The spatiotemporal patterns of water deficit and heat stress for UK rapeseed are unknown, although both stresses have been increasing due to climate change. Using historical meteorological (potential evapotranspiration (PET), rainfall and the daily maximum temperature (T-max)), soil moisture (SM), and crop productivity data, this study aimed to: a) investigate the spatiotemporal distributions of water deficit and heat stress for UK rapeseed cropping from 1981 to 2020; b) assess whether defined water deficit indices are more closely associated with vegetation health indicators than UK landscape Standardised Precipitation Index (SPI)/Standardised Precipitation Evapotranspiration Index (SPEI); c) examine the stress-rapeseed yield relationships for 2016-2022. Water deficit indices, including the cumulative water deficit (CWD), integrated PET, rainfall and SM to represent crop-level water supply-demand imbalance, while heat stress index (f(HS)) quantified exceedance of rapeseed-specific temperature thresholds (25 degrees C, etc.), unlike meteorological SPI/SPEI. Results showed that both stresses gradually intensified during flowering, albeit from low initial levels. Heat stress affected a larger area nationally and increased more rapidly, with coverage exceeding 51% of arable land in 2018, while regions experiencing both stresses expanded more slowly. Water deficit indices had stronger and more consistent correlations with growth status indicators of UK natural grassland (r = -0.18 to -0.70), particularly in water-limited southeastern regions (r = -0.62 to -0.70), compared to SPI/SPEI, suggesting their potential for assessing water deficit or productivity of rapeseed. Both stresses were generally negatively associated with rapeseed yields. Water deficit indices had stronger regression relationships between rapeseed yields than heat stress metrics, suggesting they were better yield predictors. The strongest relationship between rapeseed yield and CWD (p < 0.05), was observed in the northwestern region (R & sup2; = 0.92), whereas more moderate relationships were found in other three major growing regions (0.48 <= R-2 <= 0.68). The predictive power of combined stress indicators for rapeseed yields varied across locations. Despite the low intensities and slowly increasing trends of the studied stresses, their negative impacts-particularly those stemming from water deficit-on rapeseed yields underscore the need for further research to develop adaptation or mitigation strategies that support precision crop management for sustainable rapeseed production in the UK.

The Indus River Basin has undergone significant hydrological changes due to climate change, leading to increased flood frequency, posing a risk to agriculture-based food security. This study focused on rain-induced flood events from August to September 2022 across the provinces of Punjab, Sindh, and Baluchistan using Google Earth Engine, Sentinel-1A Synthetic Aperture Radar (SAR) data, and Landcover datasets. Flooding caused considerable damage to agricultural land and communities, affecting 49,602.92 km2 of land. In Sindh, the total land inundated is 2,042.1 km2 with 915.9 km2 of agricultural land and 609.8 km2 of built-up areas affected. Hence, district-level damage assessment includes Sukkur (497.1 km2), Sanghar (565.2 km2), and Khairpur (979.9 km2). In Baluchistan province, the flooded area was 10,733.4 km2. The agricultural land affected was 674.8 km2, and 47.8 km2 of built-up land. Heavy rain further intensified flooding affected 1002.2 km2 in Jhal Magsi, 7,266.5 km2 in Khuzdar, and 2,464.7 km2 in Lasbella. In Punjab, 4001.3 km2 of land flooded including 297.6 km2 of built-up areas, and 776.9 km2 affected agricultural land. At the District-level affected areas were D.G. Khan (1,871.3 km2), Muzaffargarh (620 km2), and Rajanpur (1,509.7 km2). integration of remote sensing and GEE provided crucial flood insights and climate risk reduction strategies, especially in data-scarce regions.

Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals-reflectance across wavelengths-from liana-infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering 775 ha of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short-wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy-reflectance signal are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Understanding how lake ecosystems respond to anthropogenic disturbance and climate change is crucial to apply suitable adaptation and remediation measures for their sustainable management and protection. However, the risk of lake eutrophication is dependent upon inherent lake system characteristics and ecological behaviour. To be able to account for all the varying factors that drive changes in lake systems, a classification scheme that can assign levels of lake resilience (or sensitivity) to change is required. For most lakes around the world there is a lack of data to apply such an approach, with profound implications on the ability to map, monitor and understand regional and global lake behaviour in response to climate change, land use/land management and further disturbance pressures. In this study, lake eutrophication risk was assessed using a typology-based approach developed using remotely sensed, modelled and open access datasets from 738 lakes and their catchments worldwide. The new framework classifies lakes according to (i) their natural sensitivity to eutrophication and, by extension, (ii) their resilience to external pressures. Support for the approach is evidenced from independent space-based water quality assessment illustrating that lakes with higher risk of eutrophication typically exhibit higher chlorophyll-a concentrations. Whilst other classifications schemes exist, the novelty of the proposed approach is that it combines explanatory variables (ten lake and catchment metrics) to develop a framework with global applicability. Results showed that 93% of the study sites exhibited low-to-moderate risk of the catchment on the water body in terms of accelerating or slowing down nutrient loading, whereas 6% of the study sites exhibited high sensitivity to such external influence, i.e. risk potential for having high rates of eutrophication. Knowing the rate at which each water body is expected to, or could become, more eutrophic provides a frame of reference in the prediction of the effect of human pressures and climate change on lake systems, both now and in the future. Targeted monitoring of more sensitive lakes can ensure that early warning signs of potentially irreversible or damaging water body change will not be missed. This global risk eutrophication assessment framework can, therefore, help to better safeguard, manage and protect freshwater resources for future societal and ecosystem wellbeing and sustainable economic growth.

Deforestation and forest degradation lead to an increase in the level of carbon in the atmosphere and disrupted the global carbon cycle. The tropical forest has received a lot of interest since it contributes around 60% of the total global forest carbon. By enhancing carbon sink, tropical forests have a great potential in mitigating climate change. Assessing aboveground biomass (AGB) and carbon stock through field inventories is crucial for this purpose as it provides the most accurate result. The research was conducted at Danum Valley Conservation Area and INFAPRO in Sabah, Malaysia. An earlier study over 35 years ago at this site suggests that restored forests accrue AGB at twice the rate of regenerating forests, though the cause of this difference between treatments is unclear. Thus, this study will focus on three principal study sites which are restored, naturally regenerating and old-growth forests to determine the forest’s potential to sequester and store carbon in the forest ecosystem. These three sites were chosen because it is a well-established plot from the earlier inventory over the last seven years. The field measuring method is a non-destructive methodology. Tree parameters such as diameter at breast height (DBH), tree height and tree species diversity were collected for calculating AGB using a species-specific allometric equation. Results showed a positive correlation between tree species, diameter at breast height, and biomass/carbon stock across three different forest treatments. The active restoration increases up to 151% carbon stock whilst the old-growth forest increased by 34% and natural regeneration increased by 73%, which active restoration can be the best solution for forest treatment. The outcome of this study will increase the ability of forest authorities and the Malaysian government to effective monitoring of carbon stock for establishing reliable standard guidelines in measuring deforestation and forest degradation toward achieving sustainable forest management.

Current policy is driving renewed impetus to restore forests to return ecological function, protect species, sequester carbon and secure livelihoods. Here we assess the contribution of tree planting to ecosystem restoration in tropical and sub-tropical Asia; we synthesize evidence on mortality and growth of planted trees at 176 sites and assess structural and biodiversity recovery of co-located actively restored and naturally regenerating forest plots. Mean mortality of planted trees was 18% 1 year after planting, increasing to 44% after 5 years. Mortality varied strongly by site and was typically ca 20% higher in open areas than degraded forest, with height at planting positively affecting survival. Size-standardized growth rates were negatively related to species-level wood density in degraded forest and plantations enrichment settings. Based on community-level data from 11 landscapes, active restoration resulted in faster accumulation of tree basal area and structural properties were closer to old-growth reference sites, relative to natural regeneration, but tree species richness did not differ. High variability in outcomes across sites indicates that planting for restoration is potentially rewarding but risky and context-dependent. Restoration projects must prepare for and manage commonly occurring challenges and align with efforts to protect and reconnect remaining forest areas. The abstract of this article is available in Bahasa Indonesia in the electronic supplementary material. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.

