Multi-Sensor Data and Time-Series Analysis: Advancing Mapping and Climate Impact Assessment in Forest and Grassland

Dear Colleagues, 

Forests and grasslands are foundational terrestrial ecosystems that support biodiversity, regulate carbon cycles, and sustain livelihoods worldwide. However, these ecosystems face unprecedented threats from climate change, including droughts, altered phenological patterns, and increased disturbance frequency, compounded by anthropogenic pressures from intensive land use, deforestation, and overgrazing. Accurate monitoring of their structure, function, and climate responses has become critical for informing conservation strategies, sustainable management practices, and climate adaptation policies.

The integration of multiple sensors, combining optical imagery (Landsat, Sentinel-2, PlanetScope), SAR, LiDAR, and UAV systems, represents a transformative approach that capitalizes on complementary strengths to produce more comprehensive ecosystem assessments. Multi-sensor fusion enhances spatial and temporal coverage, enables cross-scale validation of targeted metrics, and provides robust long-term time-series by leveraging both decades-old archives and recent high-resolution data. The synergistic use of different sensor types allows for optical data to capture seasonal dynamics, SAR to penetrate cloud cover for structure and moisture information, and LiDAR to deliver precise three-dimensional measurements. Machine learning further amplifies these benefits through advanced data processing and improved climate–ecosystem modeling.

This multi-sensor paradigm is particularly valuable for addressing critical challenges such as understanding how fine-scale variability propagates across spatial resolutions, standardizing data fusion methodologies, managing the very large data volumes associated with high-resolution imagery, and bridging the gap between satellite-derived metrics and ground-based ecological measurements.

This Special Issue is a joint Special Issue of the journal Forests (ISSN: 1999-4907) and Agriculture (ISSN: 2077-0472). It aims to consolidate and advance research that leverages multi-sensor data and time-series analysis to improve ecosystem mapping and climate impact assessment for forests and grasslands worldwide. Our core goal is to bridge technical innovations in remote sensing with applied ecological insights, supporting evidence-based forestry management and climate resilience planning. The scope encompasses both technical methodologies and ecological applications, with no geographic restrictions (e.g., Mediterranean ecosystems, tropical rainforests, temperate grasslands, boreal forests). Key thematic areas include the following:

• Multi-sensor fusion strategies (e.g., optical/SAR/LiDAR/UAV) for mapping forest/grassland structure (e.g., species composition, canopy density) and function (e.g., productivity, phenology), including cross-scale validation and harmonization approaches;
• Time-series analysis to quantify climate-driven changes (e.g., drought-induced effects, phenological shifts, post-disturbance recovery), leveraging both long-term archives and high-resolution recent data;
• ML/AI approaches to enhance data processing (e.g., cloud removal, gap-filling) and model climate-ecosystem interactions;
• Linking sensor-derived metrics to ground-based measurements and ecological outcomes (e.g., carbon sequestration, biodiversity dynamics, soil moisture), including validation strategies using PhenoCams and flux towers;
• Addressing ecosystem-specific challenges (e.g., fragmented grasslands, dense tropical forests, fire-prone landscapes).

We invite original research articles, review papers, and technical notes that advance theory, methodology, or applications in multi-sensor data and time-series analysis for forests and grasslands. This Special Issue will aid researchers, forest managers, and policymakers, balancing technical rigor with practical relevance for conservation and sustainable land use.

Dr. Laura Stendardi
Dr. Rocco Pace
Dr. Gianluca Pappaccogli
Guest Editors

Manuscript Submission Information

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