Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 38 for “"Land Cover Classification"”.
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Inter-annual stability of land cover classification: explorations and improvements
Land cover information is a key input to many earth system models, and thus accurate and consistent land cover maps are critically important to global change science. However, existing global land cover products show unrealistically high levels of year-to-year change. This thesis explores methods …
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Evaluating time-series smoothing algorithms for multi-temporal land cover classification
… algorithms in noise reduction for improving land cover classification in the Great Lakes Basin, and providing groundwork to support cyanobacteria and cyanotoxin monitoring efforts. We used inter-class separability and intra-class variability, at varying levels of pixel homogeneity, to …
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Application of deep learning to land cover classification: practical issues and strategies
Land Use and Land Cover (LULC) change is a process of essential importance to urban studies and planning. Large-scale databases provide comprehensive records, but in many circumstances, they need to be supplemented or substituted by alternative data sources. The advent of deep learning provides an …
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A Genetic Bayesian Approach for Texture-Aided Urban Land-Use/Land-Cover Classification
Urban land-use/land-cover classification is entering a new era with the increased availability of high-resolution satellite imagery and new methods such as texture analysis and artificial intelligence classifiers. Recent research demonstrated exciting improvements of using fractal dimension, …
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Fusion of Full Waveform LiDAR and Passive Remote Sensing for Improved Land-Cover Classification
Land-cover classification is a crucial step in interpreting remote sensing data, and the accuracy determines the reliability of the product for further downstream applications. Hyperspectral sensors have been widely utilized for classification because of the discrimination afforded by its rich …
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Comparison of Random Forests, Support Vector Machine and Artificial Neural Network Methods for Agriculture Land Cover Classification
Land cover classification is critical in remote sensing. Reliable classification on land cover is required to address a wide range of environmental issues. Over recent years, the application of machine learning techniques in remote sensing has attracted wide attention. Machine learning has the …
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Development of a Support-Vector-Machine-based Supervised Learning Algorithm for Land Cover Classification Using Polarimetric SAR Imagery
Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land …
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Automatic land-cover-classification derived from high-resolution Ikonos satellite image in the urban atlantic forest in Rio de Janeiro, Brasil by means of an objects-oriented approach
The city of Rio de Janeiro carried out a Land-cover forest classification with visual interpretation using SPOT data. This work produced a compatible thematic map in the scale 1:50,000. The scale of these maps permit to have a global vision of the land change cover but unfortunately do not …
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Land Cover and Impervious Surface Mapping Using Multispectral Airborne Laser Scanner Data
Impervious surfaces are land covers that do not allow water penetration. Water runoff from impervious surfaces can cause major flooding in extreme climates; therefore, mapping such surface covers in urban areas is of great importance for water resources, climatology, and urban studies. Automated …
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The landscape pattern surrounding the Venda sacred site of Thathe Forest
… groups as a point of connection to the land. They are also acknowledged for their disproportionate biodiversity contribution. These natural remnant patches have, however, recently come under threat from surrounding anthropogenic land-uses. This study aims to establish the spatial …
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Multitemporal Imagery Based Analysis of Urban Land in St. Tammany Parish in Conjunction with Socioeconomic Data
… to the remotely sensed data. In this paper, six Landsat 5 TM images were used to create land cover classification maps of the developed or built-up land in St. Tammany Parish from 1984 to 2008. It was found that, in addition to St. Tammany expanding in population, the urban areas are becoming …
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Mammal Distributions and Habitat Models for South Dakota
… is compared to protection offered from public lands, identifying species that are not adequately protected (i.e., have "gaps" in protection). Then, conservation may be suitable for unprotected species. South Dakota's Gap Analysis Project began in 1997 with information gathered on vegetation, …
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Mapping and Assessment of Land Use/Land Cover Using Remote Sensing and GIS in North Kordofan State, Sudan
… mass immigration. Spatial data on dynamics of land use and land cover is scarce and/or almost nonexistent. The study area in the North Kordofan State is located in the centre of Sudan and falls in the Sahelian eco-climatic zone. The region generally yields reasonable harvests of rainfed crops …
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Mapping the maize growth period using multi-temporal sentinel 1 and 2 imagery: a case study in Kasisi area of Chongwe district.
… variations in maize growth and mapping its coverage from November 2019 to April 2020. The analysis focused on tracking maize phenological stages— sowing, emergence, vegetative growth, and maturity—through biweekly observations of SAR backscatter and NDVI. Dual-polarized SAR data (VV and VH) …
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Mapping the maize growth period using multi-temporal sentinel 1 and 2 imagery: a case study in Kasisi area of Chongwe district.
… variations in maize growth and mapping its coverage from November 2019 to April 2020. The analysis focused on tracking maize phenological stages— sowing, emergence, vegetative growth, and maturity—through biweekly observations of SAR backscatter and NDVI. Dual-polarized SAR data (VV and VH) …
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Weighting Landsat Digital Data According to Land Cover Emissivity for Surface Temperature Mapping
… require efficient and accurate data concerning land use/land cover and temperature gradients for informed decision making. Remotely-sensed data provide a method for acquiring such information in a dependable and efficient manner. Regular data acquisition and a synoptic view make the Landsat …
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Remote sensing of recent changes in permafrost-influenced wetlands
… spatial and temporal scales. With the declassification of the Landsat archive in 2008, it is now possible to conduct a near-complete yearly time series of homogenous geospatial imagery to assess the trends seen in thermokarst lake surface area. By implementing an automated land- cover …
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Land Cover Quantification using Autoencoder based Unsupervised Deep Learning
… work aims to develop a deep learning model for land cover quantification through hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological …
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Remote Sensing of Vegetation Change Across a Latitudinal Gradient in the Canadian Arctic
… change (e.g., phenology, greening, percent cover, and biomass) over time. It is important to document how Arctic vegetation is changing, as it will have large implications related to global carbon and surface energy budgets. The research reported here examined vegetation greening across …
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