Examination of Water Phase Transitions in Black Spruce by Magnetic Resonance and Magnetic Resonance Imaging

This study examines the phase transitions of water in wood by magnetic resonance and magnetic resonance imaging. The goal was to observe and understand the behavior of water below 0°C in wood. The species studied, black spruce (Picea mariana Mill.), presented one abrupt phase change that occurred at about -3°C, which was attributed to the phase transition of free water. A more diffuse change occurred below -60°C, which was attributed to a phase transition of bound water. A recently developed portable unilateral magnetic resonance instrument is demonstrated as a powerful tool in the study of water in wood. This portable magnet used a bulk spin-spin relaxation time measurement that quantifies observable bound and free water in wood. Imaging was used to verify the unilateral magnetic resonance results and to better understand realistic freeze-thaw behavior of log samples in the field. A ring boundary behavior during the thawing process was observed, and likewise there were differences in the thawing behaviors of heartwood and sapwood samples

Identification of Log Characteristics in Computed Tomography Images Using Back-Propagation Neural Networks with the Resilient Back-Propagation Training Algorithm and Textural Analysis: Preliminary Results

This research addressed the feasibility of identifying internal log characteristics in computed tomography (CT) images of sugar maple and black spruce logs by means of back-propagation (BP) neural networks with a resilient BP training algorithm. Five CT images were randomly sampled from each log. Three of the images were used to develop the corresponding classifier, and the remaining two images were used for validation. The image features that were used in the classifier were gray-level values, textual, and distance features. The important part of the classifier topology, ie the hidden node number, was determined based on the performance indicators: overall accuracy, mean square error, training iteration number, and training time. For the training images, the classifiers produced class accuracies for heartwood, sapwood, bark, and knots of 99.3, 100, 96.7, and 97.9%, respectively, for the sugar maple log; and 99.7, 95.3, 98.4, and 93.2%, respectively, for the black spruce log. Overall accuracies were 98.5% for sugar maple and 96.6% for black spruce, respectively. High overall accuracies were also achieved with the validation images of both species. The results also suggest that using textural information as the inputs can improve the classification accuracy. Moreover, the resilient BP training algorithm made BP artificial neural networks converge faster compared with the steepest gradient descent with momentum algorithm. This study indicates that the developed BP neural networks may be applicable to identify the internal log characteristics in the CT images of sugar maple and black spruce logs

Tabusintac Bay (New Brunswick, Canada): an important spring migratory stopover for Atlantic Brant (Branta bernicla hrota)

Atlantic Brant (Branta bernicla hrota) is an Arctic-breeding migratory waterfowl that relies heavily on Common Eelgrass (Zostera marina) for food during migration and overwintering. Although the abundance of Atlantic Brant along the coasts of the Maritime provinces has declined drastically over the past decades, some flocks continue to migrate through the area in spring. Here, we present field observations of Atlantic Brant spring staging in the Tabusintac Bay, New Brunswick, Canada. We surveyed the Tabusintac Bay seven times between 26 May and 6 June 2018. We observed a maximum daily count of 1259 individuals, which is comparable to high counts from the 1970s. These spring surveys indicate the continuing importance of Tabusintac Bay to Atlantic Brant for spring staging. There is a pressing need to increase monitoring and research in the region and to preserve or enhance the quality of the area for spring staging brant

Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) synthetic aperture radar data

Structural parameters of the woody component in African savannahs provide estimates of carbon stocks that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component can be characterised by various quantifiable woody structural parameters, such as tree cover, tree height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species diversity and phenological status – a defining but challenging set of characteristics typical of African savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody component because of their ability to sense within-canopy properties of the vegetation and its insensitivity to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s structure can be sensed differently with SAR depending on the frequency or wavelength of the sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency was more effective in the modelling of the CC (coefficient of determination or R2 of 0.77), TCV (R2 of 0.79) and AGB (R2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and C-band) both as individual and combined (X + C-band) datasets. The addition of the shortest wavelengths also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g. dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration of all three frequencies (X + C + L-band) yielded the best overall results for all three metrics (R2 = 0.83 for CC and AGB and R2 = 0.85 for TCV), the improvements were noticeable but marginal in comparison to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring in this environment.Council for Scientific and Industrial Research (CSIR) – South Africa, the Department of Science and Technology, South Africa (Grant Agreement DST/CON 0119/2010, Earth Observation Application Development in Support of SAEOS) and the European Union’s Seventh Framework Programme (FP7/2007-2013, Grant Agreement No. 282621, AGRICAB) for funding this study. The Xband StripMap TerraSAR-X scenes were acquired under a proposal submitted to the TerraSAR-X Science Service of the German Aerospace Center (DLR). The C-band Quad-Pol RADARSAT-2 scenes were provided by MacDonald Dettwiler and Associates Ltd. – Geospatial Services Inc. (MDA GSI), the Canadian Space Agency (CSA), and the Natural Resources Canada’s Centre for Remote Sensing (CCRS) through the Science and Operational Applications Research (SOAR) programme. The L-band ALOS PALSAR FBD scenes were acquired under a K&C Phase 3 agreement with the Japanese Aerospace Exploration Agency (JAXA). The Carnegie Airborne Observatory is supported by the Avatar Alliance Foundation, John D. and Catherine T. MacArthur Foundation, Gordon and Betty Moore Foundation, W.M. Keck Foundation, the Margaret A. Cargill Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr., and William R. Hearst III. The application of the CAO data in South Africa is made possible by the Andrew Mellon Foundation, Grantham Foundation for the Protection of the Environment, and the endowment of the Carnegie Institution for Science.http://www.elsevier.com/locate/isprsjprs2016-07-31hb201

Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields

Crop nitrogen (N) needs to be accurately predicted to allow farmers to effectively match the N supply to the crop N demand during crop growth in order to minimize environmental impacts as excess N could seep into the water supplies around the field. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral MicaSense imagery validated with ground hyperspectral measurements to predict canopy nitrogen weight (g/m2) of wheat and cornfields in Ontario. A simple linear regression was established to predict the canopy nitrogen weight from various vegetation indices (VI). Ratio Vegetation Index (RVI) performed the best out of all the tested vegetation indices, with an R2 of 0.93 for the wheat fields and 0.83 for the corn fields. RVI estimation was also consistent throughout the growing season, which is optimal in precision agriculture. Once applied the RVI-based regression model to the UAV imagery, the best RMSE was 0.95 g/m2 for the wheat McColl field using the image of May 24th and 0.66 g/m2 for the corn Jack North field using the image of June 7th. Such information for accurately predicting nitrogen is important for farmers as it will lead to a more efficient fertilizer application program

Evaluation of Soil Properties, Topographic Metrics, Plant Height, and Unmanned Aerial Vehicle Multispectral Imagery Using Machine Learning Methods to Estimate Canopy Nitrogen Weight in Corn

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R2 of 0.73 and Root Mean Square Error (RMSE) of 2.21 g/m2. Of the model’s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management