Downwelling spectral irradiance as measured in different depths, remote sensing reflectance and phytoplankton pigment concentration during POLARSTERN cruises ANT-XXIV/1, ANT-XXIV/4, ANT-XXVI/4 and Maria S. Merian cruise MSM18/3

The composition and abundance of algal pigments provide information on phytoplankton community characteristics such as photoacclimation, overall biomass and taxonomic composition. In particular, pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by high-performance liquid chromatography (HPLC) techniques applied to filtered water samples. This method, as well as other laboratory analyses, is time consuming and therefore limits the number of samples that can be processed in a given time. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an empirical orthogonal function (EOF) analysis to remote-sensing reflectance data derived from ship-based hyperspectral underwater radiometry and from multispectral satellite data (using the Medium Resolution Imaging Spectrometer - MERIS - Polymer product developed by Steinmetz et al., 2011, doi:10.1364/OE.19.009783) measured in the Atlantic Ocean. Subsequently we developed multiple linear regression models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multispectral resolution is chosen (i.e., eight bands, similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. As a demonstration of the utility of the approach, the fitted model based on satellite reflectance data as input was applied to 1 month of MERIS Polymer data to predict the concentration of those pigment groups for the whole eastern tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photophysiology.

Remote Sensing of Vegetation Change Across a Latitudinal Gradient in the Canadian Arctic

Global air surface temperatures and precipitation have increased over the last several decades resulting in a trend of greening across the Circumpolar Arctic. The spatial variability of warming and the inherent effects on plant communities has not proven to be uniform or homogeneous on global or local scales. We can apply remote sensing vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to map and monitor vegetation change (e.g., phenology, greening, percent cover, and biomass) over time. It is important to document how Arctic vegetation is changing, as it will have large implications related to global carbon and surface energy budgets. The research reported here examined vegetation greening across different spatial and temporal scales at two disparate Arctic sites: Apex River Watershed (ARW), Baffin Island, and Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU. To characterize the vegetation in the ARW, high spatial resolution WorldView-2 data were processed to create a supervised land-cover classification and model percent vegetation cover (PVC) (a similar process had been completed in a previous study for the CBAWO). Meanwhile, NDVI data spanning the past 30 years were derived from intermediate resolution Landsat data at the two Arctic sites. The land-cover classifications at both sites were used to examine the Landsat NDVI time series by vegetation class. Climate variables (i.e., temperature, precipitation and growing season length (GSL) were examined to explore the potential relationships of NDVI to climate warming. PVC was successfully modeled using high resolution data in the ARW. PVC and plant communities appear to reside along a moisture and altitudinal gradient. The NDVI time series demonstrated an overall significant increase in greening at the CBAWO (High Arctic site), specifically in the dry and mesic vegetation type. However, similar overall greening was not observed for the ARW (Low Arctic site). The overall increase in NDVI at the CBAWO was attributed to a significant increase in July temperatures, precipitation and GSL.

New developments in UAV remote sensing for pest management and the implications for cotton

Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.

Airborne remote sensing of ocean wave directional wavenumber spectra in the marginal ice zone

Interactions between surface waves and sea ice are thought to be an important, but poorly understood, physical process in the atmosphere-ice-ocean system. In this work, airborne scanning lidar was used to observe ocean waves propagating into the marginal ice zone (MIZ). These represent the first direct spatial measurements of the surface wave field in the polar MIZ. Data were compared against two attenuation models, one based on viscous dissipation and one based on scattering. Both models were capable of reproducing the measured wave energy. The observed wavenumber dependence of attenuation was found to be consistent with viscous processes, while the spectral spreading of higher wavenumbers suggested a scattering mechanism. Both models reproduced a change in peak direction due to preferential directional filtering. Floe sizes were recorded using co-located visible imagery, and their distribution was found to be consistent with ice breakup by the wave field.

A REMOTE SENSING APPROACH TO CHARACTERIZE THE HYDROGEOLOGY OF MOUNTAINOUS AREAS: APPLICATION TO THE QUITO AQUIFER SYSTEM (QAS), ECUADOR

Climate change, intensive use, and population growth are threatening the availability of water resources. New sources of water, better knowledge of existing ones, and improved water management strategies are of paramount importance. Ground water is often considered as primary water source due to its advantages in terms of quantity, spatial distribution, and natural quality. Remote sensing techniques afford scientists a unique opportunity to characterize landscapes in order to assess groundwater resources, particularly in tectonically influenced areas. Aquifers in volcanic basins are considered the most productive aquifers in Latin America. Although topography is considered the primary driving force for groundwater flows in mountainous terrains, tectonic activity increases the complexity of these groundwater systems by altering the integrity of sedimentary rock units and the overlying drainage networks. Structural controls affect the primary hydraulic properties of the rock formations by developing barriers to flow in some cases and zones of preferential infiltration and subterranean in others. The study area focuses on the Quito Aquifer System (QAS) in Ecuador. The characterization of the hydrogeology started with a lineament analysis based on a combined remote sensing and digital terrain analysis approach. The application of classical tools for regional hydrogeological evaluation and shallow geophysical methods were useful to evaluate the impact of faulting and fracturing on the aquifer system. Given the spatial extension of the area and the complexity of the system, two levels of analysis were applied in this study. At the regional level, a lineament map was created for the QAS. Relationships between fractures, faults and lineaments and the configuration of the groundwater flow on the QAS were determined. At the local level, on the Plateaus region of the QAS, a detailed lineament map was obtained by using high-spatial-resolution satellite imagery and aspect map derived from a digital elevation model (DEM). This map was complemented by the analysis of morphotectonic indicators and shallow geophysics that characterize fracture patterns. The development of the groundwater flow system was studied, drawing upon data pertaining to the aquifer system physical characteristics and topography. Hydrochemistry was used to ascertain the groundwater evolution and verify the correspondence of the flow patterns proposed in the flow system analysis. Isotopic analysis was employed to verify the origin of groundwater. The results of this study show that tectonism plays a very important role for the hydrology of the QAS. The results also demonstrate that faults influence a great deal of the topographic characteristics of the QAS and subsequently the configuration of the groundwater flow. Moreover, for the Plateaus region, the results demonstrate that the aquifer flow systems are affected by secondary porosity. This is a new conceptualization of the functioning of the aquifers on the QAS that will significantly contribute to the development of better strategies for the management of this important water resource.

