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High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany V.6
No full textOpen access publication under review.
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Abstract:
Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.
We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomassmodels. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.
Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validationwith estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation ofmodel prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.
Data:
This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error
Current and future European potential vegetation types V1.1
No full textThis dataset contains Potential Natural Vegetation (PNV) estimates for the European continent at 1km grain size. Estimates are made for six different vegetation types following the MAES Ecosystem classification at level 1. The predictions have been made through an ensemble of Bayesian Habitat distribution models available through the ibis.iSDM package (Jung 2023). For more information on the methodology, original data and used covariates, please see the accompanying preprint (Jung 2024)
LAMASUS - Response curves for changes in forest biomass carbon stocks following de-intensification of managemet across Europe
No full textAs part of the knowledge base developed within the EU project LAMASUS (deliverable D5.1) to quantify the climate impacts of land use management (LUM) changes, we present an ensemble of response functions that describe the effects of forest management de-intensification on forest biomass carbon stocks. These functions are supported by spatially explicit maps of model coefficients at a 0.5° (~50 km) resolution covering the European Union (EU27+UK, excluding overseas territories)
Global Pasture Watch - Annual buffalo density layers at 1-km for 2000–2022 (including 95% prediction interval)
No full textGlobal annual layers of buffalo density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 313,604 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution).
Raster cell values represent heads km2 including:
Mean predicted values (_m_)
Upper prediction interval based on 97.5th percentiles (_p.975_)
Lower prediction interval based on 2.5th percentiles (_p.025_)
Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.
Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.
In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine
IAM_COMPACT_Study_8_Trade
No full textThis dataset contains the underling raw data of IAM COMPACT "Study 8 – Trade dynamics"
Global Pasture Watch - Annual maps of potential land for livestock production at 1-km for 2000–2022 (including production systems)
No full textMaps produced by per-pixel integration of cultivated and natural/semi-natural grassland (Parente et al., 2024) and cropland extent (Potapov, et al., 2022) considering fractions of land use inside 1 km2 grid cell. For this, a global map of agricultural production systems (Venier-Cambron, et al., 2024) was used to establish fraction thresholds for livestock supported by:
Natural/semi-natural grassland systems,
Multivated grassland systems,
Mosaic cropland and grassland systems, and
Cropland systems.
The annual time series (2000-2022) of integrated maps provided 1 km spatial resolution ranging from 1–100%, with potential land used for livestock production
Data and code for: Islands on the brink: Mapping global threat exposure in a changing world
No full textThis repository contains the data and code necessary to reproduce the analyses described in the paper "Islands on the brink: Mapping global threat exposure in a changing world." The objective of this study is to assess the exposure of worldwide islands to global change, encompassing three main threats to biodiversity: climate change, biological invasions, and land-use change. This repository contains:
the input dataset (Dataset S1), which includes all markers collected at the island level for the three threats;
the output dataset (Dataset S2), comprising individual threat exposure and cumulative exposure to global change for all 16,578 islands by 2050 under the SSP3 scenario;
the scripts to reproduce the figures and perform the statistical analyses.
The input dataset (Dataset_S1_Markers_3_threats_all_islands.xlsx) contains, for each island from the Weigelt et al. (2013) island list, its identifier, name, biogeographic information, and marker values for three global threats (i.e., CC: climate change, LU: land-use change, BI: biological invasions). Column names and descriptions are detailed in the "METADATA" sheet, and data are displayed in the "Input_Markers_all_isl" sheet. Raw data of the markers were obtained from the sources listed in the "Data collection" section of the Methods. They were freely accessible, and we followed the conditions to access them.
The script to standardize the markers across all islands, calculate exposure values, and derive descriptive statistics is Script1_calculate_exposure.R. It sources the functions that are all enclosed in the script FUN_calculate_global_exposure.R. The input file for this script is based on Dataset S1. However, the Dataset_S1_Markers_3_threats_all_islands.rds file can be used directly to run this script. Then, one can run the scripts Script2_Assess_threat_relative_contribution.R and Script3_characterize_hotspots.R, which perform the relative contribution analysis of each threat within the hotspots and characterize exposure hotpots, respectively.
The output dataset (Dataset_S2_Exposure_values_all_islands.xlsx) contains the identifier, name, latitude, longitude, individual threat exposure values, and cumulative exposure of each island, calculated under the SSP3 scenario for 2050 (see Methods). Column names and descriptions are detailed in the "METADATA" sheet, and data are displayed in the "Output_Exposure_values" sheet
GLOBIOM output data for the aquaculture and aquafeed supply chains in CLEVER D7.3 deliverable
No full textSupplement for CLEVER D7.3 deliverable including output data for the aquaculture & aquafeed supply chain results. It analyzes future projections to 2050 for the blue food and agricultural sectors, including aquaculture and fish-based vs crop-based aquafeed requirements and related impacts on terrestrial biodiversity through land use. The scenarios include a business-as-usual future to 2050, as well as sensitivity scenarios around non-fed aquaculture and marine catch capacity developement, changes in aquaculture feeding practices, changes in consumer preferences for blue food products, and a counterfactual scenario in which the blue food sector remains constant to 2020.
The record contains a readme file (readme_D73_AquafeedSupplyChains.docx) and a model output reporting csv file (GLOBIOM_outputs_D73_AquafeedSupplyChains_Global.csv)
URGED: coefficients and projections of street green space and its heat reduction potential in world cities (pre-release version)
No full textURGED: coefficients and projections of street green space and its heat reduction potential in world cities
Pre-release version, February 2025
Refer to the code repository on Github: https://github.com/giacfalk/URGE