Scope
Land degradation (LD) remains one of the most critical global challenges, with severe implications for food security, ecosystem functioning, biodiversity, and human livelihoods. International policy frameworks—including the United Nations Convention to Combat Desertification (UNCCD), the Sustainable Development Goals (especially SDG 15.3), and regional land rehabilitation initiatives—rely increasingly on spatially explicit information to support monitoring, planning, and targeted mitigation.
Remote Sensing provides an unprecedented means for observing land dynamics across space and time. Continuous advances in sensor platforms, spatial and temporal resolution, open-access archives, cloud-based analytical environments, and machine learning now enable integrative LD assessments at local, regional, and global scales. Nevertheless, the operational use of Remote Sensing in LD assessments continues to face several persistent challenges, such as:
- heterogeneity of LD definitions and conceptual frameworks
- scale-dependencies of degradation processes and drivers
- mismatch between socio-economic and environmental scales of assessment
- difficulty in separating short-term climatic variability from long-term degradation
trends - limited availability of in situ reference data for validation
- insufficient methodological harmonization and transferability.
Moreover, major LD manifestations—declines in vegetation productivity, bush encroachment, soil salinization, rangeland degradation, or loss of soil carbon—are often subtle, gradual, and heterogeneous in space. Addressing these complexities requires multi-source Earth Observation data combined with field-based evidence and spatially explicit modelling.
This special session aims to bring together researchers from academia, applied researchinstitutions, and international organizations to exchange current advances in Remote Sensing-based LD monitoring, to discuss challenges in multi-scale assessment, and to highlight application-oriented solutions relevant for science, policy, and land management.
Objectives
- Present recent EO methods for LD mapping/monitoring.
- Harmonize cross-scale assessment and validation approaches.
- Integrate biophysical and socio-economic data for driver attribution.
- Showcase policy-relevant applications (e.g., SDG 15.3 reporting, rehabilitation
planning).
Topics
- Methods & variables: vegetation productivity/cover decline; optical & SAR time
series (MODIS, Landsat, Sentinel-1/2); salinity/soil/biophysical indicators; NPP and
carbon-related metrics. - Multi-/cross-scale assessment: aggregation/disaggregation; parcel-to-landscape
linkages; accuracy impacts of scale; scale transfer. - Drivers & attribution: separating climatic variability from human pressures;
proximate vs. underlying drivers; ecosystem service impacts. - Data integration & modelling: fusion of optical/SAR/hyperspectral/LiDAR; AI/ML
for classification, regression, and trend estimation; coupling EO with process-based
models. - Validation & uncertainty: in situ strategies; reference datasets; uncertainty
quantification; transferability across ecosystems. - Applications & policy: prioritizing rehabilitation; payment for ecosystem services;
national LD accounting and SDG 15.3 workflows.
Organisers
Prof. Dr. Olena Dubovyk, University of Hamburg