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Object-Based Wetland Classification Using Multi-Feature Combination of Ultra-High Spatial Resolution Multispectral Images Can. J. Remote Sens. (IF 2.126) Pub Date : 2021-01-19 Renfang Geng; Shuanggen Jin; Bolin Fu; Bin Wang
Abstract The Unmanned Aerial Vehicle (UAV) and Google Earth (GE) RGB images have ultra-high spatial resolution. But it is difficult to get a high classification accuracy due to the poor spectral resolution. In this article, the object-based wetland classification is investigated using multi-feature combination of ultra-high spatial resolution multispectral images (MSI). A Gram-Schmidt (GS) transformation
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Characterizing the Great Lakes Coastal Wetlands with InSAR Observations from X-, C-, and L-Band Sensors Can. J. Remote Sens. (IF 2.126) Pub Date : 2021-01-13 Zhaohua Chen; Sarah Banks; Amir Behnamian; Lori White; Benoit Montpetit; Jon Pasher; Jason Duffe
Abstract We investigated the potential of using Synthetic Aperture Radar (SAR) imagery from three different frequencies: X-, C-, and L-band, to characterize coastal wetlands in the Great Lakes. Three sets of SAR data acquired over the Bay of Quinte, Ontario, Canada between 2016 and 2018 from Radarsat-2, 2016 from TerraSAR-X, and 2018 from ALOS-2 satellites were processed using small baseline subset
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Multilevel Extraction of Vegetation Type Based on Airborne LiDAR Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2021-01-13 Lexin Chang; Ziyi Zhang; Yuxuan Li; Xuegang Mao
Abstract Precise determination of vegetation type is important in remote sensing of the ecological environment. Many studies have explored ecosystem structure on explicit spatial scales using specific remote sensing data, but few studies have considered vegetation information extraction at various landscape levels using LiDAR-derived raster layers. This study determined vegetation information based
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A High Spatial Resolution Satellite Remote Sensing Time Series Analysis of Cape Bounty, Melville Island, Nunavut (2004–2018) Can. J. Remote Sens. (IF 2.126) Pub Date : 2021-01-12 V. Freemantle; J. Freemantle; D. Atkinson; P. Treitz
Abstract Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004–2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image
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RADARSAT-2 Derived Glacier Velocities and Dynamic Discharge Estimates for the Canadian High Arctic: 2015–2020 Can. J. Remote Sens. (IF 2.126) Pub Date : 2021-01-06 Wesley Van Wychen; David Burgess; Will Kochtitzky; Natalija Nikolic; Luke Copland; Laurence Gray
Abstract RADARSAT-2 imagery collected each winter from 2015/2016 to 2019/2020 is used to quantify and characterize the variability in the motion of, and the discharge from, the major marine-terminating ice masses of the Queen Elizabeth Islands (QEI: Devon, Ellesmere and Axel Heiberg Islands) in the Canadian High Arctic. The majority of the glaciers did not experience significant variations in flow
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Modeling Watershed-Scale Historic Change in the Alpine Treeline Ecotone Using Random Forest Can. J. Remote Sens. (IF 2.126) Pub Date : 2021-01-06 David R. McCaffrey; Chris Hopkinson
Abstract Historic changes in Alpine Treeline Ecotone were modeled using 21 topographic, climatic, geologic, and disturbance variables in a random forest model. Airborne LiDAR and oblique historic repeat photography were used to identify changes in canopy cover in the West Castle Watershed (WCW), Alberta, Canada (49.3° N, 114.4° W). A Random Forest model was trained on ∼30% of the watershed which was
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Detecting Individual-Tree Crown Regions from Terrestrial Laser Scans with an Anchor-Free Deep Learning Model Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-12-20 Zhouxin Xi; Chris Hopkinson
Abstract Detecting individual-tree crowns provides a fundamental analysis unit bridging macro ecological patterns and micro physiological functions. This study adapted an anchor-free deep learning model, CenterNet, to detect individual crown locations and regions from dense 3 D terrestrial laser scans. A total of 1181 crowns from twelve plots were manually delineated as reference, among which eight
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Digital Terrestrial Photogrammetry to Enhance Field-Based Forest Inventory across Stand Conditions Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-10-14 Christopher Mulverhill; Nicholas C. Coops; Piotr Tompalski; Christopher W. Bater
