Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection

Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data

Rapid flood impact assessment methods need complete and accurate flood maps to provide reliable information for disaster risk management, in particular for emergency response and recovery and reconstruction plans. With the aim of improving the rapid assessment of flood impacts, this work presents a new impact assessment method characterized by an enhanced satellite multi-sensor approach for flood mapping, which improves the characterization of the hazard. This includes a novel flood mapping method based on the new multi-temporal Modified Normalized Difference Water Index (MNDWI) that uses multi-temporal statistics computed on time-series of Sentinel-2 multi-spectral satellite images. The multi-temporal aspect of the MNDWI improves characterization of land cover over time and enhances the temporary flooded areas, which can be extracted through a thresholding technique, allowing the delineation of more precise and complete flood maps. The methodology, if implemented in cloud-based environments such as Google Earth Engine (GEE), is computationally light and robust, allowing the derivation of flood maps in matters of minutes, also for large areas. The flood mapping and impact assessment method has been applied to the seasonal flood occurred in South Sudan in 2020, using Sentinel-1, Sentinel-2 and PlanetScope satellite imagery. Flood impacts were assessed considering damages to buildings, roads, and cropland. The multi-sensor approach estimated an impact of 57.4 million USD (considering a middle-bound scenario), higher than what estimated by using Sentinel-1 data only, and Sentinel-2 data only (respectively 24% and 78% of the estimation resulting from the multi-sensor approach). This work highlights the effectiveness and importance of considering multi-source satellite data for flood mapping in a context of disaster risk management, to better inform disaster response, recovery and reconstruction plans.

Local feature acquisition and global context understanding network for very high-resolution land cover classification

Very high-resolution remote sensing images hold promising applications in ground observation tasks, paving the way for highly competitive solutions using image processing techniques for land cover classification. To address the challenges faced by convolutional neural network (CNNs) in exploring contextual information in remote sensing image land cover classification and the limitations of vision transformer (ViT) series in effectively capturing local details and spatial information, we propose a local feature acquisition and global context understanding network (LFAGCU). Specifically, we design a multidimensional and multichannel convolutional module to construct a local feature extractor aimed at capturing local information and spatial relationships within images. Simultaneously, we introduce a global feature learning module that utilizes multiple sets of multi-head attention mechanisms for modeling global semantic information, abstracting the overall feature representation of remote sensing images. Validation, comparative analyses, and ablation experiments conducted on three different scales of publicly available datasets demonstrate the effectiveness and generalization capability of the LFAGCU method. Results show its effectiveness in locating category attribute information related to remote sensing areas and its exceptional generalization capability. Code is available at https://github.com/lzp-lkd/LFAGCU .

Determination of long-term volume change in lakes by integration of UAV and satellite data: the case of Lake Burdur in Türkiye

Monitoring the water levels and volume changes of lakes and reservoirs enables us to understand the importance of better protection and managing water resources in an ecologically optimum manner. Although some lakes, such as Burdur Lake, are not a source of drinking water, they are home to many endangered animals, endemic plants, and some species. Therefore, monitoring the changes in these lakes over time is important for various reasons. While water level measurement stations in lakes and wetlands provide important information, it may not always be possible to obtain this data. In this study, we investigated the long-term changes in Burdur Lake, a Ramsar site, by integrating the Digital Elevation Model (DEM) obtained by the unmanned aerial vehicle (UAV) with shoreline information obtained from the Landsat mission. This study aimed to investigate the usability, advantages, and disadvantages of the UAV-Landsat integration for volume calculation. As a result, we successfully determined the water level as r= 0.999 and the cumulative volume loss at a rate of 97.5%. Burdur Lake experienced a significant reduction in its area decreasing from 206 to 120 km2 (42%) between 1984 and 2022. Furthermore, the water volume of the lake decreased by 2.70 km3 over a span of 38 years. This study demonstrates the potential and limitations of the presented UAV-remote sensing integration. Our proposed method is beneficial for determining short and long-term water levels and volumetric changes with high accuracy.

The effects of climate change-induced flooding on harvest failure in Burkina Faso: case study

Background

Climate change leads to more frequent and severe extreme weather events including floods, heatwaves, heavy rainfalls, and droughts. In contrast to the majority of research on weather extremes in sub-Saharan Africa, which focus primarily on how a lack of rainfall causes droughts, this paper aims to elucidate the effect of flooding on harvest failure in rural Burkina Faso.

Methods

We conducted a case study in north-western Nouna, Burkina Faso, between August and December 2021 covering a study population of n = 180 participants. The study comprised four components: (i) interviews with farmers (n = 180) on whether any of their fields had been inundated and if so, on harvest loss on these fields; (ii) determining the feasibility of using Sentinel-2 satellite images to validate study participants reports of floods; (iii) characterizing short-term weather including frequency and duration, of extreme rainfall events within the study area, as well as comparing cumulative rainfall (long-term) over the past 50 years; and (v), estimating both the food energy and economic loss of harvest failure due to flooding.

Results

49% of interviewed farmers (n = 88) reported that floods had damaged at least one of their fields. Some fields (n = 13, 7%) had no harvest due to flooding, while some farmers (n = 14, 8%) had lost part of their harvest. Images from the Sentinel-2-Satellite indicated that reported and remotely observed flooding were consistent. According to time series of data from the local weather station, there has been an increase irregular rainfall distribution and at the same time of cumulative annual rainfall in Nouna. Furthermore, a first illustrative calculation allowed us to estimate the amount of energy lost when one hectare of a common crop is flooded.

Conclusion

This case study demonstrated that flood-related harvest failures leading to crop losses in sub-Saharan Africa, exemplified by Burkina Faso, are likely to be substantial. This study serves as a proof-of-principle for flooding effects on food security. This could provide more detail for agricultural adaptation and mitigation strategies. Inundation-vulnerable fields need alternative and novel management practices, which may only be effectively implemented if agricultural institutions and national policy-making bodies receive evidence of flooding e.g., from remote sensing.