ocular sampling (Animalia)

Knowledge graph centered on ocular sampling (Animalia) with 13 nodes and 23 connections. Top connected: NDVI, Grand Junction, imaging spectroscopy, habitat-based sampling, presence-absence data.

Method synopsis

Manual creation of presence and absence sampling points for ecological analysis using Google Earth Engine's marker tool on false color imagery combined with vegetation indices to distinguish target land cover types.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — Creating Presence and Absence Points (2023). Implementations in other papers (listed below) may differ.

1. Create presence and absence training points manually
   Hover over Geometry imports box and click + new layer. Create FeatureCollection named 'presence' with property 'presence' set to value 1. Manually place points on aspen forest areas visible in high-resolution imagery. Repeat process to create 'absence' FeatureCollection with property 'presence' set to value 0, placing points in areas without aspen forest.
   Quantities: Variable number of presence and absence points placed manually based on visual interpretationDuration: Manual digitization stepConditions: Using NAIP 1m imagery for visual identification of aspen forest presence/absence
   Equipment: Google Earth Engine, geometry drawing tools, NAIP high-resolution imagery

Typical Equipment

• Google Earth Engine
• marker tool
• NAIP imagery
• NDVI
• NLCD
• Google Drive

Output Measurements

• presence/absence coordinates
• land cover classification points

Papers Using This Protocol (1)

Places Where Used