Abstract Forest inventories in uncertain future economic and environmental conditions require the development of cost-effective measurement techniques to provide robust and accurate information on forests across regional and global scales. Digital terrestrial photogrammetry (DTP) can be used to detect and measure trees on sample plots. In this study, a method was developed which used spherical images
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Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-11-11 Ivan Huuva; Henrik J. Persson; Maciej J. Soja; Jörgen Wallerman; Lars M. H. Ulander; Johan E. S. Fransson
Abstract Above-ground biomass change accumulated during four growth seasons in a hemi-boreal forest was predicted using airborne L- and P-band synthetic aperture radar (SAR) backscatter. The radar data were collected in the BioSAR 2007 and BioSAR 2010 campaigns over the Remningstorp test site in southern Sweden. Regression models for biomass change were developed from biomass maps created using airborne
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Autumn Crop Yield Prediction using Data-Driven Approaches:- Support Vector Machines, Random Forest, and Deep Neural Network Methods Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-10-19 Chaoya Dang; Ying Liu; Hui Yue; JiaXin Qian; Rong Zhu
Abstract Accurate prediction of crop yield before harvest is critical to food security and importation. The calculated ten explanatory factors and autumn crop yield data were used as data sources in this research. Firstly, a Redundancy Analysis (RDA) was employed to carry out explanatory factors and feature selection. The simple effects of RDA were used to evaluate the interpretation rates of the explanatory
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Editorial Note – Special Issue on the 40th Canadian Symposium on Remote Sensing: “Remote Sensing and Geomatics: Common Perspectives and NewSpace” Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-14 Brigitte Leblon; Dirk Werle; Desmond Power
(2020). Editorial Note – Special Issue on the 40th Canadian Symposium on Remote Sensing: “Remote Sensing and Geomatics: Common Perspectives and NewSpace”. Canadian Journal of Remote Sensing: Vol. 46, Special Issue on the 40th Canadian Symposium on Remote Sensing: “Remote Sensing and Geomatics: Common Perspectives and New Space”, pp. 387-389.
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Evaluation of Features Derived from High-Resolution Multispectral Imagery and LiDAR Data for Object-Based Support Vector Machine Classification of Tree Species Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-09-08 Matthew Roffey; Jinfei Wang
Abstract Remote sensing can play a key role in understanding the make-up of urban forests. This study analyzes how high-resolution Geoeye-1 multispectral imagery and LiDAR point clouds allow for improved classification of urban tree species using object-based and support vector machine classification (SVM). Five common urban trees are classified: Acer platanoides; Acer platanoides ‘Schwedleri’; Picea
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Comparison of LiDAR Building Point Cloud with Reference Model for Deep Comprehension of Cloud Structure Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-10-05 Fayez Tarsha Kurdi; Mohammad Awrangjeb
Abstract This paper studies the fidelity level of the extracted LiDAR (Light Detection And Ranging) building point cloud in relation to the original building. In this context, the building point cloud is compared with a reference model. This comparison allows a deep understanding of the point cloud structure with respect to both the actual building and the constructed model. Consequently, the source
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Refinements in Eelgrass Mapping at Tabusintac Bay (New Brunswick, Canada): A Comparison between Random Forest and the Maximum Likelihood Classifier Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-09-26 David Forsey; Armand LaRocque; Brigitte Leblon; Marc Skinner; Angela Douglas
Abstract Eelgrass (Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass
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Estimation of Phytoplankton Chlorophyll-a Concentrations in the Western Basin of Lake Erie Using Sentinel-2 and Sentinel-3 Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-09-24 Saied Pirasteh; Somayeh Mollaee; Sarah Narges Fatholahi; Jonathan Li
Abstract Algae blooms have been a serious problem in coastal and inland water bodies across Canada. The temporal and spatial variability of algae blooms makes it difficult to use in situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Color Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie.
