Vegetation

A bird's eye view: Multispectral and 3D monitoring of vegetation

Vegetation affects life on earth in many ways: plants play a key role in the earth's natural ecosystem. However, the systematic use of plants in agriculture and the timber industry is indispensable for supplying a constantly growing world population with food and energy. In this context, too, advanced technology can help to detect changes in vegetation and to use resources economically and sustainably. Today, agriculture and forestry already rely on modern sensor systems within the framework of concepts such as »precision farming« and »precision harvesting«. By means of satellite or air remote sensing, the condition of plants is observed via the so-called vegetation index; land and forest use plans are drawn up based on the measurement data. However, these data are very limited in their temporal and spatial resolution. To improve them, it was hitherto necessary to collect relevant data manually and thus inefficiently.

Lightweight Airborne Profiler LAP

The LAP, developed by Fraunhofer IPM, is an innovative monitoring system allowing vegetation areas to be measured and monitored fast and efficiently. The measuring unit, weighing less than 3 kilograms, can easily be mounted on light UAVs, thus enabling flexible, cost-effective and reliable monitoring of vegetation.

The LAP consists of a laser scanner, several cameras and optional positioning sensors such as GNSS or IMU. The system captures an area of several hundred square meters in less than ten minutes. Across the flight direction, the laser scanner generates up to 60 measuring profiles per second with 500 measuring points each. The precision of a single point measurement is around 1 cm.

The combination of multispectral camera data and geometric data can be used to derive very different, relevant parameters for the characterization of vegetation: the height of individual trees or entire forest stands, the segmentation of individual trees and crown diameters, the number of trees and stand density, the 3D coordinates of tree crowns, the classification of deciduous trees and conifers, the trunk diameter at breast height, the volume and growth of wood, biomass estimation or the delimitation of forest areas. This applies similarly to field plants.
 

Automated interpretation with 3D-AI

Today, the analysis of measurement data of large landscape areas is usually carried out manually or semi-automated. The Deep Learning Framework 3D-AI developed by Fraunhofer IPM offers a multiple of efficiency, resolution and reliability for data evaluation: The framework projects the recorded 3D scanner data reliably and accurately into the images of the color cameras. Each RGB image or multispectral image of the scene is assigned a corresponding depth channel. With the RGB-D(epth) data processed in this way and a trained artificial neural network, the data evaluation proves to be very robust against object variations, different shooting angles or varying lighting conditions.