A crowdsourced global data set for validating built-up surface layers

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas

Combining Landsat time series and GEDI data for improved characterization of fuel types and canopy metrics in wildfire simulation

Wildfires in the Chornobyl Exclusion Zone (CEZ) and other radioactively contaminated areas threaten human health and well-being with the potential to resuspend radionuclides. Wildfire behavior simulation is a necessary tool to examine the efficiency of fuel treatments in the CEZ, but it requires systematically updated maps of fuel types and canopy metrics. The objective of this study was to demonstrate an effective approach for mapping fuel types, canopy height (CH), and canopy cover (CC) in territories contaminated by radionuclides using Landsat time series (LTS) and Global Ecosystem Dynamics Investigation (GEDI) LiDAR observations. We combined LTS and GEDI data to map fuel types and canopy metrics used in wildfire simulations within the CEZ. Our classifi-cation model showed an adequate overall accuracy (75%) in mapping land covers and associated fuel types. The phenology metrics extracted from LTS reliably distinguished spectrally similar vegetation types (such as grass-lands and croplands) which exhibit different flammability through the year. We also predicted a suite of relative heights metrics and CC at Landsat 30-m pixel level (R2 = 0.23-0.26) using the nearest neighbor technique. The imputed maps adequately captured the dynamics of CH and CC in the CEZ after recent large wildfires occurred in 2015, 2020, and 2022. Thus, we illustrate a LTS processing approach to produce wall-to-wall maps of canopy characteristics that are important for wildfire simulations. We conclude that continuous updating of land cover and canopy fuel data is crucial to ensure relevant fire management of radioactively contaminated landscapes and support local decision-making

The Wildfire Problem in Areas Contaminated by the Chernobyl Disaster

This paper examines the issue of radionuclide resuspension from wildland fires in areas contaminated by the Chernobyl Nuclear Power Plant explosion in 1986. This work originated from a scientific exchange among scientists from the USDA Forest Service, Ukraine and Belarus that was organized to assess science and technology gaps related to wildfire risk management. A wildfire risk modeling system was developed to predict likely hotspots for large fires and where wildfire ignitions will most likely result in significant radionuclide (Cesium, 137Cs) resuspension. The system was also designed to examine the effect of fuel breaks in terms of reducing both burn probability and resuspension. Results showed substantial spatial variation in fire likelihood, size, intensity, and potential resuspension within the contaminated areas. The potential for a large wildfire and resuspension was highest in the Belorussian Polesie Reserve, but the likelihood of such an event was higher in the Ukrainian Chernobyl Exclusion Zone due to a higher predicted probability of ignition. Fuel breaks were most effective in terms of reducing potential resuspension when located near areas that had both high ignition probability and high levels of 137Cs contamination. Simulation outputs highlighted how human activities shape the fire regime and likelihood of a large fire in the contaminated areas. We discuss how the results can be used to develop a fire management strategy that integrates ignition prevention, detection, effective suppression response, and fuel breaks. Specifically, the modeling system can now be used to explore a wide range of fire management scenarios for the contaminated areas and contribute to a comprehensive fire management strategy that targets specific drivers of fire by leveraging multiple tools including fire prevention and long-term fuel management. Wildfire-caused emissions of radionuclides in Belarus, Ukraine, and Russia are a socio-ecological problem that will require defragmenting existing risk management systems and leveraging multiple short- and long-term mitigation measures

Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m3 ha−1 and for live biomass of about 2 t ha−1 over the study area