diff --git a/catalog/USFS/USFS_GTAC_LCMS_v2021-7.jsonnet b/catalog/USFS/USFS_GTAC_LCMS_v2021-7.jsonnet index c19477a19..250963c07 100644 --- a/catalog/USFS/USFS_GTAC_LCMS_v2021-7.jsonnet +++ b/catalog/USFS/USFS_GTAC_LCMS_v2021-7.jsonnet @@ -1,4 +1,5 @@ local id = 'USFS/GTAC/LCMS/v2021-7'; +local version = '2021.7'; local latest_id = id; local predecessor_id = 'USFS/GTAC/LCMS/v2020-5'; local subdir = 'USFS'; @@ -25,32 +26,39 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; ee_const.ext_ver, ], id: id, - title: 'USFS Landscape Change Monitoring System v2021.7 (Conterminous United States and Southeastern Alaska)', - version: 'v2021.7', + title: + 'USFS Landscape Change Monitoring System v' + version +' ' + '(Conterminous United States and Southeastern Alaska)', + version: version, 'gee:type': ee_const.gee_type.image_collection, description: ||| - This product is part of the Landscape Change Monitoring System (LCMS) data suite. - It shows LCMS-modeled change, land cover, and/or land use classes for each year. - - LCMS is a remote sensing-based system for mapping and monitoring landscape change across the - United States. Its objective is to develop a consistent approach using the latest technology - and advancements in change detection to produce a "best available" map of landscape change. - - Outputs include three annual products: change, land cover, and land use. - Change relates specifically to vegetation cover and includes slow loss, fast loss (which also - includes hydrologic changes such as inundation or desiccation), and gain. These values are - predicted for each year of the Landsat time series and serve as the foundational products for - LCMS. Land cover and land use maps depict life-form level land cover and broad-level land use + This product is part of the Landscape Change Monitoring System (LCMS) data + suite. It shows LCMS-modeled change, land cover, and/or land use classes for each year. - Because no algorithm performs best in all situations, LCMS uses an ensemble of models as - predictors, which improves map accuracy across a range of ecosystems and change processes - (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer - a holistic depiction of landscape change across the United States over the past four decades. + LCMS is a remote sensing-based system for mapping and monitoring landscape + change across the United States. Its objective is to develop a consistent + approach using the latest technology and advancements in change detection to + produce a "best available" map of landscape change. - Predictor layers for the LCMS model include outputs - from the LandTrendr and CCDC change detection algorithms, and terrain information. These - components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). + Outputs include three annual products: change, land cover, and land use. + Change relates specifically to vegetation cover and includes slow loss, fast + loss (which also includes hydrologic changes such as inundation or + desiccation), and gain. These values are predicted for each year of the + Landsat time series and serve as the foundational products for LCMS. Land + cover and land use maps depict life-form level land cover and broad-level + land use for each year. + + Because no algorithm performs best in all situations, LCMS uses an ensemble + of models as predictors, which improves map accuracy across a range of + ecosystems and change processes (Healey et al., 2018). The resulting suite + of LCMS change, land cover, and land use maps offer a holistic depiction of + landscape change across the United States over the past four decades. + + Predictor layers for the LCMS model include outputs from the LandTrendr and + CCDC change detection algorithms, and terrain information. These components + are all accessed and processed using Google Earth Engine (Gorelick et al., + 2017). Landsat Tier 1 and Sentinel 2A, 2B Level-1C top of atmosphere reflectance data are used directly in CCDC and to produce annual composites for @@ -62,11 +70,11 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; 2). For LandTrendr, the annual medoid is then computed to summarize cloud and cloud shadow-free values from each year into a single composite. - The composite time series is temporally segmented using LandTrendr - (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). + The