Showing papers by "Linda See published in 2011"

Global livestock production systems.

Abstract: Informed livestock sector policy development and priority setting is heavily dependent on a good understanding of livestock production systems. In a collaborative effort between the Food and Agriculture Organization and the International Livestock Research Institute, stock has been taken of where we have come from in agricultural systems classification and mapping; the current state of the art; and the directions in which research and data collection efforts need to take in the future. The book also addresses issues relating to the intensity and scale of production, moving from what is done to how it is done. The intensification of production is an area of particular importance, for it is in the intensive systems that changes are occurring most rapidly and where most information is needed on the implications that intensification of production may have for livelihoods, poverty alleviation, animal diseases, public health and environmental outcomes. A series of case studies is provided, linking livestock production systems to rural livelihoods and poverty and examples of the application of livestock production system maps are drawn from livestock production, now and in the future; livestock's impact on the global environment; animal and public health; and livestock and livelihoods. This book provides a formal reference to Version 5 of the global livestock production systems map, and to revised estimates of the numbers of rural poor livestock keepers, by country and livestock production system. These maps and data are freely available for download via FAO's web pages: www.fao.org/AG/againfo/resources/en/glw/home.html. It is hoped that this publication will stimulate further work in this field and encourage the use of livestock production systems information and maps in research and analysis.

Highlighting continued uncertainty in global land cover maps for the user community

Abstract: In the last 10 years a number of new global datasets have been created and new, more sophisticated algorithms have been designed to classify land cover. GlobCover and MODIS v.5 are the most recent global land cover products available, where GlobCover (300 m) has the finest spatial resolution of other comparable products such as MODIS v.5 (500 m) and GLC-2000 (1 km). This letter shows that the thematic accuracy in the cropland domain has decreased when comparing these two latest products. This disagreement is also evident spatially when examining maps of cropland and forest disagreement between GLC-2000, MODIS and GlobCover. The analysis highlights the continued uncertainty surrounding these products, with a combined forest and cropland disagreement of 893 Mha (GlobCover versus MODIS v.5). This letter suggests that data sharing efforts and the provision of more in situ data for training, calibration and validation are very important conditions for improving future global land cover products.

Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets

Abstract: This paper presents a methodology for the creation of a cropland map for Africa through the combination of five existing land cover products: GLC-2000, MODIS Land Cover, GlobCover, MODIS Crop Likelihood and AfriCover. A synergy map is created in which the products are ranked by experts, which reflects the likelihood or probability that a given pixel is cropland. The cropland map is then calibrated with national and sub-national crop statistics using a novel approach. Preliminary validation of the map was undertaken and the results are presented. The resulting cropland map has an accuracy of 83%, which is higher than the accuracy of any of the individual maps. The cropland map is freely available at agriculture.geo-wiki.org.

Building a crowd-sourcing tool for the validation of urban extent and gridded population

Abstract: This paper provides an overview of the crowd-sourcing tool Geo-Wiki, which is used to collect in-situ land cover validation data from the public. This tool is now being modularized in order to allow for domain specific land cover validation. Agriculture and biomass versions of Geo-Wiki are already operational. The next module, which is called urban.geo-wiki.org, is aimed at the validation of urban extent and gridded population data. The aim of this paper is to outline the structure of this module and the datasets that will aid in the validation of this land cover type and gridded population data. The ultimate aim of Geo-Wiki is to produce a hybrid land cover map that is better than existing products, including estimates of urban extent.

Improving neural network for flood forecasting using radar data on the Upper Ping River

Abstract: Artificial Neural Networks (ANNs) and other data-driven methods are appearing with increasing frequency in the literature for the prediction of water discharge or stage. Unfortunately, many of these data-driven models are used as the forecasting tools only short lead times where unsurprisingly they perform very well. There have not been much documented attempts at predicting floods at longer and more useful lead times for flood warning. In this paper ANNs flood forecasting model are developed for the Upper Ping River, Chiang Mai, Thailand. Raw radar reflectively data are used as the primary inputs and water stage are used as the additional inputs, also four input determination techniques (Correlation, Stepwise regression, combination between Correlation and Stepwise Regression and Genetic algorithms) are applied to select the most appropriated inputs. Normally, the ANNs model can predict up to 6 hours when only water stage used as the input data and the lead time can be increased up to 24 hours by using only radar data. In addition, combination of the input between water stage and radar data, gave the overall result better then using only water stage or radar data, also selecting different appropriated inputs could improve model's performance.

