Classification of Forest Tree Species Using Remote Sensing Technologies: Latest Advances and Improvements

Dear Colleagues,

Knowledge of the diversity, distribution, abundance, or absence of tree species is crucial for responsibly and sustainably managing resources, conserving/protecting species in a timely manner, ensuring biodiversity, sequestering carbon, and effectively promoting ecosystem health.

Remote sensing technologies provide a unique opportunity as they can be used to create an instant portrait of a forest area as well as the ability to monitor forests through repeat data acquisitions at a comparatively low cost. The last few decades have seen great advances in this technology, particularly high spectral and spatial resolution imaging which has mitigated the shortcomings of the traditional methods to rapidly map tree species at various spatial scales. However, several challenges remain. For example, spectral similarity constrains the discernibility and distinction between similarly looking tree species, e.g., balsam fir and white spruce in mixed natural forests, and high canopy densities can obscure our view and hence prevent us from completely inventorying the stands, to state a few. Researchers note that a lack of good quality field reference data that are well geopositioned, the limited portability of models to new regions for generalization or efficient implementation, and the lack of standardized methods to enable comparisons are some of the key obstacles.

Emerging modern remote sensing technologies that can enable a high frequency of visits to an area (e.g., ultra-high-resolution satellites like WorldView 3, SkySat, VLEOs (very low orbital satellites) that will soon be launched (Stingray, Albedo), small satellites (GHOSt hyperspectral microsatellite), UAVs (below cloud flights), democratized acquisitions, technologies with high spectral bands (hyperspectral satellites such as PRISMA, EnMap, ALOS-3), and advanced techniques) have received increasing attention from the scientific community and shown great potential in recent years. This Special Issue aims to disseminate state-of-the-art research and applications which use these emerging remote sensing techniques for hydrological studies. Topics for this Special Issue include, but are not limited to:

• Reviews of state-of-the-art models, algorithms, methods, products, and applications of remote sensing for tree species classification;
• The application of new analysis methods, including machine and deep learning approaches;
• The standardization of data acquisition and classification for monitoring and generalization;
• Close-range sensing observations to allow for the use of attribute estimation in broader scale models and for high-resolution monitoring at the site scale;
• Combining different close-range sensing data and approaches to create new knowledge;
• The synergetic use of data acquired from close-range sensing with airborne and satellite remote sensing observations for large-area applications, e.g., through automated in situ investigations;
• Insights into the use of close-range sensing systems and analysis approaches to further our understanding of terrestrial carbon functioning, climate change, CO₂ absorption, and biodiversity.

Dr. Udayalakshmi Vepakomma
Prof. Dr. Costas Armenakis
Guest Editors

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• tree species classification
• remote sensing
• machine learning
• data standardization
• cross platform portability
• bench marking