composite time series is temporally segmented using LandTrendr (Kennedy + et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). - All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm - (Zhu and Woodcock, 2014). + All cloud and cloud shadow free values are also temporally segmented using + the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and @@ -78,14 +86,16 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps - analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010). + analysts visualize and interpret the Landsat data record from 1984-present + (Cohen et al., 2010). **Additional Resources** * [A more detailed code example of using LCMS data](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js). - * The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a web-based application that - provides users the ability to view, analyze, summarize and download LCMS data. + * The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a + web-based application that provides users the ability to view, analyze, + summarize and download LCMS data. * Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2021-7_Methods.pdf) for more detailed information regarding methods and accuracy assessment, or the @@ -199,7 +209,10 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; 'usfs', ], providers: [ - ee.producer_provider('USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC)', 'https://apps.fs.usda.gov/lcms-viewer/'), + ee.producer_provider( + 'USDA Forest Service (USFS) Geospatial Technology and ' + + 'Applications Center (GTAC)', + 'https://apps.fs.usda.gov/lcms-viewer/'), ee.host_provider(self_ee_catalog_url), ], extent: ee.extent(-135.286387, 20.38379, -56.446306, 52.459364, @@ -223,13 +236,16 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; { name: 'Change', description: ||| - Final thematic LCMS change product. A total of three change classes (slow loss, fast loss, - and gain) are mapped for each year. Each class is predicted using a separate Random Forest - model, which outputs a probability (proportion of the trees within the Random Forest model) - that the pixel belongs to that class. Because of this, individual pixels have three different - model outputs for each year. Final classes are assigned to the change class with the highest - probability that is also above a specified threshold. Any pixel that does not have any value - above each class's respective threshold is assigned to the Stable class. + Final thematic LCMS change product. A total of three change classes + (slow loss, fast loss, and gain) are mapped for each year. Each class + is predicted using a separate Random Forest model, which outputs a + probability (proportion of the trees within the Random Forest model) + that the pixel belongs to that class. Because of this, individual + pixels have three different model outputs for each year. Final classes + are assigned to the change class with the highest probability that is + also above a specified threshold. Any pixel that does not have any + value above each class's respective threshold is assigned to the + Stable class. |||, 'gee:classes': [ { @@ -262,16 +278,19 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; { name: 'Land_Cover', description: ||| - Final thematic LCMS land cover product. A total of 14 land cover classes are mapped on an - annual basis using TimeSync reference data and spectral information derived from Landsat - imagery. Each class is predicted using a separate Random Forest model, which outputs a - probability (proportion of the trees within the Random Forest model) that the pixel belongs - to that class. Because of this, individual pixels have 14 different model outputs for each - year, and final classes are assigned to the land cover with the highest probability. Seven of - the 14 land cover classes indicate a single land cover, where that land cover type covers - most of the pixel's area and no other class covers more than 10% of the pixel. There are also - seven mixed classes. These represent pixels in which an additional land cover class covers at - least 10% of the pixel. + Final thematic LCMS land cover product. A total of 14 land cover + classes are mapped on an annual basis using TimeSync reference data + and spectral information derived from Landsat imagery. Each class