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Mass urbanisation and intensive agricultural development across river deltas have driven ecosystem degradation, impacting deltaic socio-ecological systems and reducing their resilience to climate change. Assessments of the drivers of these changes have so far been focused on human activity on the subaerial delta plains. However, the fragile nature of deltaic ecosystems and the need for biodiversity conservation on a global scale require more accurate quantification of the footprint of anthropogenic activity across delta waterways. To address this need, we investigated the potential of deep learning and high spatiotemporal resolution satellite imagery to identify river vessels, using the Vietnamese Mekong Delta (VMD) as a focus area. We trained the Faster R-CNN Resnet101 model to detect two classes of objects: (i) vessels and (ii) clusters of vessels, and achieved high detection accuracies for both classes (f-score = 0.84–0.85). The model was subsequently applied to available PlanetScope imagery across 2018–2021; the resultant detections were used to generate monthly, seasonal and annual products mapping the riverine activity, termed here the Human Waterway Footprint (HWF), with which we showed how waterborne activity has increased in the VMD (from approx. 1650 active vessels in 2018 to 2070 in 2021 - a 25 % increase). Whilst HWF values correlated well with population density estimates (R 2 = 0.59–0.61, p < 0.001), many riverine activity hotspots were located away from population centres and varied spatially across the investigated period, highlighting that more detailed information is needed to fully evaluate the extent, and type, of human footprint on waterways. High spatiotemporal resolution satellite imagery in combination with deep learning methods offers great promise for such monitoring, which can subsequently enable local and regional assessment of environmental impacts of anthropogenic activities on delta ecosystems around the globe.

Currently, remote sensing platforms provide state-of-the-art data for multiple purposes including applications related to coastal wetlands. Mangrove above-ground biomass (MAGB) together with its extent is considered well correlated with the habitats’ environmental and economic values. Above-ground biomass can be estimated by models that integrate remote sensing, field data and statistical information. However, it remains difficult to decide which model and which data offer the best performance for any one study location. Hence, this study aims to assess the spatial change in MAGB over a 45-year period and investigate different approaches to quantify this change through linear and multi linear regression models. Specifically, we test a non-linear model (Multivariate Adaptive Regression Splines; MARS), and non-parametric machine learning models, to predict MAGB using vegetation indices and biophysical variables derived from optical remote sensing data from Sentinel-2, Landsat-8, SPOT-7 and synthetic aperture radar remote sensing data from ALOS-2. The multi linear regression (MLR) and the MARS models were trained by field measured MAGB data to a good level of accuracy (R2 = 0.80 and RMSE = 5.56 Mg ha−1 for MLR and R2 = 0.89, RMSE = 5.42 Mg ha−1 for MARS). These models were subsequently applied to Landsat 2, 5 and 8 time-series images to assess changes in MAGB values and mangrove forest extent over the period 1975 to 2020. To ensure accurate training data for the models, we conducted field work to measure MAGB in 24 plots measured in May 2019. Findings showed that the MARS model generated MAGB values with higher accuracy than linear regression and multi linear regression models. Uses of vegetation indices (Normalized Differenced Vegetation Index, Soil-adjusted Vegetation Index, Green-Normalized Differenced Vegetation Index, Simple Ratio, and Red-edge Simple Ratio) generated MAGB values with accuracy slightly higher than using biophysical variables (Leaf area index, Fraction of Absorbed Radiation, Fractional vegetation cover, and Leaf chlorophyll content). Sentinel-2 and Landsat 8 were effective data sources for MAGB estimates, while SPOT-7 and ALOS-2 produced acceptable MAGB accuracy. Modelling the Landsat time series found an increase in both MAGB values and forest extent over the 1975–2020 period. The MARS model, Sentinel-2, Landsat 8 and vegetation indices are the recommended models and data to use to measure MAGB and could be used to understand changes in MAGB and forest extent at national and regional scales.

