Author: Jana S. Stewart
Publisher:
ISBN:
Category :
Languages : en
Pages : 406
Book Description
Assessment of Alternative Methods for Stratifying Landsat TM Data to Improve Land Cover Classification Accuracy Across Areas with Physiographic Variation
Handbook of Environmental Health, Two Volume Set
Author: Herman Koren
Publisher: CRC Press
ISBN: 143983296X
Category : Science
Languages : en
Pages : 1560
Book Description
The two-volume Handbook of Environmental Health, Fourth Edition provides a comprehensive but concise discussion of important environmental health areas, including energy, ecology and people, environmental epidemiology, risk assessment and risk management, environmental law, air quality management, food protection, insect control, rodent control, pe
Publisher: CRC Press
ISBN: 143983296X
Category : Science
Languages : en
Pages : 1560
Book Description
The two-volume Handbook of Environmental Health, Fourth Edition provides a comprehensive but concise discussion of important environmental health areas, including energy, ecology and people, environmental epidemiology, risk assessment and risk management, environmental law, air quality management, food protection, insect control, rodent control, pe
Handbook of Environmental Health, Volume II
Author: Herman Koren
Publisher: CRC Press
ISBN: 0849378001
Category : Science
Languages : en
Pages : 905
Book Description
The Handbook of Environmental Health-Pollutant Interactions in Air, Water, and Soil includes Nine Chapters on a variety of topics basically following a standard chapter outline where applicable with the exception of Chapters 8 and 9. The outline is as follows:1. Background and status2. Scientific, technological and general information3. Statement o
Publisher: CRC Press
ISBN: 0849378001
Category : Science
Languages : en
Pages : 905
Book Description
The Handbook of Environmental Health-Pollutant Interactions in Air, Water, and Soil includes Nine Chapters on a variety of topics basically following a standard chapter outline where applicable with the exception of Chapters 8 and 9. The outline is as follows:1. Background and status2. Scientific, technological and general information3. Statement o
Improving Image Classification Accuracy by Combining Maximum Likelihood Classifier and Artificial Neural Networks
Analysis of Spaceborne Synthetic Aperture Radar Images to Assist in Statewide Land Cover Mapping and Long-term Ecological Research
Author: Jonathan Ward Chipman
Publisher:
ISBN:
Category :
Languages : en
Pages : 388
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 388
Book Description
Register implementation for land cover legends
Author: Food and Agriculture Organization of the United Nations
Publisher: 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.
Publisher: 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.
Earth Resources
Author:
Publisher:
ISBN:
Category : Astronautics in earth sciences
Languages : en
Pages : 852
Book Description
Publisher:
ISBN:
Category : Astronautics in earth sciences
Languages : en
Pages : 852
Book Description
Technical Papers
Environmental Stratification
Author: Lawrence R. Pettinger
Publisher:
ISBN:
Category : Blackfoot River Watershed (Idaho)
Languages : en
Pages : 13
Book Description
Objectives are: "1) to produce vegetation and land cover maps using computer-assisted classification of Landsat digital data for the Blackfoot River watershed, and 2) to determine the classification accuracy, defined as the agreement of digital classification with the visual interpretation of high-altitude color-infrared aerial photographs" (p. 1587).
Publisher:
ISBN:
Category : Blackfoot River Watershed (Idaho)
Languages : en
Pages : 13
Book Description
Objectives are: "1) to produce vegetation and land cover maps using computer-assisted classification of Landsat digital data for the Blackfoot River watershed, and 2) to determine the classification accuracy, defined as the agreement of digital classification with the visual interpretation of high-altitude color-infrared aerial photographs" (p. 1587).
Improving Large Area Land Cover Classification Using Multi-temporal Remote Sensing Data
Author: W. Olthof
Publisher:
ISBN:
Category :
Languages : en
Pages : 79
Book Description
Land cover is described in other studies as the (bio)physical cover of the Earth’s surface and includes vegetated areas, artificial areas, bare areas and water bodies. Land cover is prone to changes due to anthropogenic activities and natural processes. These changes influence climate, e.g. by their effect on emissions of CO2 and other greenhouse gases and changes in carbon storage capacity. Therefore, accurate and continuous information on land cover is needed on a global scale. User requirements analysis conducted by the Climate Change Initiative Land Cover consortium (CCI-LC) proved that current land cover products derived from remotely sensed data are lacking accuracy and consistency. These issues often arise due to the inability of the input data to capture temporal dynamics by using a limited time span. Furthermore, land cover changes are often not taken into account in current classification approaches. This research aims to improve current classification approaches by investigating 1) how time series parameters, e.g. phenological metrics, can be extracted from multi-temporal MERIS data and 2) how these can be utilized for classification purposes. Furthermore, a comparison was made between classification results with and without these parameters in order 3) to determine to what extent these influence the classification result. In addition, given the fact that vegetation is highly dynamic, another goal of this study was to investigate 4) how temporally stable locations can be separated from unstable areas in order to ultimately limit classification to the stable period within a time series. The use of phenological metrics was emphasized during this study in order to include vegetation dynamics in the classification approach. During this study an operational method was developed to extract phenological metrics from MTCI and NDVI time series which were successfully used for land cover classification. The use of this method seems to increase the overall accuracy of the classification results and has the potential to be used on a large scale. In addition, an explorative study was conducted on the separation of temporary land cover change from permanent land cover change. This resulted in a fast method that may be effectively added to the classification process and applied on a larger scale.
Publisher:
ISBN:
Category :
Languages : en
Pages : 79
Book Description
Land cover is described in other studies as the (bio)physical cover of the Earth’s surface and includes vegetated areas, artificial areas, bare areas and water bodies. Land cover is prone to changes due to anthropogenic activities and natural processes. These changes influence climate, e.g. by their effect on emissions of CO2 and other greenhouse gases and changes in carbon storage capacity. Therefore, accurate and continuous information on land cover is needed on a global scale. User requirements analysis conducted by the Climate Change Initiative Land Cover consortium (CCI-LC) proved that current land cover products derived from remotely sensed data are lacking accuracy and consistency. These issues often arise due to the inability of the input data to capture temporal dynamics by using a limited time span. Furthermore, land cover changes are often not taken into account in current classification approaches. This research aims to improve current classification approaches by investigating 1) how time series parameters, e.g. phenological metrics, can be extracted from multi-temporal MERIS data and 2) how these can be utilized for classification purposes. Furthermore, a comparison was made between classification results with and without these parameters in order 3) to determine to what extent these influence the classification result. In addition, given the fact that vegetation is highly dynamic, another goal of this study was to investigate 4) how temporally stable locations can be separated from unstable areas in order to ultimately limit classification to the stable period within a time series. The use of phenological metrics was emphasized during this study in order to include vegetation dynamics in the classification approach. During this study an operational method was developed to extract phenological metrics from MTCI and NDVI time series which were successfully used for land cover classification. The use of this method seems to increase the overall accuracy of the classification results and has the potential to be used on a large scale. In addition, an explorative study was conducted on the separation of temporary land cover change from permanent land cover change. This resulted in a fast method that may be effectively added to the classification process and applied on a larger scale.