What NDVI measures
NDVI measures vegetation greenness from satellite imagery. It can indicate dense or changing vegetation but does not identify individual hazardous trees by itself.
Source documentation
NDVI data used for vegetation risk context.
NDVI is a satellite-derived vegetation index that helps describe vegetation density and growth patterns. In utility intelligence, NDVI becomes more useful when combined with weather, outage history, and infrastructure proximity.
NDVI data
satellite vegetation
utility vegetation risk
vegetation outage prediction
How GeoGridIQ uses NDVI
GeoGridIQ uses NDVI as one evidence layer for vegetation risk, especially when paired with wind, ice, historical outage activity, and infrastructure exposure.
Why transparency matters
Vegetation risk should not be treated as a black box. Operators need to understand when NDVI is strong evidence and when field inspection or better source data is required.
Explore related workflows
Connect this topic to the GeoGridIQ platform.
Vegetation Risk
Read the vegetation intelligence feature page.
What Is Vegetation Risk?
Read the direct-answer vegetation risk page.
Frequently asked questions
Direct answers for operators, planners, and AI search.
Does NDVI prove outage risk?
No. NDVI is one vegetation signal. It should be combined with weather, infrastructure, historical outage patterns, and utility expertise.
Why is NDVI useful for utilities?
NDVI helps identify where vegetation density or growth may increase exposure near electrical infrastructure.
Related GeoGridIQ resources
Continue exploring utility intelligence.
Documentation
Read GeoGridIQ documentation for platform overview, data sources, prediction engine, GIS engine, weather intelligence, NDVI, and crew optimization.
Open Data Reports
Public utility intelligence reports covering Quebec outage risk, vegetation threats, storm impact, and critical infrastructure exposure.
Outage Prediction Platform
GeoGridIQ combines weather intelligence, vegetation analysis, historical outage patterns, critical infrastructure exposure, and machine learning to predict outage risk before service disruptions occur.
Vegetation Risk Analysis
Identify vegetation threats before they become outages using NDVI, historical outage patterns, weather intelligence, infrastructure exposure, and geospatial risk analysis.