Mapping fuels at multiple scales: landscape application of the Fuel Characteristic Classification SystemThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.

2007 ◽  
Vol 37 (12) ◽  
pp. 2421-2437 ◽  
Author(s):  
D. McKenzie ◽  
C.L. Raymond ◽  
L.-K.B. Kellogg ◽  
R.A. Norheim ◽  
A.G. Andreu ◽  
...  
Keyword(s):  
Spatial Data ◽  
Classification System ◽  
Stand Structure ◽  
Multiple Scales ◽  
National Forest ◽  
Rule Based ◽  
Continental Scale ◽  
Fuel Mapping ◽  
Fuel Characteristic ◽  
Data Layers

Fuel mapping is a complex and often multidisciplinary process, involving remote sensing, ground-based validation, statistical modelling, and knowledge-based systems. The scale and resolution of fuel mapping depend both on objectives and availability of spatial data layers. We demonstrate use of the Fuel Characteristic Classification System (FCCS) for fuel mapping at two scales and resolutions: the conterminous USA (CONUS) at 1 km resolution and the Wenatchee National Forest, in Washington State, at 25 m resolution. We focus on the classification phase of mapping — assigning a unique fuelbed to each mapped cell in a spatial data layer. Using a rule-based method, we mapped 112 fuelbeds onto 7.8 million 1 km cells in the CONUS, and mapped 34 fuelbeds onto 18 million 25 m cells in the Wenatchee National Forest. These latter 34 fuelbeds will be further subdivided based on quantitative spatial data layers representing stand structure and disturbance history. The FCCS maps can be used for both modelling and management at commensurate scales. Dynamic fuel mapping is necessary as we move into the future with rapid climatic and land-use change, and possibly increasing disturbance extent and severity. The rule-based methods described here are well suited for updating with new spatial data, to keep local, regional, and continental scale fuel assessments current and inform both research and management.

Fuel Characteristic Classification System (FCCS) field sampling and fuelbed development guide

2019 ◽  
Author(s):  
Susan J. Prichard ◽  
Anne G. Andreu ◽  
Roger D. Ottmar ◽  
Ellen Eberhardt
Keyword(s):  
Classification System ◽  
Field Sampling ◽  
Fuel Characteristic

Politics of Scale and Networks of Association in Public Participation GIS

2007 ◽  
Vol 39 (8) ◽  
pp. 1961-1980 ◽  
Author(s):  
Rina Ghose
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Public Participation ◽  
Social Relations ◽  
Spatial Data ◽  
Multiple Scales ◽  
Community Organizations ◽  
Theoretical Framework ◽  
Geographic Information ◽  
Politics Of Scale

The public participation geographic information systems (PPGIS) research agenda has explored the issue of equitable access and use of geographic information systems (GIS) and spatial data among traditionally marginalized citizens, in order to facilitate effective citizen participation in inner-city revitalization activities. However, prior research indicates that PPGIS is a complex process, with uneven outcomes. The author contends that such unevenness can be explained by use of a new theoretical framework drawn from the literature of politics of scale and networks. The author contends that the PPGIS process occurs in ‘spaces of dependence’, containing localized social relations and place-specific conditions. The politics of securing this space leads to the creation of ‘spaces of engagement’ at multiple scales. Within these spaces, networks of association evolve to connect multiple actors from public and private sectors with community organizations. Such networks can contain structural inequities, hierarchical dominance, and fluctuating resources. But these networks also transcend political boundaries and are dynamic and flexible, enabling individuals to manipulate and modify them. In trying to control the revitalization agendas and the material resources required, the actors and community organizations construct politics of scale. For some community organizations, such scalar politics and creative alliances with critical actors allow them to navigate territorially scaled networks of power skillfully in order to gain an effective voice in decisionmaking activities. But other community organizations lag behind, and are not able to form relationships in order to secure their urban space. By the use of new empirical data, coupled with a new theoretical framework, the author aims to contribute both to greater theorization and to better understanding of the uneven and contradictory nature of PPGIS processes.

scholarly journals Tree height integrated into pantropical forest biomass estimates

2012 ◽  
Vol 9 (8) ◽  
pp. 3381-3403 ◽  
Author(s):  
T. R. Feldpausch ◽  
J. Lloyd ◽  
S. L. Lewis ◽  
R. J. W. Brienen ◽  
M. Gloor ◽  
...  
Keyword(s):  
East Africa ◽  
Carbon Storage ◽  
Stand Structure ◽  
Forest Biomass ◽  
Small Diameter ◽  
Tree Height ◽  
Total Biomass ◽  
Guiana Shield ◽  
Tree Biomass ◽  
Continental Scale

Abstract. Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.

