A biophysically based and objective satellite seasonality observation method for applications over the Arctic

Despite wide applications of remote-sensing data with high temporal resolution for monitoring phenology, two persistent problems have prevented the realization of their full potential. The first is the subjectivity in defining thresholds for a phenological event (e.g. the start or end of growing season ? SOS or EOS). The second is the use of various arbitrarily selected filtering and smoothing algorithms for constructing vegetation index seasonal profiles in order to reduce the noise caused by residue cloud contamination and aerosol variations. In this study, we addressed both problems by developing a biophysically based and objective satellite seasonality observation method (BLOSSOM) for application over Canada’s Arctic. Application of the BLOSSOM method to three northern Canadian national parks (Ivvavik, Wapusk, and Sirmilik) proved that the method is operational. Using the uncertainties in the vegetation index and its threshold, we estimated the overall mean uncertainties as being ?5.3 to 3.4 days, ?4.2 to 5.2 days, and ?6.2 to 8.4 days, respectively, for SOS, EOS, and growing season length (GSL). Further independent tests against SOS, determined using records of snow cover at nearby climate stations (as ‘truth’), indicate that the mean absolute error is less than 3.6 ± 0.2 days.     

Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985–2006

Long‐term changes in the Normalized Difference Vegetation Index (NDVI) have been evaluated in several studies but results have not been conclusive due to differences in data processing as well as the length and time of the analysed period. In this research a newly developed 1 km Advanced Very High Resolution Radiometer (AVHRR) satellite data record for the period 1985–2006 was used to rigorously evaluate NDVI trends over Canada. Furthermore, climate and land cover change as potential causes of observed trends were evaluated in eight sample regions. The AVHRR record was generated using improved geolocation, cloud screening, correction for sun‐sensor viewing geometry, atmospheric correction, and compositing. Results from both AVHRR and Landsat revealed an increasing NDVI trend over northern regions where comparison was possible. Overall, 22% of the vegetated area in Canada was found to have a positive NDVI trend based on the Mann–Kendal test at the 95% confidence level. Of these, 40% were in northern ecozones. The mean absolute difference of NDVI measurements between AVHRR and Landsat data was <7%. When compared with results from other studies, similar trends were found over northern areas, while in southern regions the results were less consistent. Local assessment of potential causes of trends in each region revealed a stronger influence of climate in the north compared to the south. Southern regions with strong positive trends appeared to be most influenced by land cover change.     

Pigeons (Columba livia) associate time intervals with symbols in a touch screen task: Evidence for ordinality but not summation.

The present experiments examined whether pigeons can sum symbols that are associated with various temporal consequences in a touch screen apparatus. Pigeons were trained to discriminate between two visual symbols that were associated with 0, 1, 2, 3, 4, or 5 s either of delay to 4 s of hopper access (delay group) or duration of hopper access (reward group). In Experiment 1, the pigeons in both groups learned to select the symbol associated with the more favorable outcome, and they successfully transferred this discrimination to novel symbol pairs. However, when tested with 2 pairs of symbols associated with different summed durations, they responded on the basis of a simple response rule rather than the sum of the symbol pair. In Experiment 2, the reward group was presented with four symbols at once and was allowed to successively choose one symbol at a time. All pigeons chose the symbols in order from largest to smallest. This indicates that pigeons formed an ordered representation of symbols associated with different time intervals, even though they did not sum the symbols. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Den Energieniveaus auf der Spur

Tracking energy levels – Photoelectron spectroscopy helps in finding novel semiconductor materials Research on novel semiconducting materials keeps attracting considerable interest due to the potential to develop more cost effective processing compared to conventional inorganic technology. Maybe more importantly, novel device concept become possible such as flexible or semitransparent displays and solar cells. Two of the most promising candidates are introduced here: organic semiconductors as well as halide perovskites. In both material classes, it is possible to tune the absorption or emission properties over a wide range by adjusting the structure or composition or the material. In order to effectively integrate these materials in devices, it is necessary to understand their fundamental properties. Here, the vacuum based photoelectron spectroscopy plays an important role, due to the possibility to directly map the electronic structure of the semiconducting or adjacent layers, which in turn determines the charge transport throughout the device.     

Evaluating and reducing errors in seasonal profiles of AVHRR vegetation indices over a Canadian northern national park using a cloudiness index

High-temporal coarse resolution remote-sensing data have been widely used for monitoring plant phenology and productivity. Residual errors in pre-processed composite data from these sensors can still be substantial due to cloud contamination and aerosol variations, especially over high cloud-cover areas such as the Arctic. Commonly used smoothing and filtering methods try to reform the often heavily distorted seasonal profiles of vegetation indices one way or another, instead of explicitly dealing with the errors that cause the distortion. As the distortion varies from year to year for a pixel or from pixel to pixel, so does the performance of various smoothing and filtering methods. Consequently, change detection results are likely method dependent. In this study, we investigate alternative methods in order to eliminate bias caused by cloud contamination and reduce random errors due to aerosol variations in the 10 day Advanced Very High Resolution Radiometer (AVHRR) composite data, so that accurate seasonal profiles of vegetation indices can be constructed without the need to apply a smoothing and filtering method. The best alternative method corrects cloud contaminations by spatially pairing averages of simple ratio over cloud-contaminated and clear-sky pixels in a class (SPAC). The SPAC method eliminates bias caused by cloud contamination and reduces the relative random errors to <14% near the start/end of a growing season, and to <8% during the middle growing season for the six treeless wetland and tundra classes in Wapusk National Park. In comparison, with the method whereby all pixels in a class (average all pixels in the class (AAC)) are averaged in a period, the bias could be up to 40% if all the pixels in the composite period are heavily cloud contaminated.     

Signature extension through space for northern landcover classification: A comparison of radiometric correction methods

Northern landcover mapping for climate change and carbon modeling requires greater detail than what is available from coarse resolution data. Mapping landcover with medium resolution data from Landsat presents challenges due to differences in time and space between scene acquisitions required for full coverage. These differences cause landcover signatures to vary due to haze, solar geometry and phenology, among other factors. One way to circumvent this problem is to have an image interpreter classify each scene independently, however, this is not an optimal solution in the north due to a lack of spatially extensive reference data and resources required to label scenes individually. Another possible approach is to stabilize signatures in space and time so that they may be extracted from one scene and extended to others, thereby reducing the amount of reference data and user input required for mapping large areas. A radiometric normalization approach was developed that exploits the high temporal frequency with which coarse resolution data are acquired and the high spatial frequency of medium resolution data. The current paper compares this radiometric correction methodology with an established absolute calibration methodology for signature extension for landcover classification and explores factors that affect extension performance to recommend how and when signature extension can be applied. Overall, the new normalization method produced better extension and classification results than absolute calibration. Results also showed that extension performance was affected more by geographical distance than by differences in anniversary dates between acquisitions for the range of data examined. Geographical distance in the north-south direction leads to poorer extension performance than distance in the east-west direction due in part to differences in vegetation composition assigned the same class label in the latitudinal direction. While extension performance was somewhat variable and in some cases did not produce a best classification result by itself, it provided an initial best guess of landcover that can subsequently be refined by an expert image interpreter.     

Judgments of ordinality and summation of number symbols by squirrel monkeys (Saimiri sciureus).

In Experiment 1, 2 squirrel monkeys (Saimiri sciureus) were given choices between all possible pairs of the arabic numbers 0, 1, 3, 5, 7, and 9, with choice of any number yielding that number of pieces of peanut as a reward. Both monkeys learned to choose the larger number in all pairings and learned to choose the largest number within a set of 4 numbers. In Experiments 2–4, the monkeys were tested on problems in which they chose between pairs of stimuli containing 2 numbers versus 2 numbers, 1 number versus 2 numbers, and 3 numbers versus 3 numbers. Both monkeys showed a significant tendency to choose the stimulus that contained the largest sum. Various tests indicated that this effect could not be explained by choice of the stimulus with the largest single number, by avoidance of the stimulus with the smallest single number, or by experimenter cuing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Evaluation of compositing period and AVHRR and MERIS combination for improvement of spring phenology detection in deciduous forests

Accurate and precise detection of phenology events is needed to assess trends in seasonal vegetation development indicative of climate or other environmental change processes. In this research, detection accuracy of start of season (SOS) phenology for deciduous forest across Eastern Canada was assessed using satellite time series and in situ PlantWatch observations. Several aspects were evaluated regarding performance of phenology information extraction: 1) effect of compositing period, 2) individual performance of the Advanced Very High Resolution Radiometer (AVHRR) and the Medium Resolution Imaging Spectrometer (MERIS) sensors, and 3) performance for these sensors combined. The AVHRR and MERIS sensors were used as they are overlapping operational missions with planned future continuity. Three approaches to utilizing the multi-sensor data were tested: 1) inter-calibrating NDVI data between sensors and using the multi-sensor data stream to detect SOS, 2) combining independently derived SOS estimates from AVHRR and MERIS based on a weighted average, and 3) combining approaches 1 and 2. Comparison with in situ observations of leaf out and first bloom showed that combining independent SOS estimates from AVHRR and MERIS was better than using the inter-calibrated multi-sensor data. Combining SOS estimates from both sensors reduced error by 1-2 days compared to the individual sensor results. Composite periods from 7 to 11 days produced the best results for leaf out with a mean absolute error (MAE) of 5 days. Results for first bloom were not as good as those for leaf out, producing a MAE of 6.5 days. For first bloom, compositing periods greater than 11 days did not increase error at the same rate as seen for leaf out. However, the larger MAE observed for first bloom may have masked this effect.     

Monitoring land cover change and ecological integrity in Canada's national parks

Canada's national parks system includes 43 terrestrial parks covering 3% (276,275 km²) of the country's landmass and representing its full range of natural regions. Considering the vast and often remote areas under protection, Parks Canada Agency envisions Earth Observation technology to be the basis for a Park Ecological Integrity Observing System (Park-EIOS), and integral component of a larger national parks ecological integrity (EI) monitoring program. Park-EIOS is planned for operational use beginning in 2008 and includes coarse filter EI indicators corresponding to landscape pattern, succession and retrogression, net primary productivity, and focal species distributions within parks and their surrounding greater park ecosystems. A primary input to produce all four indicators is a time series of land cover information derived from medium (~ 30 m) resolution, Landsat-class sensors. This paper describes a generic, end-to-end change detection framework developed for Park-EIOS, labelled Automated Multi-temporal Updating through Signature Extension (AMUSE). AMUSE involves radiometric normalization steps, production of a baseline land cover, change vector analysis to identify changed pixels, and a new constrained signature extension approach to update the land cover of changed areas. We present the method and results applied to six pilot parks using time series of Landsat TM/ETM+ imagery from 1985-2005.