Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forest types,structural attributes and age in sub-tropical coastal region of KwaZulu Natal,South Africa using remote sensing

Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forests is central in deriving accurate and reliable knowledge and understanding of the extent to which these species contribute to the global carbon cycle and towards minimizing climate change effects. The principal objective of this study was to quantify the variability and allocation patterns of aboveground carbon stocks across Pinus and Eucalyptus plantation forests, tree-structural attributes (i.e. stems, barks, branches and leaves) and age groups, using models developed based on remotely sensed data. The results of this study demonstrate that aboveground carbon stocks significantly (α = 0.05) vary across different plantation forest species types, structural attributes and age. Pinus taeda and Eucalyptus grandis species contained aboveground carbon stocks above 110 t C ha⁻¹, and Eucalyptus dunii had 20 t C ha⁻¹. Across plantation forest tree structural attributes, stems contained the highest aboveground carbon stocks, when compared to barks, branches and leaves. Aboveground carbon stock estimates also varied significantly (α = 0.05) with stand age. Mature plantation forest species (i.e. between 7 and 20 years) contained the highest aboveground carbon stock estimates of approximately 120 t C ha⁻¹, when compared to younger species (i.e. between 3 and 6 years), which had approximately 20 t C ha⁻¹. The map of aboveground carbon stocks showed distinct spatial patterns across the entire study area. The findings of this study are important for understanding the contribution of different plantation forest species, structural attributes and age in the global carbon cycle and possible climate change moderation measures. Also, this study demonstrates that data on vital tree structural attributes, previously difficult to obtain, can now be easily derived from cheap and readily-available satellite data for inventorying carbon stocks variability.     

FREDERICK JACKSON TURNER AS A REGIONALIST∗

Basic contemporary portfolio models are applied to an interregional economic analysis of U.S. Census Regions during 1963-1978. The models are well suited the type of analysis that partitions a region's economic cycle into national and local components. Portfolio theory provides a logic for interregional cross investment; results of an empirical test indicate portfolio theory may be superior to simple neoclassical theory in explaining spatial patterns of capital investment.     

Exploring Regional Firm-Size Structure in Canadian Housebuilding: Ontario, 1991 and 1996

This paper is concerned with the regional firm-size structure of housebuilding in Canada based on a case study of the province of Ontario. Using an innovative database of builders in Ontario and geographic information systems, measures of regional firm-size structure are developed and are modeled with data from the Canadian census. The results corroborate established models in the city systems and industrial organization literatures in that industrial concentration is negatively associated with regional population size, economic diversity, and economic performance. Small housebuilding firms abound in large, diverse and economically vibrant regions, especially large urban regions, and thereby maintain industrial deconcentration.     

Measuring the significance of a divide to local drainage patterns

This article presents a framework for estimating a new topographic attribute derived from digital elevation models (DEMs) called maximum branch length (B _max). Branch length is defined as the distance travelled along a flow path initiated at one grid cell to the confluence with the flow path passing through a second cell. B _max is the longest branch length measured for a grid cell and its eight neighbours. The index provides a physically meaningful method for assessing the relative significance of drainage divides to the dispersion of materials and energy across a landscape, that is, it is a measure of ‘divide size’. B _max is particularly useful for studying divide network structure, for mapping drainage divides, and in landform classification applications. Sensitivity analyses were performed to evaluate the robustness of estimates of B _max to the algorithm used to estimate flow lengths and the prevalence of edge effects resulting from inadequate DEM extent. The findings suggest that the index is insensitive to the specific flow algorithm used but that edge effects can result in significant underestimation along major divides. Edge contamination can, however, be avoided by using an appropriately extensive DEM.     

Stochastic model-based methods for handling uncertainty in areal interpolation

Handling of uncertainty in the estimation of values from source areas to target areas poses a challenge in areal interpolation research. Stochastic model-based methods offer a basis for incorporating such uncertainty, but to date they have not been widely adopted by the GIS community. In this article, we propose one use of such methods based in the problem of interpolating count data from a source set of zones (parishes) to a more widely used target zone geography (postcode sectors). The model developed also uses ancillary statistical count data for a third set of areas nested within both source and target zones. The interpolation procedure was implemented within a Bayesian statistical framework using Markov chain Monte Carlo methods, enabling us to take account of all sources of uncertainty included in the model. Distributions of estimated values at the target zone level are presented using both summary statistics and as individual realisations selected to illustrate the degree of uncertainty in the interpolation results. We aim to describe the use of such stochastic approaches in an accessible way and to highlight the need for quantifying estimation uncertainty arising in areal interpolation, especially given the implications arising when interpolated values are used in subsequent analyses of relationships.     

