Search Results (169)

Extreme climate events are becoming more intense, and how coastal mangroves respond to the alternating intense cyclones and severe droughts is less understood, which challenges the sustainability of the ecosystem services they provide to coastal communities. To address this, we analyzed spatiotemporal dynamics of coastal mangroves in a Caribbean island in response to major hurricanes in 2017, which followed a severe multi-year drought in 2014–2015, using multiple indices derived from multispectral optical images. We further explored the roles of hurricane forces, local hydro-geomorphic environment, and rainfall dynamics in the damage and the following recovery. In addition to the hurricane forces, such as gusty wind and rainfall, the local hydro-geomorphic environment largely determines the spatial variations of damage. Lower-lying, flatter, and wetter mangrove areas sustained more damage, possibly due to prolonged inundation susceptibility and tall canopy configurations. Recovery is mainly limited by the severity of damage. However, sufficient rainfall gradually becomes important to facilitate the recovery. While the pre-hurricane severe drought (2014–2015) largely degraded the mangroves at dry sites, the drought after the hurricanes exacerbated the hurricane damage and retarded the recovery. We also found that the spectral distance and the mangrove vegetation index revealed slower and more spatiotemporally heterogenous mangrove recovery than indices of greenness, implying they are better measures for monitoring mangroves’ response to disturbance. Six years after the disturbance, the greenness of mangroves near the hurricane landfall reached 84% of the pre-hurricane values. However, the mangrove vegetation index showed that healthy mangrove coverage was only 10%, in comparison to 76% before the disturbance. The sluggish recovery at this site with the severest damage may be associated with the loss of pre-established seedlings and the difficulty to have new ones established, thus human efforts are in need to restore the system. Full article

In recent years, the altered hydrological regimes and frequent extreme hydrological events in its watershed have significantly affected the stability and biodiversity of the dish-shaped lakes (DSLs) ecosystem in Poyang Lake. This study uses long-term water level records from the Xingzi hydrological station, multi-source remote sensing imagery, and field surveys to assess how altered hydrological regimes and frequent extreme hydrological events influence the coupled hydro-ecological evolution of DSLs under different gate-controlled conditions. The results reveal the following: (1) After 2003, average monthly water levels declined by 0.84 m, shifting prolonged inundation depths from the 10.0 to 14.0 m range into the 5.5 to 9.5 m range. Extreme hydrological events disrupted the hydrological regimes, triggering a clear “collapse–recovery” succession in submerged plants and major shifts in shoal wetland vegetation. (2) Gate-controlled DSLs (GC DSLs) mitigated many of these impacts by reducing the autumnal drawdown in the water area change rate to 0.324 km²/d, curbing the upward expansion of emergent and hygrophytic vegetation during high-water-level years, and stabilizing habitats during low-water-level years, although different management strategies and substrate characteristics may still lead to divergent habitat trajectories. (3) The habitat heterogeneity exhibited by the DSLs’ vegetation communities along the elevation gradient had differential effects on migratory birds, and GC DSLs can offer migratory birds relatively stable resting habitats and food resources during extreme hydrological events. The study recommends that DSL management should adopt a hierarchical dynamic regulation strategy to balance natural hydrological fluctuations with human interventions, thereby strengthening the resilience of DSL wetland habitats to extreme hydrological events. Full article

Sea level rise is an escalating threat to saltmarsh ecosystems as increased inundation can lead to decreased biomass, lowered productivity, and plant death. Another potential stressor is elevated nitrogen often brought into coastal regions via freshwater diversions. Nitrogen has a controversial impact on belowground biomass, potentially affecting saltmarsh stability. In this study, we examined the effects of inundation and nitrogen on common saltmarsh plants (Spartina alterniflora and Spartina patens) placed within two marsh organs (a collection of PVC pipes at different levels, the varied elevation levels expose the plants to different inundation amounts) located in the Pascagoula River, Mississippi, USA, with six rows and eight replicates in each row. We randomly fertilized four replicates in each row with 25 g/m² of NH4⁺-N every two-three weeks during the growing season in 2021 and 2022. We concurrently collected vegetative traits such as plant height and leaf count to better understand strategies saltmarshes utilize to maximize survival or growth. We harvested half of the vegetation in Year 1 and the remaining in Year 2 to evaluate the impact of inundation and nitrogen on above- and belowground biomass at different temporal scales. We developed Bayesian models that show inundation had a largely positive impact on S. alterniflora and a mostly negative impact S. patens, suggesting that S. alterniflora will adapt better to increasing inundation than S. patens. Additionally, fertilized plants from both species had higher aboveground biomass than non-fertilized plants for both years, with nitrogen addition only showing impact on belowground biomass in the long term. Our results highlight the importance of long-term study to facilitate more-informed restoration and conservation efforts in coastal wetlands while accounting for climate change and sea level rise. Full article

