• Energy Research

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.

This dataset consists of structure, biomass (carbon density) and biodiversity (plant species richness) from forest inventory plots at forest restoration sites in South and Southeast Asia and the code for the analyses of these data as conducted in Banin, Raine et al (2023). The recorded data consists of plot level censuses carried out up to May 2021 collated from published studies, grey literature and data provided by co-authors. This represents the collation of data from 11 sites in areas where disturbance had led to the clearance or degradation of natural forest. Plots where tree seedlings were planted (active restoration) and plots where no seedling planting took place (natural regeneration) were censused for structure, biomass and/or biodiversity. Some of the sites in the dataset also recorded data at old growth forest plots for reference, and/or provided repeat measures of forest metrics over time. The dataset also includes the code used for analysis of this plot level data, used to compare the outcome of different restoration approaches. Data were compiled from i) publications which contained the necessary information or ii) primary data supplied by dataset and paper co-authors. The search and screening process is described in full in the documentation. The data have been standardised so they could be presented in common units across the dataset and in some cases, categorised based either on numbers or on text descriptions within the publications. For comparisons, plot-level data were matched according to certain criteria (e.g. disturbance history, time, location). Calculations we made to draw comparisons (log response ratios, as described in full in the documentation). All data were checked at least once by co-lead author and spot-checked by another co-lead author.

article i nfo Lakes are major repositories of biodiversity, provide multiple ecosystem services and are widely recognised as key indicators of environmental change. However, studies of lake response to drivers of change at a pan-European scale are exceptionally rare. The need for such studies has been given renewed impetus by concerns over environmental change and because of international policies, such as the EU Water Framework Directive (WFD), which impose legal obligations to monitor the condition of European lakes towards sustain- able systems with good ecological status. This has highlighted the need for methods that can be widely ap- plied across large spatial and temporal scales and produce comparable results. Remote sensing promises much in terms of information provision, but the spatial transferability and temporal repeatability of methods and relationships observed at individual or regional case studies remains unproven at the continental scale. This study demonstrates that NOAA Advanced Very High Resolution Radiometer (AVHRR) thermal data are capable of producing highly accurate (R 2 >0.9) lake surface temperature (LST) estimates in lakes with vari- able hydromorphological characteristics and contrasting thermal regimes. Validation of the approach using archived AVHRR thermal data for Lake Geneva produced observations that were consistent with field data for equivalent time periods. This approach provides the basis for generalizing temporal and spatial trends in European lake surface temperature over several decades and confirms the potential of the full 30 year NOAA AVHRR archive to can provide AVHRR-derived LST estimates to help inform European policies on lake water quality.

During the 2005–2007 June–September ablation seasons, meteorological conditions were recorded on the lower and upper parts of the debris‐covered ablation zone of Miage Glacier, Italy. In 2005, debris temperature and subdebris ice melt were also monitored at 25 points with debris thickness of 0.04–0.55 m, spread over 5 km2 of the glacier. The radiative fluxes were directly measured, and near‐closure of the surface energy balance is achieved, providing support for the bulk aerodynamic calculation of the turbulent fluxes. Surface‐layer meteorology and energy fluxes are dominated by the pattern of incoming solar radiation which heats the debris, driving strong convection. Mean measured subdebris ice melt rates are 6–33 mm d−1, and mean debris thermal conductivity is 0.96 W m−1 K−1, displaying a weak positive relationship with debris thickness. Mean seasonal values of the net shortwave, net longwave, and debris heat fluxes show little variation between years, despite contrasting meteorological conditions, while the turbulent latent (evaporative) heat flux was more than twice as large in the wet summer of 2007 compared with 2005. The increase in energy output from the debris surface in response to increasing surface temperature means that subdebris ice melt rates are fairly insensitive to atmospheric temperature variations in contrast to debris‐free glaciers. Improved knowledge of spatial patterns of debris thickness distribution and 2 m air temperature, and the controls on evaporation of rainwater from the surface, are needed for distributed physically based melt modeling of debris‐covered glaciers.

Abstract El Niño events generate periods of relatively low precipitation, low cloud cover and high temperature over the rainforests of Southeast Asia, but their impact on tree physiology remains poorly understood. Here we use remote sensing and functional trait approaches—commonly used to understand plant acclimation to environmental fluctuations—to evaluate rainforest responses to an El Niño event at a site in northern Borneo. Spaceborne measurements (i.e. normalised difference vegetation index calculated from Moderate Resolution Imaging Spectroradiometer data) show the rainforest canopy greened throughout 2015, coinciding with a strengthening of the El Niño event in Sabah, Malaysia, then lost greenness in early 2016, when the El Niño was at its peak. Leaf chemical and structural traits measured for mature leaves of 65 species (104 branches from 99 tree canopies), during and after this El Niño event revealed that chlorophyll and carotenoid concentrations were 35% higher in mid 2015 than in mid 2016. Foliar concentrations of the nutrients N, P, K and Mg did not vary, suggesting the mineralisation and transportation processes were unaffected by the El Niño event. Leaves contained more phenolics, tannins and cellulose but less Ca and lignin during the El Niño event, with concentration shifts varying strongly among species. These changes in functional traits were also apparent in hyperspectral reflectance data collected using a field spectrometer, particularly in the shortwave infrared region. Leaf-level acclimation and leaf turnover could have driven the trait changes observed. We argue that trees were not water limited in the initial phase of the El Niño event, and responded by flushing new leaves, seen in the canopy greening trend and higher pigment concentrations (associated with young leaves); we argue that high evaporative demand and depleted soil water eventually caused leaves to drop in 2016. However, further studies are needed to confirm these ideas. Time-series of vegetation dynamics obtained from space can only be understood if changes in functional traits, as well as the quantity of leaves in canopies, are monitored on the ground.

The full realization of the potential of remote sensing as a source of environmental information requires an ability to generalize in space and time. Here, the ability to generalize in space was investigated through an analysis of the transferability of predictive relations for the estimation of tropical forest biomass from Landsat TM data between sites in Brazil, Malaysia and Thailand. The data sets for each test site were acquired and processed in a similar fashion to facilitate the analyses. Three types of predictive relation, based on vegetation indices, multiple regression and feedforward neural networks, were developed for biomass estimation at each site. For each site, the strongest relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed specifically for the site (r>0.71, significant at the 99% level of confidence). However, with each type of approach problems in transferring a relation to another site were observed. In particular, it was apparent that the accuracy of prediction, as indicated by the correlation coefficient between predicted and measured biomass, declined when a relation was transferred to a site other than that upon which it was developed. Part of this problem lies with the observed variation in the relative contribution of the different spectral wavebands to predictive relations for biomass estimation between sites. It was, for example, apparent that the spectral composition of the vegetation indices most strongly related to biomass differed greatly between the sites. Consequently, the relationship between predicted and measured biomass derived from vegetation indices differed markedly in both strength and direction between sites. Although the incorporation of test site location information into an analysis resulted in an increase in the strength of the relationship between predicted and actual biomass, considerable further research is required on the problems associated with transferring predictive relations.

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.