DREVO AI - Artificial Intelligence for Managing Living Ecosystems

Artificial intelligence for managing living ecosystems

WOOD AI— an intelligent digital platform that unites all projectsDREVO Living Mountainsinto a unified system of analysis, forecasting and decision support.

Unlike classic monitoring systems, DREVO AI not only collects data, but alsounderstands the relationships between water, soil, forests, animals, climate, and human activities, offering optimal environmental solutions.

"Observation is not enough. We need to understand, predict, and help nature recover."

Mission

To create digital intelligence that will help humanity manage natural ecosystems based on objective data, scientific models, and principles of nature-like development.

Main tasks

Analysis of the state of ecosystems.

Climate change forecast.

Forest restoration management.

Water resources management.

Biodiversity monitoring.

Prevention of natural disasters.

Support for scientific research.

Automation of decision making.

Architecture DREVO AI

Satellites

│

TREE AeroSense

│

River Rover

│

Living Mountain Observatory

│

Mountain Digital Twin

│

──────────────

│

▼

WOOD AI

──────────────

│

├── Water AI

├── Forest AI

├── Wildlife AI

├── Soil AI

├── Climate AI

├── AI Agriculture

├── River AI

├── Restoration AI

├── Risk AI

├── Carbon AI

└── Decision AI

Main intelligent modules

Water AI

Analyzes:

springs;

rivers;

streams;

swamps;

groundwater;

reservoirs.

Predicts:

droughts;

floods;

change in flow rate;

water quality.

Forest AI

Controls:

forest growth;

tree health;

age structure;

diseases;

pests;

fires;

windfalls.

Offers optimal recovery programs.

River AI

Analyzes:

change of riverbeds;

flow speed;

siltation;

erosion;

water quality;

spawning grounds.

Works in conjunction with:

River Rover Scout;

River Rover Clean;

River Rover Restore.

Soil AI

Controls:

humidity;

humus content;

erosion;

compaction;

microbiological activity;

carbon accumulation.

Wildlife AI

Analyzes:

animal migration;

population size;

genetic diversity;

interaction of species.

AI Pollinator

Controls:

flowering time;

pollinator activity;

food base;

climate influence.

Climate AI

Models:

temperature;

precipitation;

snow cover;

humidity;

extreme events.

Restoration AI

Defines:

order of restoration;

optimal tree species;

reclamation methods;

the effectiveness of the measures taken.

Carbon AI

Calculates:

carbon accumulation;

CO₂ binding;

ecosystem carbon balance;

climate effect of projects.

Decision AI

Generates recommendations for:

foresters;

ecologists;

farmers;

municipalities;

national parks;

scientific organizations.

Data sources

Living Mountain Observatory

Data is coming in:

sensors;

camera;

weather stations;

acoustic stations;

camera trap.

TREE AeroSense

Transmitted:

LiDAR;

multispectral imaging;

thermal imaging data;

vegetation maps.

River Rover

Transmitted:

bottom maps;

depths;

water quality;

channel objects;

state of the banks.

Satellite data

Used:

forest dynamics;

snow cover;

surface temperature;

change in humidity.

Mountain Digital Twin

DREVO AI uses a digital twin of the entire territory.

It contains:

history of changes;

cards;

photographs;

hydrological models;

forest models;

information about animals;

research results.

Forecasting capabilities

Fires

Definition:

risk of occurrence;

propagation speeds;

potential damage.

Droughts

Forecasts:

lowering the water level;

drying up of springs;

forest stress.

Floods

The following are simulated:

flood zones;

water movement;

destruction of the coast.

Forest diseases

Early detection:

mushrooms;

insect pests;

drying out of trees.

Automatic recommendations

The system can recommend:

where to plant a forest;

what breeds to use;

where to build water retention structures;

where to restore swamps;

where to clean rivers;

which areas require urgent protection.

Project integration

Stages of development

Version 1.0

monitoring;

data visualization;

basic analytics.

Version 2.0

forecasting;

automatic recommendations;

scenario modeling.

Version 3.0

semi-autonomous control;

coordination of robotic platforms;

decision-making support.

Version 4.0

self-learning system;

global network of ecosystems;

international exchange of environmental data;

management of large environmental programs.

Key performance indicators

volume of restored water resources;

area of ​​restored forests;

area of ​​restored wetlands;

biodiversity index;

accumulation of organic matter in soils;

increase in populations of key species;

reducing damage from fires, floods and erosion;

volume of fixed carbon;

economic efficiency of environmental protection measures.

The main principle

DREVO AI is the digital intelligence of living nature.

It does not replace specialists and does not manage nature in place of humans. Its task iscombine data, scientific knowledge and practical experienceto make more accurate, timely and environmentally sound decisions.

In the ecosystemDREVO Living MountainsDREVO AI becomes the central intelligent platform that unitesobservation (Living Mountain Observatory), digital modeling (Mountain Digital Twin) And robotic systems (AeroSense, River Rover and others)into a unified system of long-term restoration of natural landscapes.