Deep Fuzzy Rule-based Classification System with Improved Wang-Mendel Method

2021 ◽  
pp. 1-1
Author(s):  
Yuangang Wang ◽  
Haoran Liu ◽  
Wenjuan Jia ◽  
Shuo Guan ◽  
Xiaodong Liu ◽  
...  
Keyword(s):  
Classification System ◽  
Fuzzy Rule ◽  
Rule Based

A Framework of Local Geospatial Data Infrastructure for Sustainable Urban Development

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Azman Ariffin ◽  
Nabila Ibrahim ◽  
Ghazali Desa ◽  
Uznir Ujang ◽  
Hishamuddin Mohd Ali ◽  
...  
Keyword(s):  
Urban Development ◽  
Spatial Data ◽  
Geospatial Data ◽  
Sustainable Urban Development ◽  
Data Infrastructure ◽  
Shape Category ◽  
Key Dimensions ◽  
Data Layers ◽  
Conceptual Data

This paper addresses the need to develop a Local Geospatial Data Infrastructure (LGDI) for sustainable urban development. This research will highlight the effective and efficient framework for the development of local infrastructure. This paper presents a framework (a combination of domain based and goal based frameworks) for developing a Local Geospatial Data Infrastructure. The basis of this research is on a case study conducted in a Malaysian city. The main focus of the case study was on measuring and assessing sustainability. Six conceptual frameworks were produced based on 6 key dimensions of sustainability. The developed framework consists of 6 conceptual data models and 6 conceptual data structures. It was concluded that 30 spatial data layers are needed of which 12 data layers are categorized as point shape, 17 data layers are categorized as polygon shape and 1 data layer as line shape category.

Ireland

2010 ◽  
pp. 105-130
Author(s):  
Edward Dwyer ◽  
Kathrin Kopke ◽  
Valerie Cummins ◽  
Elizabeth O’Dea ◽  
Declan Dunne
Keyword(s):  
Reference Material ◽  
Spatial Data ◽  
Data Sources ◽  
Web Gis ◽  
Internet Resource ◽  
Digital Atlas ◽  
Wide Range ◽  
Gis Environment ◽  
Data Layers ◽  
Additional Reference

The Marine Irish Digital Atlas (MIDA) is an Internet resource built in a web GIS environment, where people interested in coastal and marine information for Ireland can visualize and identify pertinent geospatial datasets and determine where to acquire them. The atlas, which is being constantly maintained, currently displays more than 140 data layers from over 35 coastal and marine organizations both within Ireland and abroad. It also features an “InfoPort” which is a repository of text, imagery, links to spatial data sources and additional reference material for a wide range of coastal and marine topics. The MIDA team has been active in the creation of the International Coastal Atlas Network and the Atlas was chosen as one of the nodes for the Semantic Interoperability Demonstrator.

scholarly journals Compact Belief Rule Base Learning for Classification with Evidential Clustering

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 443 ◽  
Author(s):  
Lianmeng Jiao ◽  
Xiaojiao Geng ◽  
Quan Pan
Keyword(s):  
Classification System ◽  
Optimization Procedure ◽  
Classification Model ◽  
Rule Base ◽  
Data Sets ◽  
Classification Problems ◽  
Ecm Algorithm ◽  
Rule Based ◽  
Belief Rule Base ◽  
Belief Function Theory

The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classification system. However, in the BRBCS, high numbers of instances and features generally induce a belief rule base (BRB) with large size, which degrades the interpretability of the classification model for big data sets. In this paper, a BRB learning method based on the evidential C-means clustering (ECM) algorithm is proposed to efficiently design a compact belief rule-based classification system (CBRBCS). First, a supervised version of the ECM algorithm is designed by means of weighted product-space clustering to partition the training set with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then, a systematic method is developed to construct belief rules based on the obtained credal partitions. Finally, an evidential partition entropy-based optimization procedure is designed to get a compact BRB with a better trade-off between accuracy and interpretability. The key benefit of the proposed CBRBCS is that it can provide a more interpretable classification model on the premise of comparative accuracy. Experiments based on synthetic and real data sets have been conducted to evaluate the classification accuracy and interpretability of the proposal.

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