First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94%, a false-alarm rate of 12% and an overall correctness of classification of 97%. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets

Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada

Forest inventory and monitoring programs are needed to provide timely, spatially complete (i.e. mapped), and verifiable information to support forest management, policy formulation, and reporting obligations. Satellite images, in particular data from the Landsat Thematic Mapper and Enhanced Thematic Mapper (TM/ETM +) sensors, are often integrated with field plots from forest inventory programs, leveraging the complete spatial coverage of imagery with detailed ecological information from a sample of plots to spatially model forest conditions and resources. However, in remote and unmanaged areas such as Canada's northern forests, financial and logistic constraints can severely limit the availability of inventory plot data. Additionally, Landsat spectral information has known limitations for characterizing vertical vegetation structure and biomass; while clouds, snow, and short growing seasons can limit development of large area image mosaics that are spectrally and phenologically consistent across space and time. In this study we predict and map forest structure and aboveground biomass over 37 million ha of forestland in Saskatchewan, Canada. We utilize lidar plots—observations of forest structure collected from airborne discrete-return lidar transects acquired in 2010—as a surrogate for traditional field and photo plots. Mapped explanatory data included Tasseled Cap indices and multi-temporal change metrics derived from Landsat TM/ETM + pixel-based image composites. Maps of forest structure and total aboveground biomass were created using a Random Forest (RF) implementation of Nearest Neighbor (NN) imputation. The imputation model had moderate to high plot-level accuracy across all forest attributes (R2 values of 0.42–0.69), as well as reasonable attribute predictions and error estimates (for example, canopy cover above 2 m on validation plots averaged 35.77%, with an RMSE of 13.45%, while unsystematic and systematic agreement coefficients (ACuns and ACsys) had values of 0.63 and 0.97 respectively). Additionally, forest attributes displayed consistent trends in relation to the time since and magnitude of wildfires, indicating model predictions captured the dominant ecological patterns and processes in these forests. Acknowledging methodological and conceptual challenges based upon the use of lidar plots from transects, this study demonstrates that using lidar plots and pixel compositing in imputation mapping can provide forest inventory and monitoring information for regions lacking ongoing or up-to-date field data collection programs

From imagery to ecology: Leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics

https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.24Published versio

Accommodation requirements for microgravity science and applications research on space station

Scientific research conducted in the microgravity environment of space represents a unique opportunity to explore and exploit the benefits of materials processing in the virtual abscence of gravity induced forces. NASA has initiated the preliminary design of a permanently manned space station that will support technological advances in process science and stimulate the development of new and improved materials having applications across the commercial spectrum. A study is performed to define from the researchers' perspective, the requirements for laboratory equipment to accommodate microgravity experiments on the space station. The accommodation requirements focus on the microgravity science disciplines including combustion science, electronic materials, metals and alloys, fluids and transport phenomena, glasses and ceramics, and polymer science. User requirements have been identified in eleven research classes, each of which contain an envelope of functional requirements for related experiments having similar characteristics, objectives, and equipment needs. Based on these functional requirements seventeen items of experiment apparatus and twenty items of core supporting equipment have been defined which represent currently identified equipment requirements for a pressurized laboratory module at the initial operating capability of the NASA space station

Durable and ductile double-network material for dust control

Dust generation is a world-wide issue due to its serious deleterious effects on the environment, human health and safety, and the economy. Although various dust suppression methods have been used for decades, some critical drawbacks in state-of-the-art technology still remain unsolved, such as short-lasting, ground water impact, and prone to water. This work reports a soil stabilizer based on non-toxic material and forms a ductile and durable double-network in soil, namely “D3 soil stabilizer”, which not only improves soil mechanical toughness of surface soil but also suppresses dust generation. A copolymer comprising hydrophilic and hydrophobic components combined with enzyme-induced carbonate precipitation is utilized as an in-situ gelation binder to soil particle. The tunable hydrophobic-to-hydrophilic component ratio minimizes undesirable soil matrix expansion and mechanical strength loss upon experiencing wet-dry processes, while still retains good water affinity. We further demonstrated controllable treatment depth by fine-tuning precursor composition, which is essential to minimize environmental impact. The double-network morphology with carbonate precipitate embedded uniformly in polymer matrix is observed via microscopic imaging. The nature of outstanding ductility, high durability against water, and good long-term stability were supported by systematic unconfined compressive strength (UCS) measurements on treated soil, which show strong inter-particles binding, good retention of peak strength, increased strain at peak strength, and increased toughness after soil samples have experienced wet-dry processes

