Multi-model PlanetScope snow cover classification

Knowledge graph centered on Multi-model PlanetScope snow cover classification with 71 nodes and 142 connections. Top connected: Delphinium, Spruce, snowpack persistence, snow water equivalent, elevation gradient.

Method synopsis

Machine learning approach for snow cover mapping using PlanetScope imagery with multiple model configurations incorporating spectral, topographic, and vegetation variables. Performance evaluation across different forest canopy structures.

Synthesized from method descriptions across 2 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA (2024), The Cryosphere. The protocol was originally introduced by High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery (2022), Remote Sensing. Implementations in other papers (listed below) may differ.

1. Machine learning spatial density modeling
   Tested three regression techniques: multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN). Examined 16 km² area closely bounded by GPR survey. Used normalized predictor variables including bare-earth elevation, snow-covered elevation, snow depth, vegetation height, aspect, slope, derivatives of elevation rasters, distance to nearest vegetation, maximum upwind slope, and wind factor parameters.
   Quantities: Generated models using lidar information from entire ~16 km² domain. Model hyperparameters developed using cross-validation with R² of approximately 0.8 and RMSE ≈ 15 kg m⁻³Duration: Model development and validation phaseConditions: Computational analysis using MATLAB Regression Learner application
   Equipment: MATLAB Regression Learner application, cross-validation algorithms

Typical Equipment

• PlanetScope satellite constellation
• Machine learning software
• Computing resources
• Statistical analysis software

Output Measurements

• F-score performance metrics
• Snow cover classifications

Papers Using This Protocol (3)

Species Studied With This Protocol

Places Where Used