Community Research and Development Information Service - CORDIS

Periodic Report Summary 1 - FORESTMAP (3D Forest Structure Monitoring and Mapping)

Summary description of the project objectives

The aim of ForeStMap is to develop an innovative and original methodology to derive 3-D information on forest structure, based on a multi-sensor remote sensing approach. The specific objectives are to: 1) develop a methodology for integrating active (LiDAR & Radar) and passive (optical / near-infrared) RS data to estimate forest structure parameters (biomass, CBD and FC); 2) provide spatially-explicit estimates of forest structure at regional scale based on an up-scaling approach; and 3) to provide spatial information on the uncertainty of the estimates at each scale.

Description of the work performed since the beginning of the project

During the first phase of the project (01/01/2015 – 31/12/2016) I have carried out different training and research activities aimed to fulfil the objectives of the project.
The research activities carried out include: 1) development of the core of the methodology to provide forest structure estimates using a multi-sensor multi-scale approach over Mediterranean areas. A two-step methodology has been proposed to estimate forest structure and monitor the dynamics of forest aboveground biomass and the impact of disturbance factors such as megafires, using airborne LiDAR and multispectral imagery; 2) Uncertainty quantification of our final forest structure estimates using parametric and non-parametric approaches. 3) The methodology has been extended to extrapolate forest structure over boreal-temperate transition forests in North America, using airborne LiDAR and satellite L-band SAR data; 4) Evaluation of data models and impact of point density on LiDAR-derived metrics and forest aboveground biomass estimations; and 5) Assessment of the potential of imaging spectroscopy to predict forest structure using structural properties derived from LiDAR data.
The results of this research have been disseminated in different ways including talks in conferences and seminars and publication of the results in different scientific journals.
The training activities realized include: 1) 2-day Software Carpentry workshop at Caltech; 2) 1-week summer course at UC Santa Barbara: Spatio-temporal analysis and big data processing using free and open source software; 3) Radar: including different tutorials and seminars; 4) Ecological and Climate modelling: weekly seminars organized by the Carbon Club at NASA-Jet Propulsion Laboratory.

Description of the main results achieved so far

After concluding the outgoing phase of the project, significant advances towards the accomplishment of the objectives of ForeStMap have been done.
• The core of the methodology to provide multi-scale estimates of forest structure and its dynamics by the integration of different remotely sensed and ancillary data along with the uncertainties associated has been developed.
• The methodology has been tested in different ecosystems, from Mediterranean to temperate-boreal transition forests.
• We have shown the impact of the point density of the LiDAR data and the data model used (canopy height model - raster vs. echo-based) on the metrics derived and the retrieval of biomass.
• We demonstrated that structural information can be retrieved using imaging spectroscopy data. This result has been extended to larger regions and to estimate important structural canopy properties.

Expected final results and their potential impact and use (including the socio-economic impact and the wider societal implications of the project so far)

ForeStMap is expected to provide a significant progress towards the development of accurate global products of forest structure and its dynamics, for better understanding the carbon cycle, to design forest sustainable management strategies and mitigate greenhouse gas emissions from deforestation and forest degradation, as well as to halt the loss of biodiversity and to reduce fire risk. The methods developed will reduce the uncertainty in the carbon released by wildfires as well as determination of burning efficiency factors, which will in turn reduce the uncertainty of global models and improve input information used for air quality and carbon cycle models. Likewise, methods developed by ForeStMap will provide accurate spatially explicit information on canopy fuel properties that will be readily integrated into national programs like the LANDFIRE project. This information is critical to develop adequate strategies of fuel management.
Our results on the impact of point density and data model used on the LiDAR forest aboveground biomass estimates are very significant for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD+ due to its implications for data acquisition costs.
Likewise, investigating the ability of satellite SAR data to extrapolate LiDAR-based canopy height information is of paramount importance for upcoming satellite LiDAR missions like the ICESat-2/ATLAS ( or the Global Ecosystem Dynamics Investigation (GEDI;, as well as SAR missions such as the BIOMASS ( and the NASA-ISRO Synthetic Aperture Radar (NISAR- Despite the LiDAR missions will provide global coverage (GEDI: ± 51.6° latitude; ICESAT2: ± 88° latitude), this systems are multi-beam samplers that will not provide continuous coverage; therefore, it is necessary to develop data fusion algorithms to extrapolate structure estimates using other remotely sensed data.
Finally, our results on the potential of imaging spectroscopy for canopy structure classification are of significance for future satellite missions like HyspIRI or EnMAP. In addition, a better understanding of the effect of forest structure on the signal gathered by hyperspectral sensors, will help improve the retrieval of canopy biochemical and biophysical properties.

Reported by

United Kingdom


Life Sciences
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