Applying Cloud-Based Computing and Emerging Remote Sensing Technologies to Inform Land Management Decisions

Monica Vermillion; Josh Enterkine; Lucas Spaete; Nancy Glenn

Applying Cloud-Based Computing and Emerging Remote Sensing Technologies to Inform Land Management Decisions

Monica Vermillion
, Josh Enterkine
, Lucas Spaete
, Nancy Glenn

Division of Research and Economic Development

Boise State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Abstract

Who: Boise State University and Mountain Home Air Force Base

What: Creating a species level classification map through the use of Google Earth Engine (GEE), a cloud-based computing platform, to map invasive species

When: In-situ data collected in Summer 2018, a continuation of data collected in Summer 2016. Classification was created in Fall 2018. Unmanned aerial vehicles (UAV) flights in August 2018.

Where: Mountain Home Air Force Base (MHAFB) in southwest Idaho, ecosystem is in the Great Basin Range (GBR)

Why: The introduction of exotic species like cheatgrass (Bromus tectorum) has drastically altered the fire cycle of the Northern Great Basin (NGB) from 50 – 100 year burn intervals to 10 year intervals (1). Factors such as soil, elevation, temperature, and precipitation can affect the resilience of a sagebrush steppe ecosystem to invasive species. Remote sensing techniques allow large scale analysis of invasive encroachment and assessment of conservation efforts and land management.

Original language	American English
Title of host publication	ASPRS Annual Conference and International Lidar Mapping Forum 2019
State	Published - 2019
Event	ASPRS 2019 Annual Conference and 19th International Lidar Mapping Forum 2019, ILMF 2019 - Denver, United States Duration: 28 Jan 2019 → 30 Jan 2019

Conference

Conference	ASPRS 2019 Annual Conference and 19th International Lidar Mapping Forum 2019, ILMF 2019
Country/Territory	United States
City	Denver
Period	28/01/19 → 30/01/19

EGS Disciplines

Earth Sciences
Geophysics and Seismology

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Cite this

@inproceedings{936c1ea4edcd46969c7073718c152ae7,

title = "Applying Cloud-Based Computing and Emerging Remote Sensing Technologies to Inform Land Management Decisions",

abstract = "Who: Boise State University and Mountain Home Air Force Base What: Creating a species level classification map through the use of Google Earth Engine (GEE), a cloud-based computing platform, to map invasive species When: In-situ data collected in Summer 2018, a continuation of data collected in Summer 2016. Classification was created in Fall 2018. Unmanned aerial vehicles (UAV) flights in August 2018. Where: Mountain Home Air Force Base (MHAFB) in southwest Idaho, ecosystem is in the Great Basin Range (GBR) Why: The introduction of exotic species like cheatgrass (Bromus tectorum) has drastically altered the fire cycle of the Northern Great Basin (NGB) from 50 – 100 year burn intervals to 10 year intervals (1). Factors such as soil, elevation, temperature, and precipitation can affect the resilience of a sagebrush steppe ecosystem to invasive species. Remote sensing techniques allow large scale analysis of invasive encroachment and assessment of conservation efforts and land management.",

author = "Monica Vermillion and Josh Enterkine and Lucas Spaete and Nancy Glenn",

year = "2019",

language = "American English",

booktitle = "ASPRS Annual Conference and International Lidar Mapping Forum 2019",

note = "ASPRS 2019 Annual Conference and 19th International Lidar Mapping Forum 2019, ILMF 2019 ; Conference date: 28-01-2019 Through 30-01-2019",

}

TY - GEN

T1 - Applying Cloud-Based Computing and Emerging Remote Sensing Technologies to Inform Land Management Decisions

AU - Vermillion, Monica

AU - Enterkine, Josh

AU - Spaete, Lucas

AU - Glenn, Nancy

PY - 2019

Y1 - 2019

N2 - Who: Boise State University and Mountain Home Air Force Base What: Creating a species level classification map through the use of Google Earth Engine (GEE), a cloud-based computing platform, to map invasive species When: In-situ data collected in Summer 2018, a continuation of data collected in Summer 2016. Classification was created in Fall 2018. Unmanned aerial vehicles (UAV) flights in August 2018. Where: Mountain Home Air Force Base (MHAFB) in southwest Idaho, ecosystem is in the Great Basin Range (GBR) Why: The introduction of exotic species like cheatgrass (Bromus tectorum) has drastically altered the fire cycle of the Northern Great Basin (NGB) from 50 – 100 year burn intervals to 10 year intervals (1). Factors such as soil, elevation, temperature, and precipitation can affect the resilience of a sagebrush steppe ecosystem to invasive species. Remote sensing techniques allow large scale analysis of invasive encroachment and assessment of conservation efforts and land management.

AB - Who: Boise State University and Mountain Home Air Force Base What: Creating a species level classification map through the use of Google Earth Engine (GEE), a cloud-based computing platform, to map invasive species When: In-situ data collected in Summer 2018, a continuation of data collected in Summer 2016. Classification was created in Fall 2018. Unmanned aerial vehicles (UAV) flights in August 2018. Where: Mountain Home Air Force Base (MHAFB) in southwest Idaho, ecosystem is in the Great Basin Range (GBR) Why: The introduction of exotic species like cheatgrass (Bromus tectorum) has drastically altered the fire cycle of the Northern Great Basin (NGB) from 50 – 100 year burn intervals to 10 year intervals (1). Factors such as soil, elevation, temperature, and precipitation can affect the resilience of a sagebrush steppe ecosystem to invasive species. Remote sensing techniques allow large scale analysis of invasive encroachment and assessment of conservation efforts and land management.

UR - https://scholarworks.boisestate.edu/geo_facpubs/485

M3 - Conference contribution

BT - ASPRS Annual Conference and International Lidar Mapping Forum 2019

T2 - ASPRS 2019 Annual Conference and 19th International Lidar Mapping Forum 2019, ILMF 2019

Y2 - 28 January 2019 through 30 January 2019

ER -

Applying Cloud-Based Computing and Emerging Remote Sensing Technologies to Inform Land Management Decisions

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