Multi-source satellite and aerial imagery analysis to monitor land cover, vegetation health, water resources, and environmental change — delivering actionable insights at any scale from a single field to an entire nation.
Remote sensing is the science of acquiring information about Earth's surface without direct physical contact — using electromagnetic radiation detected by satellite or airborne sensors. The data it provides is irreplaceable for monitoring large areas quickly, detecting change over time, and understanding phenomena invisible to the naked eye.
Planora accesses data from 15+ satellite constellations ranging from free 10m Sentinel-2 imagery to commercial sub-metre WorldView-3 and Planet Labs daily captures. We process optical, multispectral, hyperspectral, thermal, and SAR (radar) datasets across the full analytical spectrum.
Our remote sensing team combines ENVI, Google Earth Engine, and custom Python/R pipelines to deliver land cover classifications, vegetation indices, change detection maps, and time-series analyses that support decisions at every level of government and enterprise.
Planora has processed over 80,000 sq km of satellite imagery for agriculture, forestry, environment, and urban monitoring projects in the last 5 years alone.
NDVI, NDRE, SAVI, and EVI vegetation index mapping for crop stress detection, growth monitoring, and yield prediction.
Supervised and object-based classification of land cover into 10–30+ classes for planning, taxation, and environmental compliance.
Multi-temporal analysis to detect urban expansion, deforestation, water body changes, and encroachment over years or decades.
SAR-based flood extent mapping, drought severity index, soil moisture mapping, and crop damage assessment.
Thermal infrared analysis of surface temperature, heat island intensity, and green cover impact on urban thermal comfort.
Shoreline change detection, mangrove health mapping, sediment plume tracking, and coastal erosion quantification.
Above-ground biomass estimation, canopy cover mapping, and REDD+ compliant deforestation monitoring for carbon credits.
Spectral analysis for soil type mapping, mineral indicator identification, and land degradation assessment.
Identifying optimal satellites, dates, and cloud-cover thresholds. Free Sentinel/Landsat data is downloaded directly; commercial imagery is procured from Maxar, Airbus, or Planet as needed.
Radiometric calibration, atmospheric correction (FLAASH/DOS), geometric correction, orthorectification, and pan-sharpening. All processing is done to Level-2 surface reflectance standard.
Generating vegetation indices (NDVI, NDRE, EVI, SAVI), water indices (NDWI, MNDWI), built-up indices (NDBI), and spectral band composites for visual interpretation.
Supervised (Random Forest, SVM), unsupervised (K-means, ISODATA), or object-based (eCognition) classification as appropriate. Deep learning classification using DeepLabv3+ for high-res imagery.
Producer's, user's, and overall accuracy calculated from stratified random sample of field-verified reference points. Kappa coefficient and confusion matrix reported for all classifications.
Final classified maps, change detection outputs, and index rasters produced in GIS-ready formats. Full analysis report with methodology, accuracy statistics, and interpretation guidance.
Time-series stacks enable change detection across months to decades — revealing trends invisible in single-date imagery.
Petabyte-scale cloud geospatial computing platform for large-area time-series analysis and multi-sensor data fusion.
Access to 40+ PB archiveIndustry-standard remote sensing analysis platform with spectral tools, classification algorithms, and hyperspectral processing.
50+ classification algorithmsFree ESA toolbox for Sentinel-1 SAR, Sentinel-2 optical, and Sentinel-3 data processing with full radiometric calibration.
Sentinel 1/2/3 optimisedObject-based image analysis for high-resolution imagery classification — dramatically outperforms pixel-based methods.
Object-based AI classificationCustom spectral indices, batch processing automation, ML model training, and result validation pipelines.
Open-source, fully scriptableFull remote sensing suites for change detection, hyperspectral analysis, land change modeller, and LULC forecasting.
IDRISI / ERDAS full suite