This study evaluated three geometric transformations in an image registration method applied to non-georeferenced multispectral images acquired at close range over greenhouse cucumber plants with a Micasense®RedEdge camera. The detection of matching points was performed using SURF features, and outliers matching points were removed using the MSAC algorithm. For each geometric transformation (affine, similarity, and projective), we mapped the matching points of the blue, green, red, and NIR band images into the red-edge band space and computed the root mean square error (RMSE in pixel) to estimate the accuracy of each transformation. Our results achieved an RMSE of less than 1 pixel with the similarity and affine transformations and of less than 2 pixels with the projective transformation, whatever the band image. We determined that the best transformation was the affine transformation because it produces RMSEs of less than 1 pixel and having a Gaussian distribution.

Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal temperate regions. This plant is considered an important coastal species, but little research has been done to map its distribution and location within Atlantic Canada. The purpose of this study was to assess the capability of Sentinel-2 and UAV imagery to map the presence of eelgrass beds within an estuary of Atlantic Canada. Both imageries were classified using the nonparametric Random Forests classifier and then validated using sonar data. The Sentinel-2 classified image had a lower validation accuracy of 77.7%, compared to the UAV classified image (90.9%). This accuracy difference is related to the difference in spatial resolution between both imageries. The limitations of this study and recommendations for future work are also presented.

An automated method has been developed to detect male eiders (COEI_M) on aerial multispectral images. Potential bird regions were determined after filtering the images with an average filter, masking out the ocean background and the land areas, and applying a watershed segmentation algorithm. Large and small objects were then removed. For each remaining object and its neighborhood, spectral and geometrical variables were computed. The variables that were not significantly different between the true and false COEI_M classes were removed based on a p-value of higher than 0.08 computed by an ANOVA. The objects were divided into training and validation datasets. A support vector machine using a radial basis function was used to classify the objects in the two classes. We achieved an overall accuracy of 98.6% with the validation data set. The user's accuracy and producer's accuracy for the true COEI_M class in the validation dataset were 86.4% and 92.7%, respectively.

This study presents preliminary results on detecting Atlantic Brant geese on UAV RGB images acquired over a lagoon in Atlantic Canada. The Atlantic Brant geese were identified using several photo-interpretation criteria. Geese were more easily detected when they were moving. Also, over-exposed images were found to give a better contrast between birds and water. Birds over water having light color seafloor are easier to be detected. Geese were distinctively different than the surrounding waves because they cast a visible shadow. Other bird species were also detected over the images, such as gulls and cormorants. The study also shows that images acquired during noon time produce extensive sun glint, while those early in the morning or late in the evening have a too low illumination to detect birds.

The paper presents an overview of 20 years of research at the Faculty of Forestry and Environmental Management, U. New Brunswick, Canada on fuel moisture estimation in boreal forests using optical, thermal infrared, and radar remote sensing, with a particular focus on the volumetric soil moisture content or its surrogate, the drought code (DC) of the Fire Weather Index System (FWI). Fire starts were shown to be better predicted using DC estimations from NOAA-AVHRR NDVI and Tsimages from those from weather stations. We developed a deterministic model to derive the ratio between the actual and potential evapotranspiration that was linked to FWI. ERS-1 C-VV and RADSARSAT-1 C-HH SAR backscatters were correlated to DC. Empirical algorithms to estimate soil volumetric moisture content (SVMC) maps were established with multi-date RADARSAT-2 polarimetric SAR variables and produced an RMSE of 6.7%.

Fully polarimetric Radarsat-2 imagery from wet and dry conditions over the South African Lowveld is compared to assess its value for fuel moisture mapping. Imagery was acquired at two different dates, in May (end of summer, wet) and in August (mid of winter, dry). Sample plots were classified into two broad Lowveld site types (herbaceous-dominated and shrub and tree-dominated). Linear and circular polarized backscatters, polarimetric discriminators and polarimetric decomposition parameters were computed to find suitable parameters for fuel moisture estimation. The results show a significant distinction between wet and dry conditions for C-HH, C-HV, C-RR, and C-LL, all Freeman-Durden and van Zyl decomposition parameters and some polarimetric discriminators (dmin, Prmax, Prmin, Smax, Smin). In almost all cases the normalized difference between wet and dry condition is lower for the shrub and tree-dominated sites. The Freeman-Durden double bounce scattering decomposition parameter performs best in both site types.

