◽  
Landsat 7

Se han propuesto distintos criterios para medir la diversidad a nivel local (alfa y beta diversidad) a nivel regional (gama diversidad) y también a nivel del paisaje (ecodiversidad). De todas estas aproximaciones a la complejidad biótica en los ecosistemas, la ecodiversidad permite conocer la disponibilidad y la conectividad entre hábitat y lograr una idea de la variabilidad espacial de los ecosistemas. Se presentan siete índices de ecodiversidad y se discute sus ventajas y desventajas para el análisis de paisajes muy disturbados. Se analizó el paisaje del Establecimiento Las Marías, en el NE de Corrientes, que tiene algo más de 30.000 ha, comprendiendo 12.000 ha de sistemas forestales nativos y cultivados y 18.000 ha. dedicadas a té, yerba mate y policultivos. Se utilizó imágenes Landsat 7 y el procedimiento de Ecological Land Classification (ELC), para identificar las principales unidades de paisaje (bosques, pasturas, cuerpos de agua, diferentes cultivos). Se encontraron tres subsistemas de paisaje diferentes, se obtuvo información del número de polígonos y de la superficie comprendida en cada uso del paisaje y se la comparó cuantitativamente mediante varios índices. Se concluye que una determinada unidad de paisaje puede tener muy diferente diversidad, según el contexto de paisaje en el que se encuentre incluida.

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