The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements. This 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation below. The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico.
The USGS Upper Midwest Environmental Sciences Center developed a Monarch Relevant Land Cover data set covering the area of Mexico. We used the 2010 land cover data set produced by the tri-national North American Land Change Monitoring System (NALCMS) and supported by the Commission for Environmental Cooperation (CEC) that depicts year 2010 land cover across North America at 30-meter spatial resolution, and incorporated additional spatially-explicit information to develop this land cover map. Additional sources of information included 2004 railroad data provided by Instituto Nacional de Estadística y Geografía and the CEC, 2011 roads data provided by Instituto Nacional de Estadística y Geografía, 2017 protected areas data provided by the CEC, and 2006 Mexico municipalities data provided by Sistemas de Información Geográfica.
This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.
description: This data set replaces the 2010 edition (Edition 1.0) of the 2005 Land Cover of North America. Following the release of the first 2005 land cover data, several errors were identified in the data, including both errors in labeling and misinterpretation of thematic classes. To correct the labeling errors, each country focused on its national territory and corrected the errors which it considered most critical or misleading. For the continental data sets (including surrounding water fringe) 17440830 pixels (4.33% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 10223412 pixels changed (6.44%); Mexico, 141142 pixels changed (0.45%), and U.S., 6878656 pixels changed (4.54%). The countries worked together to produce a definitive list of land cover classifications for the 2005 data; this document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005). Version 1 of the 2005 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comisin Nacional Para el Conocimiento y Uso de la Biodiversidad) and the National Forestry Commission of Mexico (Comisin Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements. The data set of 2005 Land Cover of North America at a resolution of 250 meters is the first step toward this goal. The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS. Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets.; abstract: This data set replaces the 2010 edition (Edition 1.0) of the 2005 Land Cover of North America. Following the release of the first 2005 land cover data, several errors were identified in the data, including both errors in labeling and misinterpretation of thematic classes. To correct the labeling errors, each country focused on its national territory and corrected the errors which it considered most critical or misleading. For the continental data sets (including surrounding water fringe) 17440830 pixels (4.33% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 10223412 pixels changed (6.44%); Mexico, 141142 pixels changed (0.45%), and U.S., 6878656 pixels changed (4.54%). The countries worked together to produce a definitive list of land cover classifications for the 2005 data; this document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005). Version 1 of the 2005 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comisin Nacional Para el Conocimiento y Uso de la Biodiversidad) and the National Forestry Commission of Mexico (Comisin Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements. The data set of 2005 Land Cover of North America at a resolution of 250 meters is the first step toward this goal. The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS. Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets.
Annual (1986-2020) land-use/land cover maps at 30-meter resolution of the Tucson metropolitan area, Arizona and the greater Santa Cruz Watershed including Nogales, Sonora, Mexico. Maps were created using a combination of Landsat imagery, derived transformation and indices, texture analysis and other ancillary data fed to a Random Forest classifier in Google Earth Engine. The maps contain 13 classes based on the National Land Cover Classification scheme and modified to reflect local land cover types. Data are presented as a stacked, multi-band raster with one "band" for each year (Band 1 = 1986, Band 2 = 1987 and so on). Note that the year 2012 was left out of our time series because of lack of quality Landsat data. A color file (.clr) is included that can be imported to match the color of the National Land Cover Classification scheme. This data release also contains two JavaScript files with the Google Earth Engine code developed for pre-processing Landsat imagery and for image classification, and a zip folder "Accuracy Data" with five excel files: 1) Accuracy Statistics describing overall accuracy for each LULC year, 2) Confusion Matrices for each LULC year, 3) Land Cover Evolution - changes in pixel count for each class per year, 4) LULC Change Matrix - to and from class changes over the period, and 5) Variable Importance - results of the Random Forest Classification.
The 2010 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements. The initial data set of North American Land Cover at 250 meters reflected land cover information for 2005. This 2010 data set was produced by updating the 2005 data to show land cover changes as determined from more recent data. No changes were mapped in Hawaii because newer data were not available. Land cover classification changed between 2005 and 2010 for approximately 1 percent of the continental area. For the continental data sets (including surrounding water fringe) 4150241 pixels (1.03% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 3264779 pixels changed (2.05%); Mexico, 47070 pixels changed (0.15%), and U.S., 836706 pixels changed (0.55%). The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS. Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets. The countries worked together to produce a definitive list of land cover classifications for the 2005 data; the same classifications were used for the 2010 data. This document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005).
