Cover Change Research Articles

Analysing Thermal Land Surface Characteristics and Urban Expansion in Kenitra City, Morocco, using Remote Sensing and GIS

Abstract This research investigates how land use and cover the affect land surface temperature in Kenitra. It employs Geographic Information System techniques and remote sensing methods to monitor and control surface temperature changes. The present study integrates remote sensing and geographic information system to process satellite images. Furthermore, it focuses, also, on analyzing land surface temperature and its correlation with land cover changes over three time periods (1989, 2000, 2020) using landsat satellite data. Thus, supervised classification is employed to map these changes and derive urban heat islands from thermal band data. This study provides a comprehensive evaluation of the relationship between land use and land surface temperature. It utilizese a landscape dynamics assessment based on multi-source and multi-sensor remote sensing technologies. Specifically, the study uses landsat satellite data (TM for 1989 and OLI-8 for 2002 and 2020) to assess the effects of land use and land cover changes on land surface temperature distribution in the Kenitra city. Spatial and statistical analyses are performed by comparing maps from remotely sensed data generated using geographic information system. Therefore, these analyses reveal various changes in the kenitra region between 1989 and 2020. The primary changes observed include an increase in built-up areas and bare ground with a decrease alongside in natural areas (vegetation, etc.). The average temperatures in 1989, 2002, and 2020 were 29°C, 31.05°C, and 32°C, respectively. The obtained results are valuable for planning and managing climate scientists, land-use planners, and researchers focusing on sustainable urbanization areas. The study’s findings will assist urban planners and policymakers in adopting appropriate measures for sustainable planning in the city to mitigate the adverse effects of land surface temperature.

National Statistics of Endoscopic Submucosal Dissection for Early Gastric Cancer in Korea.

Endoscopic submucosal dissection (ESD) is the standard treatment for early gastric cancer (EGC) with a low risk of lymph node metastasis. In Korea, ESD was included in the National Health Insurance (NHI) coverage in 2011, which was expanded in 2018. In the present study, we investigated the status and trends of ESD for EGC over the past decade since its incorporation into the NHI system. We analyzed the data from the National Health Insurance Service (NHIS) database from 2011 to 2021, focusing on patient characteristics, number of ESD procedures, in-hospital length of stay (LOS), and total medical cost (TMC) per admission. In addition, we conducted an interrupted time series analysis to assess the impact of changes in insurance coverage on these variables. Overall, 95,348 cases of ESD for EGC were identified. A consistent annual increase in ESD procedures was observed, particularly in tertiary care hospitals and among patients aged >60 years. The overall median LOS and TMC were 4 days and 2,123,000 KRW, respectively. The 2018 insurance coverage expansion did not significantly affect the number of ESD procedures or LOS; however, the TMC increased significantly. Our study illustrates decade-long trends in the ESD for EGC in Korea. The policy needs to be revised continuously to optimize ESD use and improve resource allocation within healthcare systems.

Coral responses to a catastrophic marine heatwave are decoupled from changes in total coral cover at a continental scale.

The services provided by the world's coral reefs are threatened by increasingly frequent and severe marine heatwaves. Heatwave-induced degradation of reefs has often been inferred from the extent of the decline in total coral cover, which overlooks extreme variation among coral taxa in their susceptibility and responses to thermal stress. Here, we provide a continental-scale assessment of coral cover changes at 262 shallow tropical reef sites around Australia, using ecological survey data on 404 coral taxa before and after the 2016 mass bleaching event. A strong spatial structure in coral community composition along large-scale environmental gradients largely dictated how coral communities responded to heat stress. While heat stress variables were the best predictors of change in total coral cover, the pre-heatwave community composition best predicted the temporal beta-diversity index (an indicator of change in community composition over time). Indicator taxa in each coral community differed before and after the heatwave, highlighting potential winners and losers of climate-driven coral bleaching. Our results demonstrate how assessment of change in total cover alone may conceal very different responses in community structure, some of which showed strong regional consistency, and may provide a telling outlook of how coral reefs may reorganize in a warmer future.

Machine Learning Approaches for Mapping and Predicting Landslide-Prone Areas in São Sebastião (Southeast Brazil)

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.

