GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

The LIA extent was identified and extracted using known techniques of band combinations in remote sensing but applied to look at a terrestrial landscape through a new lens. The marginal zone of the LIA is denoted by little or no vegetation, disturbed sediment, moraines, trimlines, little or no soil development and the exposed rock surfaces are unweathered. Sentinel 2 images were downloaded from USGS Earth Explorer from July- September 2021 to show the peak vegetation season. Scenes with less than 10% cloudiness were chosen. The band combination to identify the LIA extent is B11 (Short Wave Infrared: SWIR), B8 (Near Infrared: NIR), and B2 (Blue).

The Reclassify Spatial Analyst tool was used to perform an unsupervised classification and geoprocessing to change the value in a raster, from a range to a single value. Image classification is the conversion of a multi-band raster image, such as Sentinel-2, to a single-band raster with defined categories to represent the desired land cover.

The mask is a visual map of the entire area covered by the GrIS during the LIA maximum.

The LIA mask was created in projection Stereographic North (ESPG3413) to match the BedMachine product. The data is available for use in 30 x 30m, 150 x 150m and 1 x 1km resolutions in NetCDF Files. It is also available as .tif in 30 x 30m, 150 x 150m and 1 x 1km resolutions and a .shp and .kmz files to be useable in modelling, GIS (Arc & QGIS), and Google Earth.

The Unpublished Digital Geologic Map of Glen Canyon National Recreation Area and Vicinity, Utah, Arizona is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. These data formats also fully represent all of the features present on a GRI digital map, as well as containing related ancillary information GIS data tables. The data is also available as a 2.2 KMZ/KML file for use in Google Earth. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Utah Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (glca_metadata.xml; available at http://nrdata.nps.gov/glca/nrdata/geology/gis/glca_metadata.xml). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:100,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (glca_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 12N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Glen Canyon National Recreation Area, as well as Rainbow Bridge National Monument (RABR), Canyonlands National Park (CANY), Capitol Reef National Park (CARE) and Grand Canyon National Park (GRCA).

This dataset consists of a shapefile of the reefs, islands, sand banks, cays and rocks of the whole Great Barrier Reef (GBR) including Torres Strait. This dataset is an extension of the mapping in the GBR Marine Park to include Torres Strait. The Torres Strait region was mapped at a scale of 1:50,000 (Lawrey, E. P., Stewart M., 2016) and these new features are referred to as the "Torres Strait Reef and Island Features" dataset.

The Complete GBR Reef and Island Features dataset integrates the "Torres Strait Reef and Island Features" dataset with the existing "GBR Features" (Great Barrier Reef Marine Park Authority, 2007) to create a single composite dataset of the whole Great Barrier Reef. This dataset includes 9600 features overall with 5685 from the "GBR Features" dataset and 3927 from the "Torres Strait Reef and Island Features" dataset.

These two datasets can be easily separated if necessary based on the "DATASET" attribute.

All new mapped features in Torres Strait were allocated permanent IDs (such as 10-479 for Thursday Island and 09-246 for Mabuiag Reef). These IDs are for easy unambiguous communication of features, especially for unnamed features.

The reference imagery used for the mapping of the reefs is available on request as it is large (~45 GB). These files are saved in the eAtlas enduring repository.

Methods:

This project mapped Torres Strait using a combination of existing island datasets as well as a semi-automated and manual digitising of marine features (reefs and sand banks) from the latest aerial and satellite imagery. No features were added to the dataset without confirmed evidence of their existence and position from at least two satellite image sources. The Torres Strait Reef and Island Feature mapping was integrated with the existing "GBR Features" dataset by GBRMPA to ensure that there were no duplicate feature ID allocations and to create a single dataset of the whole GBR. The overall dataset development was as follows: 1. Dataset collation and image preparation: - Collation of existing maps and datasets. - Download and preparation of the Landsat 5, 7, and 8 satellite image archive for Torres Strait. - Spatial position correction of Landsat imagery against a known reference image. 2. Sand Bank features: - Manual digitisation of sand banks from Landsat 5 imagery. - Conversion to a polygon shapefile for integration with the reef features. 3. Reef features: - Semi-automated digitisation of the marine features from Landsat 5 imagery. - Manual trimming, cleaning and checking of marine features against available aerial and satellite imagery. 4. Island features: - Compilation of island features from existing datasets (DNRM 1:25k Queensland Coastline, and Geoscience Australia Geodata Coast 100k 2004) - Correction of the island features from available aerial and Landsat imagery. 5. Merging: of marine and island features into one dataset. 6. Classification: of mapped features, including splitting fringing reefs based on changes in classification. 7. ID allocation: - Clustering to make groups of related features (i.e. an island, plus its fringing reefs and related sand banks; a reef plus its neighbouring patch reefs, etc.).
- Merging with the GBR Features dataset. This was to ensure that there were no duplicate allocations of feature IDs. This involved removing any overlapping features above the Great Barrier Reef Marine Park from the GBR Feature dataset. - Allocation of group IDs (i.e. 10-362) following the scheme used in the GBR Features dataset. Using R scripting. - Allocation of subgroup IDs (10-362b) to each feature in the dataset. Using R scripting. 8. Allocation of names: - Names of features were copied from some existing maps (Nautical Charts, 250k, 100k Topographic maps, CSIRO Torres Strait Atlas). For more information about the methods used in the development of this dataset see the associated technical report (Lawrey, E. P., Stewart M., 2016)

Limitations:

This dataset has mapped features from remote sensing and thus in some parts of Torres Strait where it is very turbid this may result in an underestimate of boundary of features. It also means that some features may be missing from the dataset.