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This landuse / landcover (LULC) map displays a basic depiction of the Los Planes watershed in Baja California Sur, Mexico. This simplified, 7-class LULC map displays classes that are useful for hydrologic modeling and broad vegetation mapping in the region. It was created from analysis of six Sentinel-2 satellite images and other existing geospatial datasets. These satellite images are provided at 10-meter spatial resolution and were calibrated for topographic illumination effects to enhance its accuracy in rugged, mountainous terrain like that seen in the watershed. A novel filtering methodology was also applied to minimize the "salt-and-pepper effect" from the principle component analysis (PCA) and image classification methodology. See "lulc_los_planes_watershed_final_2_legend.jpg" for a low-resolution overview of the image and legend.
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This dataset includes three GeoTIFF raster files representing land cover classifications for the Yucatán Peninsula, encompassing parts of Mexico, Guatemala, and Belize. The land cover maps were derived from Landsat 8 imagery spanning 2013 to 2022 and were generated using a Random Forest classification model. For model training, the official land use and vegetation dataset from INEGI was utilized. The data were grouped into three temporal periods: P1 (2013–2016), P2 (2016–2019), and P3 (2019–2022).
These data were compiled for the creation of a continuous, high-resolution transboundary land cover map of the Sonoran and Mojave Desert ecoregion within Bird Conservation Region 33 (BCR 33). Objective(s) of our study were to, 1) develop a machine learning algorithm trained to classify vegetation land cover using remote sensing spectral data and phenology metrics from 2013-2021, over the Sonoran and Mojave Deserts BCR 33; 2) calibrate, validate, and refine the final machine learning derived vegetation map using a collection of openly sourced remote sensing and ground-based ancillary data, images, and limited fieldwork; and 3) harmonize a new transboundary classification system by expanding existing land cover mapping resources from the United States portion of BCR 33 into Mexico. These data represent the final land cover maps produced using a Random Forest Classifier (RF), with additional ancillary labels for urban and agriculture areas. These data were created within BCR33 which spans an extent from Nevada, in the United States to Sinaloa, in Mexico for the time period from April 2013 to December 2021. These data were created by researchers at the University of Arizona, Vegetation Index and Phenology Lab who collected, processed, and analyzed all data and developed the random forest model used to produce the final continuous, high-resolution transboundary land cover map. These data can be used to guide land management and conservation decisions within the Sonoran and Mojave Desert ecoregion within BCR 33.
To provide processed satellite images of key areas along the U. S.-Mexico border for use in a broad spectrum of studies. Landsat data have been used by government, commercial, industrial, civilian, and educational communities in the U.S. and worldwide. They are being used to support a wide range of applications in such areas as global change research, agriculture, forestry, geology, resources management, geography, mapping, water quality, and oceanography. Landsat data have potential applications for monitoring the conditions of the Earth's land surface.
The passage of the North American Trade Agreement (NAFTA), establishment of the Border Environmental Cooperation Commission as well as the EPA U.S./Mexico Border XXI Program has focused attention to the environmental social-cultural, and economic conditions in the United States-Mexico frontier and to the enhanced necessity of a binational, transborder approach in addressing problems. Towards this end, this U.S.-Mexico borderlands Thematic Mapper selection is designed to be utilized as fundamental part of a basic geographic information system database for natural resource, environmental, and land-management studies.
These data were compiled for the creation of a continuous, transboundary land cover map of Bird Conservation Region 33, Sonoran and Mojave Deserts (BCR 33). Objective(s) of our study were to, 1) develop a machine learning (ML) algorithm trained to classify vegetation land cover using remote sensing spectral data and phenology metrics from 2013-2020, over a large subregion of the Sonoran and Mojave Deserts BCR, 2) Calibrate, validate, and refine the final ML-derived vegetation map using a collection of openly sourced remote sensing and ground-based ancillary data, images, and limited fieldwork, and 3) Harmonize a new transboundary classification system by expanding existing land cover mapping resources from the United States portion of BCR 33 into Mexico. These data represent the final land cover maps produced by the developed random forest model, with additional ancillary labels for urban and agriculture areas. These data were created within a subregion of the Sonoran and Mojave Deserts BCR which spans from Phoenix, Arizona, US to Hermosillo, Sonora, Mexico for the time of April 2013 to December 2020. These data were created by the University of Arizona Vegetation Index and Phenology Lab who collected, processed, and analyzed all of the data and developed the random forest model used to produce the final mapping results. These data can be used to guide land management and conservation decisions within the Sonoran and Mojave Deserts BCR.