Our study tests the use of dual-polarized (HH, HV) RADARSAT-2 C-band and ALOS-PALSAR L-band SAR images for mapping wetland areas in New Brunswick. The study also uses LANDSAT-5 TM and DEM data. The resulting maps were compared to GPS field data as well as to two wetland maps currently in use by the Province of New Brunswick. Overall the Random Forests classifier gave better classification accuracies than the maximum likelihood classifier. The comparison with the 146 wetland truth sites shows that 73.3% are correctly identified over the LANDSAT-5 TM classified image. For the SAR-based classified images, the number of correctly identified wetland ground truth sites is higher when the image acquired during the flooding is considered, the difference being higher with the ALOS-PALSAR images than with the RADARSAT-2 images. The number of correctly identified sites is the highest when both the ALOS-PALSAR images and RADARSAT-2 images are used (98.6%). These percentages of correctly identified wetland sites are well above of those computed using the DNR wetland and forested wetland maps (44.5 %).

Our study compared the traditional method of teaching remote sensing with a new method that use teaching platform and Internet. We showed that the new teaching method has more advantages than the traditional one. In addition, the paper presented three lectures that can be used to teach not only geography but also mathematics high school curricula.

The woody component in African Savannahs provides essential ecosystem services such as fuel wood and construction timber to large populations of rural communities. Woody canopy cover (i.e. the percentage area occupied by woody canopy or CC) is a key parameter of the woody component. Synthetic Aperture Radar (SAR) is effective at assessing the woody component, because of its capacity to image within-canopy properties of the vegetation while offering an all-weather capacity to map relatively large extents of the woody component. This study compared the modelling accuracies of woody canopy cover (CC), in South African Savannahs, through the assessment of a set of modelling approaches (Linear Regression, Support Vector Machines, REPTree decision tree, Artificial Neural Network and Random Forest) with the use of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) datasets. This study illustrated that the ANN, REPTree and RF non-parametric modelling algorithms were the most ideal with high CC prediction accuracies throughout the different scenarios. Results also illustrated that the acquisition of L-band data be prioritized due to the high accuracies achieved by the L-band dataset alone in comparison to the individual shorter wavelengths. The study provides promising results for developing regional savannah woody cover maps using limited LiDAR training data and SAR images.

Permafrost is an important ground condition in high latitudes. Climate warming may lead to thickening of active layer, reducing permafrost thickness and extent, melting ground ice, causing ground subsidence and thermokarst erosions. In order to better map the distribution and dynamics of permafrost, there is a need to develop and test permafrost models that can be used with high spatial resolution remote sensing data. The purpose of this study is to calibrate the Northern Ecosystem Soil Temperature (NEST) model over the Victor Mine area located in the Hudson Bay Lowlands, Northern Ontario, Canada. The area is near the southern margin of permafrost region where permafrost exists only in isolated patches. We estimated and calibrated model input parameters using data from 1932 to 2012. The outputs were compared to field observations acquired between 2009 and 2012 at seven peat monitoring stations and two flux towers. Simulated soil temperatures show good agreement with observations at various depths for the different peatland types. The model shows the existence of permafrost only at palsa sites, which is in agreement with field observations. The calibrated model will be used to map permafrost over the whole area using remote sensing images.

This study assesses the potential of single-pass TanDEM-X interferometric SAR (InSAR) data to map forest canopy height, when a corresponding accurate digital terrain model (DTM) is available. In the proposed method, the forest canopy height model (CHM) is extracted by subtracting an airborne lidar DTM from the TanDEM-X digital surface model (DSM). We showed that the TanDEM-X coherence is influenced by local incidence angles and tree basal area. These factors should be taken into account when estimating forest canopy height using TanDEM-X combined to lidar data.

In this paper a neural network is used to perform a mapping between Synthetic Aperture Radar (SAR) backscatter information and LiDAR measurements, and the performance of the neural network model is evaluated against that of a multiple linear regression model. Our aim is to find a relationship between SAR backscatter information and the LiDAR tree volume measurements on a number of land uses in South Africa's Kruger National Park, using a linear as well as a non-linear model. We also seek to find the optimal grid cell size as well as the best combination of SAR polarisation-and decomposition parameters. Our findings suggest that there exists a linear or at least a near-linear relationship between the SAR backscatter information and the LiDAR measurements in South African savannas and that the addition of polarisation-and decomposition parameters to the input of the models aid in improving the Root Mean Squared Error (RMSE) performance.

Our study analyzed eleven RADARSAT-1 images acquired over burned and unburned boreal forests in June 2000 in order to assess their potential to map fire danger variables, like the CFFDRS FWI codes and indices. RADARSAT-1 can acquire images almost daily, but the acquisition is done under different beam modes. Fine beam mode images produced highly variable low radar backscatters. Incidence angles had a predominant effect in the day-to-day /spl sigma//sup 0/ variations, but these variations were also explained by rain events and by FWI variations. It is suggested that RADARSAT-1 radar backscatters are sensitive to moisture-related variables, but there is necessary to either correct the images for incidence angle effects or to only consider images acquiring in similar beam modes.