This dataset depicts wetlands in Vermont. It was created with an automated feature-extraction process that relied on high-resolution LiDAR and multispectral imagery. In particular, a Compound Topographic Index (CTI) layer derived from LiDAR was used to identify landscape features that have suitable topography and flow potential for wetlands. Moderate-scale (10 m) statistical models developed by Patrick Raney of Ducks Unlimited were also used during classification in a data-fusion approach that maximized the value of individual inputs. After initial identification, mapped features were assigned to one of three primary wetlands classes: Emergent, Scrub\Shrub, and Forested. The class assignments were based primarily on vegetation height (as estimated from LiDAR) and spectral characteristics (e.g., features with short vegetation that appeared very bright in leaf-off multispectral imagery were assigned to the Emergent category). The initial map was then generalized to eliminate unnecessary detail using a minimum mapping unit of 0.1 acres. In a final step, the automated output was manually reviewed against multispectral imagery and obvious errors of commission and omission were corrected. More than 57,000 manual corrections were incorporated into the final layer. Overall, the combination of automated feature extraction and manual corrections was biased toward over-prediction, focusing on capture of borderline features whose functional status cannot be definitvely established with remote-sensing data alone (i.e., it is generally easier to discount false wetland features than it is to locate omitted ones). Known areas of overestimation include managed forestlands with extensively-modified drainage patterns and wide river and stream channels. This map is considered current as of 2016, the year of the most-recent multispectral imagery used during manual review.

The Government of Canada acquired a national image coverage from the Systeme Pour l'Observation de la Terre (SPOT 4 - 5) satellites that includes four multispectral bands in the visible to shortwave infrared region at 20m spatial resolution. Five years from 2005 - 2010 were necessary to image all of Canada under clear-sky conditions, while acquisition anniversary dates were less important provided the data were imaged during the snow-free period. These data were downloaded from the GeoBase Orthoimage 2005 - 2010 dataset (http://www.geobase.ca/geobase/en/data/imagery/imr/description.html) and used to map 2005 - 2010 land cover south of treeline. Northern Canada has not currently been remapped since circa 2000 due to technical challenges associated with land cover variability and image acquisition dates relative to short summers. This land cover product includes 16 generic classes based on plant functional and a minimum mapping unit of 20m. Radiometric normalization was applied to balance images acquired near mid-summer during the 'peak-of-season' temporal window. The combined Enhancement and Classification by Progressive Generalization methods were used to classify large-area balanced mosaics over twenty mapping zones. Image interpretation was guided using high resolution imagery and other content in Google Earth. Knowledge of land cover spectral signatures, field experience and published reports were also used to assist interpretation in many regions. Remaining images acquired outside the peak-of-season window in early spring or late fall were subsequently classified using decision trees trained on data from overlapping classified peak-of-season images. Accuracy was assessed using ground truth data acquired during several field campaigns conducted with other government departments such as Parks Canada and the Geological Survey. This sample was enhanced using points interpreted in Google Earth as described above to provide a more even spatial coverage of Canada. Overall accuracy assessed at 71% using 1566 reference points, more than half of which were acquired in the field. When assessed using only land cover that was homogeneous within 3 by 3 pixels to account for potential geolocation errors, accuracy increased to 85% for 349 points that were biased towards easily classified classes such as water.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Greater Hunter Native …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Greater Hunter Native Vegetation Mapping supplied by NSW Office of Water on 13/05/2014 The GHM geodatabase builds on a wealth of information and previous mapping from the Hunter region. Existing field data, mapping, classification and remote sensing interpretation were augmented with new survey data to produce the vegetation community classification used in this project. The classification used a series of well documented analyses as well as expert review to achieve its end-point. The GHM geodatabase contains two principal vegetation layers. The GHM Vegetation Type layer and the Canopy Cover (v2) layer (individual tree crowns or clumps of tree crowns). The GHM also contains field plot localities, associated species information and plotspecific photographs. Data specific to each polygon (e.g. crown cover) and to each native vegetation community type (e.g. common name, scientific name) are included. Polygons, the fundamental spatial units, are built from computer-based feature recognition which delineates landscapes patterns. The GHM Vegetation Type map is built by attributing individual polygons with vegetation type from the GHM floristic classification through a multi-stage process. The process includes visual interpretation of SPOT 5 and ADS40 imagery as well as species distribution modelling and expert review. The project included a review of existing mapping and classification and established equivalences between these and the GHM Classification. VIS ID 3855 Dataset History Vegetation patterns at the stand scale were delineated using automated feature recognition software. Definiens eCognition was used to define segments with low internal variation (low heterogeneity). Pan-sharpened SPOT5 data (5m) from multiple years formed the basis of the segmentation. The data had been pre-processed to accentuate the range of spectral responses or colours. The spatial resolution is 5m and the minimum mappable unit was set to 400m2. The polygon boundaries have been smoothed and narrow slivers were eliminated. There were two stages in the feature recognition approach. The first stage was optimised to differentiate woody and non-woody vegetation. The second stage was optimised to differentiate vegetation patterns within the extent of woody vegetation. The first stage employed multi-temporal pan-sharpened SPOT - 5 data (5m). Only the red band (610-680nm) from each SPOT image was used to maximise the characteristic stability of woody vegetation over time. Each object was then classified as woody, non-woody and 'other' using the Crown Cover v2 layer and visual interpretation. For stage two the boundaries within the woody vegetation were dissolved and new objects were created within their boundaries using stretched, multi-temporal imagery. The contrast of all bands was increased using an adaptive equalisation stretch to maximise the separability of discrete vegetation patches within mosaics. The vegetation map was created by attributing vegetation polygons with a vegetation type. There are multiple stages involved but the fundamental steps are as follows: Survey sites that meet quality criteria are assigned a GHM type label using PATN analysis. Vegetation map units were defined using a hierarchical modelling approach that included the manual allocation of Keith Formation using visual identification, the use of a species distribution model to calculate the probability of GHM type in each polygon using environmental layers and a set of expert rules is developed to combine the formation classification and the modelled results. The results undergo visual quality assurance, again using manual image interpretation. Dataset Citation NSW Office of Environment and Heritage (2014) Greater Hunter Native Vegetation Mapping. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/8f575981-3730-4989-84ce-c90204158406.

