Welcome to Higher Research Seminar 241115
Postat den 6th November, 2024, 12:45 av Diana Unander
When? Friday November 15th 14-16
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the PhD-seminar via this link by https://forms.gle/GdaiE6W6J1RLPWa7A November 13th (especially important if you plan on attending onsite so we have fika for everyone)
Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Tower-based radar observations of sub-daily water dynamics in boreal forests – Johan Fransson
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – Enhancing Forest Attribute Prediction Using ResNet and DeepLab Architectures with Airborne Laser Scanning Data – Shafiullah Soomro
15.50 -16.00 Sum up and plan for the December seminar
Abstracts
Abstracts
Tower-based radar observations of sub-daily water dynamics in boreal forests – Johan Fransson
Radar remote sensing observations are predominantly affected by the concentration and spatial distribution of water in natural scenes. This motivates the utilization of high-resolution spaceborne radar observations for monitoring the water status of vegetation and the impacts of climate change on forests globally. While current satellite-based synthetic aperture radar observations are limited to temporal resolutions of days, tower-based radar observations of forests are capable of capturing detailed sub-daily physiological responses to variations in soil water availability and meteorological conditions. Such experiments demonstrate the scientific value of prospective sub-daily space-borne observations in the future.
The BorealScat tower-based radar experiment conducted in southern Sweden from 2017 to 2021 has captured various ecophysiological phenomena in a boreo-nemoral forest, including water stress and degradation induced by spruce bark beetles (Ips typographus). To gain a deeper insight into the sub-daily impacts of forest water dynamics on radar observations, the BorealScat-2 tower-based radar experiment was initiated in a boreal forest, located in northern Sweden in 2022. Along with in-situ sensors characterizing the water status on the tree level and an eddy-covariance flux tower, this initiative aims to compile a comprehensive and open dataset. The goal is to enhance our understanding and modelling of the relationship between traditional ground-based forest information, eddy-covariance flux measurements and radar remote sensing observables.
The data gathered by BorealScat-2 stands out as the most radiometrically precise high-resolution time series ever recorded in forest environments, resolving the subtle water content-induced signatures in radar measurements. Preliminary findings from the 2022 growing season, highlight the detectability of a diurnal radar signature across all conventional radar remote sensing bands (i.e. C-, L- and P-band). Moreover, metrics akin to tree water deficit, as measured by high-resolution point dendrometers, can be derived from interferometric radar observations. The fine temporal resolution of the data also unveils distinct signatures corresponding to intercepted precipitation in time series measurements. These findings underscore the need for sub-daily observations from space-borne satellites to monitor vegetation water status.
Enhancing Forest Attribute Prediction Using ResNet and DeepLab Architectures with Airborne Laser Scanning Data – Shafiullah Soomro
This study explores the application of advanced deep learning architectures, including ResNet and DeepLab, in conjunction with Airborne Laser Scanning (ALS) data for predicting forest attributes in Sweden. Utilizing a high-precision Digital Elevation Model (DEM) generated from ALS surveys conducted between 2016 and 2020, we integrated raster data and laser metric data, including point clouds, RGB imagery, and infrared imagery. We employed pre-trained model architectures, leveraging Transfer Learning to enhance model performance on a dataset comprising approximately 18,435 plots from the Swedish National Forest Inventory (NFI). The models were trained to predict key forest metrics such as stem volume, basal area, mean tree height, and mean stem diameter. Performance was evaluated through Root Mean Square Error (RMSE) calculations, revealing significant advancements over traditional modeling approaches. The results underscore the potential of employing deep learning techniques for improved forest planning and management in Sweden.
Det här inlägget postades den November 6th, 2024, 12:45 och fylls under General