CA City: Simulating Urban Growth through the Application of Cellular Automata

Abstract: It is estimated that 3.5 billion people currently live in cities, which equates to more than 50% of the total population (UN, 2010). These cities vary in size from 500,000 to over 10 million inhabitants (termed mega-cities). The number of cities, in particularly mega-cities, are set to significantly increase by 2050. However, this rapid urban population growth is taking place at differing speeds and spatial scales across the globe. Developing countries are experiencing a much higher level of urbanisation than developed countries. For example, by 2050, it is expected that Europe will be 84% urbanised (compared to 82% in 2010), whilst Asia and Africa will be 65% and 62% urbanised by 2050, respectively, compared to 40% in 2010 (UN, 2010). Such rapid urbanisation, normally unplanned and spontaneous, brings its own set of problems, in both the social and physical environment. These include spatial segregation of the rich and poor, shortages in urban housing and basic services, and the production of vast volumes of waste and harmful synthetic materials (Pacione, 2005), as well as urban poverty. Psychologically, urbanisation can engender feelings of loneliness, self-centeredness, loss of a sense of community, and increasing crime rates (Knox, 1994). To mitigate for these types of problems as well as to manage and plan future urban growth, a range of modelling techniques has been applied. The development of urban theory and modelling has a long history, e.g. Industrial Location Theory (Weber, 1909), Central Place Theory (Christaller, 1933), the Concentric Zone Model (Burgess, 1925), the Sector Model (Hoyt, 1939) and the Multiple Nuclei Model (Harris and Ullman, 1945). These classical theories and models have formed the foundation for studying urban structure and growth, but they have been criticised for being overly simplistic, unrealistic in their assumptions, and not applicable to the structure of today’s cities (Chapin and Kaiser, 1979; Briassoulis, 2000; Batty, 1994, 1996). Another major criticism levelled at these models is their static nature. They are unable to explain the spontaneous growth that has taken place in modern cities under a non-equilibrium status, which has evolved diversely from highly dispersed edge cities to

The development and evaluation of a new model for measuring land fragmentation

Abstract: Land fragmentation is a major problem in different parts of the world. This paper presents a new land fragmentation index which overcomes the weaknesses of existing indices by integrating a number of land fragmentation factors, allowing the user to select which factors are included and to apply weights to the factors selected. When applied to a case study area in Cyprus, the new "global land fragmentation index" outperforms the existing indices. The paper also introduces a new "parcel shape index" and a new transformation process known as the "mean standardisation method." Sensitivity analysis is carried out to indicate the impact of changing weights of selected factors under particular scenarios and utilisation of the new parcel shape index versus existing indices.

A multi-attribute decision-making module for the evaluation of alternative land consolidation plans

Abstract: This paper explains the "Evaluation module" of a computerised land consolidation planning support system. It is a multi-attribute decision-making approach that evaluates alternative land redistribution solutions generated by a Design module. The method introduces the idea of a "parcel concentration coefficient" for measuring the dispersion of holdings and a "landowner satisfaction rate" for estimating the acceptance of the new land redistribution plan by the landowners involved. The paper explains how the evaluation criteria have been selected, weighted, standardised and used for ranking the alternative solutions. Non-linear value functions for each evaluation criterion are introduced through the involvement of five land consolidation experts. A sensitivity analysis is reported and a case study application of the whole "Evaluation module" is presented together with screenshots of the user interface.

Understanding Ethnic Sorting and School Performance through a Classification of Primary Schools in London

Abstract: Pupil characteristics have a major influence on the performance of schools which these pupils attend. Using the ‘ k means ’ clustering method, fourteen categories of primary schools in London have been identified on the basis of their ethnic composition and certain socio-economic characteristics. The typology that has been created and reported in this paper provides a framework for examining ethnic sorting and academic performance at the school level in London.