Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally.
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Evaluation of predicted loss of different land use and land cover (LULC) due to coastal erosion in Bangladesh
Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988–2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments.
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- Award ID(s):
- 1660447
- NSF-PAR ID:
- 10408511
- Date Published:
- Journal Name:
- Frontiers in Environmental Science
- Volume:
- 11
- ISSN:
- 2296-665X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.
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Abstract. The land of the conterminous United States (CONUS) hasbeen transformed dramatically by humans over the last four centuries throughland clearing, agricultural expansion and intensification, and urban sprawl.High-resolution geospatial data on long-term historical changes in land useand land cover (LULC) across the CONUS are essential for predictiveunderstanding of natural–human interactions and land-based climatesolutions for the United States. A few efforts have reconstructed historicalchanges in cropland and urban extent in the United States since themid-19th century. However, the long-term trajectories of multiple LULCtypes with high spatial and temporal resolutions since the colonial era(early 17th century) in the United States are not available yet. Byintegrating multi-source data, such as high-resolution remote sensingimage-based LULC data, model-based LULC products, and historical censusdata, we reconstructed the history of land use and land cover for theconterminous United States (HISLAND-US) at an annual timescale and 1 km × 1 km spatial resolution in the past 390 years (1630–2020). The results showwidespread expansion of cropland and urban land associated with rapid lossof natural vegetation. Croplands are mainly converted from forest, shrub,and grassland, especially in the Great Plains and North Central regions.Forest planting and regeneration accelerated the forest recovery in theNortheast and Southeast since the 1920s. The geospatial and long-termhistorical LULC data from this study provide critical information forassessing the LULC impacts on regional climate, hydrology, andbiogeochemical cycles as well as achieving sustainable use of land in thenation. The datasets are available at https://doi.org/10.5281/zenodo.7055086 (Li et al., 2022).more » « less
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Shoreline erosion is a common problem for coastal systems, especially in areas of high human use. Small‐scale living shoreline (LSL) and oyster reef restoration projects can potentially slow or reverse shoreline erosion in low‐energy coastal environments. These projects are often easier and cheaper to implement when compared to hard armoring. However, their small size can also lead to limited funds for long‐term monitoring and an underestimation of their impact on altering local erosion dynamics. Universal metrics are popular for monitoring the biological success of small‐scale restoration projects, but may lack an affordable, easy method to quantify shoreline change over large spatial and temporal scales, including understanding losses prior to project initiation. Digital Shoreline Analysis System (DSAS) is a viable method to quantify cumulative shoreline change for multiple small‐scale restoration projects in one ecosystem. For example, over the past 12 years, 89 oyster reef and 14 small‐scale LSL restoration projects have occurred in Mosquito Lagoon, FL, U.S.A. While each project has been individually monitored, there has yet to be a combined quantification of the cumulative impact to shoreline erosion. Using imagery from USGS's Earthexplorer and the DSAS tool in ArcGIS, we calculated cumulative shoreline change impacts of small‐scale restorations in one ecosystem. Both small‐scale LSL and oyster reef restoration reversed shoreline erosion to accretion. Cumulative net shoreline gain for the LSL and oyster restoration projects were 347.62 m2/year and 288.91 m2/year, respectively. Quantifying cumulative shoreline change of multiple small‐scale restoration projects is a beneficial addition to universal monitoring.
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Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 105km2of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fenvs.2023.1144686