Assessing the impact of agricultural drought on maize prices in Kenya with the approach of the SPOT-VEGETATION NDVI remote sensing

The high cost of maize in Kenya is basically driven by East African regional commodity demand forces and agricultural drought. The production of maize, which is a common staple food in Kenya, is greatly affected by agricultural drought. However, calculations of drought risk and impact on maize production in Kenya is limited by the scarcity of reliable rainfall data. The objective of this study was to apply a novel hyperspectral remote sensing method to modelling temporal fluctuations of maize production and prices in five markets in Kenya. SPOT-VEGETATION NDVI time series were corrected for seasonal effects by computing the standardized NDVI anomalies. The maize residual price time series was further related to the NDVI seasonal anomalies using a multiple linear regression modelling approach. The result shows a moderately strong positive relationship (0.67) between residual price series and global maize prices. Maize prices were high during drought periods (i.e. negative NDVI anomalies) and low during wet seasons (i.e. positive NDVI anomalies). This study concludes that NDVI is a good index for monitoring the evolution of maize prices and food security emergency planning in Kenya. To obtain a very strong correlation for the relationship between the wholesale maize price and the global maize price, future research could consider adding other price-driving factors into the regression models.

Irrigation management with remote sensing: Evaluating irrigation requirement for maize under Mediterranean climate condition

tWater use control methods and water resources planning are of high priority. In irrigated agriculture, theright way to save water is to increase water use efficiency through better management. The present workvalidates procedures and methodologies using remote sensing to determine the water availability in thesoil at each moment, giving the opportunity for the application of the water depth strictly necessaryto optimise crop growth (optimum irrigation timing and irrigation amount). The analysis is applied tothe Irrigation District of Divor, Évora, using 7 experimental plots, which are areas irrigated by centre-pivot systems, cultivated to maize. Data were determined from images of the cultivated surface obtainedby satellite and integrated with atmosphere and crop parameters to calculate biophysical indicatorsand indices of water stress in the vegetation—Normalized Difference Vegetation Index (NDVI), Kc, andKcb. Therefore, evapotranspiration (ETc) was estimated and used to calculate crop water requirement,together with the opportunity and the amount of irrigation water to allocate. Although remote sensingdata available from satellite imagery presented some practical constraints, the study could contribute tothe validation of a new methodology that can be used for irrigation management of a large irrigated area,easier and at lower costs than the traditional FAO recommended crop coefficients method. The remotesensing based methodology can also contribute to significant saves of irrigation water.

Development of a Remote Sensing Core Curriculum submitted for the sessions on educational initiatives in Geoscience and Remote Sensing

A Remote Sensing Core Curriculum (RSCC) development project is currently underway. This project is being conducted under the auspices of the National Center for Geographic Information and Analysis (NCGIA). RSCC is an outgrowth of the NCGIA GIS Core Curriculum project. It grew out of discussions begun at NCGIA, Initiative 12 (I-12): 'Integration of Remote Sensing and Geographic Information Systems'. This curriculum development project focuses on providing professors, teachers and instructors in undergraduate and graduate institutions with course materials from experts in specific subject matter for areas use in the class room.

Mandate for remote sensing education and the remote sensing core curriculum

Consensus was developed by the remote sensing community during the 1980s and early 1990s regarding the need for an organized approach to teaching remote sensing fundamentals for collegiate institutions. Growth of the remote sensing industry might be seriously hampered without concerted efforts to bolster the capacity to teach state-of-the-practice remote sensing theory and practice to the next generation of professionals. A concerted effort of educators, researchers, government, and industry began in 1992 to meet these demands leading to the creation of the Remote Sensing Core Curriculum. The RSCC is currently sustained by cooperative efforts of the ASPRS, ICRSE, NASA, NCGIA, and others in the remote sensing community. Growth of the RSCC into the K-12 community resulted from its Internet teaching foundation that enables comprehensive and response reference links to the whole of the education community.

Impact of temperature on childhood pneumonia estimated from satellite remote sensing

The effect of temperature on childhood pneumonia in subtropical regions is largely unknown so far. This study examined the impact of temperature on childhood pneumonia in Brisbane, Australia. A quasi-Poisson generalized linear model combined with a distributed lag non linear model was used to quantify the main effect of temperature on emergency department visits (EDVs) for childhood pneumonia in Brisbane from 2001 to 2010. The model residuals were checked to identify added effects due to heat waves or cold spells. Both high and low temperatures were associated with an increase in EDVs for childhood pneumonia. Children aged 2–5 years, and female children were particularly vulnerable to the impacts of heat and cold, and Indigenous children were sensitive to heat. Heat waves and cold spells had significant added effects on childhood pneumonia, and the magnitude of these effects increased with intensity and duration. There were changes over time in both the main and added effects of temperature on childhood pneumonia. Children, especially those female and Indigenous, should be particularly protected from extreme temperatures. Future development of early warning systems should take the change over time in the impact of temperature on children’s health into account.