The High Resolution Layer - Grassland is a dataset produced within the pan-European component of the land cover and use monitoring services of the Copernicus program (CLMS). It refers to the year 2015 and was produced using high resolution multi-temporal and multi-spectral satellite data, in particular Sentinel-2A, Sentinel-1A and B and Landsat 8, as well as available ancillary data. The Grassland layer (GRA), which represents the main product, has a binary classification (0 – 1) and provides information on the presence or absence of grassland cover defined as herbaceous vegetation with at least 30% land cover, of which at least 30% of graminaceous species. Non-woody plants such as lichens, mosses and ferns may be present in the grassland product as well as no more than 10% scattered shrubs and trees. Two additional products (for advanced users) complement the Grassland HRL: the Grassland Vegetation Probability Index (GRAVPI) which provides a measure of classification confidence (low values ​​indicate that the time series of input data is limited) and the Plowing Indicator (PLOUGH) which focuses on historical land cover series in order to indicate plowing activities in previous years. All products have a spatial resolution of 20 metres, the GRA has a minimum mapping unit (MMU) of 1 ha, the GRAVPI and the PLOUGH of 20 metres. Only the GRA is also available in the aggregated version with 100 meters of resolution. The data update frequency is 3 years. The production of HRL Grassland and additional products was coordinated by the European Environment Agency within the European Copernicus programme, involving the National Reference Centers on Land Cover of the Eionet network (for Italy, represented by ISPRA). Further information can be found at https://land.copernicus.eu/pan-european/high-resolution-layers/grassland.

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.

Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.

Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.

Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle.

To summarize, the MODIS L1B 500 m data product consists of:

1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution

2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands

3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf.

4. Calibration data for all channels (scale and offset)

5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MOD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ .

The shortname for this product is MOD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MOD02HKM file size is approximately 135 MB.

Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.

See the MODIS Characterization Support Team webpage for more C6 product information at:

https://mcst.gsfc.nasa.gov/l1b/product-information

or visit Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/

The MODIS/Terra Calibrated Radiances 5-Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.

Separate L1B products are available for the five 500m resolution channels (MYD02HKM) and the twenty-nine 1km resolution channels (MYD021KM). For the 500m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500m resolution. Similarly, for the 1km product, all 36 MODIS channels are available since the data from the two 250m and five 500m channels have been aggregated into equivalent 1km pixel values.

Spatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle.

The shortname for this product is MYD02QKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size will be approximately 140 MB and the total daily volume is around 22GB.

Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.

See the MODIS Characterization Support Team webpage for more C6.1 product information at:

http://mcst.gsfc.nasa.gov/l1b/product-information

or visit Science Team homepage at: http://modis.gsfc.nasa.gov/data/dataprod/

Advanced Very High Resolution Radiometer (AVHRR) data were obtained from the USGS Global AVHRR 10-day composite data (http://edcdaac.usgs.gov/1KM/1kmhomepage.asp) (Markon et al. 1995). Glaciers and oceans were masked out using information from the Digital Chart of the World (ESRI 1993). The image is composed of 1 x 1-km pixels. The color of each pixel was determined by its reflectance at the time of maximum greenness, selected from 10-day composite images from 11 July to 30 August 1993 and 1995. These intervals cover the vegetation green-up-to-senescence period during two relatively warm years when summer-snow cover was at a minimum in the Arctic. Maximum greenness was determined from the normalized difference vegetation index (NDVI). Vegetation greenness is calculated as: NDVI = (NIR - R) / (NIR + R), where NIR is the spectral reflectance in the AVHRR near-infrared channel (0.725-1.1 µ, channel 2) where light-reflectance from the plant canopy is dominant, and R is the reflectance in the red channel (0.58 to 0.68 µ, channel 1), the portion of the spectrum where chlorophyll absorbs maximally. The resulting image shows the Arctic with minimum snow and cloud cover. The channel 1 and channel 2 values were then stacked to create as a false-color CIR image (RGB = ch. 2, ch. 1, ch. 1). Back to Alaska Arctic Tundra Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI, Bioclimate Subzone, Elevation, False Color-Infrared, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Markon, C. J., M. D. Fleming, and E. F. Binnian. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record 31:179-190.

What is NAIP?The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the contiguous U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Production and Conservation Business Center through the Aerial Photography Field Office in Salt Lake City. The APFO as of August 16, 2020 has transitioned to the USDA FPAC-BC's Geospatial Enterprise Operations Branch (GEO). This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.How can I Access NAIP?On the web GEO (APFO) public image services can be accessed through the REST endpoint here. Compressed County Mosaics (CCMs) are available to the general public through the USDA Geospatial Data Gateway. All years of available imagery may be downloaded as 1/2, 1, or 2 meter CCMs depending on the original spatial resolution. CCMs with a file size larger than 8 GB are not able to be downloaded from the Gateway. Full resolution 4 band quarter quads (DOQQs) are available for purchase from FPAC GEO. Contact the GEO Customer Service Section for information on pricing for DOQQs and how to obtain CCMs larger than 8 GB. A NAIP image service is also available on ArcGIS Online through an organizational subscription.How can NAIP be used?NAIP is used by many non-FSA public and private sector customers for a wide variety of projects. A detailed study is available in the Qualitative and Quantitative Synopsis on NAIP Usage from 2004 -2008: Click here for a list of NAIP Information and Distribution Nodes.When is NAIP acquired?NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, a three-year cycle began in 2009, NAIP was on a two-year cycle until 2016, currently NAIP is on a 3 year refresh cycle. Click here >> for an interactive PDF status map of NAIP acquisitions from 2002 - 2018. 2021 acquisition status dashboard is available here.What are NAIP Specifications?NAIP imagery is currently acquired at 60cm ground sample distance (GSD) with a horizontal accuracy that matches within four meters of photo-identifiable ground control points.The default spectral resolution beginning in 2010 is four bands: Red, Green, Blue and Near Infrared.Contractually, every attempt will be made to comply with the specification of no more than 10% cloud cover per quarter quad tile, weather conditions permitting.All imagery is inspected for horizontal accuracy and tonal quality. Make Comments/Observations about current NAIP imagery.If you use NAIP imagery and have comments or find a problem with the imagery please use the NAIP Imagery Feedback Map to let us know what you find or how you are using NAIP imagery. Click here to access the map.**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: National Agriculture Imagery Program (NAIP) History 2002-2021Item Type: Web Mapping Application URL Summary: Story map depicting the highlights and changes throughout the National Agriculture Imagery Program (NAIP) from 2002-2021.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: URL referencing this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=445e3dfd16c4401f95f78ad5905a4cceFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8eb6c5e7adc54ec889dd6fc9cc2c14c4UID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

The Land Use and Occupation Charter (COS) is a product of the Land Occupation Monitoring System (SMOS), an innovative initiative, designed and developed by the Directorate-General of the Territory, with the aim of continuously producing cartographic information on land use and occupation. SMOS uses the latest developments in space technologies and Artificial Intelligence to create products with more detail, quality, speed and made available with open data policy. All products can be viewed on Vismos (https://smos.dgterritorio.gov.pt/vi-smos).

COS2010v2.0 is a thematic mapping of land use and land use for mainland Portugal for the year 2010, and the Directorate-General of the Territory (DGT) is the entity responsible for its production.

COS2010v2.0 is an improved version of COS2010 (known as COS2010v1.0) and was produced simultaneously with the new versions of the COS for 2018 (COS2018v2.0), 2015 (COS2015v2.0), 2007 (COS2007v3.0) and 1995 (COS1995v2.0), with the aim of having a spatially and temporally consistent COS series.

