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With the recent thrust to convert forests in Ontario's Clay Belt to agricultural land, a vital need arises to assess the attendant effects on carbon and greenhouse gas (GHG) emissions. This paper examines the possible effect of land conversion on soil organic carbon and GHG emissions within a study area in Northern Ontario, Canada, during the next two decades under different land management schemes. The study established a framework to conduct simulations with the DNDC model for agricultural lands and the CBM for forested areas. The methodology involves a unique change detection method for models' land cover and disturbance inputs. The work highlights the improvement in carbon simulation accuracy from better inputs to carbon models. Furthermore, it addresses modalities to ensure fewer uncertainties are introduced while merging data from multiple geospatial data sources. The simulations demonstrated that the carbon sequestration potential in the forests was almost double the soil organic carbon accumulation in the agricultural lands. Validations done for the estimation of carbon sequestered included comparisons of the carbon model outputs from field survey data from 2018–2021. In most sites, the carbon amounts from the computer models compared to those from the field survey, within limits of error. The average uncertainties in GHG emissions ranged from ~0.5% to 12.8%. [ABSTRACT FROM AUTHOR]

The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America. Despite much progress in targeting habitat management in staging and wintering areas, methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required. This is particularly true for species that breed in remote, inaccessible areas such as the American black duck, an intensively managed game bird in Eastern North America. Although evidence suggests that black ducks prefer productive, nutrient-rich waterbodies, such as beaver ponds, information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge. Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap. In this study, we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario. The use of multi-source data, including a Digital Elevation Model, a Sentinel-2 Multi-Spectral Image, and RadarSat 2 Polarimetric data, enabled us to identify individual beaver ponds on the landscape. Our model correctly identified an average of 83.0% of the known beaver dams and 72.5% of the known beaver ponds based on validation with an independent dataset. This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents. Furthermore, the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife. [ABSTRACT FROM AUTHOR]

With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisource data at the feature level, while few systematically examined their positive or negative contributions to tree species classification. This study aimed to investigate fusion approaches at the feature and decision levels deployed with support vector machine and random forest algorithms to classify five dominant tree species: Norway maple, honey locust, Austrian pine, white spruce, and blue spruce in individual crowns. Spectral, textural, and structural features derived from multispectral imagery (MSI), a very high-resolution panchromatic image (PAN), and LiDAR data were systematically exploited to assess their contributions to accurate classifications. Among the various classification schemes that were explored, both feature- and decision-level fusion approaches demonstrated significant improvements in tree species classification compared with the utilization of MSI (0.7), PAN (0.74), or LiDAR (0.8) in isolation. Notably, the decision-level fusion approach achieved the highest overall accuracies (0.86 for SVM and 0.84 for RF) and kappa coefficients (0.82 for SVM and 0.79 for RF). The misclassification analysis of fusion approaches highlighted the potential and flexibility of decision-level fusion in tree species classification. [ABSTRACT FROM AUTHOR]

The Frequent Image Frames Enhanced Digital Orthorectified Mapping (FIFEDOM) camera was designed to provide a cost-effective remote-sensing method for accurate acquisition of forest information, such as spatial distributions of individual tree species and tree structures for forest monitoring and management. Compared with existing regular digital cameras, the FIFEDOM camera has several unique features as follows: 1) it can collect data not only in the visible bands (550 and 670 nm) but also in the near-infrared band (800 nm); 2) it has a frame rate of up to 3 frames/s with a frame size of 3500 x 2300; and 3) it has a wide angular field view with 150[degrees] along track and 78.8[degrees] across track. Its high frame rate and wide angular field view allow it to obtain a sequence of images that oversample ground target areas. The multiangle database and bidirectional reflectance signatures of forest canopies can be generated from the oversampled image data, which can be used to identify forest species and estimate tree structures. In addition, the multiframe highly overlapped FIFEDOM data can also be used to generate a very dense, high-quality, and reliable digital surface model. Effective methods for radiometric and geometric calibration of the FIFEDOM camera were developed in this paper. A data-acquisition campaign was carried out in 2004 over the Algoma boreal forest, Ontario, Canada. The FIFEDOM data were validated using the data acquired by the Compact Airborne Spectrographic Imager instrument, which was flown together with the FIFEDOM camera. Index Terms--BRDF, multi-angle, remote sensing, wideangular-field-view camera.

Methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from remotely sensed data using advanced classification algorithms through two hierarchical approaches. The data utilized included multispectral optical and thermal data (Landsat-5, and Landsat-8), radar imagery (Sentinel-1), and a digital elevation model. Goals were to determine the best way to combine imagery to classify wetlands through hierarchically based classification approaches to produce more accurate and efficient maps compared to standard classification. Algorithms used were Random Forest (RF), and Naïve Bayes. A hierarchically based RF classification methodology produced the most accurate classification result (91.94%). The hierarchically based approaches also improved classification accuracies for low-quality data, as defined through feature analysis, when compared to a nonhierarchical classifier. The hierarchical approaches also produced a significant increase in classification accuracy for the Naïve Bayes classifier versus the standard approach (∼12% increase) while not significantly increasing computation time – comparable in accuracy to the RF tests for around 20% the computational effort. Preselection of spectral bands, polarizations and other input parameters (Normalized Difference Vegetation Index, Normalized Difference Water Index, albedo, slope, etc.) using log-normal or RF variable importance analysis was very effective at identifying low-quality features and features which were of higher quality. [ABSTRACT FROM AUTHOR]

Forest roads are important features of the environment that have significant impacts on wildlife habitats. The U-net and U-shaped Fully Convoluted Network (FCN) deep-learning methods have demonstrated great potential in successfully extracting paved roads in urban environments; however, they require a large amount of pixel-based training samples, which is resource-consuming. During this study, a convolutional neural network (CNN) aided method for forest road identification and extraction was developed. The algorithm utilized the multivariate Gaussian and Laplacian of Gaussian (LoG) filters and VGG 16 on high spatial resolution multispectral imagery to extract both primary road and secondary roads in forested areas. It was tested on imagery over two areas in the Hearst forest located in central Ontario, Canada. Based on validation against manually digitized roads, over 74% of the roads from both test areas were successfully extracted. [ABSTRACT FROM AUTHOR]