Analysis of Multiple Tourist Agents’ Symbiotic Relationship on the View of Tourist Enterprises Based on a Grey Correlation Model: A Case Study of Qinghai Lake,China

Tourism symbiosis is a social phenomenon consisting of many complex factors, and the reciprocal cooperation among multiple tourist agents at tourist destinations is the crux of the sustainable development of tourism. This study is from the perspective of tourist enterprises, and introduces the Symbiosis Theory of genecology. A quantitative evaluation is used to analyze both the equilibrium state of the combined symbiotic behavior routes and the behavior patterns of tourist enterprises with local governments, community residents, tourists and tourist enterprises around Qinghai Lake. The findings reveal: (1) the symbiotic behavior routes of the multiple tourist agents “E→G-R-T-E” in the Qinghai Lake area are constituted of intense symbiotic indications, while the maximum dimensionality of symbiotic interest of “E→G-R-T-E” is still in the state of disequilibrium and dissymmetry; (2) the symbiotic model of multiple tourist agents “E→G-R-T-E” in the Qinghai Lake area is an asymmetrically positive symbiotic model. It is proposed that, by establishing symbiotic mechanisms for guidance, decisions, supervisory control and profit distribution, the participation mechanism for multiple agents “E-G-R-T” can be further standardized. Moreover, tourist enterprises should be regarded as the primary agents to optimize the symbiotic model for “E→G-R-T-E” through the reinforcement of integrative supply and the construction of integrative effect, and finally promote the integrative symbiotic model of symmetrical reciprocity of the E-R-G-T model “driven by scenic areas, responsive to community residents, affected by local governments and enjoyed by tourists.”     

Rangeland production: use of models incorporating aggregated knowledge and fuzzy construction

This work reports on a method using fuzzy membership functions to construct an aggregated interaction matrix in which the summation of variables is scaled according to the way rainfall and soil variables affect water availability to plants and hence influence rangeland productivity. Aggregation of the variables gives a comprehensive value which can be used to predict production. The model increases the predicability of production to 81% compared to models using rainfall alone and a multiplicative parametric one which give predictibilities of 61 and 76% respectively. The results showed that (1) the importance of rainfall in determining production was most important at lower rainfalls i.e. <350 mm; (2) soil texture and particularly slope were important through out the rainfall range (149–700 mm) investigated; and (3) soil depth was only important at the higher >350 mm rainfalls. The aggregated interaction matrix gives a measure of land productive capability.     

Structural Spatial Interaction*

This paper identifies a concept of structural spatial interaction. Although it is defined differently from its counterpart of structural unemployment in economics, it attempts to define a similar idea. In economics, the focus is on equilibrium in the supply and demand of labor. In this paper structural interaction is that which minimizes aggregate distance travelled, subject to constraints on origins and destinations. The structural interaction model produces an equilibrium, therefore, in the supply and demand of movers, while identifying a spatially efficient trip distribution. It is portrayed as a type of relaxed interaction model and is illustrated using an example of structural migration with Canadian interprovincial migration data.     

城市边缘带识别研究进展与展望

城市边缘带作为城市与乡村相交接的特殊地带,是城市化进程中最为敏感的区域;城市边缘带的定量识别是研究边缘带社会结构、景观格局和发展梯度的基础工作,也是城市化生态环境效应定量分析的前提条件。但边缘带识别的理论与方法未受到足够重视,缺乏系统梳理。本文在对比分析城市边缘带及相关概念的基础上,明确了城市边缘带识别的实质与内涵;提出了基于指示要素—空间单元—判别模型的城市边缘带识别概念框架,并从上述三方面系统回顾了国内外城市边缘带识别研究进展;对未来研究进行了展望,认为城乡作用机理探析、多要素综合识别、非参数化模型融合、多尺度转换以及长时间序列动态监测等将是下一步重点研究方向。