High Spatial Resolution Data Set Research Articles

Three Dimensional (3D) Echocardiography as a Tool of Left Ventricular Assessment in Children with Dilated Cardiomyopathy: Comparison to Cardiac MRI.

BACKGROUND:Left ventricular (LV) volumes and ejection fraction (EF) is Strong prognostic indicators for DCM. Cardiac MRI (CMRI) is a preferred technique for LV volumes and EF assessment due to high spatial resolution and complete volumetric datasets. Three-dimensional echocardiography is a promising new technique under investigations.AIM:Evaluate 3D echocardiography as a tool in LV assessment in DCM children about CMRI.PATIENTS AND METHODS:A group of 20 DCM children (LVdiastolic diameter < 2 Z score, LVEF < 35%) at Children s Hospital, Ain-Shams University (gp1) (mean age 6.6 years) were compared to 20 age and sex-matched children as controls (gp2). Patients were subjected to: clinical examination, conventional echocardiography, automated 3D LV quantification, 3D speckle tracking echocardiography (3D-STE) (VIVID E9 Vingmed, Norway) and CMRI (Philips Achieva Nova, 1.5 Tesla scanner) for LV end systolic volume (LVESV), LVend diastolic volume (LVEDV) that were indexed to body surface area, EF% and wall motion abnormalities assessment.RESUTS:No statistically significant difference was found between automated 3D LV quantification echocardiography, 3D-STE, and CMRI in ESV/BSA and EDV/BSA assessment (p = 1, 0.99 respectively), between automated LV quantification echocardiography and CMRI in EF% assessment (p = 0.99) and between CMRI and 3D-STE in LV Global hypokinesia detection (P = 0.255). As for segmental hypokinesia CMRI was more sensitive [45% of patients vs. 40%, (P = 0,036), basal septal hypokinesia 85% vs. 75%, (p = 0.045), mid septal hypokinesia 80% vs. 65%, (p = 0.012) and lateral wall hypokinesia 75% vs. 65%, (p = 0.028)].CONCLUSION:Automated 3D LV quantification echocardiography and 3D-STE are reliable tools in LV volumetric and systolic function assessment about CMRIas a standard method. 3D speckle echocardiography is comparable to CMRI in global wall hypokinesia detection but less sensitive in segmental wall hypokinesia which mandates further studies.

Spatial heterogeneity of phytoplankton community shaped by a combination of anthropogenic and natural forcings in a long narrow bay in the East China Sea

Phytoplankton communities in semi-enclosed bays vary on spatial scales under natural and anthropogenic forcings (e.g., water exchange, sediment resuspension, and eutrophication). However, their relative importance in shaping phytoplankton spatial heterogeneity remains poorly quantified. The long, narrow, semi-enclosed Xiangshan Bay (XSB) has been subject to extensive human activities, including eutrophication and thermal discharge from power plants, which profoundly influence the phytoplankton community, particularly in cold seasons. We hypothesize that phytoplankton spatial heterogeneity in this bay is largely structured by anthropogenic forcings. Here, high spatial resolution datasets of phytoplankton communities were obtained during four seasons in 2015. Results show that diatoms and cryptophytes dominate the phytoplankton community, followed by dinoflagellates. Colonial planktonic and single-celled benthic/meroplanktonic diatoms and cryptophytes thrive in branched bays and in the upper main bay, which is characterized by high transparency, abundant nutrients, and relatively stable water. Large-celled planktonic and colonial benthic/meroplanktonic diatoms and allochthonous species (e.g., Trichodesmium thiebautii) are dominant in the turbulent, turbid, high-salinity lower and outer sections. Phytoplankton abundances were significantly higher in branched bays and in the upper section than in other regions. Thermal discharge stimulated the winter bloom of Skeletonema and Chaetoceros curvisetus in the inner bay. A nonmetric multidimensional scaling and analysis of similarity indicated highly significant spatial heterogeneity of community composition throughout different regions. Canonical correspondence analysis and Spearman's rank correlation demonstrated the significance of light penetration, nutrients, salinity, and temperature in structuring phytoplankton spatial distribution. However, weak top-down control on phytoplankton by mesozooplankton was also observed. Variance partitioning analysis revealed that phytoplankton community variations were highly explained by the interaction of anthropogenic (eutrophication and thermal discharge) and natural (water exchange and sediment resuspension) factors in all seasons, although anthropogenic variables explained more variation than that of natural variables in cold seasons. This study demonstrated that a combination of anthropogenic and natural forces contributed to phytoplankton spatial heterogeneity in the XSB.

