RMA Platform
Rangeland Metrics and Analytics - a scalable platform built by RCS.
RMA combines satellite monitoring, field calibration, AI
Rangeland Evaluation and practical decision support to help
farmers, institutions and landscape partners monitor grass
resources, bush resources and rangeland condition over time.
The rangeland data problem
Too little objective field data
Rangeland monitoring often relies on few field
observations spread over vast areas, limiting
accuracy and representativeness.
High cost of traditional
monitoring
Extensive field campaigns are time consuming
and expensive, restricting coverage and reducing monitoring frequency.
Difficult to monitor change
over time
Inconsistent methods and sparse data make it
hard to detect trends and evaluate interventions reliably.
How RMA works
1. Satellite Monitoring
High-resolution satellite data tracks vegetation cover, biomass and rangeland condition at scale and over time.
2. Field Calibration
Targeted field data validates
satellite signals and improves
accuracy over time across
different ecosystems.
3. AI Rangeland Evaluation
Structured, geo-referenced photos are analysed to extract indicators of grass, woody cover, bare ground, scene validity and
context.
4. Decision Support
Integrated analytics and reporting
turn data into actionable insights
for better management and
investment decisions.
AI Rangeland Evaluation allows structured farmer and field-team photos to expand data collection at radically lower cost.
Suitable pilot structure
1. Select farms or landscapes
Define pilot scope, select participating farms
or landscapes and agree on objectives.
2. Collect satellite + field +
photo data
Combine satellite monitoring, field calibration
and structured photos to build a robust, cost effective dataset.
3. Deliver reports, insights
and learning
Provide actionable reports, insights and
continuous learning to improve management
and scale impact.
Current outputs and future direction
Grass monitoring outputs
Maps and trends of grass cover,
biomass and productivity by
season and landscape.
Bush quantification outputs
Woody cover, density and
structure maps to support
sustainable bush management.
Farm reports and trends
Custom reports with key
indicators, trend analysis and
benchmarking at farm level.
Future stocking and
economic models
Integrating rangeland metrics with
stocking and economic models to
estimate productivity and financial
outcomes.
AI Rangeland Evaluation
Farmers and field teams submit structured, geo-referenced rangeland photos. AI analyses these photos for indicators such as cover, visible grass condition, woody presence, bare ground, scene validity and general rangeland context. This lowers the cost of data collection dramatically while increasing monitoring points and improving calibration over time.
What the farmer gets
Simple photo capture guidance;
feedback on rangeland condition;
insights to inform management and stocking decisions.
More monitoring points, better calibration, stronger trend detection
and lower cost per data point.