Intelligent network of sensors, monitoring and digital management of mountain ecosystems
Concept
Living Mountain Observatory (LMO)— is a unified digital system for continuous monitoring, analysis and management of all processes in a projectDREVO Living Mountains.
It integrates thousands of smart sensors, drones, satellite data, robotic platforms, and artificial intelligence into a single digital ecosystem.
The main task of the system issee the state of the mountain ecosystem in real time and predict changes before critical situations arise.
The main principle:
It is impossible to effectively restore what cannot be accurately measured and understood.
Main tasks
Living Mountain Observatory provides:
continuous monitoring of the state of the mountains;
control of all nine cascades;
early detection of threats;
flood forecasting;
drought forecasting;
fire monitoring;
forest restoration control;
biodiversity assessment;
decision-making support.
System architecture
The system consists of several interconnected levels.
1. Ground-based sensor network
Installed throughout the entire catchment area.
Controlled by:
air temperature;
air humidity;
soil temperature;
soil moisture;
infiltration rate;
groundwater level;
consumption of springs;
stream flow;
water level;
water quality;
wind speed;
solar radiation;
amount of precipitation;
snow reserves;
snow depth;
slope movement;
soil deformation.
2. Hydrological stations
Placed:
on streams;
rivers;
springs;
infiltration basins;
floodplains;
river mouths.
Measured:
water consumption;
flow rate;
turbidity;
mineralization;
temperature;
pH;
electrical conductivity;
dissolved oxygen content.
3. Soil stations
Control:
humidity;
density;
temperature;
organic matter content;
carbon content;
activity of microorganisms;
mycorrhiza activity;
nitrogen content;
phosphorus content;
potassium content.
4. Biological monitoring
Observed:
tree growth;
development of shrubs;
condition of herbs;
fungal communities;
insects;
birds;
mammals;
invasive species;
rare species.
Used:
cameras;
acoustic stations;
automatic species recognition;
spectral methods.
5. Climate stations
Measured:
temperature;
humidity;
pressure;
wind speed;
wind direction;
solar radiation;
evaporation;
dew point;
probability of fog formation.
This data is used by the systemDREVO Cloud & Mist Systemfor local humidity control.
TREVO AeroSense Drone
Drones regularly scan the area.
Used:
LiDAR;
Ultra-high resolution RGB cameras;
multispectral cameras;
hyperspectral cameras;
thermal imagers;
gas analyzers (if necessary).
Main tasks:
digital elevation model;
biomass assessment;
forest condition;
search for fire sources;
erosion control;
discovery of new ravines;
humidity assessment;
monitoring the condition of plantings.
Satellite monitoring
Open and commercial satellite data are used.
Controlled by:
change in forest cover;
surface temperature;
vegetation index;
humidity;
snow cover;
change of coastline;
There will be a change.
DREVO Mountain Rover
Ground robots automatically perform:
equipment inspection;
sensor maintenance;
inspection of hydraulic structures;
delivery of devices;
conducting local measurements.
Artificial Intelligence DREVO AI
AI analyzes information from all sources.
He is capable of:
predict floods;
predict droughts;
identify forest degradation;
calculate the risk of fires;
predict erosion;
identify a decrease in spring flow;
calculate the efficiency of restoration;
propose optimal measures.
Mountain Digital Twin
All data is fed into the digital twin.
It models:
hydrology;
climate;
forest growth;
soil development;
biodiversity change;
climate change scenarios;
efficiency of engineering solutions.
This allows testing various scenarios without interfering with the natural environment.
Control of nine cascades
Cascade 1
CONTROL:
precipitation;
snow;
fog;
wind speed.
Cascade 2
CONTROL:
infiltration;
erosion;
slope stability.
Cascade 3
CONTROL:
forest growth;
humidity;
humus formation.
Cascade 4
CONTROL:
stream;
sediments;
flow rates.
Cascade 5
CONTROL:
swimming pools;
groundwater;
infiltration.
Cascade 6
CONTROL:
floodplains;
steel;
seasonal flooding.
Cascade 7
CONTROL:
water quality;
salinization;
states of deltas.
Cascade 8
CONTROL:
coastline;
dune;
lagoon.
Cascade 9
CONTROL:
sea temperatures;
sea level;
state of marine ecosystems.
Automatic alerts
The system automatically reports:
fire risk;
probability of landslide;
approaching flood;
the beginning of erosion;
the emergence of invasive species;
water pollution;
equipment malfunctions;
deterioration of forest conditions.
Digital passport
Each object receives its own digital passport.
It includes:
coordinates;
photographs;
observation history;
technical condition;
environmental parameters;
AI recommendations;
service log.
Passports are created for:
trees;
forest areas;
springs;
rivers;
slopes;
steel;
sensors;
hydraulic structures;
nature conservation areas.
Integration
Living Mountain Observatory is the digital core of the entire DREVO platform.
It unites:
DREVO Mountain Sponge;
Mountain Springs Recovery;
Mountain Forest Corridors;
DREVO Cloud & Mist System;
Mountain Digital Twin;
TREVO AeroSense Drone;
WOOD AI;
DREVO Mountain Rover;
WOOD BioFire System;
DREVO Smart Agriculture.
Expected results
After the implementation of the system it is expected:
continuous monitoring of the state of the entire mountain ecosystem;
early detection of natural hazards;
reducing damage from fires, floods and erosion;
increasing the efficiency of forest restoration;
optimization of water resource use;
accumulation of a long-term scientific database;
support for adaptive project management.
Project mission
Living Mountain Observatoryis the digital nervous system of the projectDREVO Living Mountains. If Mountain Spongeretains water,Mountain Forest Corridorsform a living ecological network, andMountain Springs Recoveryrestores the underground water cycle, thenLiving Mountain ObservatoryEnsures their continuous monitoring, analysis, and coordination. By combining sensors, unmanned systems, satellite observations, and artificial intelligence, the project is able not only to respond to changes but also to predict them, managing the mountain ecosystem based on objective data and long-term development models.