Publications
13 results found
Schmidt L, Forkel M, Zotta R-M, et al., 2023, Assessing the sensitivity of multi-frequency passive microwave vegetationoptical depth to vegetation properties, BIOGEOSCIENCES, Vol: 20, Pages: 1027-1046, ISSN: 1726-4170
Schmidt L, Forkel M, Zotta R-M, et al., 2022, Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties
<jats:p>Abstract. Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter Vegetation Optical Depth (VOD), and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellites operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation status at large scales. VOD has been used to determine above-ground biomass, monitor phenology or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scale. Several VOD products exist differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as normalised difference vegetation index, leaf area index, gross primary production, biomass, vegetation height or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select certain VOD wavelengths for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) of Ku-, X- and C-bands of the harmonised VODCA dataset and level 3 L-band derived from SMOS and SMAP sensors. Within a multivariable regression random forest model for simulating these VOD signals, leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 0.95 of the variance (coefficient of determination). Thereby, the variance in L-band VOD is reproduced spati
Schmidt L, Forkel M, Zotta R-M, et al., 2022, Supplementary material to "Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties"
Kuhn-Régnier A, Voulgarakis A, Nowack P, et al., 2021, Quantifying the Importance of antecedent fuel-related vegetationproperties for burnt area using random forests, Biogeosciences, Vol: 8, ISSN: 1726-4170
The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence mayhelp to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediateimpact of climate, vegetation, and human influences in agiven month and tested the impact of various combinationsof antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global,climatological out-of-sample R2from 0.579 to 0.701, but theinclusion of antecedent vegetation conditions on timescales≥ 1 year had no impact on simulated burnt area. Currentmoisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were importantfor fuel build-up. The models also enabled the visualisationof interactions between variables, such as the importanceof antecedent productivity coupled with instantaneous drying. The length of the period which needs to be consideredvaries across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longertime periods (∼ 4 months), while moisture-limited regionsare more sensitive to current conditions that regulate fuel drying.
Kuhn- Regnier A, Voulgarakis A, Nowack P, et al., 2021, The importance of antecedent vegetation and drought conditions as global drivers of burnt areas, Biogeosciences, Vol: 18, Pages: 3861-3879, ISSN: 1726-4170
The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.
Kuhn-Regnier A, 2021, wildfires
This package facilitates the analysis of global gridded data. Iris is used internally to manage data and custom Dask algorithms enable efficient scaling of algorithms (including some from scikit-learn) across a cluster.This release is more robust and fully-featured than previous releases, with a new caching backend and additional support for data processing, for example.
Kuhn-Régnier A, Voulgarakis A, Nowack P, et al., 2021, Supplementary material to "Quantifying the Importance of Antecedent Fuel-Related VegetationProperties for Burnt Area using Random Forests", Biogeosciences, ISSN: 1726-4170
Kuhn-Regnier A, Jumelle M, Rajaratnam S, 2021, ALEPython
Calculation of ALEs (Accumulated Local Effects) in one and two dimensions.This is a robust technique - given correlated features - allowing for the visualisation of relationships fitted by machine learning models. The code can calculate and plot ALEs and approximate uncertainties.
Cocconi L, Kuhn-Regnier A, Neuss M, et al., 2021, Reconstructing the intrinsic statistical properties of intermittent locomotion through corrections for boundary effects, Bulletin of Mathematical Biology, Vol: 83, Pages: 1-17, ISSN: 0092-8240
Locomotion characteristics are often recorded within bounded spaces, a constraint which introduces geometry-specific biases and potentially complicates the inference of behavioural features from empirical observations. We describe how statistical properties of an uncorrelated random walk, namely the steady-state stopping location probability density and the empirical step probability density, are affected by enclosure in a bounded space. The random walk here is considered as a null model for an organism moving intermittently in such a space, that is, the points represent stopping locations and the step is the displacement between them. Closed-form expressions are derived for motion in one dimension and simple two-dimensional geometries, in addition to an implicit expression for arbitrary (convex) geometries. For the particular choice of no-go boundary conditions, we demonstrate that the empirical step distribution is related to the intrinsic step distribution, i.e. the one we would observe in unbounded space, via a multiplicative transformation dependent solely on the boundary geometry. This conclusion allows in practice for the compensation of boundary effects and the reconstruction of the intrinsic step distribution from empirical observations.
Kuhn-Regnier A, 2020, era5analysis
Package designed to ease processing of ERA5 data.The CDS API client is used to download data, which is then processed asynchronously to yield the mean or maximum, or the diurnal temperature range, for example.
Kuhn-Regnier A, 2020, fuel-build-up
First release of code related to the fuel build-up paper.The code is primarily organised into a set of Jupyter Notebooks, which perform dedicated functions, from RF model fitting to ALE plotting. Notebooks are separated into folders based on the specific model they relate to (i.e. which predictor variables are included).Parts of the code are designed to be run on the Imperial College London HPC system, e.g. RF model fitting, SHAP value calculation, and LOCO evaluation.
Kuhn-Regnier A, Cocconi L, Neuss M, 2020, bounded-rand-walkers
This is the first release of the software used in our paper investigating bounded random walks.We implemented an adaptive rejection sampling algorithm in both Python and C++, allowing the investigation of bounded random walks given user-specified intrinsic step distributions and convex geometries. Multiple binning techniques are used throughout in order to enable analysis of both 2D gridded and 1D radially-averaged data, and a custom integrator is used to achieve high numerical accuracy where needed.
Kuhn-Regnier A, Voulgarakis A, Harrison S, et al., 2020, The Importance of Vegetation Build Up for Burnt Area Seasonality
<jats:p> &lt;p&gt;Vegetation build up is a major controlling factor for wildfires globally. The exact nature of the dependency of wildfire activity on past vegetation productivity is still under debate, however. Given the potential future rise in conditions conducive to extremely damaging fires in many regions of the world, controlling factors like this need to be investigated urgently to better understand and manage especially extreme wildfire events.&lt;br&gt;To improve our understanding of wildfires and the advice given to policy makers, a comprehensive understanding of all contributing factors is required. Changes to land management can be controversial and thus concrete evidence is required to assess and modify longstanding management practices and regulations if needed.&lt;br&gt;We therefore used global satellite datasets extending from 2005 to 2011 to assess the relationship between burnt area and various biophysical variables. Vegetation proxy data included vegetation optical depth and the fraction of absorbed photosynthetically activate radiation. Different regions and time periods were analysed separately to isolate regional and temporal effects respectively. The relationship between pre-season vegetation productivity and burnt area was modelled as a regionally and temporally varying weighted sum of past monthly productivity proxies.&lt;br&gt;As expected, significant differences in fire regimes were found across biomes, signified for example by significant shifts in the seasonality of burnt area. Understanding these shifts in the seasonality of both burnt area and the accompanying temporal dependence on past vegetation growth is key to reproducing observed wildfire regimes in fire models. As these relationships were found to vary both temporally and regionally, judicious inclusion of biophysical variables in fire models coupled with algorithms able to capture these relationships i
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