Knowledge Discovery from Satellite Images for Drought Monitoring in Food Insecure Areas

Attributed to climatic change and uncertainty of weather conditions, drought has become a recurrent phenomenon. It is manifested by erratic and uncertain rainfall distribution in rainfall dependent farming areas. The hitherto methods of monitoring drought employed conventional methods that rely on availability of metrological data. The objectives of this research were to: 1) identify the critical factors for efficiently implementing geo-spatial information for drought monitoring, 2) develop a new approach for extracting knowledge from satellite imageries for real time drought monitoring in food insecure areas, and 3) validate and calibrate the new approach for national and regional applications. For this research, satellite data from MSG and NOAA AVHRR were used. The preliminary results confirmed that real time MSG satellite data can be used for monitoring drought in food insecure areas. The output of this research helps decision makers in taking the appropriate actions in time for saving millions of lives in drought affected areas using advanced satellite technology

Mapping moth induced birch forest damage in northern Sweden, with MODIS satellite data

Large synchronous outbreaks of herbivory geometrids is regularly occurring at 9-10 years intervals when they reach peak densities in the Fennoscandian birch forest, in the northern part of Scandinavia (Tenow 1972, Bylund 1995). Climate change is likely to increase the frequency, intensity and extent of the outbreak due to increasing temperatures in the area (Callaghan 2010, Heliasz et al. 2011, Wolf et al. 2008). The consequence is a detrimental effect to the birch forest since the forest might not have enough time to recover between outbreaks, which will potentially decrease the proliferation and distribution of birch forest (Tenow et al. 2001, 2003, Karlsson et al 2004). This will have an ecological cost by making the forest inhabitable and non-resourceful for the animals and people that depend on it (Helle 2001). However, the effects on the Birch forest are not well known, therefore it is important to continue studying the distribution of forest damage, to gain a better understanding of its dynamics and the underlying spatio-temporal factors controlling the synchronous outbreaks. The most current year of infestation in the study area of the surroundings of lake Torneträsk in northern Sweden was 2012. To map the distribution and dynamics of the geometrids of the birch forest, time series data of MODIS 16-day NDVI composites were analyzed. To facilitate the analysis, a tree cover map with high resolution was created based on Lidar data. The Lidar based forest cover map was created to mask the forest. The topographical distribution of infested forest at four altitudinal intervals with 100 meter equidistance in between was also studied. A method was developed in this study to separate infested from non-infested forest with a threshold value based on z-score, which was successful at showing the distribution of the geometrid outbreak in 2012. The size of the infested area was 80km², equal to 54.3% of the forest in the area. If the forest classified as “likely infested” would have been included, the ratio of infested forest would increase to 64.4%. This is a significant proportion of the forest that will certainly affect the forest in future years. The topographical distribution of the infestation over the study area was relatively evenly distributed, without displaying any range of altitude that was more prone to infestation.Stora synkrona utbrott av björkmätare förekommer vart 9-10 år i de norra delarna av Skandinavien i den Fenoskandiska björkskogen. Björkmätaren konsumerar bladen och kan orsaka stora skador under ett toppår. Man misstänker att klimatförändringarna kommer att öka utbrottens omfattning och intensitet på grund av ökad temperatur i området. Konsekvenserna kan bli förödande för björkskogen, eftersom tiden mellan utbrotten riskerar att bli för korta för skogen att återhämta sig. Det kan resultera i att träden dör och skogen försvinner i de värst drabbade områdena. Om skogen minskar eller försvinner kan det få ekologiska konsekvenser för de djur som är beroende av den och människor som utnyttjar dess resurser. Hur skogen kommer att påverkas vet man inte säkert, därför är det viktigt att studera utbredningen av skogsskador och på så sätt få bättre kunskap om de underliggande faktorerna som kontrollerar utbrottens dynamik. Ett utbrott inträffade sommaren 2012 i studieområdet vilket var indelat i 2 olika stora överlappande område; Ett större som täcker området runt sjön Torneträsk i norra Sverige och ett mindre som täcker den västra delen av sjön runt Abisko. För att kartlägga omfattningen av björmatarutbrottet användes tidsserier av MODIS 16-dagars NDVI kompositer som analyserades men en utvecklad förändrings analys för projektet. Som en del i analysen skapades en högupplöst skogsutbredningskarta med Lidar data. Den topografiska utbredningen av utbrotten studerades också genom att dela in området i fyra höjdintervall med 100 meters ekvidistans mellan intervallen. För att identifiera skadade område utvecklades en metod baserat på standardiserade z-värde och definierade gränsvärde för klassificeringen av skogen. Det angripna området i Torneträsk var 251 km² stort, motsvarande 32% av skogen. I studieområdet i Abisko var utbrottet 80km2 stort, vilket motsvarar 54.3% av skogen i studieområdet. Om skogen klassad som ”troligen angripen” inkluderas i beräkningen, skulle den del av skogen som var angripen ökas ytterligare. Det är en signifikant andel skog som med stor sannolikhet kommer att påverka skogen i flera år. Den topografiska utbredningen av utbrottet i studieområdet var relativt jämnt fördelat, inget höjdintervall visade sig vara extra känsligt för att bli angripen

