Author: Alain J. FontainePublisher:
ISBN: 9781100190877
Category : Biodiversity conservation
Languages : en
Pages : 104
Book Description
Detection and Classification of Land Cover Classes of Southampton Island, Nunavut, Using Landsat ETM+ Data
Author: Alain J. FontainePublisher:
ISBN: 9781100190877
Category : Biodiversity conservation
Languages : en
Pages : 104
Book Description
Detection and Classification of Land Cover Classes of Southampton Island, Nunavut, Using Landsat ETM+ Data
Author: Alain J. FontainePublisher:
ISBN:
Category : Biodiversity conservation
Languages : en
Pages : 108
Book Description
Digital Classification of Landsat Data for Vegetation and Land-cover Mapping in the Blackfoot River Watershed, Southeastern Idaho
Author: Lawrence R. PettingerPublisher:
ISBN:
Category : Blackfoot River Watershed (Idaho).
Languages : en
Pages : 44
Book Description
A case study, including step-by-step procedures for computer-assisted analysis of Landsat digital data, with emphasis on assessment of classification accuracy and generation of output products.
Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data
Author: Zhe ZhuPublisher:
ISBN:
Category :
Languages : en
Pages : 322
Book Description
Abstract: Land cover mapping and monitoring has been widely recognized as important for understanding global change and in particular, human contributions.This research emphasizes the use of the time domain for mapping land cover and changes in land cover using satellite images. Unlike most prior methods that compare pairs or sets of images for identifying change, this research compares observations with model predictions. Moreover, instead of classifying satellite images directly, it uses coefficients from time series models as inputs for land cover mapping. The methods developed are capable of detecting many kinds of land cover change as they occur and providing land cover maps for any given time at high temporal frequency.One key processing step of the satellite images is the elimination of "noisy" observations due to clouds, cloud shadows, and snow. I developed a new algorithm called Fmask that processes each Landsat scene individually using an object-based method. For a globally distributed set of reference data, the overall cloud detection accuracy is 96%. A second step further improves cloud detection by using temporal information.The first application of the new methods based on time series analysis found change in forests in an area in Georgia and South Carolina. After the difference between observed and predicted reflectance exceeds a threshold three consecutive times a site is identified as forest disturbance. Accuracy assessment reveals that both the producers and users accuracies are higher than 95% in the spatial domain and approximately 94% in the temporal domain.The second application of this new approach extends the algorithm to include identification of a wide variety of land cover changes as well as land cover mapping. In this approach, the entire archive of Landsat imagery is analyzed to produce a comprehensive land cover history of the Boston region. The results are accurate for detecting change, with producers accuracy of 98% and users accuracies of 86% in the spatial domain and temporal accuracy of 80%. Overall, this research demonstrates the great potential for use of time series analysis of satellite images to monitor land cover change
Digital and Visual Classification of Land Use/land Cover Using Landsat-MSS and High Altitude Photography Data
Author: Ramiro SalcedoPublisher:
ISBN:
Category : Aerial photography in regional planning
Languages : en
Pages : 188
Book Description
Land Cover Change Detection Using Classified Landsat Data
Author: Kerry Rand BrooksPublisher:
ISBN:
Category : Landsat satellites
Languages : en
Pages : 224
Book Description
Extracting Land Cover Change Classes in the Cosumnes River Watershed from Landsat TM/ETM+ Images Using Spectral Indices
Author: Nina Vasilievna NoujdinaPublisher:
ISBN:
Category :
Languages : en
Pages : 156
Book Description
Classification of Land-Cover Types for the Fort Benning Ecoregion Using Enhanced Thematic Mapper Data: January 2003 Imagery
Author: Sam S. JacksonPublisher:
ISBN:
Category : Ecosystem management
Languages : en
Pages : 9
Book Description
Information regarding regional land cover is a fundamental requirement to support the long-term baseline ecosystem monitoring plan under the Strategic Environmental Research and Development Program (SERDP), Ecosystem Management Project (SEMP), Ecosystem Characterization and Monitoring Initiative (ECMI). The land cover characterization phase of this plan provides the foundation needed to derive vegetation density indices and land cover patterns. These characteristics are the primary visible expressions of the underlying ecosystem structure, function, and process at all spatial scales (Kress 2000). To meet the requirement for land cover information, Landsat 7 Enhanced Thematic Mapper (ETM) data were used to classify land cover types for the Fort Benning ecoregion. This technical note describes the procedures used to extract land cover information from the satellite imagery.
Author: Wafi al- Fares
Author: