The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

The GIS database contains the data of aufeis (naleds) in the Indigirka River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects). Historical data collection is created based on the Cadastre of aufeis (naleds) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 896 aufeis with a total area 2063.6 km² within the studied basin. Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2017. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 15 and June, 18), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 1213, with their total area about 1287 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

The GIS database contains the data of aufeis (naleds) in the Kolyma River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects). Historical data collection is created based on the Cadastre of aufeis (naled) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 1755 aufeis with a total area 1945.2 km² within the studied basin. Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2019. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 17 and June, 16), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 2216, with their total area about 879.7 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

The GIS database contains the data of aufeis (naled) in the Chukotka region from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects). Historical data collection is created based on the Cadastre of aufeis (naled) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 2024 aufeis with a total area 2976.9 km² within the studied area. Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2019. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season, to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 2758, with their total area about 1147.1 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

The GIS database contains the data of aufeis (naled) in the Yana River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects). Historical data collection is created based on the Cadastre of aufeis (naled) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 381 aufeis with a total area 731.6 km² within the studied basin. Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2017. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 15 and June, 18), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 571, with their total area about 432 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

This layer presents the Universal Transverse Mercator (UTM) zones of the world. The layer symbolizes the 6-degree wide zones employed for UTM projection.To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to World UTM Zones Grid.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Since 1998, approximately 148,134,806 m2 (14,813.5 Ha) of seagrass habitat has been mapped across the United Kingdom coastline. This was calculated from a total of 1,872 polygons from 67 datasets of mapped seagrass meadows using various methodologies of surveying (diving, aerial photography, hydrographic surveys, walking surveys, drone mapping, in situ assessments) and various resolutions. This data was collated from a wide range of databases outlined in the paper by Green et al. (2021; doi:10.3389/fpls.2021.629962). The datasets were subsequently projected into QGIS, merged, and dissolved into one layer as one single shapefile. The resulting dataset is a composite shapefile depicting mapped seagrass distribution across the United Kingdom, saved as a QGIS polygon shapefile, using the Assigned Coordinate Reference System (CRS) EPSG:4326 – WGS 84.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).

GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.

GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.

GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

fid - Unique identifier

bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.

bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.

gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).

bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).

prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).

cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.

doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.

heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.

feat_code - Code describing the type of building, pulled from the Building Footprint Data.

groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.

qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'

notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.

classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)

digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)

newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)

original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.

address - Address based on MapPLUTO, joined to the dataset based on bbl.

borough - Borough abbreviation pulled from MapPLUTO.

ownertype - Owner type field pulled from MapPLUTO.

zonedist1 - Zoning District 1 type pulled from MapPLUTO.

spdist1 - Special District 1 pulled from MapPLUTO.

bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

xcoord - Longitude in decimal degrees.

ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

The GIS database contains the data of aufeis (naled) in the Chukotka region from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects).

Historical data collection is created based on the Cadastre of aufeis (naled) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 2024 aufeis with a total area 2976.9 km² within the studied area.

Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2019. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season, to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections.

According to Landsat data, the number of aufeis reaches 2758, with their total area about 1147.1 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.