The North American Landscape Characterization (NALC) project is a component of the Landsat Pathfinder Program, which is part of a larger Pathfinder Program initiated by the National Aeronautics and Space Administration (NASA) in 1989. The NALC project is a cooperative effort between NASA, the U.S. Environmental Protection Agency, and the U.S. Geological Survey to make Landsat data available to the widest possible user community for scientific research and for the general public interest. The objectives of the NALC project are to develop standardized remotely sensed data sets and analysis methods in support of investigations of changes in land cover, to develop inventories of terrestrial carbon stocks, to assess carbon cycling dynamics, and to map terrestrial sources of greenhouse gas (CO, CO2, CH4, and N2) emissions. The NALC data set is comprised of hundreds of triplicates (i.e., multispectral scanner (MSS) data acquired in the years 1973, 1986, and 1991 plus or minus 1 year, thus, the name triplicate). The NALC triplicates also include digital elevation model data. The specific temporal windows vary for geographical regions based on the seasonal characteristics of the vegetation cover. In accordance with the Landsat Pathfinder Program concept, the Pathfinder basic data sets are to be comprised of data which have had systematic radiometric and systematic geometric corrections applied to them. The NALC triplicates, however, are precision corrected for geocoding purposes. The Land Processes Distributed Active Archice Center (LP DAAC), located at the U.S. Geological Survey's EROS Data Center, has primary responsibility for producing the NALC Landsat MSS triplicate data sets as well as the responsibility for archiving, managing, and distributing NALC data.
Reference:
Lunetta, R. S., and J. A. Sturdevant. 1994, The North American Landscape Characterization Landsat Pathfinder Project, in Pecora 12 Symposium, Land Information from Space-Based Systems, 12th, Sioux Falls, South Dakota, August 24-26, 1993, Proceedings: Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, pp. 363-371.
The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.
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Benchmark set at 77.1% O.A at: https://doi.org/10.1117/1.JRS.14.048503
The dataset consists of 60,000 images, corresponding to Landsat patches of 33x33 pixels with 102 bands. Randomly selected from Mexico (country). Each patch is labeled with one of 12 Land Use and Vegetation classes according to the classification described at https://doi.org/10.3390/rs6053923.
The zip file contains 12 folders numbered 1-12 and each contains 5,000 .npy python files (can be loaded with the NumPy library).
The labeled classes correspond to the following identifier.
1, Temperate Coniferous forest
2, Temperate Decidius Forest
3, Temperate Mixed Forest
4, Tropical Evergreen Forest
5, Tropical Deciduous Forest
6, Scrubland
7, Wetland Vegetation
8, Agriculture
9, Grassland
10, Water body
11, Barren Land
12, Urban Area
To build that dataset, we take the information of the National Continuum of Land Use and Vegetation series number 5 generated by the National Institute of Statistics and Geography from Mexico (INEGI) from The National Commission for the Knowledge and Use of Biodiversity (CONABIO) web page (http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s5ugw.html).
The file used for this dataset construction is the shape format file with geographic coordinates located in http://www.conabio.gob.mx/informacion/gis/maps/geo/usv250s5ugw.zip.
Later, a transformation to Albers equal-area conic projection was done with the followings parameters:
Fake east: 2500000.0
Fake North: 0.0
Origin longitude: -102.0º
Origin latitude: 12.0º
First standard parallel: 17.5º
Second standard parallel: 29.5º
Linear unit: Meter (1.0)
Reference ellipsoid: GRS80
Once the data was projected, using the classes identified in the National Continuum of Land Use and Vegetation, correspondence was applied to the classes identified in https://doi.org/10.3390/rs6053923, these classes being: Agriculture, Barren land, Grassland, Scrubland, Temperate coniferous forest, Temperate deciduous forest, Temperate mixed forest, Tropical deciduous forest, Tropical evergreen forest, Urban area, Waterbody and Wetland vegetation.
Once the information layer was generated with the 12 classes indicated above, the reference layer was rasterized.
Thus, a national grid of 1,975,940 regions of 1 x 1 kilometers was generated and the percentage of pixels of the dominant class in each corresponding 1 km region was associated.
A total of cells with 70% or more pixels from one dominant class corresponds to 1,640,827 which represents a total of 83% of the Mexican territory. That means, only 17% of cells have less than 70% of their pixels from one dominant class.
Then, 5000 regions were randomly selected from each land cover class at the national level. For this random selection only were selected the regions in which cells have 70% or more of their pixels from one dominant class. The above, for looking to have consistent and reliable data for the automatic classification task. This random selection generates a total of 60,000 regions selected.
Image patches were extracted from the selected regions in the sample.