Removing thick clouds in landsat 8 OLI images with cloudy time-series auxiliary data

Abstract Thick cloud occlusion severely limits the subsequent application of optical remote-sensing images. Existing mainstream cloud removal methods often use cloud-free homologous temporal images to provide auxiliary information. However, due to the limitation of cloud occlusion and satellite revisit cycle, the time interval between the cloud-free auxiliary image and the target cloudy image is often too large, and the possible land cover changes during the period increase the uncertainty of effective information on the auxiliary images. In this case, more accurate reconstructions may be obtained by using temporally closer and partially cloud-contaminated images. In this paper, a deep network, convLSTM, is developed for cloud removal (i.e., CR-convLSTM), which makes full use of the complementary effective information on partially cloud-contaminated time-series images. Experiments based on simulated clouds indicated that CR-convLSTM can obtain more accurate predictions than the classical cloud removal method MNSPI. CR-convLSTM is an effective cloud removal method with high computational efficiency while ensuring predicting accuracy.

Multi-temporal assessment of a wildfire chronosequence by remote sensing

The study aimed to develop a methodological framework to identify forest ecosystems affected by wildfires and evaluate their recovery chronologically. To do this remote sensing analysis, sites with burn scars were selected based on various criteria (fire severity, affected area, vegetation and soil type, slope, aspect, and one-time occurrence of wildfire in the last 23 years). Spectral vegetation indices (VIs) from satellite imagery were used to estimate burn severity and vegetation cover changes. Images of surface reflectance were obtained from the collection of Landsat 5 ETM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, available and processed on the Google Earth Engine Platform (GEE). Indices VIs (i) the normalized difference vegetation index (NDVI), (ii) the normalized burn ratio (NBR), and (iii) the differenced normalized burn ratio (dNBR) were calculated to classify burn severity. The one-time occurrence selection was performed using the LandTrendr algorithm to monitor changes in land cover and burned areas. To validate the selection, the chosen sites within the chronosequence were clustered on 4 seasons of soil properties and litter accumulation recovery. Our result can guide methodological comparisons and forest management practices on large surfaces by comparing parches of different time-affected ecosystems. Validation sites of the cluster chronosequence shows consistent recovery of soil properties as soil carbon, bulk density and litter accumulation through the studied years•The study developed a framework to identify wildfire-affected forest ecosystems and evaluate their recovery using remote sensing and local data.•Vegetation indices (NDVI, NBR, dNBR) from Landsat satellite imagery processed on the Google Earth Engine were used to assess burn severity and vegetation changes over time.•Selected sites were validated using the LandTrendr algorithm and monitored for seasonal changes in soil properties and litter accumulation.

Environmental impacts of the billion tree Tsunami project in Khyber Pakhtunkhwa on the dynamics of Agro-Meteorological Droughts

Changes in forest cover are closely associated with the variability in meteorological and hydrological variables. Therefore, this study delves into investigating how forest cover changes impact the environment (i.e., hydro-meteorological variables, including precipitation, streamflow, relative humidity (RH), evapotranspiration (ET), and temperature) using Trend Projection (TP) methods during 1980–2019. The study is carried out in the Khyber Pakhtunkhwa (KP) province of Pakistan, which witnessed deforestation between 1980 and 2010 followed by afforestation (through billion tree tsunami project, BTTP) initiated in 2014. A new drought index, named as agro-meteorological drought index (AMDI), is developed in this study using the remotely sensed data to analyze the impact of forest cover on drought severity. The robust least square regression (RLSR) model is used to regress the normalized difference vegetation index (NDVI) with AMDI at various time scales to investigate the impact of forest cover on drought severity. The RLSR and paired t-test are used to quantify the impact of forest cover and BTTP, in particular, on the environment. Land use maps prepared for KP province over a span of the past four decades revealed significant deforestation during 1985–2005, transitioning gradually to afforestation in the past decade. Results indicated a decline in streamflow throughout different seasons with an increase in forest cover, particularly during the period of afforestation (i.e., 2015–2019). The precipitation, RH (maximum/minimum), and ET displayed an increasing trend over time, whereas a decrease trend is observed in Tmax/Tmin and streamflow at most of the stations. The trend analyses depicted a significant change before and after the BTTP. The paired t-test results revealed that BTTP has statistically significant impact on the environmental variables. Furthermore, the time series plots of AMDI at different time scales indicated that drought events were frequent and severe prior to 2003, whereas significant decrease in both the frequency and severity of drought was observed in the last decade (2010–2019). The RLSR results at pixel scales demonstrated the crucial role of forest covers in alleviating both the frequency and severity of drought events. The elasticities revealed that increase in the forest cover resulted in substantial increase/decrease in each hydro-meteorological variable. Overall, the results highlighted a positive and statistically significant impact of forest cover (i.e., BTTP) on both the environment and drought variability.

