Global Land Monitoring

Screenshot of the 2020 forest age visualization in Google Earth Engine.
Screenshot of the 2020 forest age visualization in Google Earth Engine. (CC BY 4.0 Besnard et al. (2021); Mapping global forest age from forest inventories, biomass and climate data, Earth Syst. Sci. Data, 13, 4881–4896, https://doi.org/10.5194/essd-13-4881-2021)
Drone deployment
(Photo: Johannes Wilk)
Measuring instrument
(Photo: Johannes Wilk)
Scientists reading out measurement data
(Photo: Johannes Wilk)
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Global Land Monitoring (GLM)

Picture Team
Group photo from the GLM science retreat, March 2025 (Photo: GFZ)

The Global Land Monitoring (GLM) group develops and applies innovative methodological approaches to improve systematic, large-scale monitoring of land dynamics worldwide. Our work bridges near-sensing and satellite-based Earth observation, focusing on time-series analysis and multi-sensor data integration through data science and big data analytics.

Our efforts are grounded in a global monitoring perspective and a strong commitment to national and international collaboration.

The biosphere plays a critical role in regulating Earth's carbon balance, water availability, and nutrient cycle of the Earth system. To monitor and understand these dynamics at scale, we rely on Earth Observation (EO): consistent, repeated satellite measurements that reveal environmental change over time. Our focus on large-scale EO enables us to track biosphere processes across regional to global extents, with particular attention to forests and their structural, functional, and land-use dynamics.

Building on Section 1.4 methodological focus, the LEO subgroup develops data-driven methods to integrate large-scale EO with ecological and carbon cycle science. We focus on understanding forest dynamics, including structural changes and land-use transitions, and how they affect and are affected by the carbon cycle. For this, we integrate various EO products at different scales and incorporate ground data such as National Forest Inventories.

Research Areas

  1. Forest Land Use and Land Cover Dynamics: We explore how to build robust maps of forest land use using EO and machine learning, and how regrowth patterns vary across land-use systems, including secondary, primary, and plantation forests.
  2. Vertical Structure and Forest Dynamics: We study how vertical forest structure, observed via spaceborne lidar like GEDI, influences biodiversity and carbon cycling. We also examine structural changes before and after disturbance and advance signal processing techniques for 3D forest mapping.
  3. Carbon Emissions and Policy Implications: We work on attributing deforestation-related carbon emissions to commodity supply chains, supporting policies like the EU Deforestation Regulation (EUDR). We also compare forest carbon flux estimates from global products and national datasets.
  4. Energy Efficiency of Data Analytics Workflows: We aim to optimise EO data workflows for scalability and energy efficiency, assess the environmental footprint of large-scale analytics, and explore trade-offs in computing strategies.

Projects

Integrating measurements of changing land surfaces aims to enhance the physical underpinnings and fundamental abilities to measure how and why land surfaces are changing. The availability of data from near-sensing (UAVs) and from new satellite sensors (i.e. ENMAP, CHIME, GEDI) now allows for a full exploration of the spectral and 3D dimension for the purpose integrated large-area monitoring. Long-term, global in-situ networks of spectral and structural vegetation measurements, and the integration of active sensors and the third dimension (i.e. from RADAR and LIDAR) are essential for such a comprehensive assessment of land characteristics and changing land surfaces that will complement current global optical remote sensing approaches building on simple vegetation indices and a broad set of global land cover classes only. 

In this context, our in-situ and proximal sensing activities develop and use a range of sensing techniques like terrestrial and UAV-borne laser scanning, UAV-borne hyperspectral sensing and automated monitoring technology (e.g. IoT). These data streams are used to produce high quality reference data at local scale across networks of sites to represent large areas and multiple environmental conditions for the calibration and validation of EO data. Furthermore, we take a role in coordination of international networks.

Projects

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