The image used is the result of the application of multiple time series analysis algorithms on a cube of image data with mainly Tier 1 (T1) quality and a few Tier 2 (T2) as described in https: // www. usgs.gov/land-resources/nli/landsat/landsat-collection-1. An Open Data Cube (ODC, https://www.opendatacube.org/) was constructed from 3,515 Landsat 5 and 7 images corresponding to the year 2011, which is the same reference year of the National Continuum of Land Use and Vegetation Series 5.
From the analysis of the ODC images, the Geomedian (https://doi.org/10.1109/TGRS.2017.2723896) was calculated, which generated a national cloud-free mosaic from 2011, pixels at 30 meters resolution and 6 spectral bands (blue, green, red, nir, swir 1, swir 2). Finally, 15 spectral indices were calculated for each pixel in the image. This resulted in 15 national mosaics from the analysis of the time series of each pixel available for the year 2011 using all the combinations of normalized difference indices, which were possible with the 6 bands that were incorporated into the data cube, with which resulted in 102 information channels. Since Landsat images have a resolution of 30 meters, we have images of 33 pixels x 33 pixels for each region of 1 km x 1 km.
The 102 channels in the patches correspond to:
Geomedian Bands (6): blue, green, red, nir, swir 1, swir 2
Geomedian Based Indexes (15): evi, bu, sr, arvi, ui, ndbi, ibi, ndvi, ndwi, mndwi, nbi, brba, nbai, baei, bi
Geomedian Based Tasseled cap transformation (6): brightness, greenness, wetness, fourth, fifth, sixth
2011 Landsat Time Analysis Series by Pixel
(red-swir 1)/(red+swir 1); (5): min, mean, max, std, median
(red-nir)/( red+nir); (5): min, mean, max, std, median
(swir 1-swir 2)/( swir 1+swir 2); (5): min, mean, max, std, median
(nir-swir 2)/(nir+swir 2); (5): min, mean, max, std, median
(nir-swir 1)/( nir+swir 1); (5): min, mean, max, std, median
(red-swir 2)/( red+swir 2); (5): min, mean, max, std, median
(green-swir 2)/(green+swir 2); (5): min, mean, max, std, median
(green-swir 1)/(green+swir 1); (5): min, mean, max, std, median
(green-red)/(green+red); (5): min, mean, max, std, median
(green-nir)/(green+nir); (5): min, mean, max, std, median
(blue-swir 2)/(blue+swir 2); (5): min, mean, max, std, median
(blue-swir 1)/(blue+swir 1); (5): min, mean, max, std, median
(blue-red)/(blue+red); (5): min, mean, max, std, median
(blue-nir)/(blue+nir); (5): min, mean, max, std, median
(blue-green)/( blue+green); (5): min, mean, max, std, median
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This dataset contains all the data that is required to carry out the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.
The dataset includes data for three different case studies: The Asturias Central Area (Spain), the Ariège Valley (France) and Marqués de Comillas (Mexico). For the Asturias Central Area and the Ariège Valley, the dataset includes Land Use Cover (LUC) maps for several years of reference as well as data (simulation outputs, model drivers) for different modelling exercises. For Marqués de Comillas, the dataset includes a LUC map and a set of reference points used to validate it.
The dataset includes a readme file listing all the files it contains and auxiliary files describing the data. For further information on the study area and the files used in the practical exercises, users are referred to Chapter 1 of the book.
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The MOD-LSP project contains MODIS-based land and surface (soil and vegetation) parameters for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994), release 5.0 and later (Hamman et al., 2018). The MOD-LSP spatial domain covers the continental United States, Mexico, and southern Canada. This spatial domain and 0.625° (6 km) grid resolution are compatible with the gridded daily meteorological forcings of (Livneh et al. 2015) ("L2015" hereafter), which can be disaggregated to hourly time step via the MetSim tool (Bennett et al. 2018) using domain files archived on Zenodo (Bohn et al. 2018). These domain files also can accompany the MOD-LSP parameter files as inputs to VIC simulations.
These parameters have two main purposes: (1) to improve upon previous widely-used parameters over the region (e.g., L2015) with updated, higher-resolution land cover maps and spatially explicit observations of surface properties; and (2) to expand from a single parameter set corresponding to one point in time to a series of parameter sets that account for temporal variability at seasonal to decadal scales.
A detailed description of methods, the data sources and purposes of different VIC parameter sets within MOD-LSP, and how to use them with VIC, can be found in the MOD-LSP User Guide.pdf, included here. The scripts that were used to create the MOD-LSP parameters are archived on GitHub (https://github.com/tbohn/VIC_Landcover_MODIS_NLCD_INEGI/releases/tag/v1.1) (Bohn 2019).