Mudflats are critical interfaces between the marine and terrestrial environment of muddy coasts. Mapping mudflat surface sediment types and analyzing their dynamic changes are helpful to understand the variations of sedimentary environments and their responses to tidal current movements. To obtain the surface sediment types of the mudflats, this study first took the chlorophyll-a content as an environmental variable and combined it with satellite spectral reflectance data processed by fractional-order derivative (FOD) to establish a machine learning model for retrieving sediment component content (SCC) of the sand, silt, and clay. Then, the three SCCs were linear equilibrium corrected and inputted into Folk's ternary classification model, and the spatial distribution map of the sediment types was accurately obtained. The results showed that the use of the FODs for satellite image enhancement could provide richer spectral information and improve the accuracy of the chlorophyll-a content and SCCs inversed by the grid search-support vector machine (GS-SVM) model. When compared to direct spectral modeling, considering the effect of the chlorophyll-a content as the key input factor, the coefficients of determination (R2) for the sand, silt, and clay contents inversion were increased by 15.1, 9.2, and 38.2%, and the root mean square error (RMSE) were reduced by 9.7, 5.5, and 2.5%, respectively. Surface sediment type maps obtained from the three SCCs showed that the main sediments in the mudflats on the central coast of Jiangsu Province, China were silty sand and sandy silt. Between 2019 and 2021, the sediment types in 84.46% of the total area were unchanged. For the changing area, the sediment transitioned toward finer-grained types near the seawall of the coastal region. Whereas in the offshore area, especially along the edge of the huge tidal channels, the sediments tended to be coarser because of strong hydrodynamic screening. The findings will be helpful for improving the ability of mudflat sedimentation environmental monitoring and spatial resource management via remote sensing.

Climate change plays a key role in changing vegetation productivity dynamics, which ultimately affect the hydrological cycle of a watershed through evapotranspiration (ET). Trends and correlation analysis were conducted to investigate vegetation responses across the whole Upper Jhelum River Basin (UJRB) in the northeast of Pakistan using the normalized difference vegetation index (NDVI), climate variables, and river flow data at inter-annual/monthly scales between 1982 and 2015. The spatial variability in trends calculated with the Mann-Kendall (MK) trend test on NDVI and climate data was assessed considering five dominant land use/cover types. The inter-annual NDVI in four out of five vegetation types showed a consistent increase over the 34-year study period; the exception was for herbaceous vegetation (HV), which increased until the end of the 1990s and then decreased slightly in subsequent years. In spring, significant (p<0.05) increasing trends were found in the NDVI of all vegetation types. Minimum temperature (Tmin) showed a significant increase during spring, while maximum temperature (Tmax) decreased significantly during summer. Average annual increase in Tmin (1.54°C) was much higher than Tmax (0.37°C) over 34 years in the UJRB. Hence, Tmin appears to have an enhancing effect on vegetation productivity over the UJRB. A significant increase in NDVI, Tmin and Tmax during spring may have contributed to reductions in spring river flow by enhancing evapotranspiration observed in the watershed of UJRB. These findings provide valuable information to improve our knowledge and understanding about the interlinkages between vegetation, climate and river flow at a watershed scale.

With the rapid development of wind power generation, many marine wind farms have been developed on the offshore intertidal sandbank (OIS) along the coastal regions of Jiangsu Province, China during the last decade. In order to quantitatively assess the stability of offshore wind turbines and their induced topographic changes on the OIS, a digital elevation model (DEM)-based analysis supported by satellite remote sensing is adopted in the present study. Taking the Liangsha OIS at the middle of Jiangsu coast, China as the research area, we first used an enhanced waterline method (EWM) to construct the 30 m resolution DEMs for the years 2014 and 2018 with the embedment of tidal creeks to effectively express the detailed characteristics of the micro-terrain. Then, a hypothetical sandbank surface discrimination method (HSSDM) was proposed. By comparing the height difference between the hypothetical and the real terrain surface during the operation period, the wind turbine-induced topographic change rate (TCR) was estimated from the DEM of 2018. The results show that 73.47% of the 49 wind turbines in the Liangsha OIS have an erosional/depositional balanced influence on the intertidal sand body, 8.16% show a weak depositional influence, and 18.36% lead to weak erosion. The average erosional depth, 58.6 cm, reached nearly 6% to 10% of the maximum possible erosion estimated by the hydrodynamic model. Furtherly, using two DEMs for the years 2014 and 2018, the topographic change depths at the location of wind turbines were calculated. By comparing the wind turbine-induced terrain change with the naturally erosional/depositional depths of the OIS, the average contribution rate caused by the wind turbines achieved 42.17%, which meant that the impact of wind turbines on terrain changes could not be ignored. This work shows the potential of utilizing satellite-based remote sensing to monitor topographic changes in the OIS and to assess the influence of morphological variations caused by wind turbines, which will be helpful for offshore wind farm planning and intertidal environment protection.

