Attribute_Accuracy_Report:
The 1973 era raster images have a roughly 60 m horizontal registration uncertainty, whereas the 2002 and 2017 raster images have a roughly 30 m horizontal registration uncertainty. Uncertainty was inherited from the original Landsat scenes that were mosaicked to produce each raster image. There are no vertical values.
Source_Information:
Source_Citation:
Citation_Information:
Originator:
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center
Publication_Date: 2017
Title:
Landsat Imagery from Landsat 1 of Tier 2 Scenes for Time-Series Analysis
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Sioux Falls, South Dakota, USA
Publisher:
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center
Other_Citation_Details:
The following is a list of the Landsat scene identifier and the date of the scene.
LC80690172017084LGN00, 20160829,
LC80690182017084LGN00, 20160829,
LC80690172017084LGN00, 20170325,
LC80690182017084LGN00, 20170325
Online_Linkage: https://www.usgs.gov/centers/eros
Online_Linkage: https://earthexplorer.usgs.gov/
Source_Scale_Denominator: 120000
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 19730817
Single_Date/Time:
Calendar_Date: 19740304
Single_Date/Time:
Calendar_Date: 19750210
Single_Date/Time:
Calendar_Date: 19750403
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat_73_75
Source_Contribution:
Landsat satellite imagery acquired between 1973 and 1975 to classify vegetation into 9 general classes based on vegetation height, density and photosynthetic activity.
Source_Information:
Source_Citation:
Citation_Information:
Originator:
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center
Publication_Date: 2017
Title:
Landsat Imagery from Landsat 7 of Tier 1 Scenes for Time-Series Analysis
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Sioux Falls, South Dakota, USA
Publisher:
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center
Other_Citation_Details:
The following is a list of the Landsat scene identifier and the date of the scene.
LE70690172002211EDC00, 20020730,
LE70690182002211EDC00, 20020730,
LE70690172002051EDC00, 20020220,
LE70690182002051EDC00, 20020220
Online_Linkage: https://www.usgs.gov/centers/eros
Online_Linkage: https://earthexplorer.usgs.gov/
Source_Scale_Denominator: 120
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 20200220
Single_Date/Time:
Calendar_Date: 20020730
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat_02
Source_Contribution:
Landsat satellite imagery acquired for 2002 to classify vegetation into 9 general classes based on vegetation height, density and photosynthetic activity.
Source_Information:
Source_Citation:
Citation_Information:
Originator:
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center
Publication_Date: 2017
Title:
Landsat Imagery from Landsat 8 of Tier 1 Scenes for Time-Series Analysis
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Sioux Falls, South Dakota, USA
Publisher:
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center
Other_Citation_Details:
The following is a list of the Landsat scene identifier and the date of the scene.
LC80690172017084LGN00, 20160829,
LC80690182017084LGN00, 20160829,
LC80690172017084LGN00, 20170325,
LC80690182017084LGN00, 20170325
Online_Linkage: https://www.usgs.gov/centers/eros
Online_Linkage: https://earthexplorer.usgs.gov/
Source_Scale_Denominator: 120
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 20160829
Single_Date/Time:
Calendar_Date: 20170325
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat_16_17
Source_Contribution:
Landsat satellite imagery acquired between 2016 and 2017 to classify vegetation into 9 general classes based on vegetation height, density and photosynthetic activity.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological Survey
Publication_Date: 20200514
Title: USGS National Map Viewer
Geospatial_Data_Presentation_Form: view
Other_Citation_Details: 3DEP View (V1.0)
Online_Linkage:
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20200514
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: 3DEP_View
Source_Contribution:
The 3DEP Viewer is used to find and download digital elevation products such as Digital Elevation Models produced by the U.S. Geological Survey.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Kenai Peninsula Borough
Publication_Date: Unknown
Title: 2016_Imagery (MapServer)
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Kenai Peninsula
Publisher: Kenai Peninsula Borough
Other_Citation_Details: ArcGIS REST Services Directory
Online_Linkage: http://maps.kpb.us/gis/rest/services/2016_Imagery/MapServer
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: Unknown
Ending_Date: Unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Kenai_Web1
Source_Contribution:
Kenai Peninsula Borough ArcGIS REST Services Directory for 2016 areal imagery.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Kenai Peninsula Borough
Publication_Date: Unknown
Title: Vegetation (MapServer)
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Kenai Peninsula
Publisher: Kenai Peninsula Borough
Other_Citation_Details: ArcGIS REST Services Directory
Online_Linkage: http://maps.kpb.us/gis/rest/services/Vegetation/MapServer
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: Unknown
Ending_Date: Unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Kenai_Web2
Source_Contribution:
Kenai Peninsula Borough ArcGIS REST Services Directory for vegetative analysis and classification.
