LCMS_MLSNF_v2019-03_Gain

Metadata:

Identification_Information:
Citation:
Citation_Information:
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:
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:
Abstract:
This product is part of the Landscape Change Monitoring System (LCMS). It shows areas LCMS detected gain for each year

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:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1985
Ending_Date: 2019
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -111.943373358
East_Bounding_Coordinate: -108.866304455
North_Bounding_Coordinate: 40.0257653185
South_Bounding_Coordinate: 37.5326124954
Keywords:
Theme:
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:
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:
Landscape Change Monitoring System (USDA Forest Service)
Native_Data_Set_Environment:
Google Earth Engine
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center
Contact_Address:
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:
Horizontal_Coordinate_System_Definition:
Planar:
Grid_Coordinate_System: EPSG:5070
Planar_Coordinate_Information:
Coordinate_Representation:
Abscissa_Resolution: 30.0
Ordinate_Resolution: 30.0
Planar_Distance_Units: meters
Entity_and_Attribute_Information:
Manti-La Sal National Forest v2019-03 Gain 1985-2019 Description:
Each year has a modelled probability of gain using TimeSync model calibration data in a Random Forest model.
The modelled probability is then thresholded.
Any value of 1 indicates gain for the given year

Detailed_Description:
Entity_Type:
Entity_Type_Label: LCMS_Manti-La Sal National Forest_v2019-03_Gain_1985-2019
Entity_Type_Definition: LCMS Manti-La Sal National Forest Gain
Entity_Type_Definition_Source: Landscape Change Monitoring System
Attribute:
Attribute_Label: Gain
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.
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 1
Range_Domain_Maximum: 1
Attribute_Units_of_Measure:1 indicates gain for each year (The only unmasked value)
Distributor_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center
Contact_Address:
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:
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:
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