is + predicted using a separate Random Forest model, which outputs a + probability (proportion of the trees within the Random Forest model) + that the pixel belongs to that class. Because of this, individual + pixels have 14 different model outputs for each year, and final + classes are assigned to the land cover with the highest + probability. Seven of the 14 land cover classes indicate a single land + cover, where that land cover type covers most of the pixel's area and + no other class covers more than 10% of the pixel. There are also seven + mixed classes. These represent pixels in which an additional land + cover class covers at least 10% of the pixel. |||, 'gee:classes': [ { @@ -354,12 +373,14 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; { name: 'Land_Use', description: ||| - Final thematic LCMS land use product. A total of 6 land use classes are mapped on an annual - basis using TimeSync reference data and spectral information derived from Landsat imagery. - Each class is predicted using a separate Random Forest model, which outputs a probability - (proportion of the trees within the Random Forest model) that the pixel belongs to that class. - Because of this, individual pixels have 6 different model outputs for each year, and final - classes are assigned to the land use with the highest probability. + Final thematic LCMS land use product. A total of 6 land use classes + are mapped on an annual basis using TimeSync reference data and + spectral information derived from Landsat imagery. Each class is + predicted using a separate Random Forest model, which outputs a + probability (proportion of the trees within the Random Forest model) + that the pixel belongs to that class. Because of this, individual + pixels have 6 different model outputs for each year, and final classes + are assigned to the land use with the highest probability. |||, 'gee:classes': [ { @@ -402,260 +423,301 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; { name: 'Change_Raw_Probability_Slow-Loss', description: ||| - Raw LCMS modeled probability of Slow Loss. Defined as: Slow Loss includes the following - classes from the TimeSync change process interpretation- - - * 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. + Raw LCMS modeled probability of Slow Loss. Defined as: Slow Loss + includes the following classes from the TimeSync change process + interpretation- + + * 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. |||, }, { name: 'Change_Raw_Probability_Fast-Loss', description: ||| - Raw LCMS modeled probability of Fast Loss. Defined as: Fast Loss includes the following - classes from the TimeSync change process interpretation- + Raw LCMS modeled probability of Fast Loss. Defined as: Fast Loss + includes the following classes from the TimeSync change process + interpretation- - * Fire - Land altered by fire, regardless of the cause of the ignition (natural or - anthropogenic), severity, or land use. + * 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 - 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. + * 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. - * 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. + * 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). + * 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. + * 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 + * 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. |||, }, { name: 'Change_Raw_Probability_Gain', description: ||| - Raw LCMS modeled probability of Gain. Defined as: 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. + Raw LCMS modeled probability of Gain. Defined as: 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. |||, }, { name: 'Land_Cover_Raw_Probability_Trees', description: ||| - Raw LCMS modeled probability of Trees. Defined as: The majority of the pixel is comprised - of live or standing dead trees. + Raw LCMS modeled probability of Trees. Defined as: The majority of the + pixel is comprised of live or standing dead trees. |||, }, { name: 'Land_Cover_Raw_Probability_Tall-Shrubs-and-Trees-Mix', description: ||| - Raw LCMS modeled probability of Tall Shrubs and Trees Mix (SEAK Only). Defined - as: The majority of the pixel is comprised of shrubs greater than 1m in height and is also - comprised of at least 10% live or standing dead