Modeling Canada yew (Taxus canadensis Marsh.) distribution and abundance in the boreal forest of northeastern Ontario, Canada

Dech, Jeffery P.; Mayhew-Hammond, Shawn; James, April L.; Pokharel, Bharat

doi:10.1016/j.ecolind.2013.06.017

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…In total, 16 classification methods were employed across the 128 Canadian wetland classification studies. The RF [94][95][96], ML [97,98], Decision Tree (DT) [38,[99][100][101][102], SVM [46][47][48], Multiple Classifier System (MCS) [11,103], Iterative Self-Organizing Data Analysis Technique (ISODATA) [104,105], CNN [21,27,54], k-Nearest Neighbors (k-NN) [106,107], and Artificial Neural Network (ANN) [30,[108][109][110] were the most commonly used algorithms. The Linear Discriminant Analysis (LDA) [83,111,112], Fuzzy Rule-Based Classification Systems (FRBCSs) [11,19], Markov Random Fields (MRF)-based method [113,114], k-means, and classification methods based on polarization target decomposition [115,116] were used once or less than three times and, here, were categorized as the "Other" group.…”

Section: Table A1mentioning

confidence: 99%

Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

et al. 2021

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A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.

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Section: Table A1mentioning

confidence: 99%

Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

et al. 2021

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“…The benefit of using regression trees is that the predictors can be both categorical and continuous, and the approach is non-parametric (i.e., does not assume a normal response) [39]. The regression tree was set to have a minimum bucket value of three and a minimum complexity parameter value of 0.001, in order to follow standard procedures to identify the ideal number of nodes in the tree [40]. The result of a regression tree is a dendrogram or "tree" which splits the data into smaller, more homogenous groups based on the importance of variables on the nodes; the most important variables are found on the top node and least important on lower nodes [39].…”

Section: Hierarchical Classification Modelsmentioning

confidence: 99%

Modeling Wood Fibre Length in Black Spruce (Picea mariana (Mill.) BSP) Based on Ecological Land Classification

Townshend

Pokharel

Groot

et al. 2015

Forests

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Effective planning to optimize the forest value chain requires accurate and detailed information about the resource; however, estimates of the distribution of fibre properties on the landscape are largely unavailable prior to harvest. Our objective was to fit a model of the tree-level average fibre length related to ecosite classification and other forest inventory variables depicted at the landscape scale. A series of black spruce increment cores were collected at breast height from trees in nine different ecosite groups within the boreal forest of northeastern Ontario, and processed using standard techniques for maceration and fibre length measurement. Regression tree analysis and random forests were used to fit hierarchical classification models and find the most important predictor variables for the response variable area-weighted mean stem-level fibre length. Ecosite group was the best predictor in the regression tree. Longer mean fibre-length was associated with more productive ecosites that supported faster growth. The explanatory power of the model of fitted data was good; however, random forests simulations indicated poor generalizability. These results suggest the potential to develop localized models linking wood fibre length in black spruce to landscape-level attributes, and improve the sustainability of forest management by identifying ideal locations to harvest wood that has desirable fibre characteristics.

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Tree cover and carbon mapping of Argentine savannas: Scaling from field to region

González‐Roglich

Swenson

2016

Remote Sensing of Environment

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Modeling Canada yew (Taxus canadensis Marsh.) distribution and abundance in the boreal forest of northeastern Ontario, Canada

Cited by 4 publications

References 25 publications

Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

Modeling Wood Fibre Length in Black Spruce (Picea mariana (Mill.) BSP) Based on Ecological Land Classification

Tree cover and carbon mapping of Argentine savannas: Scaling from field to region

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