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Capturing Location Data for Field Plots

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Issue: 
Fall 2014

John Porter (VCR)

Recently I sent out a query to LTER Information Managers requesting information about good ways to collect field research location data from users.  For point data, this is relatively easy, since only a single pair of coordinates is required and these can be gotten from any Global Positioning System (GPS) unit or even smart phone,or obtained by noting coordinates displayed in mapping tools such as Google Earth.  However, things rapidly get more
complicated when complex polygons of plot boundaries are required for multiple plots and where there are many different data providers.  My goal is to create (or find) an easy-to-use system that will allow a diverse array of investigators to generate points, lines or polygons for study sites that can submitted for import into GIS software or a database for the purpose of populating metadata and research site maps.

I received several helpful responses.  Fox Peterson at the H.J. Andrews was first. She proposed a system based on using the outline and edge-detection functions in Matlab to convert outlines or areas drawn using a “paintbrush” on top of a high-resolution, georeferenced aerial image of the site drawn.  She cited information on the Matlab Mapping Toolkit (http://www.mathworks.com/help/map/_f7-12036.html ) and additional information on edge detection (http://www.mathworks.com/discovery/edge-detection.html ).

Gastil from the Moorea Coral Reef LTER recommended looking at
SeaSketch – a collaborative platform for GeoDesign (http://mcclintock.msi.ucsb.edu/projects/seasketch).  This developing tool goes well beyond capturing boundary coordinates, providing automated feedback on potential impacts of manipulations and forums for sharing and discussing sketches.  The tool is aimed at marine environments, but she suggested that the developers might be open to wider collaboration.

Theresa Valentine from the H.J. Andrews LTER provided some useful tips on dealing with capturing the individual sampling points associated with gridded plots. She uses a GPS location at one corner of the grid, along with sample point spacing, number of plots and grid orientation to automate generation of individual sampling points using the “fishnet” tool in ArcGIS.  For features that are clearly visible from aerial imagery, digitization off of high-resolution photos may be used, or field surveys using optical surveying tools may be converted from a coordinate geometry to conventional coordinates. For irregular linear features, such as trails, they combine GPS data from multiple trips down the trail. In a forested environment, dropout of GPS signals is a recurrent problem, so collection of key points such as switchbacks or turns may require revisits.

She further discussed the desirability of high-resolution GPS data so as to match up with high-quality LiDAR-based digital elevation data.  Raising the antenna well up into, or over, the canopy helps in a forested environment. Additionally, picking dates for surveys with good satellite geometries using planning tools and differential correction to a local base station help achieve the best accuracy. They do a GPS foray every summer to collect accurate GPS coordinates of study site locations.

Additionally, John Vande Castle from the LTER Network Office offered a wealth of information regarding the use of Global Positioning System data and tradeoffs between the KML data format used by Google Earth and the GPX exchange format for GPS data.  In addition to tracks recorded directly from dedicated Garmin and other

GPS devices, he uses apps, such as “Ski Tracks” or Google’s “My Tracks,” that keep a record of GPS points (a track). The apps generate KML files that contain information specific to the app along with the coordinates. For example an app aimed at skiing gives information on speed and slope (Figure 1).  He noted the benefit of transforming the KML into GPX in order to obtain the coordinates and line distances in a more consistent, less esoteric, format for general use (Figure 2). He then can manipulate the data with the (now free) Garmin BaseCamp software (Figure 3) or with other GIS tools.

ski track mapping application screenshot

conversion of ski map application data to kml

screenshot of Garmin Basecamp software

Additionally, I investigated using Google My Maps (https://www.google.com/mymaps ),  ScribbleMaps.com and Google Earth as potential tools. Each of these had some advantages and some drawbacks.  ScribbleMaps shares many similarities with a large number of map editing sites built on top of Google or ESRI frameworks such as:  Mapbox (https://www.mapbox.com – allows GPS file import),  ZeeMaps.com (points only),  Waze.com,  National Geographic MapMaker Interactive (http://mapmaker.education.nationalgeographic.com/, no way to save polygons), Map Maker (http://mapmaker.donkeymagic.co.uk/, points only) and many others.

Feature

Google “My Maps”

ScribbleMaps

Google Earth

Accounts

 Required

Optional, but all anonymously saved maps are public

None, but software must be installed

Editor

Easy to use, all types of features  can be labeled through editing

Easy to use, but only points may be labeled with a title.

Moderately easy to use. 
Editing is a secondary function so controls are harder to find.

Export Capabilities

Exports to KML retaining names and other annotations

Can export XML in either KML or GPX forms. Polygons and lines have
unique IDs but these are set by the system and hidden from the user.

KML/KMZ (zipped KML) via “Save Place as”

All of the online tools use available satellite and aerial photo services, which vary in their accuracy from location to location (fortunately for the VCR/LTER there is a high agreement between GPS and image-based sources).  However,  before using the online  tools, it is a good idea to cross-check the accuracy for known points in your area. The most common export format, KML, is an XML format that can be transformed to extract location information for use in databases and metadata. However, the “richness” of the KML varies among services. For example, ScribbleMaps allows creation of points, lines or polygons, but user-supplied identification information is only exported for points.  In contrast, Google Earth exports name, description and style information.

I would like to say that I identified the “perfect” system.  All of the suggestions have merit and there is clearly rapid evolution occurring in the online map making community. However there are still gaps that require manual intervention (from collection of GPS data to data conversions). Nonetheless, there are a number of tools and approaches that could be applied to the field data geolocation problem.