Author: Michael J. BuenoPublisher:
ISBN:
Category :
Languages : en
Pages : 84
Book Description
Quantification of Cartographic Generalization in Land Cover Maps Using Spatial Pattern Index Measurements Derived from Digital Satellite Imagery
Author: Michael J. BuenoSpatial Generalization of Thematic Maps Derived from Satellite Imagery for Operational Land Cover Mapping
Author: S. GoffredoPublisher:
ISBN:
Category : Artificial satellites in surveying
Languages : en
Pages : 14
Book Description
Scientific and Technical Aerospace Reports
Author: Earth Resources
Author: Publisher:
ISBN:
Category : Astronautics in earth sciences
Languages : en
Pages : 758
Book Description
Causes And Consequences Of Map Generalization
Author: Elsa JoaoPublisher: CRC Press
ISBN: 1000124126
Category : Computers
Languages : en
Pages : 283
Book Description
This text describes late-1990s understanding of map generalisation in the context of paper maps and GIS. Its particular value should be in helping to further automate and measure the process of map generalisation.; The research has concentrated on quantifying generalisation effects and on analysing how these effects of generalisation locked into the maps were measured. Elsa Joao's book covers the background to the problems of map generlasation; the methodology developed by the author to investigate the consequences of the map generalisation; a detailed description of results, and a conclusion that draws together consequences for the broader applications to GIS.
Multiscale Raster Treatments for Map Generalization
Author: Paulo RaposoPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Raster map data are challenging to generalize, perhaps because they do not avail themselves to the kinds of geometric reshaping that vector data do. Cartographers' manipulation and use of raster data, especially in the context of scale change, tends to be unsophisticated, with techniques used rarely going beyond resampling strategies. Also, as with procedures developed for vector data, raster data generalization is typically not objectively related to mapping scale.The overarching theme of this dissertation is the development of raster generalization procedures directly related to target mapping scale. It contributes to the presently small body of cartographic literature on raster generalization, with two contributions to digital elevation model (DEM) and topography treatments, and one to pixel land cover treatment. The dissertation consists of a suite of three projects which take three respective approaches to the overall scale-oriented goal: entropy-based quantification, analytical segmentation, and resolution change.The first project presents the development of an automated method of cartographic DEM smoothing. Associating generalization level to the objective parameter of local terrain entropy, the method variably smooths a DEM such that sharper and more salient relief features are better retained while smaller fluctuations in the surface and more monotonous areas are more aggressively smoothed. This effect is achieved by using variably-sized low-pass filter kernels, with their sizes being determined by the inverse of the local surface entropy. The algorithm is also explicitly tunable to any cartographic scale, such that multiple representations can be produced for multi-scale cartography. Derived cartographic products from the smoothed DEMs such as terrain shadings, slope shadings, and contour lines are produced at high quality at particular scales.The second project consists of a novel geographic raster segmentation algorithm, termed "water table" segmentation. The name reflects the algorithm's simulation of terrain basin flooding by incrementally raising an idealized, planar water table. As inundated basins coalesce while the water table rises, their mergers are recorded in a segmentation tree. The algorithm borrows from existing image segmentation algorithms in computer vision, with distinctions in how the resulting segmentation tree is formed and analyzed. The segmentation tree serves as an abstraction of the partonomic structure of the terrain's landforms, thereby making landform detection and analysis possible through graph analysis. The algorithm and its resulting segmentation tree are explained and associated to points of generalization, being instances along a shrinking map scale at which map symbols fail and must be reworked, replaced, or abandoned. The water table segmentation algorithm makes cartometric analysis of landforms and their associated points of generalization possible. The chapter also focuses on the segmentation's particular usefulness when applied to land surface parameters (LSPs) derived from the DEM, such as slope or roughness.In the final project, National Land Cover Database (NLCD) data are generalized and enhanced for use at multiple map scales, with particular emphasis on 1:24,000, a scale for which the data are too coarse. To produce an informative, generalized, and aesthetically pleasing land cover representation at large scale, the data are subjected to a series of processing steps, principle among which are upsampling, stochastic edge "airbrushing," and re-coloration. Each technique is used to deliberately soften and ambiguate the location of land cover class boundaries within a narrow margin. This margin, or "uncertainty corridor," is no wider than two input NLCD cells, and the deliberate visual uncertainty introduced is limited to this area. The resulting land cover map layer provides a painterly representation, and is particularly well-suited to be used as translucent and in combination with other layers such at orthoimagery and feature vectors for large-scale (approximately 1:24,000) topographic mapping.
Register implementation for land cover legends
Author: Food and Agriculture Organization of the United NationsPublisher: Food & Agriculture Org.
ISBN: 9251345600
Category : Law
Languages : en
Pages : 58
Book Description
Land cover assessment and monitoring of its dynamics are essential requirements for the sustainable management of natural resources, environmental protection, food security, humanitarian programmes as well as core data for monitoring and modelling. Land Cover (LC) data are therefore fundamental in fulfilling the mandates of many United Nations (UN), international and national institutions and programmes. Despite the recognition of such importance, current users of LC data still lack access to sufficient reliable or comparable baseline LC data. These data are essential to tackle the increasing concerns in regard to food security, environmental degradation, and climate change. Critically, maintaining and restoring land resources plays a vital task in tackling climate change, securing biodiversity, and maintaining crucial ecosystem services, while ensuring resilient livelihoods and food security.
A Land Use and Land Cover Classification System for Use with Remote Sensor Data
Author: James Richard AndersonPublisher:
ISBN:
Category : Land cover
Languages : en
Pages : 36
Book Description
Government Reports Announcements & Index
Author: Generalization in Digital Cartography
Author: Robert Brainerd McMasterPublisher: Assn of Amer Geographers
ISBN: 9780892912094
Category : Political Science
Languages : en
Pages : 134
Book Description
This monograph is part of the Resource Publications in Geography Series of the Association of American Geographers. It describes the many issues & problems associated with representing cartographic features on maps as the scale of the map changes. The first two chapters of the book discuss the graphic problems associated with scale change & give a history of map generalization. A set of maps, at different scales, is used to illustrate the conceptual & technical problems of generalization. The third chapter presents a conceptual model for the cartographic generalization process within a digital environment. The generalization process described answers the questions why we generalize, when to generalize & how to generalize. The fourth & fifth chapters identify & describe the basic operators & algorithms used in map generalization for both the vector & raster data models of feature representation. The final chapter reviews current activities & future research directions including the application of expert systems, neural networks & advanced user interfaces for developing a newer generation of generalization software.