Visualising agricultural landscapes with APPN GOBI™

This video, created from 3D point clouds captured with the GOBI’s Ouster LiDAR sensor and open source software tools, showcases high-resolution 3D structural data capture of:

• Wheat pre-breeding trials funded by CIMMYT and the Grains Research and Development Corporation (GRDC).
• Cotton trials supported by the Cotton Research and Development Corporation (CRDC).
• Pasture and riparian zones being used for Meat and Livestock Australia (MLA) funded research.
• Native forest patches within agroecosystems that support teaching and learning at the University of Sydney.

More than just a technical demonstration, this visualisation represents a leap forward in how we understand and monitor agricultural systems. LiDAR provides an unparalleled view into the physical structure of crops and landscapes, allowing researchers to quantify plant height, estimate biomass, and assess canopy architecture with remarkable precision. 

From the raw point clouds, the APPN team can generate digital terrain models (DTMs) of the underlying ground surface and digital surface models (DSMs) for the uppermost layers of vegetation or other features. These layers are the key to quantifying vegetation height, modelling hydrological processes, and integrating structural data with other spatial and spectral datasets. 

The resulting metrics are critical for evaluating crop performance, especially in breeding and agronomic trials where subtle differences in growth and structure can signal important genetic or environmental interactions.

The LiDAR point clouds can also be used to construct detailed 3D models for exploring canopy architecture, landscape features, and vegetation structure at high spatial resolution. Being able to map terrain and vegetation in three dimensions is ideal for assessing agroecosystem health indicators such as erosion, creek morphology, and riparian zone integrity. These are essential for understanding landscape function and resilience, particularly in the context of sustainable land management and restoration efforts.

The Sydney Informatics Hub has been instrumental in supporting this analytical work, contributing expertise in spatial data science and helping to develop workflows that translate these complex structural datasets into meaningful agroecological and agronomic insights.

Importantly, the GOBI platform is not limited to LiDAR. It also captures high-resolution RGB imagery and VNIR hyperspectral data using advanced multimodal systems and the data processing pipelines that transform raw data into geometrically and radiometrically corrected products. Development of this capability has been led by US-based company GRYFN, whose support has been critical as APPN deploys these tools across our national Node network.

By visualising and quantifying the structure and function of crops and landscapes, we are building tools that support innovation in breeding, agronomy, ecosystem monitoring, and sustainability assessment. This has a truly transformative potential for agricultural science.

To learn more about our scanning, phenotyping and visualisation tools – and how they could be used in your research or industry projects – contact us. 

23 October 2025