Optimization of land use structure based on ecological GREEN equivalent

Optimization of land use structure consists of economic and social and ecological optimization. Applying the minds of system engineering and principles of ecology, this paper presents such thoughts: the optimal forest-coverage rate calculated according to the reality of a district is set as main standard of ecological rationality in the district; through considering the value of ecosystem services of the land with GREEN equivalent (mainly cultivated land and grassland) and based on the rule, GREEN equivalent, this paper introduces the area conversion between woodland and cultivated land, also between woodland and grassland; this paper establishes a multi-dimension controlling model of optimization of land use structure. In addition, a multi-objective linear programming model for optimization of land use structure is designed. In the end, this paper tests and verifies this theory of ecological optimization, taking Qionghai city in Hainan Province as an example.     

Models for analyzing the driving force of cultivated land supply change

This paper focuses on a series of quantitative analysis models, such as grey relational analysis model, hierarchical cluster analysis model, principal component analysis model, linear regression model and elastic coefficient model. These models are used to analyze the comprehensive function and effect of driving forces systemically, including analysis on features, analysis for differentiating the primary and the secondary, analysis on comprehensive effects, analysis of elasticity, analysis of prediction. The primary and characteristic factors can be extracted by analysis of features and analysis for differentiating the primary and the secondary. Analysis on prediction and elasticity can predict the area of cultivated land in the future and find out which factors exert great influence on the cultivated land supply.     

Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND

Abstract

The main objective of this study is to assess the relative contribution of the state-of-the-art topo-hydrological factor, known as height above the nearest drainage (HAND), to landslide susceptibility modellling using three novel statistical models: weights-of-evidence (WofE), index of entropy and certainty factor. In total, 12 landslide conditioning factors that affect the landslide incidence were used as input to the models in the Ziarat Watershed, Golestan Province, Iran. Landslide inventory was randomly divided into a ratio of 70:30 for training and validating the results of the models. The optimum combination of conditioning factors was identified using the principal components analysis (PCA) method. The results demonstrated that HAND is the defining factor among hydrological and topographical factors in the study area. Additionally, the WofE model had the highest prediction capability (AUPRC = 74.31%). Therefore, HAND was found to be a promising factor for landslide susceptibility mapping.     

The design and composition of persuasive maps

Persuasive maps are ubiquitous in society, yet cartographers have largely neglected to conduct serious, holistic research on them. Persuasive maps represent a form of visual communication that differs markedly from scientific geovisualization. First, many of these maps' communicative goals are anathema to those of scientific representation. Second, many persuasive maps ignore and defy established cartographic conventions. This article argues two things. First, the cartographic discipline can gain insight about how maps communicate information from the longitudinal and holistic analysis of persuasive maps. By comparing the differences and similarities of persuasive maps to scientific geovisualizations, researchers can begin to understand how persuasive maps communicate differently than scientific ones. Second, breaking persuasive maps down into composite parts may make it possible to identify persuasive map norms (i.e., methods of design that are embraced by producers of persuasive maps). The results of a content analysis of 256 persuasive maps are discussed. The article concludes that it may be possible to take the most statistically significant results from this study to begin identifying different rhetorical styles of persuasive maps.     

Validating the use of object-based image analysis to map commonly recognized landform features in the United States

ABSTRACT

The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods.     

Mapping for history: the influence of the 7th Field Survey Company’s 1917 operation maps on the historiography of the 3rd Battle of Gaza

ABSTRACT

During Palestine Campaign of World War I, the British Army’s Egyptian Expeditionary Force produced a series of operation maps to track and control the progress of their offensive into Palestine. These maps demonstrated innovation in both their form and function, and they have been referenced and reproduced by both map scholars and military historians ever since. Leaning on J.B. Harley and Matthew Edney’s ideas about critical map history, this essay provides a critical examination of these operation maps to illuminate the sociocultural context of their creation and examines the maps’ influence over the broader historiography of the Palestine Campaign. In doing so it highlights the interdisciplinary value of critical map histories.     

Three-dimensional trees for virtual globes

One of the problems in virtual globes technologies is the real-time representation of vegetal species. In forest or garden representations, the low detailed plants produce a lack of realism. Efficient techniques are required to achieve accurate interactive visualisation due to the great number of polygons the vegetal species have. This article presents a multi-resolution model based on a geometric representation of vegetal species that allows the application to perform the progressive transmission of the model, that is, the transmission of a simple representation followed by successive refinements of it. It has a hardware-oriented design in order to obtain interactive frame rates. The geometric data of the objects are stored in the graphics processing unit and, moreover, the change from one approximation to another is obtained by performing mathematical calculations in this graphics hardware. The multi-resolution model presented here enables instancing: as many vegetal species as desired can be rendered with different levels of detail, while all of them are accessing the same geometric data. This model has been used to build a real-time representation of a not imaginary scenario.     

Understanding the potential relationship between the socio-economic variables and contributions to OpenStreetMap

OpenStreetMap (OSM) has seen an exponential increase in the last few years and large volumes of geodata have been received from volunteered individuals. The collected geodata are heterogeneous in terms of different dimensions such as spatial patterns of contributions, quality, patterns of contributing individuals, and type of contributions. Because contributors’ personal information is anonymously stored by the OSM administrators, alternative methods are needed to investigate the role of contributors’ characteristics on their mapping behavior. This study is intended to explore the potential socio-economic characteristics of contributors in highly contributed areas to have better insights about the latent patterns of involved individuals in a highly dynamic state of the most active country in OSM, Germany. A logistic regression model (LRM) is applied to discover the potential correlations between dependent and independent variables. The findings explain that the areas with high population density, middle level of education, high income, high rate of overnight stays, high number of foreigners, and residents aged from 18 to 69 are more likely to be involved in OSM. Furthermore, the degree of dynamism in OSM is a function of proximity to built-up areas. Finally, concluding remarks concerning the independent variables and model sensitivity are presented.     

A hierarchical spatiotemporal adaptive fusion model using one image pair

ABSTRACT

Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community. Over the past decade, although many advances have been made in the spatiotemporal fusion models, there still remain several shortcomings in existing methods. In this article, a hierarchical spatiotemporal adaptive fusion model (HSTAFM) is proposed for producing daily synthetic fine-resolution fusions. The suggested model uses only one prior or posterior image pair, especially with the aim being to predict arbitrary temporal changes. The proposed model is implemented in two stages. First, the coarse-resolution image is enhanced through super-resolution based on sparse representation; second, a pre-selection of temporal change is performed. It then adopts a two-level strategy to select similar pixels, and blends multi-sensor features adaptively to generate the final synthetic data. The results of tests using both simulated and actual observed data show that the model can accurately capture both seasonal phenology change and land-cover-type change. Comparisons between HSTAFM and other developed models also demonstrate our proposed model produces consistently lower biases.