Source_Information:
Source_Citation:
Citation_Information:
Originator: US Fish and Wildlife
Publication_Date: Unknown
Title: Aerial imagery of the Kenai Wildlife Refuge, Kenai, AK
Geospatial_Data_Presentation_Form: raster digital data
Other_Citation_Details: Kenai National Wildlife Refuge
Online_Linkage: https://www.fws.gov/refuge/kenai/
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: Unknown
Ending_Date: Unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Kenai_Web3
Source_Contribution:
We used areal imagery acquired in 1975 and provided upon request by the Kenai National Wildlife Refuge.
Source_Information:
Source_Citation:
Type_of_Source_Media: paper
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2001
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: randomForest_1
Source_Contribution:
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Andy Liaw
Originator: Matthew Wiener
Publication_Date: 20180322
Title: Breiman and Cutler's Random Forests for Classification and
Geospatial_Data_Presentation_Form: website
Online_Linkage:
Online_Linkage: DOI:10.1023/A:1010933404324
Larger_Work_Citation:
Citation_Information:
Originator: L. Breiman
Publication_Date: 2001
Title: Random Forests
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Machine Learning
Issue_Identification: 45
Publication_Information:
Publication_Place: U.C. Berkley
Publisher: U.C. Berkley
Online_Linkage: https://www.stat.berkeley.edu/~breiman/RandomForests/
Type_of_Source_Media: computer program
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: Unknown
Ending_Date: Unknown
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: randomForest_2
Source_Contribution:
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001)
Process_Step:
Process_Description:
Downloaded 1973 era Landsat Collection-1 Level-1 archived scenes from the USGS Center for Earth Resources Observation and Science (EROS) EarthExplorer web application that overlapped the study area. We acquired scenes from Landsat 1 Multispectral Scanner (MSS) that defined the era circa 1973.
For the 1973 scene, the infrared band was used from Landsat 1 (Band 7) as an index for the abundance of above-snow woody vegetation during late winter scenes.
Source_Used_Citation_Abbreviation: Landsat_73_75
Process_Date: Unknown
Process_Step:
Process_Description:
Downloaded 2002 era Landsat Collection-1 Level-1 archived scenes from the USGS Center for Earth Resources Observation and Science (EROS) EarthExplorer web application that overlapped the study area. We acquired scenes from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) that defined the era circa 2002.
For 2002 scenes, the blue band from Landsat 7 (Band 1) was used as an index for the abundance of above-snow woody vegetation.
Source_Used_Citation_Abbreviation: Landsat_02
Process_Date: Unknown
Process_Step:
Process_Description:
Downloaded 2017 era Landsat Collection-1 Level-1 archived scenes from the USGS Center for Earth Resources Observation and Science (EROS) EarthExplorer web application that overlapped the study area. We acquired scenes from Landsat 8 Operational Land Imager (OLI) that defined the era circa 2017.
For 2017 scenes, the blue bands from Landsat 8 (Band 2) was used as an index for the abundance of above-snow woody vegetation.
Source_Used_Citation_Abbreviation: Landsat_16_17
Process_Date: Unknown
Process_Step:
Process_Description:
For each era, scenes were manually clipped to remove cloud and shadow contamination if needed (ArcGIS Desktop 10.7).
Source_Used_Citation_Abbreviation: Landsat_73_75
Source_Used_Citation_Abbreviation: Landsat_02
Source_Used_Citation_Abbreviation: Landsat_16_17
Process_Date: Unknown
Process_Step:
Process_Description:
Digital numbering (DN) values across all scenes were standardized using dark object subtraction based on ice-free offshore water (DN = 0). Values were rescaled so that large snow-covered lowland lakes common to overlapping scenes had a value of DN = 140 (ArcGIS Desktop 10.7).