Offshore wind farms have developed rapidly in Jiangsu Province, China, over the last decade. The existence of offshore wind turbines will inevitably impact hydrological and sedimentary environments. In this paper, a digital elevation model (DEM) of the intertidal sandbank in southern Jiangsu Province from 2018 to 2020 was constructed based on the improved remote sensing waterline method. On this basis, the stability of the sandbank was analysed, and combined with the hypothetical sandbank surface discrimination method (HSSDM), the erosional/depositional influences of wind turbine construction on topography were quantitatively analysed. The results show that due to the frequent oscillations of the tidal channels, only 35.03% of the study area has a stable topography, and more than 90% of the wind turbines in all years have a balanced impact on the intensity of topographic change, and all see a small reduction in their impact in the following year. The remaining wind turbines with erosional/depositional impacts are mainly located in areas with unstable topography, but the overall impact of all wind turbines is balanced in 2018–2020. The impact of wind turbines on topography is both erosional and depositional, but the overall intensity of the impact is not significant. This study demonstrates the quantitative effects of wind turbine construction on topography and provides some help for wind turbine construction site selection and monitoring after turbine completion.

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Due to challenging conditions of field survey techniques, it is difficult to measure the topography of tidal flats, an important parameter to understanding the evolution and dynamics of the constantly changing zone. This study used remotely sensed sediment moisture estimates to retrieve tidal flat elevation. The method is based on the observation that the intertidal zone is gradually exposed from land to sea at low tide, meaning that higher elevations contain less moisture. Here, we investigate the nature of the relationship between reflectance and moisture content from Landsat Enhanced Thematic Mapper Plus images and the study areas as a proxy for mapping the elevation of an exposed tidal flat surface. Statistical analysis confirmed a negative correlation between moisture and elevation; however, the correlation coefficient was relatively weak, and the slope of the intersecting tidal creek was found to be a crucial factor affecting this relationship. After segmenting the slope to correspond to areas of tidal flat and nontidal flat surfaces, the correlation coefficient of the moisture and elevation increased significantly. A retrieval model was then developed to generate the tidal flat elevations of different slope grades. After verification, the retrieval accuracy of the model was up to 17.3 cm. This research study demonstrated that the remotely sensed moisture method is suitable for monitoring the surface elevation of tidal flats.

In contrast to the general trend of global glacier recession, several studies have reported stable or advancing glaciers in the sub-basins of the Karakoram - the so-called 'Karakoram Anomaly'. Snow and glacier ice melt are important components of the hydrological system and represent a major water supply for the region. In the absence of reliable and comprehensive in situ measurements, Earth observation (EO) and remote sensing retrievals of snow water equivalent (SWE), water balance (WB) and hydro-meteorological variables can be used to infer changes in snow/glacier melting. We used linear regression and Mann-Kendall (MK) methods to assess trends in annual and seasonal variables derived from satellite, gridded and reanalysis datasets of the Global Land Data Assimilation System (GLDAS) and Terraclimate. The spatial and temporal pattern of snow accumulation and ablation varies across the study region. The spatial distribution of annual and winter SWE showed a significant (p < 0.05) positive trend in the western Karakoram. This snow accumulation may be attributed to a significant decrease in summertime maximum temperature (Tmax) in the western Karakoram. By contrast, in the eastern Karakoram, significant negative trends in annual WB indicate depletion of water storage. These results, using a different dataset and approach, are consistent with previous studies where glacier mass balances have been found to be stable or positive in the Karakoram, but become more negative further east and into the Himalaya. These changes in hydrology at highly glacierised catchments have considerable im-plications for water availability and supply to large downstream populations.