trees. + Raw LCMS modeled probability of Tall Shrubs and Trees Mix (SEAK + Only). Defined as: The majority of the pixel is comprised of shrubs + greater than 1m in height and is also comprised of at least 10% live + or standing dead trees. |||, }, { name: 'Land_Cover_Raw_Probability_Shrubs-and-Trees-Mix', description: ||| - Raw LCMS modeled probability of Shrubs and Trees Mix. Defined as: The majority of the pixel - is comprised of shrubs and is also comprised of at least 10% live or standing dead trees. + Raw LCMS modeled probability of Shrubs and Trees Mix. Defined as: The + majority of the pixel is comprised of shrubs and is also comprised of + at least 10% live or standing dead trees. |||, }, { name: 'Land_Cover_Raw_Probability_Grass-Forb-Herb-and-Trees-Mix', description: ||| - Raw LCMS modeled probability of Grass/Forb/Herb and Trees Mix. Defined as: The majority of - the pixel is comprised of perennial grasses, forbs, or other forms of herbaceous vegetation - and is also comprised of at least 10% live or standing dead trees. + Raw LCMS modeled probability of Grass/Forb/Herb and Trees Mix. Defined + as: The majority of the pixel is comprised of perennial grasses, + forbs, or other forms of herbaceous vegetation and is also comprised + of at least 10% live or standing dead trees. |||, }, { name: 'Land_Cover_Raw_Probability_Barren-and-Trees-Mix', description: ||| - Raw LCMS modeled probability of Barren and Trees Mix. Defined as: The majority of the pixel - is comprised of bare soil exposed by disturbance (e.g., soil uncovered by mechanical - clearing or forest harvest), as well as perennially barren areas such as deserts, playas, - rock outcroppings (including minerals and other geologic materials exposed by surface mining - activities), sand dunes, salt flats, and beaches. Roads made of dirt and gravel are also - considered barren and is also comprised of at least 10% live or standing dead trees. + Raw LCMS modeled probability of Barren and Trees Mix. Defined as: The + majority of the pixel is comprised of bare soil exposed by disturbance + (e.g., soil uncovered by mechanical clearing or forest harvest), as + well as perennially barren areas such as deserts, playas, rock + outcroppings (including minerals and other geologic materials exposed + by surface mining activities), sand dunes, salt flats, and + beaches. Roads made of dirt and gravel are also considered barren and + is also comprised of at least 10% live or standing dead trees. |||, }, { name: 'Land_Cover_Raw_Probability_Tall-Shrubs', description: ||| - Raw LCMS modeled probability of Tall Shrubs (SEAK Only). Defined as: The majority of the - pixel is comprised of shrubs greater than 1m in height. + Raw LCMS modeled probability of Tall Shrubs (SEAK Only). Defined as: + The majority of the pixel is comprised of shrubs greater than 1m in + height. |||, }, { name: 'Land_Cover_Raw_Probability_Shrubs', description: ||| - Raw LCMS modeled probability of Shrubs. Defined as: The majority of the pixel is comprised - of shrubs. + Raw LCMS modeled probability of Shrubs. Defined as: The majority of + the pixel is comprised of shrubs. |||, }, { name: 'Land_Cover_Raw_Probability_Grass-Forb-Herb-and-Shrubs-Mix', description: ||| - Raw LCMS modeled probability of Grass/Forb/Herb and Shrubs Mix. Defined as: The majority of - the pixel is comprised of perennial grasses, forbs, or other forms of herbaceous vegetation - and is also comprised of at least 10% shrubs. + Raw LCMS modeled probability of Grass/Forb/Herb and Shrubs + Mix. Defined as: The majority of the pixel is comprised of perennial + grasses, forbs, or other forms of herbaceous vegetation and is also + comprised of at least 10% shrubs. |||, }, { name: 'Land_Cover_Raw_Probability_Barren-and-Shrubs-Mix', description: ||| - Raw LCMS modeled probability of Barren and Shrubs Mix. Defined as: The majority of the pixel - is comprised of bare soil exposed by disturbance (e.g., soil uncovered by mechanical - clearing or forest harvest), as well as perennially barren areas such as deserts, playas, - rock outcroppings (including minerals and other geologic materials exposed by surface mining - activities), sand dunes, salt flats, and beaches. Roads made of dirt and gravel are also - considered barren and is also comprised of at least 10% shrubs. + Raw LCMS modeled probability