Multi-Layer GIS Integration
Import soil maps, elevation data, land use, weather stations, and management zones. Overlay emission measurements with 50+ spatial data layers.
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.
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.
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.
Import soil maps, elevation data, land use, weather stations, and management zones. Overlay emission measurements with 50+ spatial data layers.
Aggregate emissions by watershed, HUC boundaries, or custom polygons. Support for nested watershed hierarchies and flow routing.
Combine ground measurements with satellite imagery (NDVI, soil moisture, temperature) for enhanced spatial predictions.
Kriging, inverse distance weighting, and machine learning methods to create continuous emission surfaces from point measurements.
Interpolated annual N₂O emissions (kg N₂O-N/ha/yr) based on 25 measurement sites, overlaid with soil texture and topography
GHGMET Spatial Analysis Platform, 2023 Growing Season
Ordinary kriging with soil texture as covariate. Cross-validation RMSE: 0.8 kg N₂O-N/ha/yr
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
- High-resolution emission maps (1m to 1km)
- Uncertainty quantification
- Hotspot identification
- Temporal animation (seasonal/annual)
- Multi-scenario comparisons
GHGMET collaborates with leading GIS and remote sensing organizations
No partners to display yet.
“- GHGMET's geospatial tools transformed how we approach regional emission inventories. What used to take months of GIS work now happens in hours, and the spatial accuracy is unprecedented.”
- Ready to integrate geospatial analysis into your GHG research? Our team can help with setup, training, and custom spatial modeling.
info@ghgmet.com
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