A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficultie

A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use, and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lake and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 137 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30&thinsp;m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh&thinsp;∕&thinsp;dA) for all lakes. Nearly all lakes showed a linear regression, and 42&thinsp;% of the lakes showed a strong linear relationship with a R2&thinsp;&gt;&thinsp;0.8, an average R2 of 0.91 and a standard deviation of 0.05. For these lakes and for lakes with a nearly constant lake area (coefficient of variation &lt;&thinsp;0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage and (3) a predominant lack of data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the US, Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97 with a standard deviation of 0.041, and a normalized RMSE of 7.42&thinsp;%. These results show a high potential for measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will not only provide a historical lake and reservoir volume variation record, but will also help to improve our understanding of the behaviour of lakes and reservoirs and their representation in (large-scale) hydrological models.</p

Evaluation of ESA CCI prototype land cover map at 20m

In September 2017, the ESA CCI Land Cover Team released a prototype land cover (LC) map at 20 m resolution over Africa for the year 2016. This is the first LC map produced at such a high resolution covering an entire continent for the year 2016. To help improve the quality of this product, we have assessed its overall accuracy and identified regions where the map should be improved. We have compared the product against two independent datasets developed within the Copernicus Global Land Services (CGLS): a reference land cover dataset at a 10 m resolution, which has been used as training data to produce the LC map at 100 m over Africa for the year 2015 (http://land.copernicus.eu/global/products/lc); and an independent validation dataset at a 10 m resolution, which has been developed by CGLS for independent assessment of land cover maps at resolutions finer than 100 m. According to our estimates, overall accuracy of the African CCI LC at 20 m is approximately 65%. We have highlighted regions where the spatial distribution of such classes as shrubs, crops and trees should be improved before the map at 20 m could be used as input for research questions, e.g. conservation of biodiversity, crop monitoring and climate modelling

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

A Land Cover Map of Africa. Carte de l'Occupation du Sol de l'Afrique.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Developing and applying a multi-purpose land cover validation dataset for Africa

The production of global land cover products has accelerated significantly over the past decade thanks to the availability of higher spatial and temporal resolution satellite data and increased computation capabilities. The quality of these products should be assessed according to internationally promoted requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 validation). Providing updated accuracies for the yearly maps would require considerable effort for collecting validation datasets. To save time and effort on data collection, validation datasets should be designed to suit multiple map assessments and should be easily adjustable for a timely validation of new releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-purpose assessments and its applicability is demonstrated in three different assessments focusing on validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. The validation dataset is generated primarily to validate the newly released 100 m spatial resolution land cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 100 m × 100 m. Within site, reference land cover information was collected at 100 subpixels of 10 m × 10 m allowing the land cover information to be suitable for different resolution and legends. Firstly, using this dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6 ± 2.1% (at 95% confidence level) for the African continent. Fraction cover products were found to have mean absolute errors of 9.3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). Overall accuracies for these perspectives vary between 73.7% ± 2.1% and 93.5% ± 0.9%, depending on the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the Globeland30-2010 map at 30 m spatial resolution. Using the subpixel level validation data, we derived 15,252 sample pixels at 30 m spatial resolution. Based on these sample pixels, the overall accuracy of the Globeland30-2010 map was found to be 66.6 ± 2.4% for Africa. The three assessments exemplify the applicability of multi-purpose validation datasets which are recommended to increase map validation efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or updating the dataset with sample sites in identified change areas

A Hybrid of Optical Remote Sensing and Hydrological Modelling Improves Water Balance Estimation

Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally. Remote sensing and water balance modelling are frequently cited as potential solutions, but these techniques largely rely on these same in-decline gauge data to make accurate discharge estimates. A different approach is therefore needed, and we here combine remotely sensed discharge estimates made via at-many-stations hydraulic geometry (AMHG) and the PCR-GLOBWB hydrological model to estimate discharge over the Lower Nile. Specifically, we first estimate initial discharges from 87 Landsat images and AMHG (1984-2015), and then use these flow estimates to tune the model, all without using gauge data. The resulting tuned modelled hydrograph shows a large improvement in flow magnitude: validation of the tuned monthly hydrograph against a historical gauge (1978-1984) yields an RMSE of 439 m3/s (40.8%). By contrast, the original simulation had an order-of-magnitude flow error. This improvement is substantial but not perfect: tuned flows have a one-to two-month wet season lag and a negative baseflow bias. Accounting for this two-month lag yields a hydrograph RMSE of 270 m3/s (25.7%). Thus, our results coupling physical models and remote sensing is a promising first step and proof of concept toward future modelling of ungauged flows, especially as developments in cloud computing for remote sensing make our method easily applicable to any basin. Finally, we purposefully do not offer prescriptive solutions for Nile management, and rather hope that the methods demonstrated herein can prove useful to river stakeholders in managing their own water

Impacts of past abrupt land change on local biodiversity globally

Abrupt land change, such as deforestation or agricultural intensification, is a key driver of biodiversity change. Following abrupt land change, local biodiversity often continues to be influenced through biotic lag effects. However, current understanding of how terrestrial biodiversity is impacted by past abrupt land changes is incomplete. Here we show that abrupt land change in the past continues to influence present species assemblages globally. We combine geographically and taxonomically broad data on local biodiversity with quantitative estimates of abrupt land change detected within time series of satellite imagery from 1982 to 2015. Species richness and abundance were 4.2% and 2% lower, respectively, and assemblage composition was altered at sites with an abrupt land change compared to unchanged sites, although impacts differed among taxonomic groups. Biodiversity recovered to levels comparable to unchanged sites after >10 years. Ignoring delayed impacts of abrupt land changes likely results in incomplete assessments of biodiversity change

Heavy menstrual bleeding in adolescents: incidence, diagnostics, and management practices in primary care

Background: Heavy menstrual bleeding (HMB), self-reported by 37% of adolescents, can be the first sign of a bleeding disorder (BD) during adolescence. The Dutch general practitioner (GP) guideline demands laboratory diagnostics and referral for patients at risk for a BD. How often adolescents consult the GP for HMB and which diagnostic and management strategies are used are unknown. Objectives: This study aims to estimate the incidence of HMB in adolescents in primary care and to identify diagnostic and management practices for HMB, considering the HMB GP guideline. Methods: Retrospective analyses of a GP network database containing over 200 Dutch GPs were performed. Adolescents aged 10 to 21 years, with a new diagnosis of HMB between 2010 and 2020, and a 6-month follow-up were eligible. The incidence rate and diagnostic and therapeutic strategy data were extracted. Results: We identified 1879 new diagnoses of HMB in adolescents. The average incidence rate was 7.91 per 1000 person-years. No diagnostic studies were performed in 67%. Laboratory studies were mainly restricted to hemoglobin levels (31%). Full coagulation screening occurred in 1.3%, and ferritin levels in 10%. Medication was prescribed in 65%; mostly hormonal treatment (56%) and/or nonsteroidal antiinflammatory drugs (NSAIDs) (18%). The referral rate was higher after >2 follow-up visits (6.7%) vs after 1 GP visit for HMB (1.6%; Odds ratio: 8.8; 95% CI: 5.1-15), mostly to gynecologists (>85%). Conclusion: According to this GP database study, few adolescents visit their GP with HMB despite its high self-reported incidence. Most adolescents were prescribed hormonal contraception without further diagnostics. Referral was rare and mostly occurred after multiple follow-up visits