of Barren and Shrubs Mix. Defined as: The + majority of the pixel is comprised of bare soil exposed by disturbance + (e.g., soil uncovered by mechanical clearing or forest harvest), as + well as perennially barren areas such as deserts, playas, rock + outcroppings (including minerals and other geologic materials exposed + by surface mining activities), sand dunes, salt flats, and + beaches. Roads made of dirt and gravel are also considered barren and + is also comprised of at least 10% shrubs. |||, }, { name: 'Land_Cover_Raw_Probability_Grass-Forb-Herb', description: ||| - Raw LCMS modeled probability of Grass/Forb/Herb. Defined as: The majority of the pixel is - comprised of perennial grasses, forbs, or other forms of herbaceous vegetation. + Raw LCMS modeled probability of Grass/Forb/Herb. Defined as: The + majority of the pixel is comprised of perennial grasses, forbs, or + other forms of herbaceous vegetation. |||, }, { name: 'Land_Cover_Raw_Probability_Barren-and-Grass-Forb-Herb-Mix', description: ||| - Raw LCMS modeled probability of Barren and Grass/Forb/Herb Mix. Defined as: The majority of - the pixel is comprised of bare soil exposed by disturbance (e.g., soil uncovered by - mechanical clearing or forest harvest), as well as perennially barren areas such as deserts, - playas, rock outcroppings (including minerals and other geologic materials exposed by - surface mining activities), sand dunes, salt flats, and beaches. Roads made of dirt and - gravel are also considered barren and is also comprised of at least 10% perennial grasses, - forbs, or other forms of herbaceous vegetation. + Raw LCMS modeled probability of Barren and Grass/Forb/Herb + Mix. Defined as: The majority of the pixel is comprised of bare soil + exposed by disturbance (e.g., soil uncovered by mechanical clearing or + forest harvest), as well as perennially barren areas such as deserts, + playas, rock outcroppings (including minerals and other geologic + materials exposed by surface mining activities), sand dunes, salt + flats, and beaches. Roads made of dirt and gravel are also considered + barren and is also comprised of at least 10% perennial grasses, forbs, + or other forms of herbaceous vegetation. |||, }, { name: 'Land_Cover_Raw_Probability_Barren-or-Impervious', description: ||| - Raw LCMS modeled probability of Barren or Impervious. Defined as: The majority of the pixel - is comprised of 1) bare soil exposed by disturbance (e.g., soil uncovered by mechanical - clearing or forest harvest), as well as perennially barren areas such as deserts, playas, - rock outcroppings (including minerals and other geologic materials exposed by surface mining - activities), sand dunes, salt flats, and beaches. Roads made of dirt and gravel are also - considered barren or 2) man-made materials that water cannot penetrate, such as paved roads, - rooftops, and parking lots. + Raw LCMS modeled probability of Barren or Impervious. Defined as: The + majority of the pixel is comprised of 1) bare soil exposed by + disturbance (e.g., soil uncovered by mechanical clearing or forest + harvest), as well as perennially barren areas such as deserts, playas, + rock outcroppings (including minerals and other geologic materials + exposed by surface mining activities), sand dunes, salt flats, and + beaches. Roads made of dirt and gravel are also considered barren or + 2) man-made materials that water cannot penetrate, such as paved + roads, rooftops, and parking lots. |||, }, { name: 'Land_Cover_Raw_Probability_Snow-or-Ice', description: ||| - Raw LCMS modeled probability of Snow or Ice. Defined as: The majority of the pixel is - comprised of snow or ice. + Raw LCMS modeled probability of Snow or Ice. Defined as: The majority + of the pixel is comprised of snow or ice. |||, }, { name: 'Land_Cover_Raw_Probability_Water', description: ||| - Raw LCMS modeled probability of Water. Defined as: The majority of - the pixel is comprised of water. + Raw LCMS modeled probability of Water. Defined as: The majority of the + pixel is comprised of water. |||, }, { name: 'Land_Use_Raw_Probability_Agriculture', description: ||| - Raw LCMS modeled probability of Agriculture. Defined as: Land used for the production of - food, fiber and fuels which is in either a vegetated or non-vegetated state. This includes - but is not limited