a. Change detection and time series analyses.
b. Natural and human induced disturbances: harvesting, forest fires, storms,
insects, disease, drought.
c. Monitoring regeneration, regrowth, succession, encroachment, gap dynamics,
and invasive species.
a. Use of remote sensing for the analysis of forestry CO2 emissions reduction.
b. Analysis of deforestation, degradation and increased stocks.
c. Estimates of reference levels of forest carbon.
d. Models of forest biomass and carbon.
e. Tools for monitoring, reporting and verification of forest carbon emissions.
f. Supporting the implementation of adaptation and mitigation of climate change.
a. Use of remote sensing in the main forest formations in the region.
b. Amazon rainforest.
c. Southern forests.
d. Central America rainforests.
e. Latin American mediterranean forests.
f. Island forest formations.
a. Approaches for mapping forest types, species, structure, biodiversity indicators
and ecosystems services.
b. Space biomass estimates, height, growing stock.
c. Remote sensing guided statistical estimation, uses of inventory data, sampling,
d. Uncertainty analysis.
a. Use of remote sensing and geo-spatial analysis supporting forest management
b. Forecasts productivity.
c. Models of biodiversity and habitat analysis.
a. Methods based on optical, microwave and LiDAR data.
b. Classification algorithms, continuous variable modeling.
c. Radiometric and geometric accuracy and precision.
d. Compositing and mosaicking, objects vs. pixels, visualization.
e. Multiscale analysis , data fusion
f. Radiative transfer modeling, 3-dimensional modeling.
g. Radar interferometry, aerial photogrammetry.
h. UAV experimental applications.
Abstracts will be peer-reviewed by the members of the Scientific Committee.
For each contribution accepted as oral or poster presentation at least one of the Authors is expected to attend to ForestSat-2016.
Each ForestSat2016 attendee can present a maximum of two contributions (orally, in a poster session or a mixture of both).
Please submit your paper in MS Word through the form from your user account or directly by email to email@example.com.
Important Note: Copies of overheads or PowerPoint slides are NOT ACCEPTABLE as your written paper.
ForestSat 2016 is glad to host several courses related to the use of remote sensing applied to forestry.
Classrooms are organized inside the conference building and are finalized to work on real data. For this reason participants need to bring their own laptops.
The cost of each course is US$30 per person. The participation to the courses is strictly restricted to regularly registered ForestSat 2016 participants.
Title: Forest Canopy Height Mapping with Radar Interferometry
Instructors: Naiara Pinto, Marc Simard, Michael Debina.
Maximum number of participants: 30
Forest canopy height is an essential biophysical parameter for monitoring forest ecosystems. Under specific observation scenarios, Synthetic Aperture Radar sensors can be used to produce wall-to-wall canopy height maps through a technique called Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR). Data amenable for PolInSAR have been collected by US American and European airborne sensors, and are expected to be available from ESA’s BIOMASS radar mission, planned to launch in 2020.
This workshop presents a first introduction of the PolInSAR technique. Students will learn basic SAR concepts and work on a hands-on activity with simulated SAR data. We will cover the following topics:
1) Typical scattering mechanisms at forest sites
2) Polinsar sensor observation strategy
3) Canopy height estimation Random Volume over Ground model
4) Uncertainty sources: temporal decorrelation and spatial heterogeneity
5) Examples from temperate, tropical, and boreal forest
6) Synergies with liar-derived metrics
Instructors: Martin Isenburg.
Maximum number of participants: 60
The LiDAR course will focus on how to create Canopy Height Models and plot as well as raster based forestry metrics such as canopy cover, as well as percentiles, strata, kurtosis, skewness, standard deviation, etc for both normalized heights as well as intensities. The focus of the course would be the many important choices that need to be made and their impact on the resulting output. The course will also emphasize the importance of quality checking the raw LiDAR obtained before starting to produce the needed outputs.
(1) Quality Checking
(2) Preprocessing LiDAR
(3) Derivative Generation
Title: Design of Forest Inventories based on Remote Sensing Data
Instructors: Günther Bronner, Umweltdata Austria.
Maximum number of participants: 50
The proposed workshop would bring the following topics, combined with lots of examples from real projects:
Title: Detection of Larch (Fitzroya cupressoides) by Hyperspectral imaging
Instructors: Pablo Cruz, Jesús Torralba, Paulina Vidal, Frederick Keusch, OTERRA, Chile.
Maximum number of participants: 50. Language: Spanish
During this practical exercise, students will be able to identify Larch trees, through remote sensing and hyperspectral images in the Tagua Tagua Park. The images were captured with the hyperspectral VNIR sensor HySpex 1600.
The topics of the course are:
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