A weekly, continually updated dataset of the probability of large wildfires across western US forests and woodlands

Abstract. There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near- and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250 m pixel) of the probability of large wildfires ( &gt; 405 ha) across forests and woodlands in the contiguous western US, from 2005 to the present. The dataset is automatically updated on a weekly basis using Google Earth Engine and a continuous integration pipeline. Each image in the dataset is the output of a random forest machine-learning algorithm, trained on random samples of historic small and large wildfires and represents the predicted conditional probability of an individual pixel burning in a large fire, given an ignition or fire spread to that pixel. This novel workflow is able to integrate the near-term dynamics of fuels and weather into weekly predictions while also integrating longer-term dynamics of fuels, the climate, and the landscape. As a continually updated product, the dataset can provide operational fire managers with contemporary, on-the-ground information to closely monitor the changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as the strategic targeting of fuels management, fire-smart development at the wildland–urban interface, and the analysis of trends in wildfire potential over time. Weekly large fire probability GeoTiff products from 2005 to 2017 are archived on the Figshare online digital repository with the DOI https://doi.org/10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Weekly GeoTiff products and the entire dataset from 2005 onwards are also continually uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1 (last access: 14 September 2018) and are available free of charge with a Google account. Continually updated products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets and can be accessed with the image collection ID users/mgray/wffr-preds within GEE.

Local strategies for China's carbon mitigation: An investigation of Chinese city-level CO2 emissions

Abstract This paper provides a systematic analysis that identifies the driving forces of carbon dioxide (CO2) emissions of 286 Chinese prefecture-level cities in 2012. The regression analysis confirms the economic scale and structure effects on cities' CO2 emissions in China. If China's annual economic growth continues at the rate of 7%, CO2 emissions will increase by about 6% annually. In addition, climate conditions, urbanization and public investment in RD An increase in R&D investment by 1% can help reduce CO2 emissions by 0.21%. As cities in our study vary greatly based on their industry composition, development stage and geographical location, the patterns of their CO2 emissions are also variable. Our study improves the comprehensiveness and accuracy of previous carbon accounting method by distinguishing the scope 1 and scope 2 CO2 emissions and establishing a high spatial resolution dataset of CO2 emissions (CHRED). The analysis covers almost all Chinese prefectural cities and derives useful implications for China's low carbon development.

Seasonal and Spatial Changes in Trichodesmium Associated With Physicochemical Properties in East China Sea and Southern Yellow Sea

AbstractTrichodesmium is broadly distributed and occasionally blooms in the East China Sea (ECS) and southern Yellow Sea, where it contributes to local N and C budgets. However, its population structure, spatiotemporal distribution, controlling factors, and N2 fixation remain poorly documented. Here we provide high spatial resolution data sets of Trichodesmium during the four seasons of 2011–2012 using water‐ and net‐collected methods. The net‐collected method captures colonial trichomes of Trichodesmium effectively but results in an underestimation of free trichomes. Colonies are rarely observed and occur only on the ECS shelf, which are easily missed in water‐collected samples. Depth‐integrated densities of Trichodesmium were found to be significantly higher in warm seasons than in cold seasons. Maximum densities in the water column were generally found at depths of 10–50 m. Trichodesmium thrives on the oligotrophic, warm, offshore ECS shelf (controlled by the Kuroshio and Taiwan Warm Current), but restrains in the cold southern Yellow Sea and the eutrophic, inshore ECS. Seasonal and spatial variations in Trichodesmium are closely correlated with physicochemical properties (mainly temperature and P), which are primarily controlled by circulation alteration and water mass movement. The N2 fixation rates of Trichodesmium in the ECS in summer and autumn (&gt;20°C) are roughly estimated at 17.1 and 41.7 μmol N m−2 d−1 under nonbloom conditions, which potentially contribute to 81% and 57% of biological N2 fixation, respectively. Compared with historical data since the 1970s, Trichodesmium densities have increased considerably in all seasons, and the distribution boundary has shifted northward under regional warming and hydrological changes.