If you wish to present or publish results that use these parameter sets, please cite the following paper (this record will be updated when the paper is published):
Bohn, T. J., and E. R. Vivoni, 2019: Improvements to land surface parameters for the Variable Infiltration Capacity (VIC) hydrologic model over the Continental US, Mexico, and Southern Canada. Nat. Sci. Data, (in prep).
In addition, if you use the domain files associated with the PITRI precipitation disaggregation to accompany the MOD-LSP parameter files in VIC simulations, please cite the following paper:
Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling. J. Hydrometeorol., 20, 297–317, doi:10.1175/JHM-D-18-0203.1.
Contents:
CONABIO provides online cartography through cartographic metadata distributed following the guidelines in the Standards for Digital Geospatial Metadata of FGDC-NBII (Federal Geographic Data Committee – National Biological Information Infrastructure), 1996. The cartographic information is queried through a database that is organized based on themes (biotic, physical and social aspects, regionalization and others), scales, and geographic area. The metadata content is presented as basic information, reports of the information (methodology) and spatial data information. The cartography is available online at no charge in distinct formats like: export file for Arc/Info (.E00) and shape file (ESRI), and DXF (Drawing eXchange Format). Maps is presented in cartographic projections: Lambert Conic Conformal, UTM and geographic coordinates system. GIF format of map images can be obtained as well.
This map layer is a grid map of North America including the Caribbean and most of Mexico. The map layer is an excerpt from a global assessment of forest fragmentation (Riitters et al., 2000). Each pixel value represents an index of forest fragmentation for the surrounding 81 sq. km. The map layer was created by applying spatial algorithms to a 1 sq. km. resolution map of global land cover (Loveland and Belward 1997) known as NAIGBP1_2L, obtained from the USGS Center for EROS Distributed Active Archive Center (DAAC) as part of the Global Land Cover Characteristics database (GLCC)(Loveland et al. 1991, 1999). One of six categories of fragmentation was identified for each forested pixel in North America from the amount of forest and its occurrence as adjacent forest pixels within a 9x9 pixel (81 sq. km.) window surrounding the pixel on the original land-cover map. The map layer describes one aspect of forest fragmentation at one scale. The forest fragmentation index is designed to distinguish among types of fragmentation (e.g., edges on the interior versus the exterior of a forest patch) and it also reflects differences in the absolute amount of forest present. However, no distinction was drawn between "natural" and "human-caused" fragmentation. The data available through the National Atlas of the United States are in GeoTIFF format. This is a revised version of the May 2002 map layer, with a corrected shoreline for Greenland. This map layer was previously distributed as the Forest Fragmentation Index Map of North America.
This dataset includes barrier island land cover types collected from mid-November 2015 to mid-December 2015 along randomly placed transects at seven sites throughout the east end of Dauphin Island. Specifically, this data collection included characterizing land cover types and measuring horizontal position and elevation. We characterized plant community composition and structure for a subset of these points (see Vegetation Survey Data Table). This work was conducted through a joint effort by the State of Alabama, the U.S. Geological Survey, and the U.S. Army Corps of Engineers to evaluate the feasibility of various restoration alternatives and how specific alternatives might increase the resiliency and sustainability of Dauphin Island. The overarching goal of the aforementioned effort is to preserve and enhance the ecological functions and values of the island. This product provides a powerful tool for tracking changes to barrier island habitats over time. This data release includes the following three components, which are included in the attached ZIP file: 1) Dauphin Island Habitat Map (Raster data) 2) Land Cover and Vegetation Field Data Points (Vector data) 3) Vegetation Survey Data (Tabular data)
Database of the events of occurrence of the mountain carnivores in Northwestern Mexico during a camera trap survey on 2015-2017. This script was written to model the habitat use of the mountain carnivores in Northwestern Mexico for landscape covariates: forest cover, proportion of Abundance was estimated with camera-trap surveys and refers to the number of events (at least one picture per week) during the survey. We sampled habitat variables in five concentric circles (buffers of 25-, 75-, 150-, 450- and 600-m radius) around each camera trap. Landscape covariates were: a) Terrain ruggedness index using the Mexican digital elevation model (INEGI 2015) with a 15 m resolution per pixel. (b) Forest cover of the study site using 2013 land cover maps (Hansen et al. 2013). (c) Map of vegetation types; all pine forest is considered secondary vegetation because of disturbances caused by wildfires and forest management practices.
The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements. This 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation below. The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico.