The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

BackgroundMolecular clocks drive oscillations in leaf photosynthesis, stomatal conductance, and other cell and leaf-level processes over ~24 h under controlled laboratory conditions. The influence of such circadian regulation over whole-canopy fluxes remains uncertain; diurnal CO2 and H2O vapor flux dynamics in the field are currently interpreted as resulting almost exclusively from direct physiological responses to variations in light, temperature and other environmental factors. We tested whether circadian regulation would affect plant and canopy gas exchange at the Montpellier European Ecotron. Canopy and leaf-level fluxes were constantly monitored under field-like environmental conditions, and under constant environmental conditions (no variation in temperature, radiation, or other environmental cues).ResultsWe show direct experimental evidence at canopy scales of the circadian regulation of daytime gas exchange: 20–79 % of the daily variation range in CO2 and H2O fluxes occurred under circadian entrainment in canopies of an annual herb (bean) and of a perennial shrub (cotton). We also observed that considering circadian regulation improved performance by 8–17 % in commonly used stomatal conductance models.ConclusionsOur results show that circadian controls affect diurnal CO2 and H2O flux patterns in entire canopies in field-like conditions, and its consideration significantly improves model performance. Circadian controls act as a ‘memory’ of the past conditions experienced by the plant, which synchronizes metabolism across entire plant canopies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13742-016-0149-y) contains supplementary material, which is available to authorized users.

Abstract:It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m −2 ), 50% (0.259 points m −2 ), and 75% (0.129 points m −2 ) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m −2 ). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m −2 ). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.

The global pandemic emergent from SARS-COV-2 (COVID-19) has continued to cause both health and socioeconomic challenges worldwide. However, there is limited information on the factors affecting the dynamics of COVID-19, especially in developing countries, including African countries. In this study, we have focused on understanding the association of COVID-19 cases with environmental and socioeconomic factors in Zambia - a sub-Saharan African country. We used Zambia's district-level COVID-19 data, covering 18 March 2020 (i.e., from first reported cases) to 17 July 2020. Geospatial approaches were used to organise, extract and establish the dataset, while a classification tree (CT) technique was employed to analyse the factors associated with the COVID-19 cases. The analyses were conducted in two stages: (1) the binary analysis of occurrences of COVID-19 (i.e., COVID-19 or No COVID-19), and (2) a risk level analysis which grouped the number of cases into four risk levels (high, moderate, low and very low). The results showed that the distribution of COVID-19 cases in Zambia was significantly influenced by the socioeconomic factors compared to environmental factors. More specifically, the binary model showed that distance to the airport, population density and distance to the town centres were the most combination influential factors, while the risk level analysis indicated that areas with high rates of human immunodeficient virus (HIV) infection had relatively high chances of having many COVID-19 cases compared to areas with low HIV rates. The districts that are far from major urban establishments and that experience higher temperatures have lower chances of having COVID-19 cases. This study makes two major contributions towards the understanding of COVID-19 dynamics: (1) the methodology presented here can be effectively applied in other areas to understand the association of environmental and socioeconomic factors with COVID-19 cases, and (2), the findings from this study present the empirical evidence of the relationship between COVID-19 cases and their associated environmental and socioeconomic factors. Further studies are needed to understand the relationship of this disease and the associated factors in different cultural settings, seasons and age groups, especially as the COVID-19 cases increase and spread in many countries.