The Holderness coastline of Eastern England is the fastest eroding coastline in Europe. The coast is characterised by ‘soft sediment’ tills, which make it distinctly susceptible to cliff retreat, in turn, these pose a socio-economic threat to local communities. The controls and future projections of the rates and patterns of retreat rely upon robust monitoring and process-based understanding of the geomorphological processes. Herein, we report on a 12-month monitoring study (June 2019 to May 2020) along a 220 m stretch of the Holderness coastline (Withernsea), whereby the spatial and temporal patterns of failure were captured using terrestrial LiDAR. Failure footprint, volumetric change and total eroded volume of the cliffs were estimated and compared against local hydrodynamic and meteorological records. The results reveal that >36% of individual failure events occurred solely in the upper portions (upper 75% vertical height) of the cliff, with a further >38% over the central section of the cliff face, with

We are the midst of a climate emergency requiring urgent climate action that is, as yet, unforthcoming both on the scale, and at the speed, commensurate with the associated hazard and risk. This paper presents work that considers this current state of inaction and explores how we might understand the underpinning processes of attitudinal and behavioural change needed through the emotional framework of loss.This inaction is also explored through the additional lens of the year 2020, a year of tumultuous social change created by the COVID–19 pandemic. The article draws parallels with and looks to learn from the ways in which the collective loss experienced as a result of COVID–19 may offer a sense of hope in the fight to adequately address climate change but how meeting the Sustainable Development Goals will require climate injustices to also be addressed. We argue that appropriate leadership that guides widespread climate action from all is best sought from those groups already facing the loss of climate change and therefore already engaged in climate-related social action and activism, including youth and Indigenous peoples.In this regard we present work from an ongoing project based within the Red River catchment (Vietnam), which is already experiencing enhanced hydrological extremes. Resultant floods, landslides and soil erosion in the upper region is having impacts in communities, whilst relative sea-level rises in the region are affecting the frequency and magnitude of flooding. Our research is working with young people and their communities, alongside social and environmental scientists in partnership, to identify imaginative ways to mitigate these climate change challenges and foster action. The paper will outline how this youth-led approach explores how local, traditional, and indigenous knowledges can develop understandings and strengthen local and societal resilience, incorporating peer-to-peer, intergenerational and cross-/inter-cultural forms of collaborative, and socially just, learning.

Earth science reviews 228, 104021 (2022). doi:10.1016/j.earscirev.2022.104021, Published by Elsevier, Amsterdam [u.a.]

Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water and provide useful information regarding operational flood monitoring, in particular for the improvement of rapid flood assessments. However, this application frequently requires standard and simple, yet robust, algorithms. Although thresholding approaches meet these requirements, limitations such as data inequality over large spatial regions and challenges in estimating optimal threshold values remain. Here, we propose a new method for SAR water extraction named Hammock Swing Thresholding (HST). We applied this HST approach to four SAR remote sensing datasets, namely, Sentinel-1, ALOS-2, TerraSAR-X, and RadarSAT-2 for flood inundation mapping for a case study focusing on the Tam Nong district in the Vietnam Mekong delta. A 2D calibrated Hydrologic Engineering Centers River Analysis System (HEC-RAS) model was coupled with the HST outputs in order to estimate the optimal thresholds (OTs) where the SAR-based water masks fitted best with HEC-RAS&rsquo, s inundation patterns. Our results showed that water levels extracted from Sentinel-1 data best agreed with the HEC-RAS water extent (88.3%), following by ALOS-2 (85.9%), TerraSAR-X (77.2%). and RadarSAT-2 (72%) at OTs of &minus, 15, 68, 21, and 35 decibel (dB), respectively. Generated flood maps indicated changes in the flood extent of the flooding seasons from 2010 and 2014&ndash, 2016 with variations in spatial extent appearing greater in the TerraSAR-X and RadarSAT-2 higher resolution maps. We recommend the use of OTs in applications of flood monitoring using SAR remote sensing data, such as for an open data cube (ODC).