Accurate flood monitoring and forecasting techniques are important and continue to be developed for improved disaster preparedness and mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional methods. Soil Moisture Active Passive (SMAP) fractional water (FW) was used as a reference to estimate flood areas in a long short-term memory (LSTM) model using a combination of soil moisture information, rainfall forecasts, and floodplain topography. To perform flood modeling in LSTM, datasets with different spatial resolutions were resampled to 30 m spatial resolution using bicubic interpolation. The model’s efficacy was quantified by validating the LSTM-based flood inundation area with a water mask from Senti-nel-1 SAR images for regions with different topographic characteristics. The average area under the curve (AUC) value of the LSTM model was 0.93, indicating a high accuracy estimation of FW. The confusion matrix-derived metrics were used to validate the flood inundation area and had a high-performance accuracy of ~0.9. SMAP FW showed optimal performance in low-covered vegetation, seasonal water variations and flat regions. The estimates of flood inundation areas show the methodological promise of the proposed framework for improved disaster preparedness and resilience. Full article

Accurate mapping of rice planting areas is of great significance in terms of food security and market stability. However, the existing research into high-resolution rice mapping has relied heavily on fine-scale temporal remote sensing image data. Due to cloud occlusion and banding problems, data extraction from Landsat series remote sensing images with medium spatial resolution is not optimal. Therefore, this study proposes a rice mapping method (LR) using Google Earth Engine (GEE), which uses Landsat images and integrates automatic generation of training samples and a machine learning algorithm, with the assistance of phenological methods. The proposed LR method initially generated rice distribution maps based on phenology, and 300 sample points were selected for meta-identification of rice images via an enhanced pixel-based phenological feature composite method (Eppf-CM) utilizing high-resolution imagery. Subsequently, the inundation frequency (F) and an improved sample point statistical feature, i.e., the ratio of change amplitude of LSWI to NDVI (RCLN), were introduced to combine Eppf-CM with combined consideration of vegetation phenology and surface water variation (CCVS) methods, to automate the generation of training data with the aid of phenology. The sample data were optimized by an alternate iterative method involving extraction of neighborhood information. Finally, a random forest (RF) probabilistic model trained by integrating data from different phenological periods was used for rice mapping. To test its performance, we mapped rice distribution at 30 m resolution (“LR_Rice”) across Heilongjiang Province, China from 2010 to 2022, with annual overall accuracy (OA) and Kappa coefficients greater than 0.97 and 0.95, respectively, and compared them with four existing rice mapping products. The spatial distribution characteristics of rice cultivation extracted by the LR algorithm were accurate and the performance was optimal. In addition, the extracted area of LR_Rice was highly consistent with the agricultural statistical area; the coefficient of determination R² was 0.9915, and the RMSE was 22.5 kha. The results show that this method can accurately obtain large-scale rice planting information, which is of great significance for food security, water resource management, and environmentally sustainable development. Full article

Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km², 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment. Full article