to cultivated and uncultivated croplands, hay lands, orchards, vineyards, - confined livestock operations, and areas planted for production of fruits, nuts or berries. - Roads used primarily for agricultural use (i.e. not used for public transport from town to - town) are considered agriculture land use. + Raw LCMS modeled probability of Agriculture. Defined as: Land used for + the production of food, fiber and fuels which is in either a vegetated + or non-vegetated state. This includes but is not limited to cultivated + and uncultivated croplands, hay lands, orchards, vineyards, confined + livestock operations, and areas planted for production of fruits, nuts + or berries. Roads used primarily for agricultural use (i.e. not used + for public transport from town to town) are considered agriculture + land use. |||, }, { name: 'Land_Use_Raw_Probability_Developed', description: ||| - Raw LCMS modeled probability of Developed. Defined as: Land covered by man-made structures - (e.g. high density residential, commercial, industrial, mining or transportation), or a - mixture of both vegetation (including trees) and structures (e.g., low density residential, - lawns, recreational facilities, cemeteries, transportation and utility corridors, etc.), - including any land functionally altered by human activity. + Raw LCMS modeled probability of Developed. Defined as: Land covered by + man-made structures (e.g. high density residential, commercial, + industrial, mining or transportation), or a mixture of both vegetation + (including trees) and structures (e.g., low density residential, + lawns, recreational facilities, cemeteries, transportation and utility + corridors, etc.), including any land functionally altered by human + activity. |||, }, { name: 'Land_Use_Raw_Probability_Forest', description: ||| - Raw LCMS modeled probability of Forest. Defined as: Land that is planted or naturally - vegetated and which contains (or is likely to contain) 10% or greater tree cover at some - time during a near-term successional sequence. This may include deciduous, evergreen and/or - mixed categories of natural forest, forest plantations, and woody wetlands. + Raw LCMS modeled probability of Forest. Defined as: Land that is + planted or naturally vegetated and which contains (or is likely to + contain) 10% or greater tree cover at some time during a near-term + successional sequence. This may include deciduous, evergreen and/or + mixed categories of natural forest, forest plantations, and woody + wetlands. |||, }, { name: 'Land_Use_Raw_Probability_Non-Forest-Wetland', description: ||| - Raw LCMS modeled probability of Non-Forest Wetland. Defined as: Lands adjacent to or within - a visible water table (either permanently or seasonally saturated) dominated by shrubs or - persistent emergents. These wetlands may be situated shoreward of lakes, river channels, or - estuaries; on river floodplains; in isolated catchments; or on slopes. They may also occur - as prairie potholes, drainage ditches and stock ponds in agricultural landscapes and may - also appear as islands in the middle of lakes or rivers. Other examples also include marshes, - bogs, swamps, quagmires, muskegs, sloughs, fens, and bayous. + Raw LCMS modeled probability of Non-Forest Wetland. Defined as: Lands + adjacent to or within a visible water table (either permanently or + seasonally saturated) dominated by shrubs or persistent + emergents. These wetlands may be situated shoreward of lakes, river + channels, or estuaries; on river floodplains; in isolated catchments; + or on slopes. They may also occur as prairie potholes, drainage + ditches and stock ponds in agricultural landscapes and may also appear + as islands in the middle of lakes or rivers. Other examples also + include marshes, bogs, swamps, quagmires, muskegs, sloughs, fens, and + bayous. |||, }, { name: 'Land_Use_Raw_Probability_Other', description: ||| - Raw LCMS modeled probability of Other. Defined as: 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. + Raw LCMS modeled probability of Other. Defined as: 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. |||, }, { name: 'Land_Use_Raw_Probability_Rangeland-or-Pasture', description: ||| - Raw LCMS modeled probability of Rangeland or Pasture. Defined as: This class includes any - area that is either