Eco-climatological modeling approach for exploring spatiotemporal dynamics of ecosystem service values in response to land use and land cover changes in Riyadh, Saudi Arabia

Eco-climatological modeling approach for exploring spatiotemporal dynamics of ecosystem service values in response to land use and land cover changes in Riyadh, Saudi Arabia

Detecting small-scale landslides along electrical lines using robust satellite-based techniques

Robust Satellite Technique (RST) was applied to detect small-scale landslides along electrical lines in Sicily, Italy. To this end, electrical poles were selected as targets within the study area. The methodology, implemented in Google Earth Engine (GEE) environment, exploits the Copernicus Sentinel-2 platform to identify anomalous land cover variation, in terms of Normalized Difference Vegetation Index (NDVI), possibly related to small displacements affecting electric poles. Since the applied methodology is based on land cover change, dense vegetation plays an important role in detecting small-scale landslides. Therefore, we targeted months with the highest vegetation density, such as February, March, and April from 2016–2023. The results obtained reveal that out of the five targeted electrical poles, four of them exhibited anomalies > 2-sigma indicating significant changes in land cover possibly related to local ground movement as confirmed by aerial photos collected in the period 2015–2023. Our findings reveal anomalies of −2.17 and −2.36 on 7/17/2017 and 9/05/2017 for pole 1. For pole 2, the results show an anomaly of −2.02 on 8/11/2018. The results also indicate anomalies of −4.40 and −2.99 on 7/09/2021 and 9/27/2022 for pole 3. For pole 4, the findings show an anomaly of −3.10 on 1/18/2019.

The role of society in the expansion of oil palm plantation and the changes in land cover at Kuantan Singingi, Riau

The role of society in the expansion of oil palm plantation and the changes in land cover at Kuantan Singingi, Riau

Analysis of land use land cover change dynamics in Habru District, Amhara Region, Ethiopia

This study examines spatiotemporal changes in Land Use Land Cover (LULC) patterns within Habru District, Amhara Region, Ethiopia, between 1985 and 2021. Employing Landsat imagery and a supervised classification approach, we mapped six LULC classes: water body, settlement area, cultivated land, bare land, shrub land, and forest land. Ground control points (GCP) were obtained through field observations to ensured image accuracy. Complementing the remote sensing analysis, focus group discussions (FGDs) and key informant interviews were conducted to understand local perspectives on LULC changes. The analysis revealed significant LULC transformations over the study period. Between 1985 and 2021, there was a significant increase in cultivated land and settlement areas, which expanded from 12.27 % to 0.23 %–42.12 % and 2.58 % of the total area, respectively. Conversely, shrub and forest lands saw a marked reduction during the same period, declining from 71.52 % to 12.88 %–45.35 % and 7.06 % of the total area, respectively. Water body area showed a minor increase, while bare land exhibited negligible change. Agricultural land and settlement area expansion, population growth, land tenure insecurity, economic challenge, and climatic change were identified as key drivers of observed LULC changes. Our findings underscore the urgency of implementing multifaceted interventions to address these drivers and promote sustainable resource use practices. Such actions are critical for safeguarding Habru District's natural resources and ensuring the long-term viability of ecosystem services.

Measuring Temporal Change in Scrub Vegetation Cover Using UAV-Derived Height Maps: A Case Study at Two UK Nature Reserves.

Measuring the outcome of practical interventions and actions helps to inform conservation management objectives and assess progress towards objectives and targets. Measuring success also informs future management by identifying actions that are effective and those that are not. Scrub vegetation is an important habitat type in terrestrial ecosystems, providing important shelter and food resources for biodiversity and livestock. Much of practical land management in the UK involves the monitoring and management of scrub, and current drone-based methods of scrub collection requires expensive equipment or complex methods. A 2021 paper determined a cheap and simple way to determine scrub levels, and this could potentially be used to map temporal changes, as well as identify directional change in scrub. This study looks at whether the method outlined in the 2021 study could be used to measure temporal and directional changes in scrub cover on two nature reserves in the UK: Daneway Banks in Gloucestershire and Flat Holm Island in the Severn Estuary. Scrub levels at Daneway Banks increased from 14.63% in 2015 to 16.52% in 2017, before decreasing to 14.89% in 2021 due to managed cutting and clearing. Scrub cover at Flatholm Island decreased from 10.18% in 2019 to 8.71% in 2021. The exact locations of scrub growth and loss for each site was also calculated and mapped. This approach was found to be a viable way of measuring temporal and directional change in scrub levels. The data can also be used to reframe changes in scrub levels as a shift towards vegetation succession or reduction, to better visualise how changes in scrub levels affect overall site management goals, and is a cheaper, more accessible alternative to current methods of measuring temporal vegetation changes.