This series and the versions of the COS that are part of it replace the previous ones from the date of its publication, these being without effect from that date.

COS2010v2.0 has 83 classes and now has the same number of COS2018v2.0 classes, COS2015v2.0 and COS2007v3.0. COS1995v2.0 has 44 classes.

The cartographic information of COS2010v2.0 is in vector format and divides the space into landscape units (polygons) that share the concepts of land use and occupation, not contemplating any linear or punctual elements. COS2010v2.0 has a minimum cartographic unit (UMC) of 1 ha a minimum distance between lines of 20 m and the equivalent scale is 1:25 000. The nomenclature consists of a hierarchical system of land use/occupation classes and has 83 classes at the most detailed level. Each COS2010v2.0 polygon is classified with the land cover/use code of each hierarchical level of the nomenclature. The limits of COS2010v2.0 at the border with Spain are those of the Official Administrative Charter of Portugal (CAOP) version 2010 and on the sea side are defined by photointerpretation.

COS2010v1.0 was improved to produce COS2010v2.0 based on visual interpretation of orthorectified aerial images (i.e. spatial resolution of 50 cm and four spectral bands – blue, green, red and near infrared) used in the production of the original version. Auxiliary databases from various sources, including intra-annual multi-temporal series of satellite images, were used in the improvement process as well as quality control. Landscape units smaller than the CMU (1 ha) were generalised according to well-defined rules.

The publication of the revised historical series (COS1995v2, COS2007v3, COS2010v2, COS2015v2 and COS2018v2) marks the integration of COS into SMOS which also includes the Conjuntural Soil Occupation Charter (COSC). The COS stands out for providing structural and more land use related information. COSC has a conjunctural character and is more related to land use. The SSO remains the reference mapping at national level.

The differences between the two maps must always be kept in mind. For example, an area of forest use and thus classified in the COS may be represented in COSC as Matos or spontaneous herbaceous vegetation if in that year it is temporarily dismanaged due to a cut or fire. However, in the SSO this area continues to be classified as forest. COSC is a conjuncture mapping, so in medium-term planning and management, the relevant mapping remains the SSO. COSC can be useful in planning and scheduling exercises.

If you are not familiar with spatial data being made available through viewing services (e.g. WMS) and downloading services, you can consult the Support Guides on the DGT Open Data Page (https://www.dgterritorio.gov.pt/dados-abertos).

The normalized difference vegetation index (NDVI) is a measure of greenness. NDVI was calculated as: NDVI = (NIR - R) / (NIR + R), where NIR is the spectral reflectance in the AVHRR near-infrared channel (0.725-1.1 µm, channel 2) where light-reflectance from the plant canopy is dominant, and R is the reflectance in the red channel (0.5 to 0.68 µm, channel 1), the portion of the spectrum where chlorophyll absorbs maximally. Advanced Very High Resolution Radiometer (AVHRR) data were obtained from the USGS Global AVHRR 10-day composite data (http://edcdaac.usgs.gov/1KM/1kmhomepage.asp). Glaciers and oceans were masked out using information from the Digital Chart of the World (ESRI 1993). The image is composed of 1 x 1-km pixels. The color of each pixel was determined by its reflectance at the time of maximum greenness, selected from 10-day composite images from 11 July to 30 August 1993 and 1995 (Markon et al. 1995). These intervals cover the vegetation green-up-to-senescence period during two relatively warm years when summer-snow cover was at a minimum in the Arctic (Markon et al. 1995). Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color-Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Markon, C. J., M. D. Fleming, and E. F. Binnian. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record 31:179-190.

The normalized difference vegetation index (NDVI) is a measure of greenness. NDVI was calculated as: NDVI = (NIR - R) / (NIR + R), where NIR is the spectral reflectance in the AVHRR near-infrared channel (0.725-1.1 µm, channel 2) where light-reflectance from the plant canopy is dominant, and R is the reflectance in the red channel (0.5 to 0.68 µm, channel 1), the portion of the spectrum where chlorophyll absorbs maximally. Advanced Very High Resolution Radiometer (AVHRR) data were obtained from the USGS Global AVHRR 10-day composite data website. Glaciers and oceans were masked out using information from the Digital Chart of the World (ESRI 1993). The image is composed of 1 x 1-km pixels. The color of each pixel was determined by its reflectance at the time of maximum greenness, selected from 10-day composite images from 11 July to 30 August 1993 and 1995. These intervals cover the vegetation green-up-to-senescence period during two relatively warm years when summer-snow cover was at a minimum in the Arctic (Markon et al. 1995). Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI, Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Markon, C. J., M. D. Fleming, and E. F. Binnian. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record 31:179-190.