GeoAI Ecological Zoning & Habitat Mapping for KSRNR
🔷 Solution Overview
The King Salman bin Abdulaziz Royal Natural Reserve (KSRNR) engaged GPC to design and implement a comprehensive ecological zoning and habitat mapping solution aimed at establishing a precise geospatial database of habitats, land use, and land cover across the reserve.
Covering one of the largest protected natural areas in Saudi Arabia, the project leveraged advanced geospatial technologies including artificial intelligence, machine learning, satellite earth observation, and GIS to generate high-resolution habitat maps and land cover classifications.
The solution integrated multispectral satellite imagery analysis, visual interpretation, and field validation workflows to ensure high accuracy and multi-level habitat characterization across the reserve. This enabled the identification, classification, and mapping of diverse ecological zones at different levels of detail.
In addition to generating core geospatial datasets, the engagement included capacity building and active involvement of KSRNR staff in data collection, validation, and interpretation processes, ensuring sustainable use of the outputs for long-term conservation planning.
The resulting geospatial databases provide a critical foundation for environmental planning, biodiversity assessment, KPI reporting, and the management of interactions between natural ecosystems and human activities.
🔷 Main Objectives
- Produce accurate habitat mapping across the reserve
- Develop land use and land cover geospatial databases
- Support conservation planning and environmental management
- Enable data-driven ecological decision-making
- Strengthen internal capabilities in habitat mapping and GIS usage
🔷 How GPC Empowers the Client
GPC delivered a comprehensive GeoAI-enabled solution combining advanced analytics, geospatial workflows, and knowledge transfer.
The engagement included:
- Development of satellite-derived habitat maps
- Integration of AI/ML-based classification techniques
- Implementation of multispectral analysis and visual interpretation
- Design of field validation and ground-truthing workflows
- Active involvement of KSRNR staff in data collection and validation
- Training and knowledge transfer to support long-term use
This approach ensured not only the delivery of high-quality datasets but also the development of internal capabilities for sustainable ecological monitoring and management.
🔷 The Bottom Line
A GeoAI-powered ecological intelligence platform enabling KSRNR to enhance conservation planning, biodiversity assessment, and environmental decision-making at scale.
🔷 About Our Customer
The King Salman bin Abdulaziz Royal Natural Reserve (KSRNR) is the largest natural reserve in Saudi Arabia, covering approximately 130,000 km² in the northern region of the Kingdom.
The reserve is characterized by rich biodiversity, diverse landscapes, and significant cultural and historical assets, playing a critical role in conservation, environmental protection, and sustainable development initiatives.
🔷 Challenge
- Large geographic coverage and high data volume
- Complexity of satellite imagery processing and interpretation
- Habitat classification challenges across diverse ecosystems
- Field survey constraints and access limitations
- Need for high accuracy and validation across multiple data sources
🔷 Solution Description
GPC delivered a comprehensive GeoAI-based ecological mapping solution combining:
- Satellite-derived habitat mapping
- Land use and land cover classification
- Orthomosaic / image mosaic generation
- Multispectral analysis and visual interpretation
- Field validation and ground-truthing
- Integration of AI/ML models for classification
The solution established a structured geospatial workflow for accurate and scalable habitat mapping across the reserve.
🔷 Key Features
- Satellite-derived habitat mapping
- Land use and land cover classification
- High-resolution image mosaic
- AI/ML-based classification models
- Multispectral analysis
- Field validation workflows
- Ecological zoning outputs
🔷 Platform / Integration Components
- Satellite Earth Observation
- Machine Learning (ML) and Artificial Intelligence (AI)
- Geographic Information Systems (GIS)
- ArcGIS Enterprise
- Geospatial data processing workflows
🔷 Hosting / Deployment
ArcGIS Enterprise environment supporting centralized geospatial data management and analysis.
🔷 Delivery Model / Scope
Design and implementation engagement including:
- Remote sensing analysis
- Geospatial data processing
- Habitat classification and validation
- Capacity building and training
🔷 Standards / Methods Applied
- Multispectral satellite analysis
- AI/ML classification methodologies
- Visual interpretation techniques
- Ground-truthing and validation workflows
- Geospatial data modeling and processing standards
🔷 Deliverables / Milestones
- Habitat maps
- Land use and land cover maps
- Image mosaic of the reserve
- Geospatial habitat database
- Validation and interpretation outputs
🔷 Benefits / Results
- Improved conservation planning and management
- Enhanced biodiversity assessment capabilities
- Better understanding of habitat distribution
- Support for KPI reporting and environmental monitoring
- Strengthened ecological decision-making
🔷 Platform / System KPIs
- Coverage of entire reserve area (~130,000 km²)
- High-resolution habitat and land cover datasets
- Multi-level ecological classification
- Integration of multiple data sources
🔷 Outcome KPIs
- Stakeholder entities engaged
- KSRNR environmental and conservation teams
- Datasets assessed / modeled
- Habitats, land use, land cover, ecological zones
- Standards reviewed / developed
- Geospatial classification and mapping methodologies
- Roadmap horizon / phases
- Project-based implementation (approx. 6 months)
- Target milestones / outcomes
- Deployment of ecological zoning and habitat mapping datasets

