
Rangeland, biomass and GIS decision support for regenerative African landscapes
RCS combines Namibian rangeland knowledge, field data coordination, biomass interpretation and practical GIS outputs to support grass monitoring, bush quantification, sustainable biomass utilisation and landscape decision-making. RCS is building RMA — a scalable monitoring platform that brings together satellite layers, rainfall context, field calibration and AI-assisted rangeland evaluation. For large institutional assignments, RCS works with specialist consortium partners in remote sensing, ArcGIS geoportal development, drone surveys and technical system integration.
Why this matters

Many rangeland decisions are still made with incomplete or subjective information. RCS and RMA aim to make monitoring more objective, repeatable and affordable.
How RCS and RMA work together
RCS Consulting
Field-based assessments, technical interpretation, project support and practical management guidance.
RMA Platform
A scalable data platform for maps, monitoring, reporting and future farm-level analytics.
AI Rangeland Evaluation
A scalable data platform for maps, monitoring, reporting and future farm-level analytics.
Applied solutions

Grass Monitoring
Forage availability, grass biomass, rainfall context, grazing support and seasonal monitoring.
Bush Quantification
Woody biomass, bush density classes, operational planning and resource mapping for harvesting, restoration and land management.
Why RMA can scale
Satellite, rainfall and field calibration form the core of RMA. Structured farmer and field-team photos add a further scale layer — increasing monitoring points and lowering cost per data point over time.
Satellite Monitoring
Frequent, consistent and widearea observations form the foundation.
Field Calibration
Targeted field data validates satellite signals and improves accuracy over time.
AI Rangeland Evaluation
Structured field photos are analysed by AI to expand data at scale.
Decision Support
Actionable maps, indicators and reports for better decisions.