Climate change has implications for water resources by increasing temperature, shifting precipitation patterns and altering the timing of snowfall and glacier melt, leading to shifts in the seasonality of river flows. Here, the Soil & Water Assessment Tool was run using downscaled precipitation and temperature projections from five global climate models (GCMs) and their multi-model mean to estimate the potential impact of climate change on water balance components in sub-basins of the Upper Indus Basin (UIB) under two emission (RCP4.5 and RCP8.5) and future (2020–2050 and 2070–2100) scenarios. Warming of above 6 °C relative to baseline (1974–2004) is projected for the UIB by the end of the century (2070–2100), but the spread of annual precipitation projections among GCMs is large (+16 to −28%), and even larger for seasonal precipitation (+91 to −48%). Compared to the baseline, an increase in summer precipitation (RCP8.5: +36.7%) and a decrease in winter precipitation were projected (RCP8.5: −16.9%), with an increase in average annual water yield from the nival–glacial regime and river flow peaking 1 month earlier. We conclude that predicted warming during winter and spring could substantially affect the seasonal river flows, with important implications for water supplies.

Selective logging has affected large areas of tropical forests and there is increasing interest in how to manage selectively logged forests to enhance recovery. However, the impacts of logging and active restoration, by liberation cutting and enrichment planting, on tree community composition are poorly understood compared to trajectories of biomass recovery. Here, we assess the long-term impacts of selective logging and active restoration for biomass recovery on tree species diversity, community composition, and forest structure. We censused all stems >= 2 cm diameter at breast height (DBH) on 46 permanent plots in unlogged, primary forest in the Danum Valley Conservation Area (DVCA; 12 plots, totalling 0.6 ha) and in sites logged 23-35 years prior to the census in the Ulu Segama Forest Reserve adjacent to DVCA (34 plots, totalling 1.7 ha) in Sabah, Malaysian Borneo. Active restoration treatments, including enrichment planting and climber cutting, were implemented on 17 of the logged forest plots 12-24 years prior to the census. Total plot-level basal area and pole (5-10 cm DBH) stem density were lower in logged than unlogged forests, however no difference was found in stem density amongst saplings (2-5 cm DBH) or established trees (>= 10 cm DBH). Neither basal area, nor plot-level stem density varied with time since logging at any size class, although sapling and pole stem densities were lower in actively restored than naturally regenerating logged forest. Sapling species diversity was lower in logged than unlogged forest, however there were no other significant effects of logging on tree species richness or diversity indices. Tree species composition, however, differed between logged and unlogged forests across all stem size classes (PER-MANOVA), reflected by 23 significant indicator species that were only present in unlogged forest. PER-MANOVA tests revealed no evidence that overall species composition changed with time since logging or with active restoration treatments at any size class. However, when naturally regenerating and actively restored communities were compared, two indicator species were identified in naturally regenerating forest and three in actively restored forests. Together our results suggest that selective logging has a lasting effect on tree community composition regardless of active restoration treatments and, even when species richness and diversity are stable, species composition remains distinct from unlogged forest for more than two decades post-harvest. Active restoration efforts should be targeted, monitored, and refined to try to ensure positive outcomes for multiple metrics of forest recovery.

Abstract The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object‐ versus pixel‐based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object‐based approaches which require refinement in order to accurately segment imagery across contiguous closed‐canopy forests. We conclude that the decision on whether to use a pixel‐ or object‐based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management.

Muddy intertidal flats are important resources for coastal development activities, such as coastal shoal reclamation and mudflat cultivation. Understanding changes in intertidal flat topography is essential for intertidal zone development, management, and protection. As an essential topographic factor, the slope of an intertidal flat can effectively express profile morphology in the cross-shore direction, reflect topographic undulation in the long-shore direction, and be indicative of intertidal flat erosion and deposition. Previously, intertidal flat slopes have been estimated by the two-temporal average gradient (TTAG) method using two spatially separated waterlines derived from satellite remote sensing data and their relevant water elevations. However, this method does not adequately reflect the profile morphology of different coastline types, especially sinuous coastlines, and is highly sensitive to the selected waterlines. This study proposes an effective strategy for estimating slope considering not only the characteristics of the vertical terrain undulation but also the horizontal plane-form shape of the coastline. Using waterlines extracted from sequential satellite images and their corresponding tidal height information, the slopes were estimated utilizing a profile morphology discriminant inferential (PMDI) method on straight coasts and a digital elevation model (DEM) method on sinuous coasts. We obtained the following results: (a) for straight coasts, by judging the various shapes of the coastal profiles and curve-fitting separately, the PMDI method achieved significant improvements in accuracy and robustness of slope estimates compared to results obtained using the TTAG method; and (b) for sinuous coasts, the DEM-based method performed better for addressing the intersecting waterline issue and accurately retrieved the slope, although this accuracy is strongly dependent on the areal coverage of the DEM and the precision of the terrain inversion. Using this new paired methodology, we estimated that the average slope of the intertidal flats in the Mid-Jiangsu Province between the Sheyang Estuary, the Liangduo Estuary, and the Lianxing Port was 0.96‰. In general, the slopes from north to south in this coastal area exhibited a steep-gentle-steep pattern, which was consistent with in situ observed data. We conclude that stratifying the coastlines according to their plane-form shapes and applying different methods to straight and sinuous coastlines can result in a more robust estimation of coastal slope for muddy intertidal flats than previous TTAG methods. This work demonstrates the utility of satellite-based remote sensing for retrieving critical coastal geomorphological information in dynamic terrains where in situ data are difficult to obtain.

