LCMS_MLSNF_v2019-03_Gain_Year_Sort_By_Year
Metadata:
- Identification_Information:
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- Citation:
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- Citation_Information:
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- Originator: USDA Forest Service
- Title: Landscape Change Monitoring System, Manti-La Sal National Forest
- Edition: 2019-03
- Publication Date:20191101
- Geospatial_Data_Presentation_Form: raster digital data
- Other_Citation_Details:
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References:
(1) Breiman, L. 2001. Random forests. Machine Learning 45:5–32.
(2) W.B. Cohen, Y. Zhiqiang, R.E. Kennedy, Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation,Remote Sensing of Environment, 114 (2010), pp. 2911-2924
(3) Cohen, Warren B.; Yang, Zhiqiang; Healey, Sean P.; Kennedy, Robert E.; Gorelick, Noel. 2018. A LandTrendr multispectral ensemble for forest disturbance detection.
(4) Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27.
(5) S.P. Healey, W.B. Cohen, Z. Yang, C. Kenneth Brewer, E.B. Brooks, N. Gorelick, A.J. Hernandez, C. Huang, M. Joseph Hughes, R.E. Kennedy, T.R. Loveland, G.G. Moisen, T.A. Schroeder, S.V. Stehman, J.E. Vogelmann, C.E. Woodcock, L. Yang, Z. Zhu
Mapping forest change using stacked generalization: an ensemble approach
Remote Sens. Environ., 204 (2018), pp. 717-728
(6) M. Hughes, S. Kaylor, D. Hayes, Patch-based forest change detection from Landsat time series, Forests, 8 (2017), p. 166
(7) Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing. 10, 691.
(8) Zhu, Z.; Woodcock, C.E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment. 118(2012): 83–94.
- Online_Linkage: <https://lcms-data-explorer.appspot.com/>
- Description:
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- Abstract:
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This product is part of the Landscape Change Monitoring System (LCMS). It is a summary of all annual gain into a single layer showing the most recent year LCMS detected gain.
The Landscape Change Monitoring System (LCMS) is an emerging remote sensing-based system for mapping and monitoring land cover change across the United States. The objective of LCMS is to develop a consistent approach using the latest technology and advancements in change detection to produce a “best available” map of landscape change. LCMS data are designed to advance and modernize our intra- and inter-agency monitoring capabilities using reference data and a series of change detection algorithms. Because no algorithm performs best in all situations, LCMS uses an ensemble model to improve map accuracy across a range of ecosystems and disturbance processes. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present. The LCMS ensemble model yields a “best available” map highlighting a multitude of change processes and land cover types (Healey et al 2018).
Landsat 5, 7, and 8 surface reflectance data are accessed in Google Earth Engine (Gorelick et al 2017). All data have the cFmask cloud and cloud shadow masking algorithm applied to them (Zhu and Woodcock 2012). The annual medoid is then computed to summarize each year into a single composite. Composites are run through the LANDTRENDR (Kennedy et al 2018; Cohen et al 2018) and VERDET (Hughes et al 2017) temporal segmentation algorithms. The raw composite values and fitted values from LANDTRENDR and VERDET along with the respective pair-wise differences are used as independent predictor variables in a Random Forest (Breiman 2001) model. Model calibration data are manually interpreted by analysts using the TimeSync Landsat visualization tool (Cohen et al 2010). Predicted values include: loss, gain, landcover, and landuse. These values are predicted for each year of the Landsat time series, and serve as the foundational products for LCMS.
- Time_Period_of_Content:
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- Time_Period_Information:
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- Range_of_Dates/Times:
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- Beginning_Date: 1985
- Ending_Date: 2019
- Spatial_Domain:
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- Bounding_Coordinates:
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- West_Bounding_Coordinate: -111.943373358
- East_Bounding_Coordinate: -108.866304455
- North_Bounding_Coordinate: 40.0257653185
- South_Bounding_Coordinate: 37.5326124954
- Keywords:
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- Theme:
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- Theme_Keyword_Thesaurus: None
- Theme_Keyword: Change
- Theme_Keyword: Disturbance
- Theme_Keyword: Forest
- Theme_Keyword: Landsat
- Theme_Keyword: Landscape
- Theme_Keyword: LCMS
- Theme_Keyword: Monitoring
- Theme_Keyword: Loss
- Theme_Keyword: Gain
- Access_Constraints: None
- Use_Constraints:
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The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.
