Modelling landscape complexity for land use management in Rio de Janeiro, Brazil

Papadimitriou, Fivos

doi:10.1016/j.landusepol.2012.01.004

Cited by 51 publications

(10 citation statements)

References 53 publications

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“…Research related to land unit areas has typically been limited to ecological studies, such as biodiversity, as this term originated in the ecological sciences [23][24][25][26][27]. We found only one previous study which used the LUA concept for soil research, but it did not consider SM [30].…”

Section: Derivation Of the Lua Mapmentioning

confidence: 99%

Soil Moisture Mapping in an Arid Area Using a Land Unit Area (LUA) Sampling Approach and Geostatistical Interpolation Techniques

Gharechelou

Tateishi

Sharma

et al. 2016

IJGI

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Soil moisture (SM) plays a key role in many environmental processes and has a high spatial and temporal variability. Collecting sample SM data through field surveys (e.g., for validation of remote sensing-derived products) can be very expensive and time consuming if a study area is large, and producing accurate SM maps from the sample point data is a difficult task as well. In this study, geospatial processing techniques are used to combine several geo-environmental layers relevant to SM (soil, geology, rainfall, land cover, etc.) into a land unit area (LUA) map, which delineates regions with relatively homogeneous geological/geomorphological, land use/land cover, and climate characteristics. This LUA map is used to guide the collection of sample SM data in the field, and the field data is finally spatially interpolated to create a wall-to-wall map of SM in the study area (Garmsar, Iran). The main goal of this research is to create a SM map in an arid area, using a land unit area (LUA) approach to obtain the most appropriate sample locations for collecting SM field data. Several environmental GIS layers, which have an impact on SM, were combined to generate a LUA map, and then field surveying was done in each class of the LUA map. A SM map was produced based on LUA, remote sensing data indexes, and spatial interpolation of the field survey sample data. The several interpolation methods (inverse distance weighting, kriging, and co-kriging) were evaluated for generating SM maps from the sample data. The produced maps were compared to each other and validated using ground truth data. The results show that the LUA approach is a reasonable method to create the homogenous field to introduce a representative sample for field soil surveying. The geostatistical SM map achieved adequate accuracy; however, trend analysis and distribution of the soil sample point locations within the LUA types should be further investigated to achieve even better results. Co-kriging produced the most accurate SM map of the study area.

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Section: Derivation Of the Lua Mapmentioning

confidence: 99%

Soil Moisture Mapping in an Arid Area Using a Land Unit Area (LUA) Sampling Approach and Geostatistical Interpolation Techniques

Gharechelou

Tateishi

Sharma

et al. 2016

IJGI

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“…In this paper we are interested in the effect of two different kinds of relevant complex networks in finance [1][2][3] on implied cost of capital for the listed companies, particularly in a prominent emerging market, Brazil, which have been the subject of studies on complexity in other disciplines such as epidemiology [4], geophysics [5], ecology [6,7], and information science [8,9].…”

Section: Introductionmentioning

confidence: 99%

The Complexity of Social Capital: The Influence of Board and Ownership Interlocks on Implied Cost of Capital in an Emerging Market

2018

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Starting from sociological perspectives on complexity, we show how the social capital of boards and owners networks affects the implied cost of capital of companies listed on Brazilian stock exchange. We specifically show arguments and evidence that the effect of the relational resources found in the direct, indirect, and heterogeneous board's ties reduces the cost of capital while relational resources embedded in shareholder networks increase the cost of capital. Our results show that while the increase in the relational resources of the board reduces the implied cost of capital, an increase in these shared resources in the ownership relationships of the firm increases the cost of financial capital.

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“…To address challenges faced by disaster management agencies and to improve the use of existing information for disaster damage estimations, the following issues should be taken into account: (1) Produce more global datasets through inclusive and collaborative platforms to facilitate data access and update them periodically. At the same time, make full use of existing geo-information resources so that they add value through integrated information service platforms; (2) Develop methodologies specific to each disaster and validate them in conjunction with available data sources, damage assessment indicators, and guidelines for their transfer software tools for ease of use; (3) Shift the practice of generating 'information based on data' to developing 'knowledge based on information' through in-depth requirement analyses and solutions-oriented methodologies to generate the products needed for decision-making; (4) Develop standards related to datasets, methodologies, products, and platforms for the superior integration and utilization of geo-information to enhance disaster preparedness for an effective response, as spelled out under the Sendai Framework for Disaster Risk Reduction: 2015-2030; (5) Construct a geo-information integrated platform for information resource searching, use, and development at the global scale with data catalogs, metadata, and clearly specified policies; (6) Explore the potential and modalities of new advances and innovations such as big data, cloud computing, and environment modeling for superior disaster reduction applications [56][57][58].…”

Section: Discussionmentioning

confidence: 99%

Integrating Global Open Geo-Information for Major Disaster Assessment: A Case Study of the Myanmar Flood

Cui

Liu

et al. 2017

IJGI

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Major disasters typically impact large areas, cause considerable damages, and result in significant human and economic losses. The timely and accurate estimation of impacts and damages is essential to better understand disaster conditions and to support emergency response operations. Geo-information drawn from various sources at multi spatial-temporal scales can be used for disaster assessments through a synthesis of hazard, exposure, and post disaster information based on pertinent approaches. Along with the increased availability of open sourced data and cooperation initiatives, more global scale geo-information, including global land cover datasets, has been produced and can be integrated with other information for disaster dynamic damage assessment (e.g., impact estimation immediately after a disaster occurs, physical damage assessment during the emergency response stage, and comprehensive assessment following an emergency response). Residential areas and arable lands affected by the flood disaster occurring from July to August 2015 in Myanmar were assessed based on satellite images, GlobeLand30 data, and other global open sourced information as a study case. The results show that integrating global open geo-information could serve as a practical and efficient means of assessing damage resulting from major disasters worldwide, especially at the early emergency response stage.

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Modelling landscape complexity for land use management in Rio de Janeiro, Brazil

Cited by 51 publications

References 53 publications

Soil Moisture Mapping in an Arid Area Using a Land Unit Area (LUA) Sampling Approach and Geostatistical Interpolation Techniques

Soil Moisture Mapping in an Arid Area Using a Land Unit Area (LUA) Sampling Approach and Geostatistical Interpolation Techniques

The Complexity of Social Capital: The Influence of Board and Ownership Interlocks on Implied Cost of Capital in an Emerging Market

Integrating Global Open Geo-Information for Major Disaster Assessment: A Case Study of the Myanmar Flood

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