Source_Used_Citation_Abbreviation: Landsat_73_75
Source_Used_Citation_Abbreviation: Landsat_02
Source_Used_Citation_Abbreviation: Landsat_16_17
Process_Date: Unknown
Process_Step:
Process_Description:
The 1973 scenes were ordered and mosaicked (ArcGIS Desktop 10.7) using the FIRST operator, a method that determines the pixel value from the first raster dataset encountered in a mosaic list.
For the 1973 era scenes, we used the transformed vegetation index (TVI) based on the red and infrared bands where TVI=√(((NIR-R)/(NIR+R)+0.5)).
Source_Used_Citation_Abbreviation: Landsat_73_75
Process_Date: Unknown
Process_Step:
Process_Description:
The 2002 and 2017 scenes were then ordered and mosaicked (ArcGIS Desktop 10.7) using the FIRST operator, a method that determines the pixel value from the first raster dataset encountered in a mosaic list.
For the 2002 and 2017 eras, we used the Level 2 Enhanced Vegetation Index (EVI) spectral index derived from clear-sky summer scenes, available from USGS EROS Science Processing Architecture (ESPA).
Source_Used_Citation_Abbreviation: Landsat_02
Source_Used_Citation_Abbreviation: Landsat_16_17
Process_Date: Unknown
Process_Step:
Process_Description:
The study area was overlaid with a points shapefile with points centered in the pixels of each era’s respective scenes.
Source_Used_Citation_Abbreviation: Landsat_73_75
Source_Used_Citation_Abbreviation: Landsat_02
Source_Used_Citation_Abbreviation: Landsat_16_17
Process_Date: Unknown
Process_Step:
Process_Description:
Each point was assigned a feature list containing pixel values from the winter and summer Landsat mosaics and values for slope, aspect, and elevation which were derived from the Alaska 2-Arc-second Digital Elevation Model (DEM, 60m).
Source_Used_Citation_Abbreviation: 3DEP_View
Process_Date: Unknown
Process_Step:
Process_Description:
Training points for the 1973 era were randomly distributed within training areas opportunistically distributed to capture the regional and geomorphic extent of each land cover types to the extent possible given availability of aerial imagery for each era. We used areal imagery acquired in 1975 and provided upon request by the Kenai National Wildlife Refuge.
Source_Used_Citation_Abbreviation: Kenai_Web3
Process_Date: Unknown
Process_Step:
Process_Description:
Training points for the 2002 era were randomly distributed within training areas opportunistically distributed to capture the regional and geomorphic extent of each land cover types to the extent possible given availability of aerial imagery for each era. We used previous interpretations of aerial imagery completed circa 1999 and available for download from the Kenai Peninsula Borough.
Source_Used_Citation_Abbreviation: Kenai_Web2
Process_Date: Unknown
Process_Step:
Process_Description:
Training points for the 2017 era were randomly distributed within training areas opportunistically distributed to capture the regional and geomorphic extent of each land cover types to the extent possible given availability of aerial imagery for each era. We used areal imagery acquired in 2016 and available to download from the Kenai Peninsula Borough.
Source_Used_Citation_Abbreviation: Kenai_Web1
Process_Date: Unknown
Process_Step:
Process_Description:
Training data containing a full feature list and known land cover type were read into the randomForest package in R and used to create a predictive model termed a classifier.
Within R, the classifier was run for every point in the point shapefile that overlaid the study area which output a predicted land cover type and a corresponding probability score.
1.A separate classifier was derived for each era.
2.Each classier used 500 trees and tested two variables at each node.
3.All other parameters remained in the default setting offered in the R package.
Source_Used_Citation_Abbreviation: randomForest_1
Source_Used_Citation_Abbreviation: randomForest_2
Process_Date: Unknown
Process_Step:
Process_Description:
The predicted landcover type and corresponding probability score was added to the feature list of the points shapefile using the Join Field tool in AcrGIS.
Source_Used_Citation_Abbreviation: Landsat_73_75
Source_Used_Citation_Abbreviation: Landsat_02
Source_Used_Citation_Abbreviation: Landsat_16_17
Process_Date: Unknown
Process_Step:
Process_Description:
Raster images with cell values corresponding to land cover type and probability were produced from the point shapefile for each era using the Point to Raster tool in ArcMap.
Source_Used_Citation_Abbreviation: Landsat_73_75
Source_Used_Citation_Abbreviation: Landsat_02
Source_Used_Citation_Abbreviation: Landsat_16_17
Process_Date: Unknown