- Data_Set_Credit:
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Landscape Change Monitoring System (USDA Forest Service)
- Native_Data_Set_Environment:
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Google Earth Engine
- Point_of_Contact:
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- Contact_Information:
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- Contact_Organization_Primary:
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- Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center
- Contact_Address:
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- Address_Type: mailing and physical
- Address: USDA Forest Service - GTAC
- Address: 125 S State Street, Suite 7105
- City: Salt Lake City
- State_or_Province: UT
- Postal_Code: 84138
- Country: US
- Contact_Electronic_Mail_Address: sm.fs.lcms@usda.gov
- Spatial_Reference_Information:
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- Horizontal_Coordinate_System_Definition:
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- Planar:
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- Grid_Coordinate_System: EPSG:5070
- Planar_Coordinate_Information:
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- Coordinate_Representation:
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- Abscissa_Resolution: 30.0
- Ordinate_Resolution: 30.0
- Planar_Distance_Units: meters
- Entity_and_Attribute_Information:
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- Manti-La Sal National Forest v2019-03 Gain_Year_Sort_By_Year 1985-2019 Description:
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Each year has a modelled probability of gain using TimeSync model calibration data in a Random Forest model.
The modelled probability is then thresholded.
Then the year corresponding to the most recent gain is chosen
Classes:All values represent the 4-digit year of gain -1970 (e.g. 15 = 1985, 16 = 1986, etc)
- Detailed_Description:
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- Entity_Type:
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- Entity_Type_Label: LCMS_Manti-La Sal National Forest_v2019-03_Gain_Year_Sort_By_Year_1985-2019
- Entity_Type_Definition: LCMS Manti-La Sal National Forest Gain_Year_Sort_By_Year
- Entity_Type_Definition_Source: Landscape Change Monitoring System
- Attribute:
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- Attribute_Label: Gain_Year_Sort_By_Year
- Attribute_Definition: Vegetative indices indicate a positive trend over time. Gain is categorized using one specific change process classification within the training data, described below.
GROWTH/RECOVERY - Land exhibiting an increase in vegetation cover due to growth and succession over one or more years. Applicable to any areas that may express spectral change associated with vegetation regrowth. In developed areas, growth can result from maturing vegetation and/or newly installed lawns and landscaping. In forests, growth includes vegetation growth from bare ground, as well as the over topping of intermediate and co-dominate trees and/or lower-lying grasses and shrubs. Growth/Recovery segments recorded following forest harvest will likely transition through different land cover classes as the forest regenerates. For these changes to be considered growth/recovery, spectral values should closely adhere to an increasing trend line (e.g. a positive slope that would, if extended to ~20 years, be on the order of .10 units of NDVI) which persists for several years.
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- Attribute_Domain_Values:
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- Range_Domain:
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- Range_Domain_Minimum: 15 (1985)
- Range_Domain_Maximum: 49 (2019)
- Attribute_Units_of_Measure:Year of most recent gain minus 1970
- Distributor_Information:
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- Distributor:
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- Contact_Information:
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- Contact_Organization_Primary:
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- Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center
- Contact_Address:
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- Address_Type: mailing and physical
- Address: USDA Forest Service - GTAC
- Address: 125 S State Street, Suite 7105
- City: Salt Lake City
- State_or_Province: UT
- Postal_Code: 84138
- Country: US
- Contact_Electronic_Mail_Address: sm.fs.lcms@usda.gov
- Distribution_Liability:
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The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.
- Metadata_Reference_Information:
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- Metadata_Date: 20190315
- Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
- Metadata_Standard_Version: FGDC-STD-001-1998
- Metadata_Time_Convention: local time