TerraXGreen

An Earth Intelligence Platform with Multi-Sensor Data and AI Systems for Forestry & Vegetation Monitoring

Forestry & Vegetation Intelligence

TerraXGreen captures high-resolution data from satellites, drones, LiDAR, and SAR for monitoring tree health, species diversity, forest type, and carbon stocks. By transforming this data into actionable spatial insights, it enables precise habitat mapping, wetland classification, riparian zone management, and change detection over time. Our digital win of forested landscapes allows managers to simulate growth, assess wildfire risks, track reclamation success, and plan conservation interventions in a dynamic, interactive 3D environment. TerraXGreen  provides forestry and vegetation professionals with the tools to make data-driven decisions, optimize resource management, and enhance ecosystem resilience.

Featured Applications

Intelligent Wetland Monitoring

TerraXGreen is an AI- and remote sensing–driven monitoring framework for large-scale assessment of vegetation, wetlands, and mangrove ecosystems across national and regional levels. The platform integrates multi-sensor satellite data with AI algorithms to produce accurate, high-resolution maps. This approach supports data-driven decision-making for sustainable land management, conservation, and climate resilience. Some of the key features include:

  • Automated workflows for entire process
  • Real time platform for visualization, trend analysis, and ecosystem change
  • Configurable layers for environmental agencies to prioritize restoration zones and evaluate conservation outcomes
  • A flexible, cloud-native system that can be scaled from regional studies to national ecosystem inventories

Forest Monitoring Intelligence

TerraXGreen assesses forest cover, health, and change dynamics at regional and national scales. By integrating multi-sensor satellite imagery with advanced AI analytics, the platform provides near-real-time insights into deforestation, degradation, regeneration, and carbon dynamics, enabling data-driven forest management and conservation strategies. Some of the key features include:

  • Satellite imagery for year-round observation under all weather conditions.
  • Application of AI models to classify forest types, detect clearcuts, and identify regrowth zones.
  • Multi-temporal analysis to track deforestation, fire impact, and vegetation recovery through radar and optical time series.
  • Derivation of canopy density, leaf area index, and biomass indicators to quantify forest health and productivity
  • Automated processing, visualization, and cloud dashboards for scalable monitoring and reporting

Intelligent Vegetation Monitoring

TerraXGreen provides a Synthetic Aperture Radar (SAR)–based vegetation monitoring system that enables continuous, all-weather observation of vegetation dynamics, structure, and biomass. By leveraging radar backscatter sensitivity to canopy moisture and roughness, our approach supports agricultural productivity tracking, forest management, and ecosystem monitoring even under cloud cover or low-light conditions. Some of the key features include:

  • Time-series SAR imagery to detect vegetation growth, harvesting cycles, and canopy moisture changes.
  • Extraction of biophysical parameters such as canopy height, structure, and water content using dual- and quad-pol radar data.
  • AI models for vegetation type mapping and biomass estimation.
  • Continuous tracking of deforestation, reforestation, and flood-vegetation interactions through radar intensity and coherence variations

Intelligent Permafrost Monitoring

TerraXGreen facilitates monitoring permafrost dynamics, ground subsidence, and surface deformation in Arctic and sub-Arctic regions. By integrating optical, radar, and thermal satellite observations, our system detects early signs of thaw, slope instability, and thermokarst development, supporting climate adaptation, infrastructure protection, and environmental assessment. Some of the key features include:

  • Use of Sentinel-1 and RADARSAT-Constellation data with InSAR techniques to map ground movement and surface subsidence.
  • Integration of thermal satellite data to detect permafrost thaw and freeze–thaw cycles.
  • Use of ArcticDEM and LiDAR datasets to track terrain changes, thermokarst formation, and drainage pattern evolution.
  • Application of AI models to classify thaw zones, identify slump activity, and monitor permafrost-related geomorphic features
  • Fusion of satellite observations with ground temperature sensors and weather data

Salt-affected Soil Monitoring

TerraXGreen delivers a remote sensing-based monitoring framework for detecting and assessing soil salinity and degradation across agricultural and coastal landscapes. By analyzing spectral and temporal changes, the system identifies salinity-affected zones, supporting sustainable land management and reclamation planning. The monitoring framework has been applied in Canada and elsewhere. The framework is able to detect and map specific salt-affected soil strata such as Sabkha in GCC, which is a problematic soil for construction industries. Some of the key features include:

  • Detecting salinity through indicators Monitoring salinity progression and recovery trends using time-series remote sensing data.
  • Evaluating crop health and canopy response to underlying salinity conditions.
  • Integrating topography, drainage, and groundwater data for spatial risk assessment
  • Using AI models to classify soil salinity severity and predict future expansion zones

Smart Soil Contamination Monitoring

TerraXGreen detects and evaluate soil contamination and degradation caused by oil and gas operations. By analyzing spectral and radar data, the system identifies hydrocarbon-affected zones and measures their spatial extent, supporting environmental compliance and restoration planning. Some of the key feature include:

  • Spectral anomaly detection to identify oil-affected areas
  • Fusion of radar and optical data for enhanced sensitivity
  • Time-series analysis to track contamination dynamics and recovery
  • Machine learning for accurate classification of polluted soils
  • Cloud-based mapping and reporting for monitoring and decision support

Desertification, Drought, and Land Degradation Monitoring Intelligence

TerraXGreen monitors desertification, soil degradation, and vegetation decline across arid and semi-arid landscapes. The platform integrates multispectral, radar, and climatic datasets to detect early signs of land deterioration caused by overgrazing, deforestation, and climate stress. TerraXGreen provides regional and temporal insights that support sustainable land management, restoration planning, and policy implementation. The features include satellite-derived “Land Condition Analysis” using satellite imagery to extract vegetation and soil indices (NDVI, SAVI, BSI, and LST) for long-term degradation trend assessment; “Change Detection and Temporal Monitoring” via multi-temporal analysis to track vegetation loss, shifting sand fronts, and recovery patterns following rehabilitation efforts; “SAR-Based Surface Characterization” by application of Sentinel-1 radar data to evaluate soil roughness, crust formation, and surface moisture dynamics in desert-prone areas;  “Climatic Correlation Modeling” by integration of rainfall, temperature, and evapotranspiration datasets (ERA5 and CHIRPS) to link degradation processes with climatic variability; “GIS-Based Risk Mapping” via weighted spatial modeling to identify vulnerable zones, prioritize restoration areas, and support proactive desertification control programs. Also, an index, called Temperature-Vegetation-soil Moisture Dryness Index (TVMDI) is applied to the entire region of the question to assess the dryness and its effects on social lives.