Big Data Analytics on combining RADAR and optical remote sensing imagery

Remote sensing data is big, which makes it inherently not FAIR, due to the great burden put on the users, for the data to be truly Accessible. In turn, this makes it unfeasible to process data to an Interoperable form. The ESA’s Copernicus program raised the bar for quality data in earth observation satellite sensors ..

Relationships between declining summer sea ice, increasing temperatures and changing vegetation in the Siberian Arctic tundra from MODIS time series (2000–11)

The concern about Arctic greening has grown recently as the phenomenon is thought to have significant influence on global climate via atmospheric carbon emissions. Earlier work on Arctic vegetation highlighted the role of summer sea ice decline in the enhanced warming and greening phenomena observed in the region, but did not contain enough details for spatially characterizing the interactions between sea ice, temperature and vegetation photosynthetic absorption. By using 1 km resolution data from the Moderate Resolution Imaging Spectrometer (MODIS) as a primary data source, this study presents detailed maps of vegetation and temperature trends for the Siberian Arctic region, using the time integrated normalized difference vegetation index (TI-NDVI) and summer warmth index (SWI) calculated for the period 2000-11 to represent vegetation greenness and temperature respectively. Spatio-temporal relationships between the two indices and summer sea ice conditions were investigated with transects at eight locations using sea ice concentration data from the Special Sensor Microwave/Imager (SSM/I). In addition, the derived vegetation and temperature trends were compared among major Arctic vegetation types and bioclimate subzones. The fine resolution trend map produced confirms the overall greening (+1% yr(-1)) and warming (+0.27% yr(-1)) of the region, reported in previous studies, but also reveals browning areas. The causes of such local decreases in vegetation, while surrounding areas are experiencing the opposite reaction to changing conditions, are still unclear. Overall correlations between sea ice concentration and SWI as well as TI-NDVI decreased in strength with increasing distance from the coast, with a particularly pronounced pattern in the case of SWI. SWI appears to be driving TI-NDVI in many cases, but not systematically, highlighting the presence of limiting factors other than temperature for plant growth in the region. Further unravelling those limiting factors constitutes a priority in future research. This study demonstrates the use of medium resolution remotely sensed data for studying the complexity of spatio-temporal vegetation dynamics in the Arctic

Detecting clear-cuts and decreases in forest vitality using MODIS NDVI time series

This paper examines the potential of MODIS-NDVI time series for detecting clear-cuts in a coniferous forest stand in the south of France. The proposed approach forms part of a survey monitoring the status of forest health and evaluating the forest decline phenomena observed over the last few decades. One of the prerequisites for this survey was that a rapid and easily reproducible method had to be developed that differentiates between forest clear-cuts and changes in forest health induced by environmental factors such as summer droughts. The proposed approach is based on analysis of the breakpoints detected within NDVI time series, using the “Break for Additive Seasonal and Trend” (BFAST) algorithm. To overcome difficulties detecting small areas on the study site, we chose a probabilistic approach based on the use of a conditional inference tree. For model calibration, clear-cut reference data were produced at MODIS resolution (250 m). According to the magnitude of the detected breakpoints, probability classes for the presence of clear-cuts were defined, from greater than 90% to less than 3% probability of a clear-cut. One of the advantages of the probabilistic model is that it allows end users to choose an acceptable level of uncertainty depending on the application. In addition, the use of BFAST allows events to be dated, thus making it possible to perform a retrospective analysis of decreases in forest vitality in the study area

Detecting leaf pulvinar movements on NDVI time series of desert trees: A new approach for water stress detection

Heliotropic leaf movement or leaf ‘solar tracking’ occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (¿NDVImo-mi) and between winter and summer (¿NDVIW-S). In this paper, we showed that the ¿NDVImo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ¿NDVIW-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ¿NDVImo-mi and ¿NDVIW-S. For an 11-year time series without rainfall events, Landsat ¿NDVIW-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ¿NDVImo-mi and ¿NDVIW-S have potential to detect early water stress of paraheliotropic vegetation

A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection

To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series

Global land characterisation using land cover fractions at 100 m resolution

Currently most global land cover maps are produced with discrete classes, which express the dominant land cover class in each pixel, or a combination of several classes at a predetermined ratio. In contrast, land cover fraction mapping enables expressing the proportion of each pure class in each pixel, which increases precision and reduces legend complexity. To map land cover fractions, regression rather than classification algorithms are needed, and multiple approaches are available for this task. A major challenge for land cover fraction mapping models is data sparsity. Land cover fraction data is by its nature zero-inflated due to how common the 0% fraction is. As regression favours the mean, 0% and 100% fractions are difficult for regression models to predict accurately. We proposed a new solution by combining three models: a binary model determines whether a pixel is pure; if so, it is processed using a classification model; otherwise with a regression model. We compared multiple regression algorithms and implemented our proposed three-step model on the algorithm with the lowest RMSE. We further evaluated the spatial and per-class accuracy of the model and demonstrated a wall-to-wall prediction of seven land cover fractions over the globe. The models were trained on over 138,000 points and validated on a separate dataset of over 20,000 points, provided by the CGLS-LC100 project. Both datasets are global and aligned with the PROBA-V 100 m UTM grid. Results showed that the random forest regression model reached the lowest RMSE of 17.3%. Lowest MAE (7.9%) and highest overall accuracy (72% ± 2%) was achieved using random forest with our proposed three-model approach and median vote. This research proves that machine learning algorithms can be applied globally to map a wide variety of land cover fractions. Fraction mapping expresses land cover more precisely, and empowers users to create their own discrete maps using user-defined thresholds and rules, which enables customising the result for a diverse range of uses. The three-step approach is useful for addressing the zero-inflation issue and mapping 0% and 100% fractions more accurately, and thus has already been taken up in the operational production of global land cover fraction layers within the CGLS-LC100 project. Furthermore, this study contributes to the accuracy assessment of land cover fraction maps both thematically and spatially, and these methods could be taken up by future land cover fraction mapping efforts

Weight, pregnancy and oral contraceptives affect intravenous paracetamol clearance in young women

CONCLUSIONS: Weight, pregnancy and OC result in higher clearance of IV paracetamol in young women. Besides compound specific relevance, these findings also unveil covariates of drug metabolism in young women

Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research

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