Two decades of land cover change and anthropogenic pressure around Bontioli Nature Reserve in Burkina Faso

Protected areas (PAs) are critical for ecosystem maintenance and providing services that benefit both wildlife and people. Nevertheless, climate change and anthropogenic pressures are posing an increasing challenge. Surrounded by high human population densities, there is still a paucity of information on how the land cover in Burkina Faso's PAs is changing, and what kinds of human activities are the main drivers. In this study, we examined the change in land use and land cover (LULC) in the Bontioli Nature Reserve (NR), one of Burkina Faso's most important protected areas, and assessed anthropogenic pressure within and around. Landsat imagery (ETM+ and OLI-TIRS) is used to categorise and estimate the change in LULC in 2000, 2010, and 2022 with the Random Forest algorithm on the Google Earth Engine platform. Regression analysis was applied to examine the relationship between the LULC categories and population increase. We found significant changes and correlations in LULC trends and population growth over time. From 2000 to 2022, wooded savanna, tree savanna, and shrub savanna decreased by 20.8 %, 6.8 %, and 4.5 %, respectively, while cropland increased by 26.3 %, along with grass savanna by 5 %. Population growth correlated with increased agriculture and decreased vegetative area with R2 of 0.903 and 0.793, respectively. Efforts should be made to create harmony between humans and nature through various approaches such as nature-based solutions to enable efforts for the reserve sustainable management (SDG15).

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Evaluating water balance components in a tropical ecological zone under land use changes

Water availability for domestic, institutional, and agricultural production is threatened by the continuous change in land use land cover (LULC). These changes in LULC features affect the dynamics of the hydrological processes such as surface runoff, infiltration, and evapotranspiration, resulting in variations in water availability especially, for human consumption and crop yield. Therefore, the Soil and Water Assessment Tool (SWAT) hydrological model was deployed to simulate the Pra River Basin (PRB)’s water balance response to changing LULC from 1986 to 2020. Open and closed forests reduced from an area of 11,071 km2 to 5902 km2 and 5125 km2 to 4990 km2 while settlement and cropland increased from 1100 km2 to 2392 km2 and 5303 km2 to 9320 km2 representing an average annual expansion rate of +24.7 km2/yr and +118 km2/yr respectively over the three decades. The growth in settlement and cropland resulted in an upsurge of more than 33 % and 20 % in surface runoff and water yield respectively, but decreases of over 4 % and 17 % in evapotranspiration and baseflow respectively. These changes have resulted in a seasonal increase in surface runoff, baseflow, and evapotranspiration (ET) during the wet season but a decrease during the dry season. The PRB has experienced a dramatic decline in forest areas, chiefly in areas where settlement and farmlands have been expanded. The areas where surface runoff and water yield showed significant upsurge and reduction in ET are the northwestern and southern enclaves of the basin. These findings can aid in effectively planning and managing land use to control the gradually depleting water resources in the basin.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Morphometric analysis and LULC change dynamics of Nayar watershed for the sustainable watershed management

The morphometric analysis of the watersheds is essential for the conservation of natural resources, including soil, water, and vegetation. The Morphometric analysis defines the linear, areal and relief aspects of the watershed. It involves the comprehensive analysis of various factors such as drainage network, surface water flow, and other topographical features. The aim of this study is to develop a sustainable watershed management strategy for the Nayar watershed based on morphometry and Land Use Land Cover (LULC) change dynamics. The Nayar watershed has a total area of 1956.33 km2. The multispectral satellite imagery, Digital Elevation Model (DEM) data, and Survey of India (SOI) Toposheets were used for understanding the topographical and morphological characteristics along with the LULC change dynamics. The findings of this research conclude that the Nayar watershed has parallel and dendritic drainage patterns with high relief. According to the LULC change dynamics, the Nayar watershed's area change comprising 57.60 km2 and 57.15 km2, are from Agricultural Land to Wasteland and from Forest Cover to Wasteland, respectively. This shift in the hilly region will increase the risks of landslides and erosion. Furthermore, the change of 110.03 km2 area of Forest Cover to Agricultural Land raises further challenges due to loss of natural vegetation in long run. In summary the change in LULC of Nayar watershed is vulnerable to risk of natural calamities like landslide, erosion. This work would be helpful to many experts and decision-makers for sustainable watershed management and natural resource management.