ASSESSMENT OF MULTIBEAM BACKSCATTER TEXTURE ANALYSIS FOR SEAFLOOR SEDIMENT CLASSIFICATION

Abstract. Recently, there have been many debates to analyse backscatter data from multibeam echosounder system (MBES) for seafloor classifications. Among them, two common methods have been used lately for seafloor classification; (1) signal-based classification method which using Angular Range Analysis (ARA) and Image-based texture classification method which based on derived Grey Level Co-occurrence Matrices (GLCMs). Although ARA method could predict sediment types, its low spatial resolution limits its use with high spatial resolution dataset. Texture layers from GLCM on the other hand does not predict sediment types, but its high spatial resolution can be useful for image analysis. The objectives of this study are; (1) to investigate the correlation between MBES derived backscatter mosaic textures with seafloor sediment type derived from ARA method, and (2) to identify which GLCM texture layers have high similarities with sediment classification map derived from signal-based classification method. The study area was located at Tawau, covers an area of 4.7 km2, situated off the channel in the Celebes Sea between Nunukan Island and Sebatik Island, East Malaysia. First, GLCM layers were derived from backscatter mosaic while sediment types (i.e. sediment map with classes) was also constructed using ARA method. Secondly, Principal Component Analysis (PCA) was used determine which GLCM layers contribute most to the variance (i.e. important layers). Finally, K-Means clustering algorithm was applied to the important GLCM layers and the results were compared with classes from ARA. From the results, PCA has identified that GLCM layers of Correlation, Entropy, Contrast and Mean contributed to the 98.77 % of total variance. Among these layers, GLCM Mean showed a good agreement with sediment classes from ARA sediment map. This study has demonstrated different texture layers have different characterisation factors for sediment classification and proper analysis is needed before using these layers with any classification technique.

A High Resolution Dataset of Drought Indices for Spain

Drought indices are essential metrics for quantifying drought severity and identifying possible changes in the frequency and duration of drought hazards. In this study, we developed a new high spatial resolution dataset of drought indices covering all of Spain. The dataset includes seven drought indices, spans the period 1961–2014, and has a spatial resolution of 1.1 km and a weekly temporal resolution. A web portal has been created to enable download and visualization of the data. The data can be downloaded as single gridded points for each drought index, but the entire drought index dataset can also be downloaded in netCDF4 format. The dataset will be updated for complete years as the raw meteorological data become available.

Spatial and temporal dynamics of lekking behaviour revealed by high-resolution GPS tracking

Even though leks have been widely used by biologists as a model to study sexual selection, we still do not understand how lekking individuals use space, interact and socially progress over time. This study aimed to provide new insights into lekking breeding dynamics using a high temporal and spatial resolution data set derived from the GPS tracking of adult male little bustards, Tetrax tetrax , collected over a 4-year period. We found the lekking system was dynamic at both intra- and interseasonal scales. Smaller ranges were consistent with territorial behaviour. Using Bayesian latent cluster analysis, based on the movements of each individual, we were able to distinguish between breeding behavioural modes, territorial and floating, within the breeding season. Surprisingly, only 27% of the males were found to be constantly territorial within the same year. The remaining birds showed predominantly floating behaviour or shifted between the two behavioural modes, floating and territorial, possibly indicating male turnover. Birds that were tracked for more than 1 year tended to return to the same breeding range. Floating males between years reduced their ranges within the same area and became territorial, indicating a trend for rank progression with increasing adult age. The combination of territorial site fidelity and male rank ascent based on age can explain the temporal persistence of leks within stable habitats.

