Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through case study in peatland using LiDAR derivatives, we present an analysis the effects input data characteristics on RF classifications (including out-of-bag error, independent accuracy and class proportion error). Training selection specific variables (i.e., image channels) have large impact overall classification. High-dimension datasets should be reduced so that only uncorrelated important are...

A new method of enhancing fingerprint images is described, based upon nonstationary directional Fourier domain filtering. Fingerprints are first smoothed using a filter whose orientation everywhere matched to the local ridge orientation. Thresholding then yields enhanced image. Various simplifications lead efficient implementation on general-purpose digital computers. Results enhancement presented for fingerprints various pattern classifications. comparison made with used within automated...

In this paper, we assess the use of Random Forest (RF) for mapping land cover classes within Mer Bleue bog, a large northern peatland in southeastern Ontario near Ottawa, Canada, using Synthetic Aperture Radar (SAR) and airborne Light Detection Ranging (LiDAR). Not only has RF been shown to improve classification accuracies over more traditional classifiers, but it also provides useful information on statistical importance individual input image bands classification. Our specific objectives...

Random Forests variable importance measures are often used to rank variables by their relevance a classification problem and subsequently reduce the number of model inputs in high-dimensional data sets, thus increasing computational efficiency. However, as result way that training predictor randomly selected for use constructing each tree splitting node, it is also well known if too few trees generated, rankings tend differ between runs. In this letter, we characterize effect (ntree) class...

Abstract. Peatlands store large amounts of soil carbon and freshwater, constituting an important component the global hydrologic cycles. Accurate information on extent distribution peatlands is presently lacking but needed by Earth system models (ESMs) to simulate effects climate change balance. Here, we present Peat-ML, a spatially continuous map peatland fractional coverage generated using machine learning (ML) techniques suitable for use as prescribed geophysical field in ESM. Inputs our...

Abstract Peatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, effects of which expected to worsen. Peatland types and sub‐classes vary their ecohydrological characteristics have different responses change. Large‐scale modelling frameworks such as Model for Peatlands, Fire Behaviour Prediction System Land Data Assimilation require peatland maps including information on sub‐types vegetation critical inputs. Additionally, class height variables...

The following review is the second part of a two series on use remotely sensed data for quantifying wetland extent and inferring or measuring condition monitoring drivers change environments. In first part, we introduce policy makers non-users with an effective feasibility guide how can be used. current review, explore more technical aspects processing analysis using case studies within literature. Here describe: (a) technologies used assessment monitoring; (b) latest algorithmic...

Abstract A large number of small forests typically harbor higher biodiversity than a totaling the same area, suggesting that patches are disproportionately valuable for conservation. However, policies often favor protection forest patches. Here we demonstrate global trend deforestation in patches: likelihood randomly selected plot disappeared between 1992 and 2020 increased with decreasing size patch containing plot. Our results imply disproportionate impact loss on relative to total area...

For this research, the Random Forest (RF) classifier was used to evaluate potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, as test case. The goal research prepare launch upcoming RCM by evaluating three polarizations We examined (1) if lower noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important classification, and (3) whether...

In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing statistical distribution backscatter values applied to mean each superpixel. Higher-order texture measures, such as variance, used improve accuracy removing false positives an...

Wetlands have and continue to undergo rapid environmental anthropogenic modification change their extent, condition, therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use remote sensing technologies for ‘wise wetlands’, following Ramsar Convention protocols. The objectives review are provide: (1) synthesis history wetlands, (2) feasibility study quantify accuracy remotely sensed data products when compared field based...

Although it is increasingly accepted that young (e.g., ≤30 years) stands originating from wildfire are considerably less flammable than older in the boreal forest of North America, role fuel availability and structure this phenomenon has not been thoroughly investigated. As a regional study high-frequency fire regime, detailed loading were measured 66 sites including both wetlands uplands Boreal Plains landscape Wood Buffalo National Park northwestern Canada. Overall, significant increase...

Particulate Matter (PM) emissions originating from mine waste and tailings can be hazardous to human health, depending on the ore type processes used extract ore. Until now, only a single, simple estimate of total area across all Canada has been available for calculating air quality this source. This single estimate, based manual satellite interpretation completed in 1977, was extrapolated areas years 1990 present. These estimates were annually calculate particulate matter mines Canadian Air...

Despite the enormous success of 'pre-training and fine-tuning' paradigm, widespread across machine learning, it has yet to pervade remote sensing (RS). To help rectify this, we pre-train a vision transformer (ViT) on 1.3 million satellite-derived RS images. We SatViT using state-of-the-art self-supervised learning algorithm called masked autoencoding (MAE), which learns general representations by reconstructing held-out image patches. Crucially, this approach does not require annotated data,...

Abstract. Peatlands store large amounts of soil carbon and freshwater, constituting an important component the global hydrologic cycles. Accurate information on extent distribution peatlands is presently lacking but needed by Earth System Models (ESMs) to simulate effects climate change balance. Here, we present Peat-ML, a spatially continuous map peatland fractional coverage generated using machine learning techniques suitable for use as prescribed geophysical field in ESM. Inputs our...

An original algorithm is presented for the high-quality enhancement of fingerprint images. Fingerprints are directionally smoothed using a position-dependent Fourier domain filter whose orientation everywhere matched to local ridge orientation. Thresholding then yields enhanced image. Use proposed method within working AFIS significantly improves speed and accuracy.

The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 and a Digital Elevation Model covering an area approximately 81,000 km2 in size, representing the entirety of Dease Strait, Coronation Gulf Bathurst Inlet, Nunavut. focus this research assess potential operationalize shoreline sensitivity mapping inform oil spill response contingency planning. impact varying training sample size reducing model data load were evaluated. Results...

The purpose of this research was to use empirical models monitor temporal dynamics soil moisture in a peatland using remotely sensed imagery, and determine the predictive accuracy approach on dates outside time series through statistically independent validation. A seven Moderate Resolution Imaging Spectroradiometer (MODIS) Synthetic Aperture Radar (SAR) images were collected along with concurrent field measurements over one growing season, retrieval tested Linear Mixed Effects (LMEs)....

Remote sensing image classification applications often involve determining which variables are the most important to obtain best accuracy. Common metrics for assessing variable importance such as a mean decrease in accuracy (MDA) typically provide values scaled units that difficult interpret, and do not easily accommodate user-defined groups of variables. In this letter, an improved method quantifying classifier is developed demonstrated context land-cover using random forest algorithm. The...

Detailed information on the land cover types present and horizontal position of land–water interface is needed for sensitive coastal ecosystems throughout Arctic, both to establish baselines against which impacts climate change can be assessed inform response operations in event environmental emergencies such as oil spills. Previous work has demonstrated potential accurate classification via fusion optical SAR data, though what contribution either makes model accuracy not well established,...

Differences in topographic structure, vegetation and surface wetness exist between peatland classes, making active remote sensing techniques such as SAR LiDAR promising for mapping. As the timing of green-up, senescence, hydrologic conditions vary differently comparison with upland full growing-season time series imagery was expected to produce higher accuracy classification results than using only a few select images. Both interferometric coherence, amplitude difference datasets were...

Abstract Airborne Light Detection and Ranging (LiDAR), a remote sensing data collection technique, has many applications in the field of archaeology, including aiding planning campaigns, mapping features beneath forest canopy, providing an overview broad, continuous that may be indistinguishable on ground. LiDAR was used to create high‐resolution digital elevation model (DEM) heavily vegetated area at Fort Beauséjour–Fort Cumberland National Historic Site, Canada. Previously undiscovered...