This paper presents an assessment of potential losses and damage costs to rare coastal plant species of Lake Baikal (UNESCO World Heritage Site) as a result of inundation at high water levels. The lake’s ecosystem is characterized by an exceptional diversity of rare and endemic animal and plant species. The construction of a hydroelectric power plant caused an increase in the water level of Lake Baikal, resulting in the inundation of low-lying coastal areas, the destruction of the coastline, alterations to the hydrological regime, etc. However, there are practically no works devoted to water-level modeling and the assessment of its impact on riparian vegetation, including rare species. We conducted fieldwork to determine the abundance of four vulnerable species and identified inundation zones at different high water levels on the basis of digital elevation models based on aerial photography data. The analysis revealed that at the maximum level of inundation, the number of plant species affected would total 5164, amounting to a financial loss of biodiversity estimated at 3098.4 thousand rubles. To mitigate the projected losses, it is imperative to implement measures that restrict water-level fluctuations above the 457.00 m threshold. The absence of flora as an object of state environmental monitoring, which is not specified in the regulatory legal document, must be rectified in a timely manner. Full article

The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami’s impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia’s recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions. Full article

Compound mitigation systems, integrations of natural and engineering structures against the high inundating current from tsunamis or storm surges, have garnered significant interest among researchers, especially following the Tohoku earthquake and tsunami in 2011. Understanding the complex flow phenomena is essential for the resilience of the mitigation structures and effective energy reduction. This study conducted a flume experiment to clarify flow characteristics and fluid force dissipation in a compound defense system. Vegetation models (V) with different porosities (Φ) were placed at three different positions downstream of an embankment model (E). A single-layer emergent vegetation model was considered, and a short-layer vegetation with several values of Φ was incorporated to increase its density (decreased Φ). Depending on Φ and the spacing (S) between the E and V, hydraulic jumps occurred in the physical system. The findings demonstrated that a rise in S allowed a hydraulic jump to develop inside the system and contributed to reducing the fluid force in front and downstream of V. Due to the reduced porosity of the double-layer vegetation, the hydraulic jump moved upstream and terminated within the system, resulting in a uniform water surface upstream of V and downstream of the system. As a result, the fluid force in front of and behind V reduced remarkably. Full article

Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of land use/land cover (LULC) changes on both ecosystem vulnerability and sustainable development achievements. This study addresses this gap through an innovative integration of multitemporal Landsat imagery (5, 7, and 8), SRTM-DEM, historical land use maps, and population data using the MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling to monitor LULC changes over three decades (1990–2020) and project future changes for 2025, 2030, and 2035, supporting the Sustainable Development Goals (SDGs) in Karachi, southern Pakistan, one of the world’s most populous megacities. The framework integrates LULC analysis with SDG metrics, achieving an overall accuracy greater than 97%, with user and producer accuracies above 77% and a Kappa coefficient approaching 1, demonstrating a high level of agreement. Results revealed significant urban expansion from 13.4% to 23.7% of the total area between 1990 and 2020, with concurrent reductions in vegetation cover, water bodies, and wetlands. Erosion along the riverbank has caused the Malir River’s area to decrease from 17.19 to 5.07 km² by 2020, highlighting a key factor contributing to urban flooding during the monsoon season. Flood risk projections indicate that urbanized areas will be most affected, with 66.65% potentially inundated by 2035. This study’s innovative contribution lies in quantifying SDG achievements, showing varied progress: 26% for SDG 9 (Industry, Innovation, and Infrastructure), 18% for SDG 11 (Sustainable Cities and Communities), 13% for SDG 13 (Climate Action), and 16% for SDG 8 (Decent Work and Economic Growth). However, declining vegetation cover and water bodies pose challenges for SDG 15 (Life on Land) and SDG 6 (Clean Water and Sanitation), with 16% and 11%, respectively. This integrated approach provides valuable insights for urban planners, offering a novel framework for adaptive urban planning strategies and advancing sustainable practices in similar stressed megacity regions. Full article