The spatiotemporal substrates of autobiographical recollection: Using event-related ICA to study cognitive networks in action

Higher cognitive functions depend upon dynamically unfolding brain network interactions. Autobiographical recollection – the autonoetic re-experiencing of context rich, emotionally laden, personally experienced episodes – is an excellent example of such a process. Autobiographical recollection unfolds over time, with different cognitive processes engaged at different times throughout. In this paper we apply a recently developed analysis technique – event related independent components analysis (eICA) – to study the spatiotemporal dynamics of neural activity supporting autobiographical recollection. Participants completed an in-scanner autobiographical recollection paradigm in which the recalled episodes varied in chronological age and emotional content. By combining eICA with these cognitive manipulations we show that the brain-wide response to autobiographical recollection comprises brain networks with (i) different sensitivities to psychological aspects of the to-be-recollected material and (ii) distinct temporal profiles of activity during recollection. We identified networks with transient activations (in language and cognitive control related regions) and deactivations (in auditory and sensorimotor regions) to each autobiographical probe question, as well as networks with responses that are sustained over the course of the recollection period. These latter networks together overlapped spatially with the broader default mode network (DMN), indicating subspecialisation within the DMN. The vividness of participants' recollection was associated with the magnitude of activation in left dorsolateral prefrontal cortex and deactivation in visual association cortices. We interpret our results in the context of current theories of the spatial and temporal organisation of the human autobiographical memory system. Our findings demonstrate the utility of eICA as a tool for studying higher cognitive functions. The application of eICA to high spatial and temporal resolution datasets identifies in a single experimental protocol spatially specific networks that are recruited during cognitive activity, as well as the temporal order of activation of these networks.

ON THE FORMATION OF A STABLE PENUMBRA IN A REGION OF FLUX EMERGENCE IN THE SUN

ABSTRACT We studied the formation of the first penumbral sector around a pore in the following polarity of the NOAA Active Region (AR) 11490. We used a high spatial, spectral, and temporal resolution data set acquired by the Interferometric BIdimensional Spectrometer operating at the NSO/Dunn Solar Telescope, as well as data taken by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory satellite. On the side toward the leading polarity, elongated granules in the photosphere and an arch filament system (AFS) in the chromosphere are present, while the magnetic field shows a sea-serpent configuration, indicating a region of magnetic flux emergence. We found that the formation of a stable penumbra in the following polarity of the AR begins in the area facing the opposite polarity located below the AFS in the flux emergence region, different from what was found by Schlichenmaier and colleagues. Moreover, during the formation of the first penumbral sector, the area characterized by magnetic flux density larger than 900 G and the area of the umbra increase.

Daily black carbon emissions from fires in northern Eurasia for 2002–2015

Abstract. Black carbon (BC) emitted from fires in northern Eurasia is transported and deposited on ice and snow in the Arctic and can accelerate its melting during certain times of the year. Thus, we developed a high spatial resolution (500 m × 500 m) dataset to examine daily BC emissions from fires in this region for 2002–2015. Black carbon emissions were estimated based on MODIS (Moderate Resolution Imaging Spectroradiometer) land cover maps and detected burned areas, the Forest Inventory Survey of the Russian Federation, the International Panel on Climate Change (IPCC) Tier-1 Global Biomass Carbon Map for the year 2000, and vegetation specific BC emission factors. Annual BC emissions from northern Eurasian fires varied greatly, ranging from 0.39 Tg in 2010 to 1.82 Tg in 2015, with an average of 0.71 ± 0.37 Tg from 2002 to 2015. During the 14-year period, BC emissions from forest fires accounted for about two-thirds of the emissions, followed by grassland fires (18 %). Russia dominated the BC emissions from forest fires (92 %) and central and western Asia was the major region for BC emissions from grassland fires (54 %). Overall, Russia contributed 80 % of the total BC emissions from fires in northern Eurasia. Black carbon emissions were the highest in the years 2003, 2008, and 2012. Approximately 58 % of the BC emissions from fires occurred in spring, 31 % in summer, and 10 % in fall. The high emissions in spring also coincide with the most intense period of ice and snow melting in the Arctic.

