Individual tree species identification is important for urban forest inventory and ecology management. Recent advances in remote sensing technologies facilitate more detailed estimation of individual urban tree characteristics. This study presents an approach to improve the classification of individual tree species via longitudinal profiles from very high spatial resolution airborne imagery. The longitudinal profiles represent the side view tree shape, which play a very important role in individual tree species on-site identification. Decision tree classification was employed to conduct the final classification result. Using this profile approach, six major species (Maple, Ash, Birch, Oak, Spruce, Pine) of trees on the York University (Ontario, Canada) campus were successfully identified. Two decision trees were constructed, one knowledge-based and one derived from gain ratio criteria. The classification accuracy achieved were 84% and 86%, respectively.

In current individual tree crown (ITC) delineation methods for high-resolution multispectral imagery, either a spectral band or a brightness component of the multispectral image is employed in delineation with reference to edges or shapes of crowns, whereas spectra of tree crowns are seldom taken into account. Such methods normally perform well in coniferous forests with obvious between-crown shadows, but fail in dense deciduous or mixed forests, in which tree crowns are close to each other, between-crown shadows and boundaries are unobvious, whereas adjacent tree crowns may be of distinguishable spectra. In order to effectively delineate crowns in dense deciduous or mixed forests, a new ITC delineation method using both brightness and spectra of the image is proposed in this study. In this method, a morphological gradient map of the image is first generated, treetops of multi-scale crowns are extracted from the gradient map and refined regarding the spectral differences between neighboring crowns, the gradient map is segmented using a watershed approach with treetops as markers, and the resulting segmentation map is refined to yield a crown map. Evaluated on images of a rainforest and a deciduous forest, the proposed method more accurately delineated adjacent broad-leaved tree crowns with similar brightness but different spectra than the other two typical ITC delineation algorithms, achieving a delineation accuracy of up to 76% in the rainforest and 63% in the deciduous forest.

Deep learning (DL) has shown promising performances in various remote sensing applications as a powerful tool. To explore the great potential of DL in improving the accuracy of individual tree species (ITS) classification, four convolutional neural network models (ResNet-18, ResNet-34, ResNet-50, and DenseNet-40) were employed to classify four tree species using the combined high-resolution satellite imagery and airborne LiDAR data. A total of 1503 samples of four tree species, including maple, pine, locust, and spruce, were used in the experiments. When both WorldView-2 and airborne LiDAR data were used, the overall accuracies (OA) obtained by ResNet-18, ResNet-34, ResNet-50, and DenseNet-40 were 90.9%, 89.1%, 89.1%, and 86.9%, respectively. The OA of ResNet-18 was increased by 4.0% and 1.8% compared with random forest (86.7%) and support vector machine (89.1%), respectively. The experimental results demonstrated that the size of input images impacted on the classification accuracy of ResNet-18. It is suggested that the input size of ResNet models can be determined according to the maximum size of all tree crown sample images. The use of LiDAR intensity image was helpful in improving the accuracies of ITS classification and atmospheric correction is unnecessary when both pansharpened WorldView-2 images and airborne LiDAR data were used.

Mapping the distribution and persistence of surface water in a timely fashion has broad value for tracking dynamic events like flooding, and for monitoring the effects of climate and human activities on natural resource values and biodiversity. Traditionally, surface water is mapped from optical imagery using semi-automatic approaches. However, this process is time-consuming and the accuracy of results can vary among image interpreters. In recent years, Synthetic Aperture Radar (SAR) images have been increasingly used. Microwave signals sensitive to water content make SAR systems useful for mapping surface water, saturated soils, and flooded vegetation. In this study, a fully automatic method based on robust stepwise thresholding was developed to map and track the change in the extent of surface water using Polarimetric SAR data. The application of this method in both Radarsat-2 and Sentinel-1 data in central Ontario, Canada demonstrates that the developed robust stepwise thresholding approach could facilitate rapid mapping of open water areas with a promising accuracy of over 95%. In addition, the time-series extent of surface water extracted from May 2008 to August 2016 reveals the dynamic nature of surface inundation, and the trend was consistent with the local precipitation data.

Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality.

In this study, a new noise reduction algorithm based on singular spectral analysis (SSA) was developed to reduce the noise in hyperspectral data. With this SSA‐based approach, the reflectance spectrum of a given pixel in a hyperspectral cube is transformed into its state space. The state space is dynamically constructed and characterized by irregular bases, which allows the proposed approach to reduce noises while keeping the absorption features of surface objects. The performance of the developed method was verified on three datasets: two simulated reflectance spectra with several narrow absorption features and a CHRIS (Compact High Resolution Imaging Spectrometer) data cube over agricultural fields. Our results demonstrated the effectiveness of the SSA‐based approach in improving the signal‐to‐noise ratio of hyperspectral data, while keeping the ‘sharp features’ in the reflectance spectra. The results also show that the proposed SSA method outperforms the commonly used MNF (minimum noise fraction) and wavelet‐based noise reduction methods and it improved vegetation cover classification accuracy by 6%.