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Environmental Monitoring Commons

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Full-Text Articles in Environmental Monitoring

Gis Data: Anne Arundel County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi Jan 2021

Gis Data: Anne Arundel County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …


Gis Data: Calvert County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi Jan 2021

Gis Data: Calvert County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …


Gis Data: Talbot County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi Jan 2021

Gis Data: Talbot County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …


Gis Data: Dorchester County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi Jan 2021

Gis Data: Dorchester County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …


Satellite-Based Phenology Analysis In Evaluating The Response Of Puerto Rico And The United States Virgin Islands' Tropical Forests To The 2017 Hurricanes, Melissa Collin Jan 2021

Satellite-Based Phenology Analysis In Evaluating The Response Of Puerto Rico And The United States Virgin Islands' Tropical Forests To The 2017 Hurricanes, Melissa Collin

Cal Poly Humboldt theses and projects

The functionality of tropical forest ecosystems and their productivity is highly related to the timing of phenological events. Understanding forest responses to major climate events is crucial for predicting the potential impacts of climate change. This research utilized Landsat satellite data and ground-based Forest Inventory and Analysis (FIA) plot data to investigate the dynamics of Puerto Rico and the U.S. Virgin Islands’ (PRVI) tropical forests after two major hurricanes in 2017. Analyzing these two datasets allowed for validation of the remote sensing methodology with field data and for the investigation of whether this is an appropriate approach for estimating forest …


Evaluation Of A Low-Cost Uas And Phenocams For Measuring Grapevine Greenness, Timothy J. Hoheneder Jan 2021

Evaluation Of A Low-Cost Uas And Phenocams For Measuring Grapevine Greenness, Timothy J. Hoheneder

Graduate Theses, Dissertations, and Problem Reports

Unpersoned aerial systems (UAS) could provide winegrowers with the potential to monitor vineyard productivity with ultra-high-resolution imagery and low operational costs. This ability could prove particularly valuable in the challenging cool-climate viticultural areas of Appalachia. Especially in this mountainous region of increasingly variable microclimates, there could be of great value from an ability to use UAS-measured greenness to monitor wine grape phenology and predict harvest quality and quantity. In this study, I assess how UAS-measured greenness relates to three complementary measures of field-based: leaf angle measurements, phenocam measured greenness, and leaf spectral measurements of greenness. After correlating these field-based measures …