Regional scale spatial and temporal variability of soil moisture in a prairie region

AbstractUnderstanding the dynamics of spatial and temporal variability of soil moisture at the regional scale and daily interval, respectively, has important implications for remote sensing calibration and validation missions as well as environmental modelling applications. The spatial and temporal variability of soil moisture was investigated in an agriculturally dominated region using an in‐situ soil moisture network located in central Saskatchewan, Canada. The study site evaluated three depths (5, 20, 50 cm) through 139 days producing a high spatial and temporal resolution data set, which were analysed using statistical and geostatistical means. Processes affecting standard deviation at the 5‐cm depth were different from the 20‐cm and 50‐cm depths. Deeper soil measurements were well correlated through the field season. Further analysis demonstrated that lag time to maximum correlation between soil depths increased through the field season. Temporal autocorrelation was approximately twice as long at depth compared to surface soil moisture as measured by the e‐folding frequency. Spatial correlation was highest under wet conditions caused by uniform rainfall events with low coefficient of variation. Overall soil moisture spatial and temporal variability was explained well by rainfall events and antecedent soil moisture conditions throughout the Kenaston soil moisture network. It is expected that the results of this study will support future remote sensing calibration and validation missions, data assimilation, as well as hydrologic model parameterization for use in agricultural regions. Copyright © 2016 John Wiley &amp; Sons, Ltd.

In-between forest expansion and cropland decline: A revised USLE model for soil erosion risk under land-use change in a Mediterranean region

The present study illustrates an original approach for the long-term assessment of soil erosion risk under land-use changes in a Mediterranean region (Matera, southern Italy). The study has been focused on the implementation of a modified Universal Soil Loss Equation (USLE) model at three time points (1960, 1990, 2010) with the objective to evaluate the contribution of each component to model's performance and model outcomes’ reliability. A modified USLE model was proposed for the assessment of soil erosion risk, based on the simplification of model’s parameters and the use of high spatial resolution datasets. Spatio-temporal variability in the model's outcomes was analyzed for basic land-use classes. Our approach has improved model's flexibility with the use of high spatial resolution layers, producing reliable long-term estimates of soil loss for the study area.

FORMATION OF THE PENUMBRA AND START OF THE EVERSHED FLOW

ABSTRACT We studied the variations of line of sight photospheric plasma flows during the formation phase of the penumbra around a pore in active region NOAA 11490. We used a high spatial, spectral, and temporal resolution data set acquired by the Interferometric BIdimensional Spectrometer operating at the NSO/Dunn Solar Telescope as well as data taken by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory satellite (SDO/HMI). Before the penumbra formed we observed a redshift of the spectral line in the inner part of the annular zone surrounding the pore as well as a blueshift of material associated with opposite magnetic polarity farther away from the pore. We found that the onset of the classical Evershed flow occurs on a very short timescale (1 to 3 hr) while the penumbra is forming. During the same time interval we found changes in the magnetic field inclination in the penumbra, with the vertical field actually changing sign near the penumbral edge, while the total magnetic field showed a significant increase, about 400 G. To explain these and other observations related to the formation of the penumbra and the onset of the Evershed flow we propose a scenario in which the penumbra is formed by magnetic flux dragged down from the canopy surrounding the initial pore. The Evershed flow starts when the sinking magnetic field dips below the solar surface and magnetoconvection sets in.

Detection and Delineation of Localized Flooding from WorldView-2 Multispectral Data

Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77% and 95%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance.

Xylem hydraulic properties in subtropical coniferous trees influence radial patterns of sap flow: implications for whole tree transpiration estimates using sap flow sensors

A high spatial resolution dataset of sap flux density in subtropical conifers is used to assess the minimum number and location of sap flow sensors required to monitor tree transpiration accurately. Tree transpiration is commonly estimated by methods based on in situ sap flux density (SFD) measurements, where the upscaling of SFD from point measurements to the individual tree has been identified as the main source of error. The literature indicates that the variation in SFD with radial position across a tree stem section can exhibit a wide range of patterns. Adequate capture of the SFD profile may require a large number of point measurements, which is likely to be prohibited. Thus, it is of value to develop protocols, which rationalize the number of point measurements, while retaining a satisfactory precision in the tree SFD estimates. This study investigates cross-sectional SFD variability within a tree and successively for six individual trees within a stand of Pinus elliottii var. elliottii × caribaea var. hondurensis (PEE × PCH). The stand is part of a plantation in subtropical coastal Australia. SFD is estimated using the Heat Field Deformation method simultaneously for four cardinal directions with measurements at six depths from the cambium. This yields a reference value of single tree SFD based on the twenty-four point measurements. Large variability of SFD is observed with measurement depth, cardinal direction and selected tree. We suggest that this is linked to the occurrence of successive narrow early and latewood rings with contrasting-specific hydraulic conductivities and wood water contents. Thus, an accurate placement of sensors within each ring is difficult to achieve in the field with the sensor footprint covering several rings of both early and latewood. Based on the reference dataset, we identified both an “ideal” setup and an “optimal” setup in terms of cost effectiveness and accuracy. Our study shows the need of using a systematic protocol to optimize the number of sensors to be used as a trade-off between precision and cost. It includes a preliminary assessment of the SFD variability at a high spatial resolution, and only then based on this, an appropriate placement of sensors for the long-term monitoring.

Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data

Information derived from high spatial resolution remotely sensed data is critical for the effective management of forested ecosystems. However, high spatial resolution data-sets are typically costly to acquire and process and usually provide limited geographic coverage. In contrast, moderate spatial resolution remotely sensed data, while not able to provide the spectral or spatial detail required for certain types of products and applications, offer inexpensive, comprehensive landscape-level coverage. This study assessed using an object-based approach to extrapolate detailed tree species heterogeneity beyond the extent of hyperspectral/LiDAR flightlines to the broader area covered by a Landsat scene. Using image segments, regression trees established ecologically decipherable relationships between tree species heterogeneity and the spectral properties of Landsat segments. The spectral properties of Landsat bands 4 (i.e., NIR: 0.76–0.90 µm), 5 (i.e., SWIR: 1.55–1.75 µm) and 7 (SWIR: 2.08–2.35 µm) were consistently selected as predictor variables, explaining approximately 50% of variance in richness and diversity. Results have important ramifications for ongoing management initiatives in the study area and are applicable to wide range of applications.

Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland

With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n = 5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments.

Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks

This study presents the successful application of artificial neural networks (ANNs) for downscaling Meteosat Second Generation thermal infrared satellite imagery. The scope is to examine, propose, and develop an integrated methodology to improve the spatial resolution of Meteosat satellite images. The proposed approach may contribute to the development of a general methodology for monitoring and downscaling Earth’s surface characteristics and cloud systems, where there is a clear need for contiguous, accurate, and high-spatial resolution data sets (e.g. improvement of climate model input data sets, early warning systems about extreme weather phenomena, monitoring of parameters such as solar radiation fluxes, land-surface temperature, etc.). Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to validate the downscaled Meteosat images. In terms of the ANNs, a multilayer perceptron (MLP) is used and the results are shown to compare favourably against a linear regression approach.

Investigation of the porous structure of cellulosic substrates through confocal laser scanning microscopy

At the most fundamental level, saccharification occurs when cell wall degrading enzymes (CWDEs) diffuse, bind to and react on readily accessible cellulose fibrils. Thus, the study of the diffusive behavior of solutes into and out of cellulosic substrates is important for understanding how biomass pore size distribution affects enzyme transport, binding, and catalysis. In this study, fluorescently labeled dextrans with molecular weights of 20, 70, and 150 kDa were used as probes to assess their diffusion into the porous structure of filter paper. Fluorescence microscopy with high numerical aperture objectives was used to generate high temporal and spatial resolution datasets of probe concentrations versus time. In addition, two diffusion models, including a simple transient diffusion and a pore grouping diffusion models, were developed. These models and the experimental datasets were used to investigate solute diffusion in macro- and micro-pores. Nonlinear least squares fitting of the datasets to the simple transient model yielded diffusion coefficient estimates that were inadequate for describing the initial fast diffusion and the later slow diffusion rates observed; on the other hand, nonlinear least squares fitting of the datasets to the pore grouping diffusion model yielded estimations of the micro-pore diffusion coefficient that described the inherently porous structure of plant-derived cellulose. In addition, modeling results show that on average 75% of the accessible pore volume is available for fast diffusion without any significant pore hindrance. The method developed can be applied to study the porous structure of plant-derived biomass and help assess the diffusion process for enzymes with known sizes.