Geospatial Integration

From Field to Landscape: Spatial GHG Analysis

Integrate greenhouse gas measurements with GIS data to understand spatial patterns, scale emissions, and support precision agriculture decisions.

Spatial Analysis for Agricultural GHG Science

Greenhouse gas emissions vary dramatically across landscapes due to differences in soil properties, topography, climate, and management. GHGMET's geospatial tools help researchers understand, visualize, and predict these spatial patterns.

Why Geospatial Integration Matters

Precision Agriculture

Target mitigation practices to areas with highest emission potential, optimizing both environmental and economic outcomes.

Regional Scaling

Scale field measurements to watershed, county, or national levels using spatial modeling and GIS data integration.

Hotspot Identification

Identify emission hotspots and prioritize monitoring locations for maximum research impact.

Policy Support

Provide spatially-explicit emission estimates for climate policy development and carbon market verification.

Our platform integrates seamlessly with industry-standard GIS software and supports common spatial data formats for maximum flexibility.

Key geospatial features

Multi-Layer GIS Integration

Import soil maps, elevation data, land use, weather stations, and management zones. Overlay emission measurements with 50+ spatial data layers.

Watershed-Scale Analysis

Aggregate emissions by watershed, HUC boundaries, or custom polygons. Support for nested watershed hierarchies and flow routing.

Satellite Data Integration

Combine ground measurements with satellite imagery (NDVI, soil moisture, temperature) for enhanced spatial predictions.

Advanced Spatial Interpolation

Kriging, inverse distance weighting, and machine learning methods to create continuous emission surfaces from point measurements.

Spatial Modeling Approach

Data Integration Framework

GHGMET's geospatial platform integrates multiple data sources:

Field Measurements

Point measurements from automated chambers
Eddy covariance tower footprints
Manual sampling campaigns
Historical datasets

Environmental Covariates

Soil properties (texture, organic C, pH, drainage)
Topography (elevation, slope, aspect, TWI)
Climate (temperature, precipitation, growing degree days)
Land use and crop type
Management practices (fertilizer, tillage, irrigation)

Spatial Analysis Methods

1. Geostatistical Interpolation

Variogram analysis and modeling
Ordinary and universal kriging
Uncertainty mapping

2. Machine Learning

Random forest spatial prediction
Neural networks with spatial features
Ensemble modeling

3. Process-Based Scaling

Run models at each grid cell
Integrate spatial covariates
Aggregate to desired scale

Output Products

High-resolution emission maps (1m to 1km)
Uncertainty quantification
Hotspot identification
Temporal animation (seasonal/annual)
Multi-scenario comparisons

Start Spatial Analysis

- Ready to integrate geospatial analysis into your GHG research? Our team can help with setup, training, and custom spatial modeling.

Get in Touch

info@ghgmet.com

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