Intelligent system for monitoring mountain ecosystems

Intelligent system for monitoring mountain ecosystems

Living Mountain Observatory (LMO)— is a unified network for monitoring and managing the projectDREVO Living Mountains, which combines sensors, drones, satellite data, robotic platforms and artificial intelligence to continuously monitor the health of mountain watersheds.

The main task is to move from eliminating the consequences toearly detection of changesand data-driven ecosystem management.

"You can't effectively restore what you can't measure."

Main objectives

Forest monitoring.

Monitoring of springs.

River monitoring.

Soil monitoring.

Biodiversity monitoring.

Erosion control.

Early detection of fires.

Control of illegal logging.

Air quality monitoring.

Climate change control.

System architecture

Satellites

│

TREVO AeroSense Drone

│

Living Mountain Observatory

│

├── Ground stations

├── Soil sensors

├── Spring stations

├── River stations

├── Weather stations

├── Cameras

├── Acoustic stations

├── Insect traps

├── Camera traps

├── River Rover

└── Mountain Digital Twin

Main subsystems

1. Water Observatory

CONTROL:

water level;

flow rate of springs;

groundwater level;

temperatures;

turbidity;

pH;

mineralization;

dissolved oxygen;

nitrates;

phosphates;

electrical conductivity.

2. Forest Observatory

CONTROL:

tree growth;

crown conditions;

drying out;

diseases;

pests;

fallen tree;

biomass;

age of the forest.

3. Soil Observatory

Measured:

humidity;

temperature;

density;

organic matter;

humus;

pH;

carbon content;

erosion;

infiltration;

activity of soil biota.

4. Biodiversity Observatory

Observation for:

birds;

mammals;

amphibians;

reptiles;

insects;

pollinators;

mushrooms;

lichens;

rare plants.

5. Climate Observatory

Measured:

air temperature;

humidity;

wind speed;

precipitation;

solar radiation;

evaporation;

snow;

snow depth.

6. Fire Observatory

CONTROL:

temperatures;

smoke;

infrared radiation;

moisture content of the forest litter;

rate of fire spread.

7. Landslide Observatory

Monitoring:

slope movements;

cracks;

without it;

mudflows;

rockfall.

8. River Observatory

CONTROL:

stream;

banks;

sediments;

depths;

flow speeds;

pollution;

spawning grounds.

Technologies used

TREVO AeroSense Drone

Performed by:

LiDAR scanning;

multispectral imaging;

thermal imaging control;

photogrammetry;

erosion detection;

control of forest roads.

WOOD River Rover

Used for:

river surveys;

bottom mapping;

assessment of the riverbed condition;

search for contamination;

control of hydraulic structures.

Camera trap

Control:

large animals;

nocturnal activity;

migration.

Acoustic stations

Determine:

birds;

bats;

insects;

amphibians;

noise of equipment;

illegal logging.

Automatic weather stations

They transmit data every few minutes.

Sensor network

Springs

Measured:

debit;

temperature;

water quality.

Rivers

CONTROL:

level;

consumption;

turbidity;

pollution.

Soil

Measured:

humidity;

temperature;

electrical conductivity.

Forest

Measured:

tree growth;

movement of trunks;

wood moisture content.

Mountain Digital Twin

All data is fed into the digital twin.

It stores:

measurement history;

cards;

photographs;

relief models;

the condition of each section.

WOOD AI

Artificial intelligence analyzes:

forest change;

change of springs;

climate change;

risk of fires;

risk of erosion;

animal migration;

biodiversity dynamics.

Automatic alerts

The system reports:

decrease in the spring flow rate;

drying up of the stream;

forest fire;

illegal logging;

water pollution;

mudflows;

mass drying of trees;

pest outbreaks.

Integration with DREVO

Stages of implementation

Stage I

mapping;

installation of base stations;

creation of a digital model.

Stage II

connecting sensors;

launch of unmanned monitoring;

River Rover integration.

Stage III

launch of artificial intelligence;

risk forecasting;

automatic notifications.

Stage IV

complete digital model of the catchment;

autonomous control;

international exchange of environmental data.

Expected results

early detection of environmental problems;

reducing damage from fires and erosion;

restoration of springs;

improving water quality;

biodiversity conservation;

support for scientific research;

efficient management of natural resources;

decision making based on objective data.

The main principle

Living Mountain Observatory transforms mountains into a continuously monitored living system.

In combination withMountain Digital Twin, WOOD AI, TREVO AeroSense Drone And WOOD River Roverthis network createsdigital nervous system of a mountain catchmentIt allows us to monitor the state of ecosystems in near real time, predict changes, and take timely measures to preserve water, forests, and biodiversity.

Mountain Digital Twin

DREVO Mountain Digital Twin (MDT)