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. Full article

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article

Accurate river hydraulic characterization is fundamental to assess flood risk, parametrize flood forecasting models, and develop river maintenance workflows. River hydraulic roughness and riverbed/floodplain geometry are the main factors controlling inundation extent and water levels. However, gauging stations providing hydrometric observations are declining worldwide, and they provide point measurements only. To describe hydraulic processes, spatially distributed data are required. In situ surveys are costly and time-consuming, and they are sometimes limited by local accessibility conditions. Satellite earth observation (EO) techniques can be used to measure spatially distributed hydrometric variables, reducing the time and cost of traditional surveys. Satellite EO provides high temporal and spatial frequency, but it can only measure large rivers (wider than ca. 50 m) and only provides water surface elevation (WSE), water surface slope (WSS), and surface water width data. UAS hydrometry can provide WSE, WSS, water surface velocity and riverbed geometry at a high spatial resolution, making it suitable for rivers of all sizes. The use of UAS hydrometry can enhance river management, with cost-effective surveys offering large coverage and high-resolution data, which are fundamental in flood risk assessment, especially in areas that difficult to access. In this study, we proposed a combination of UAS hydrometry techniques to fully characterize the hydraulic parameters of a river. The land elevation adjacent to the river channel was measured with LiDAR, the riverbed elevation was measured with a sonar payload, and the WSE was measured with a UAS radar altimetry payload. The survey provided 57 river cross-sections with riverbed elevation, and 8 km of WSE and land elevation and took around 2 days of survey work in the field. Simulated WSE values were compared to radar altimetry observations to fit hydraulic roughness, which cannot be directly observed. The riverbed elevation cross-sections have an average error of 32 cm relative to RTK GNSS ground-truth measurements. This error was a consequence of the dense vegetation on land that prevents the LiDAR signal from reaching the ground and underwater vegetation, which has an impact on the quality of the sonar measurements and could be mitigated by performing surveys during winter, when submerged vegetation is less prevalent. Despite the error of the riverbed elevation cross-sections, the hydraulic model gave good estimates of the WSE, with an RMSE below 3 cm. The estimated roughness is also in good agreement with the values measured at a gauging station, with a Gauckler–Manning–Strickler coefficient of M = 16–17 m^1/3/s. Hydraulic modeling results demonstrate that both bathymetry and roughness measurements are necessary to obtain a unique and robust hydraulic characterization of the river. Full article

On the African continent, Picrodendraceae are represented by four genera. Their intracontinental paleophytogeographic histories and paleoecological aspects are obscured by the lack of pre-Miocene fossils. For this study, late Eocene sediments from Kenya were investigated. The sample was prepared in the laboratory, and its organic residue was screened for pollen. We extracted fossil Picrodendraceae pollen and investigated the grains using light and scanning electron microscopy. Based on the pollen morphology, the grains were assigned to Hyaenanche. This genus is currently confined to a small area within the Cape Province, South Africa. There, the plants grow as shrubs and small trees at an elevation between 60 and 800 m, on rocky substrate, as part of open fynbos vegetation, and under a dry climate with hot summers and limited precipitation. The sedimentary context and the associated palynoflora suggest that during the Eocene of Kenya, Hyaenanche was part of lowland coastal vegetation in Eastern Africa. There, the plants grew under fully humid to winter-dry tropical climates as part of landwards margins of mangroves, seasonally inundated floodplain forests, or coastal forests. Our study shows that when evaluating paleoecological aspects of relict monotypic plants, their extant closely related genera and their fossil records need to be considered. Full article

This paper develops a method integrating Geographic Information Systems (GIS) and the Decision-Making Trials and Evaluation Laboratory (DEMATEL) for the analysis of factors influencing urban flood risk and the identification of flood-prone areas. The method is based on nine selected factors: land use/land cover (LULC: the ratio of built-up areas, the ratio of greenery areas), elevation, slope, population density, distance from the river, soil, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The DEMATEL method is used to determine the cause–effect relationship between selected factors, allowing for key criteria and their weights to be determined. LULC and population density were identified as the most important risk factors for urban floods. The method was applied to a case study—the Serafa River watershed (Poland), an urbanized catchment covering housing estates of cities of Kraków and Wieliczka frequently affected by flooding. GIS analysis based on publicly available data using QGIS with weights obtained from DEMATEL identified the vulnerable areas. 45% of the total catchment area was classified as areas with a very high or high level of flood risk. The results match the actual data on inundation incidents that occurred in recent years in this area. The study shows the potential and possibility of using the DEMATEL-GIS method to determine the significance of factors and to designate flood-prone areas. Full article