Urban forest cover and ecosystem service response to fire varies across California communities

Urban fires that result from wildfires are an emerging, extreme event affecting communities and urban forests globally. However, much of the fire effects on urban ecosystems literature is primarily focused on Wildland-Urban Interface (WUI) areas, correlates of building loss, risk mitigation, and wildland vegetation and fuels. Three recent urban fires in California USA provided an opportunity to explore these effects on urban forests: the 2017 Tubbs (Santa Rosa) and Thomas (Ventura) and the 2018 Camp fire that burned Paradise. Accordingly, we analyzed pre- and post-fire neighborhood level urban tree cover (nUTC) change over 5 years using Sentinel and LiDAR data (10m resolution). Then, we explored the effects of fire severity on changes in several regulating ecosystem services (e.g., carbon, air pollution, stormwater). Findings from the Rapid Assessment of Vegetation Condition after Wildfire severity processes and other geospatial datasets show that fire effects were patchy with fire severity within neighborhoods ranging from unburned to extreme. We found that ~5 years after the fires, and relative to adjacent non-fire affected neighborhoods, Ventura and Santa Rosa’s nUTC is recovering to pre-fire levels while Paradise’s nUTC is being consistently lost over time. Ventura and Santa Rosa had 20-25% of their fire affected area outside established WUI boundaries. However, local-scale urban tree cover and ecosystem service supply are lagged, and recovery time depends on the surrounding biome and socio-ecological context. In inland, higher elevation communities – like Paradise – the recovery of nUTC and the associated ecosystem services might materialize over a much longer timeframe. Our study provides a roadmap to assess the response of urban wildfire-affected tree cover and ecosystem services across space and time. It is also one of the first assessments of fire effects on urban ecosystems and communities outside of WUI boundaries that are now experiencing increased threat from wildfires globally.

Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network

The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.

Constraining the origin of the Norwegian strandflat – The influence of isostatic and dynamic surface changes

The Norwegian strandflat is a prominent low-relief bedrock surface found near sea level along most of the west coast of Norway. Its origin has been discussed throughout the last 130 years but is yet to be resolved. Some studies suggest that the strandflat represent a tropical weathering front of Mesozoic age that has since been buried and re-exhumed, while others relate its origin to Pleistocene periglacial and glacial processes and/or wave-induced weathering and erosion. Previous interpretations of the strandflat have considered postglacial isostatic uplift, but the impacts of isostatic changes due to glacial erosion and deposition, as well as dynamic surface changes driven by mantle convection, have been largely overlooked. Here we examine how geomorphological-driven isostatic changes and dynamic surface changes have influenced the land-surface elevation along the Norwegian coast during late Pliocene-Quaternary (the last ca. 3 million years). We employ quantitative estimates of glacial erosion and deposition to assess the flexural isostatic response from the resulting load changes. Our analyses show that patterns of geomorphic isostatic adjustments and dynamic surface changes are generally not reflected in the present elevation of the strandflat. Only the loading effect from the deposition of the North Sea Fan can clearly be correlated with the submerged strandflat found near Stad (~62 °N). Our results imply that if the strandflat formed synchronously along the Norwegian coast as a flat surface at sea level, the strandflat we observe today must have developed after the majority of late Pliocene-Quaternary glacial erosion took place, but prior to the main deposition of the North Sea Fan. This would place strandflat formation within the last few glacial cycles, but before the Last Glacial Maximum (LGM). This inferred pre-LGM age of the strandflat is generally consistent with cosmogenic nuclide exposure ages and observed striations on the strandflat. Finally, we examine ice cover and land-surface changes relative to sea level during the last 80,000 years and find no extended periods favorable for synchronous strandflat formation across all regions along the Norwegian coast. This implies that either the strandflat is diachronous, or that the processes of formation have either been extremely fast under certain conditions or are independent of sea level, for instance related to glacial erosion.