a.) Rangeland, where vegetation is a mix of native grasses, shrubs, forbs - and grass-like plants largely arising from natural factors and processes such as rainfall, - temperature, elevation and fire, although limited management may include prescribed burning - as well as grazing by domestic and wild herbivores; or b.) Pasture, where vegetation may - range from mixed, largely natural grasses, forbs and herbs to more managed vegetation - dominated by grass species that have been seeded and managed to maintain near monoculture. + Raw LCMS modeled probability of Rangeland or Pasture. Defined as: This + class includes any area that is either a.) Rangeland, where vegetation + is a mix of native grasses, shrubs, forbs and grass-like plants + largely arising from natural factors and processes such as rainfall, + temperature, elevation and fire, although limited management may + include prescribed burning as well as grazing by domestic and wild + herbivores; or b.) Pasture, where vegetation may range from mixed, + largely natural grasses, forbs and herbs to more managed vegetation + dominated by grass species that have been seeded and managed to + maintain near monoculture. |||, }, { @@ -700,97 +762,43 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; ], 'gee:visualizations': [ { - display_name: 'ChangeViz', - lookat: { - lat: 37.09024, - lon: -95.712891, - zoom: 5, - }, + display_name: 'Thematic Change', + lookat: {lon: -98.58, lat: 38.14, zoom: 4}, image_visualization: { band_vis: { - min: [ - 1.0, - ], - max: [ - 5.0, - ], + min: [1], + max: [5], palette: [ - '3d4551', - 'f39268', - 'd54309', - '00a398', - '1b1716', - 'b30088', - ], - bands: [ - 'Change', - ], + '3d4551', 'f39268', 'd54309', '00a398', '1b1716', 'b30088'], + bands: ['Change'], }, }, }, { - display_name: 'lcViz', - lookat: { - lat: 37.09024, - lon: -95.712891, - zoom: 5, - }, + display_name: 'Land Cover', + lookat: {lon: -98.58, lat: 38.14, zoom: 4}, image_visualization: { band_vis: { - min: [ - 1.0, - ], - max: [ - 15.0, - ], + min: [1], + max: [15], palette: [ - '005e00', - '008000', - '00cc00', - 'b3ff1a', - '99ff99', - 'b30088', - 'e68a00', - 'ffad33', - 'ffe0b3', - 'ffff00', - 'aa7700', - 'd3bf9b', - 'ffffff', - '4780f3', - '1b1716', - ], - bands: [ - 'Land_Cover', + '005e00', '008000', '00cc00', 'b3ff1a', '99ff99', 'b30088', + 'e68a00', 'ffad33', 'ffe0b3', 'ffff00', 'aa7700', 'd3bf9b', + 'ffffff', '4780f3', '1b1716', ], + bands: ['Land_Cover'], }, }, }, { - display_name: 'luViz', - lookat: { - lat: 37.09024, - lon: -95.712891, - zoom: 5, - }, + display_name: 'Land Sse', + lookat: {lon: -98.58, lat: 38.14, zoom: 4}, image_visualization: { band_vis: { - min: [ - 1.0, - ], - max: [ - 7.0, - ], - palette: [ - '3d4551', - 'f39268', - 'd54309', - '00a398', - '1b1716', - ], - bands: [ - 'Land_Use', - ], + min: [1], + max: [7], + palette: ['3d4551', 'f39268', 'd54309', '00a398', '1b1716'], + bands: ['Land_Use'], }, }, }, @@ -802,20 +810,24 @@ local catalog_subdir_url = ee_const.catalog_base + subdir + '/'; Salt Lake City, Utah. |||, 'gee:terms_of_use': ||| - 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. - - These data were collected using funding from the U.S. Government and can be used - without additional permissions or fees. If you use these data in a publication, presentation, or - other research product please use the following citation: + 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. + + These data were collected using funding from the U.S. Government and can be + used without additional permissions or fees. If you use these data in a + publication, presentation, or other research product please use the + following citation: USDA Forest Service. 2022. USFS Landscape Change Monitoring System v2021.7 (Conterminous United States and Southeastern Alaska). Salt Lake City, Utah. diff --git a/examples/USFS/USFS_GTAC_LCMS_v2021-7.js b/examples/USFS/USFS_GTAC_LCMS_v2021-7.js index eb46c285b..298652a2c 100644 --- a/examples/USFS/USFS_GTAC_LCMS_v2021-7.js +++ b/examples/USFS/USFS_GTAC_LCMS_v2021-7.js @@ -9,6 +9,6 @@ var lcms = dataset Map.addLayer(lcms.select('Land_Cover'), {}, 'Land Cover'); Map.addLayer(lcms.select('Land_Use'), {}, 'Land Use'); -Map.addLayer(lcms.select('Change'), {}, 'Change'); +Map.addLayer(lcms.select('Change'), {}, 'Thematic Change'); Map.setCenter(-98.58, 38.14, 4);