This dataset is NOT SUITABLE FOR NAVIGATION.

The classification of features in this dataset was determined from remote sensing and not in-situ surveys. Each feature has a confidence rating associated with this classification. Features with a 'Low' confidence should be considered only as guidance.

This project only digitised reefs in Torres Strait, no modifications were made to the features from the integrated GBR Features dataset.

Format:

This dataset is available as a shapefile, a set of associated A1 preview maps of the Torres Strait region, ArcMap MXD file with map styling and ArcMap map layer file. The shapefile is also available in KMZ format suitable for viewing in Google Earth. TS_AIMS_NESP_Torres_Strait_Features_V1b_with_GBR_Features.shp (26 MB), TS_AIMS_NESP_Torres_Strait_Features_V1b_with_GBR_Features.kmz: Torres Strait features (3927 polygon features) integrated with the (GBRMPA) GBR Features dataset (5685 polygon features). This dataset covers the entire GBR.

Data Dictionary:

• DATASET: (TS Features, GBR Features) Which dataset this feature belongs to. This attribute is used when the Torres Strait Reef and Island Features dataset is merged with the GBRMPA GBR Features dataset.
• LOC_NAME_S: (e.g. Tobin (Zagarsum) Island (10-147a)) Location Name: Name of the feature and its ID
• GBR_NAME: (e.g. Tobin (Zagarsum) Island) Name of the features with no ID
• CHART_NAME: (e.g. Tobin Island) Name of the feature on the Australian Nautical Charts
• TRAD_NAME: (Zagarsum) Traditional name. From various sources.
• UN_FEATURE: (TRUE, FALSE) Unnamed Feature: If TRUE then the feature is unnamed. Useful for limiting labels in maps to features with names.
• LABEL_ID: (10-147a) ID of the feature
• SORT_GBR_I: (10147) ID of each feature cluster made up from the Latitude ID and Group ID. Used for sorting the features.
• FEAT_NAME: (Island, Rock, Reef, Cay, Mainland, Bank, Terrestrial Reef, Other ) Classification of the feature that is used in the GBR Features dataset. See 3.6 Classification scheme for more information.
• LEVEL_1, LEVEL_2, LEVEL_3: Hierarchical classification of the features. See Appendix 3: Feature Classification Descriptions.
• Checked: (TRUE, FALSE) Flag to record if the feature was reviewed in detail (at a scale of approximately 1:5000) after the initial digitisation. Unchecked features were only reviewed at a coarser scale (1:25000) to spot significant problems.
• IMG_SOURCE: (Aerial, AGRI, Landsat, ESRI) Imagery type used for the final digitisation checking and correction. (AGRI - AGRI PRISM by GA, Landsat is Landsat 8 or Landsat 5, ESRI - ArcMap satellite basemap)
• CLASS_SRC: (Aerial, AGRI, Landsat, Google, Marine Chart) Imagery type used to determine the classification of the feature. Often the classification will be an aggregation of information from multiple image sources. This field will record the highest resolution source used. For some small features the classification was obtained from the Marine Chart, generally for Rocky Reefs.
• CLASS_CONF: (High, Medium, Low) Confidence of the classification applied to the feature. The confidence is dependent on the clarity and range of the imagery available for classification. High - Clear high resolution imagery available (Aerial, Google) with good water visibility. Key characteristics of the classification clear visible. Feature classification fits the context for the neighbouring region. For unconsolidated features (such as sand banks) a High confidence classification would be applied if the shape, colour and context fit and in particular if movement is visible over time-lapse Landsat imagery. Medium - Moderate imagery available (Landsat 8 pan sharpened, some high resolution imagery) that shows key characteristics of the feature and the classification fits the context for the neighbouring region. Low - Only Landsat 5 imagery is available, the feature is small and its origin is unclear from the neighbouring context. This is the default confidence rating for any features that were not individually checked.
• POLY_ORIG: (QLD_DNRM_Coastline_25k, New, GBR_Features, AU_GA_Coast100k_2004) Original source of the polygon prior to any modifications. New features correspond to all the mapped marine features. Most features from the other source would have been modified as part of the checking and trimming of the dataset.
• SUB_NO: (100, 101, …) Subgroup number. Numeric count, starting at 100 of each feature in a group. Matches the subgroup ID i.e. 100 -> blank, 101 -> a, 102 -> b, etc.
• CODE: (e.g. 10-147-102-101) Unique code made from the various IDs. This is a GBR Feature attribute.
• UNIQUE_ID: (10147102101) Same as the CODE but without the hyphens, This is a GBR Feature attribute. Note: Version 1b, this attribution is currently out of date.
• FEATURE_C: (100 - 110) Code applied to each of the FEAT_NAMEs.
• QLD_NAME: (Tobin Island) Same as the GBR_NAME
• X_COORD: Longitude in decimal degrees east, in GDA94.
• Y_COORD: Latitude in decimal degrees north, in GDA94.
• SHAPE_AREA: Shape Area in km2
• SHAPE_LEN: Shape perimeter length in km
• CHECKED: (TRUE, FALSE) Whether the features was carefully checked (at a scale of better than ~1:5000) and manually corrected to this level of precision. If FALSE then the feature was only checked to approximately a1:25000 scale.
• PriorityLn: (TRUE, FALSE) Priority Label - If TRUE then this feature's label should be included in a map. Usually correspond to features with names. Use to reduce near duplicate labels of the islands and their surrounding fringing reefs.
• COUNTRY: (Australia, Papua-New Guinea) Sovereignty of the feature. This is based on a spatial join with the Australian Maritime Boundaries 2014a. The Territorial Sea and the Exclusive Economic