Utilizing the Landsat spectral-temporal domain for improved mapping and monitoring of ecosystem state and dynamics

Just as the carbon dioxide observations that form the Keeling curve revolutionized the study of the global carbon cycle, free and open access to all available Landsat imagery is fundamentally changing how the Landsat record is being used to study ecosystems and ecological dynamics. This dissertation advances the use of Landsat time series for visualization, classification, and detection of changes in terrestrial ecological processes. More specifically, it includes new examples of how complex ecological patterns manifest in time series of Landsat observations, as well as novel approaches for detecting and quantifying these patterns. Exploration of the complexity of spectral-temporal patterns in the Landsat record reveals both seasonal variability and longer-term trajectories difficult to characterize using conventional bi-temporal or even annual observations. These examples provide empirical evidence of hypothetical ecosystem response functions proposed by Kennedy et al. (2014). Quantifying observed seasonal and phenological differences in the spectral reflectance of Massachusetts’ forest communities by combining existing harmonic curve fitting and phenology detection algorithms produces stable feature sets that consistently out-performed more traditional approaches for detailed forest type classification. This study addresses the current lack of species-level forest data at Landsat resolutions, demonstrating the advantages of spectral-temporal features as classification inputs. Development of a targeted change detection method using transformations of time series data improves spatial and temporal information on the occurrence of flood events in landscapes actively modified by recovering North American beaver (Castor canadensis) populations. These results indicate the utility of the Landsat record for the study of species-habitat relationships, even in complex wetland environments. Overall, this dissertation confirms the value of the Landsat archive as a continuous record of terrestrial ecosystem state and dynamics. Given the global coverage of remote sensing datasets, the time series visualization and analysis approaches presented here can be extended to other areas. These approaches will also be improved by more frequent collection of moderate resolution imagery, as planned by the Landsat and Sentinel-2 programs. In the modern era of global environmental change, use of the Landsat spectral-temporal domain presents new and exciting opportunities for the long-term large-scale study of ecosystem extent, composition, condition, and change