Remote sensing holds great potential for detecting stress in vegetation caused by hydrocarbons, but we need to better understand the effects of hydrocarbons on plant growth and specific spectral expression. Willow (Salix viminalis var. Tora) cuttings and maize (Zea mays var. Lapriora) seedlings were grown in pots of loam soil containing a hydrocarbon-contaminated layer at the base of the pot (crude or refined oil) at concentrations of 0.5, 5, or 50 g·kg−1. Chlorophyll concentration, biomass, and growth of plants were determined through destructive and nondestructive sampling, whilst reflectance measurements were made using portable hyperspectral spectrometers. All biophysical (chlorophyll concentration and growth) variables decreased in the presence of high concentrations of hydrocarbons, but at lower concentrations an increase in growth and chlorophyll were often observed with respect to nonpolluted plants, suggesting a biphasic response to hydrocarbon presence. Absorption features were identified that related strongly to pigment concentration and biomass. Variations in absorption feature characteristics (band depth, band area, and band width) were dependent upon the hydrocarbon concentration and type, and showed the same biphasic pattern noted in the biophysical measurements. This study demonstrates that the response of plants to hydrocarbon pollution varies according to hydrocarbon concentration and that remote sensing has the potential to both detect and monitor the variable impacts of pollution in the landscape.

Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing offers a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global specificity of spectral signals from liana-infested tree crowns and forest stands, examining the underlying mechanisms. We compiled a database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering hectares of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, corroborated by field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signature at canopy and stand scales across sites. This signature mainly arises at the canopy level due to direct effects of leaf angles, resulting in a larger apparent leaf area, and indirect effects from increased light scattering in the NIR and SWIR regions, linked to lianas’ less costly leaf construction compared to trees. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits identified are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life-history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas’ adaptive similarities across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits. Open data statement Of the 17 datasets used, 10 are published and publicly accessible, with links provided in this submission (Appendix S1: Section S1). Upon acceptance, remaining seven datasets will be provided via Smithsonian’s Dspace. The open-source model code is available as R-package ccrtm ( https://cran.r-project.org/web/packages/ccrtm/index.html ) and on github ( https://github.com/MarcoDVisser/ccrtm ). Code will be archived in Zenodo should the manuscript be accepted for publication

Uganda is one of the four top refugee-hosting countries in the world and the largest in Africa, a product of the surrounding geopolitical context and Uganda's progressive refugee laws and policy. Refugees in Uganda are afforded freedom of movement, the right to work, the provision of social services, and are allocated land for residential and agricultural use in settlements. High dependence on natural resources to meet needs for shelter, food, fuel and income generation has caused environmental change and degradation in and around refugee settlements. Increasing demand for fuelwood and timber amongst growing populations puts strain on forest resources, threatening biodiversity and the provision of ecosystem services critical to livelihoods. Yet these dynamics differ depending on socio-cultural, political-economic and ecological factors specific to local settlement contexts. This report generates a nuanced view of environment–livelihood interactions, informing recommendations for protracted refugee contexts. The research aims to: 'Explore how displacement impacts on environmental change and the subsequent development of sustainable livelihoods' through the following objectives:• Examine the nature and extent of environmental change in different settlements using satellite remote sensing and field-based observations.• Understand the various ways in which refugees and host communities, living in or around new and long-term refugee settlements, interact with the environment and ecosystem services.• Explore the variety of knowledges and values of refugee and host households for understanding how the environment is used.• Offer recommendations for the management of increasing pressure on land resources within sustainable livelihood practices for development and policy programming.