Resource Communication : ForestAz - Using Google Earth Engine and Sentinel data for forest monitoring in the Azores Islands (Portugal)

AIM OF STUDY: ForestAz application was developed to (i) map Azorean forest areas accurately through semiautomatic supervised classification; (ii) assess vegetation condition (e.g., greenness and moisture) by computing and comparing several spectral indices; and (iii) quantitatively evaluate the stocks and dynamics of aboveground carbon (AGC) sequestrated by Azorean forest areas. AREA OF STUDY: ForestAz focuses primarily on the Public Forest Perimeter of S. Miguel Island (Archipelago of the Azores, Portugal), with about 3808 hectares. MATERIAL AND METHODS: ForestAz was developed with Javascript for the Google Earth Engine platform, relying solely on open satellite remote sensing data, as Copernicus Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (multispectral). MAIN RESULTS: By accurately mapping S. Miguel island forest areas using a detailed species-based vegetation mapping approach; by allowing frequent and periodic monitoring of vegetation condition; and by quantitatively assessing the stocks and dynamics of AGC by these forest areas, this remote sensing-based application may constitute a robust and low-cost operational tool able to support local/regional decision-making on forest planning and management. RESEARCH HIGHLIGHTS: This collaborative initiative between the University of the Azores and the Azores Regional Authority in Forest Affairs was selected to be one of the 99 user stories by local and regional authorities described in the catalog edited by the European Commission, the Network of European Regions Using Space Technologies (NEREUS Association), and the European Space Agency (ESA).info:eu-repo/semantics/publishedVersio

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation.Peer reviewe

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing