70,557 research outputs found
Integrating remote sensing datasets into ecological modelling: a Bayesian approach
Process-based models have been used to simulate 3-dimensional complexities of
forest ecosystems and their temporal changes, but their extensive data
requirement and complex parameterisation have often limited their use for
practical management applications. Increasingly, information retrieved using
remote sensing techniques can help in model parameterisation and data
collection by providing spatially and temporally resolved forest information. In
this paper, we illustrate the potential of Bayesian calibration for integrating such
data sources to simulate forest production. As an example, we use the 3-PG
model combined with hyperspectral, LiDAR, SAR and field-based data to
simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and
SAR data are used to estimate LAI dynamics, tree height and above ground
biomass, respectively, while the Bayesian calibration provides estimates of
uncertainties to model parameters and outputs. The Bayesian calibration
contrasts with goodness-of-fit approaches, which do not provide uncertainties
to parameters and model outputs. Parameters and the data used in the
calibration process are presented in the form of probability distributions,
reflecting our degree of certainty about them. After the calibration, the
distributions are updated. To approximate posterior distributions (of outputs
and parameters), a Markov Chain Monte Carlo sampling approach is used (25
000 steps). A sensitivity analysis is also conducted between parameters and
outputs. Overall, the results illustrate the potential of a Bayesian framework for
truly integrative work, both in the consideration of field-based and remotely
sensed datasets available and in estimating parameter and model output uncertainties
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
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