LCMS_MLSNF_v2019-03_Loss_Year_Sort_By_Year

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 is a summary of all annual loss into a single layer showing the most recent year LCMS detected loss.

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 Loss_Year_Sort_By_Year 1985-2019 Description:
Each year has a modelled probability of loss using TimeSync model calibration data in a Random Forest model.
The modelled probability is then thresholded.
Then the year corresponding to the most recent loss is chosen
Classes:All values represent the 4-digit year of loss -1970 (e.g. 15 = 1985, 16 = 1986, etc)

Detailed_Description:
Entity_Type:
Entity_Type_Label: LCMS_Manti-La Sal National Forest_v2019-03_Loss_Year_Sort_By_Year_1985-2019
Entity_Type_Definition: LCMS Manti-La Sal National Forest Loss_Year_Sort_By_Year
Entity_Type_Definition_Source: Landscape Change Monitoring System
Attribute:
Attribute_Label: Loss_Year_Sort_By_Year
Attribute_Definition: Vegetative indices indicate a negative trend over time. Loss is not categorized explicitly. However, specific change process classifications are collected within the training data. The following describes the change processes collected within the training data. Any of them could be present and are collectively considered loss.
FIRE - Land altered by fire, regardless of the cause of the ignition (natural or anthropogenic), severity, or land use.
HARVEST - Forest land where trees, shrubs or other vegetation have been severed or removed by anthropogenic means. Examples include clearcutting, salvage logging after fire or insect outbreaks, thinning and other forest management prescriptions (e.g. shelterwood/seedtree harvest).
MECHANICAL - Non-forest land where trees, shrubs or other vegetation has been mechanically severed or removed by chaining, scraping, brush sawing, bulldozing, or any other methods of non-forest vegetation removal.
STRUCTURAL DECLINE - Land where trees or other woody vegetation is physically altered by unfavorable growing conditions brought on by non-anthropogenic or non-mechanical factors. This type of loss should generally create a trend in the spectral signal(s) (e.g. NDVI decreasing, Wetness decreasing; SWIR increasing; etc.) however the trend can be subtle. Structural decline occurs in woody vegetation environments, most likely from insects, disease, drought, acid rain, etc. Structural decline can include defoliation events that do not result in mortality such as in Gypsy moth and spruce budworm infestations which may recover within 1 or 2 years.
SPECTRAL DECLINE - A plot where the spectral signal shows a trend in one or more of the spectral bands or indices (e.g. NDVI decreasing, Wetness decreasing; SWIR increasing; etc.). Examples include cases where: a) non-forest/non-woody vegetation shows a trend suggestive of decline (e.g. NDVI decreasing, Wetness decreasing; SWIR increasing; etc.), or b) where woody vegetation shows a decline trend which is not related to the loss of woody vegetation, such as when mature tree canopies close resulting in increased shadowing, when species composition changes from conifer to hardwood, or when a dry period (as opposed to stronger, more acute drought) causes an apparent decline in vigor, but no loss of woody material or leaf area.
WIND/ICE - Land (regardless of use) where vegetation is altered by wind from hurricanes, tornados, storms and other severe weather events including freezing rain from ice storms.
HYDROLOGY - Land where flooding has significantly altered woody cover or other Land cover elements regardless of land use (e.g. new mixtures of gravel and vegetation in and around streambeds after a flood).
DEBRIS - Land (regardless of use) altered by natural material movement associated with landslides, avalanches, volcanos, debris flows, etc.
OTHER - Land (regardless of use) where the spectral trend or other supporting evidence suggests a disturbance or change event has occurred but the definitive cause cannot be determined or the type of change fails to meet any of the change process categories defined above.
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 15 (1985)
Range_Domain_Maximum: 49 (2019)
Attribute_Units_of_Measure:Year of most recent loss minus 1970

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