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Hotspots, Heat Vulnerability and Urban Heat Islands: An Interdisciplinary Review of Research Methodologies Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-09-15 Changchang Wang; Hsiao-Tung Chang
Abstract As a result of ongoing global warming, approximately 30% of the world's population lives in areas where the temperature reaches the death risk threshold at least 20 days a year. The distribution of heat and vulnerability, however, varies in a city; where mitigation actions should begin is a simple question that must be answered. Therefore, it is important to identify hotspots and provide a
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Hyperspectral Image Classification Based on Multilayer Perceptron Trained with Eigenvalue Decay Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-06 Gürcan Lokman; Hasan Hüseyin Çelik; Vedat Topuz
Hyperspectral Images (HSI) require sufficient labeled samples and a complex classifier to identify an area. Support Vector Machine (SVM) is one of the most competent algorithms in this field. Neural Networks (NN) is another approach used for classification problems, and both have been widely proposed in the literature. The Convolutional Neural Network (CNN) method has also received significant attention
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An Efficient Change Detection for Large SAR Images Based on Modified U-Net Framework Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-07 Jujie Wei; Yonghong Zhang; Hong’an Wu; Bin Cui
Large SAR images usually contain a variety of land-cover types and accordingly complicated change types, which cause great difficulty for accurate change detection. The U-Net is a special fully convolutional neural network that not only can capture multiple features in the image context but also enables precise pixel-by-pixel image classification. Therefore, we explore the U-Net to describe accurately
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A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-22 Mehrdad Eslami; Mohammad Saadatseresht
Today, both point cloud and imagery datasets processed for mapping aims. The precise fusion of both datasets is a major issue that leads to the fine registration problem. This article proposes a fine registration method based on a novel concept of tie plane. The assumption of our solution is that the laser scanner point cloud is much more accurate than the image interior and exterior geometric accuracy
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Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-11 Chen Shang; Michael A. Wulder; Nicholas C. Coops; Joanne C. White; Txomin Hermosilla
Wildfire is a critical process shaping the structure and composition of forest landscapes of western Canada. Spatially-explicit forest disturbance history and forest structure estimated using remotely-sensed data enables the characterization of burn probability, defined as the susceptibility of landscapes to fire hazard over time. In this research, we leveraged the Landsat archive to determine the
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Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-17 Fen Chen; Sijia Lu; Peng Zhao; Tim Van de Voorde; Wenbo Xu
Due to the fragmented compositional structure of urban scenes, many pixels are mixtures of multiple materials even in high spatial resolution airborne hyperspectral data. In the past ten years, sparse regression based spectral unmixing methods have achieved some noticeable results. Recently, Chen et al. proposed a jointly sparse spectral mixture analysis model for urban mapping. Their model has a high
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Impacts of Topography on the Land Cover Classification in the Qilian Mountains, Northwest China Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-31 Hong Wang; Chenli Liu; Fei Zang; Jianhong Yang; Na Li; Zhanlei Rong; Chuanyan Zhao
There are many disagreements and uncertainties among global land use/land cover (LULC) products, which make it unsuitable to apply these products directly to a specific region. In this study, Enhanced Vegetation Index (EVI) time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with 250 m spatial resolution, combining with geographic features, were used for LULC classification
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The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-08 Masoud Mahdianpari; Brian Brisco; Jean Elizabeth Granger; Fariba Mohammadimanesh; Bahram Salehi; Sarah Banks; Saeid Homayouni; Laura Bourgeau-Chavez; Qihao Weng
Recently, there has been a significant increase in efforts to better inventory and manage important ecosystems across Canada using advanced remote sensing techniques. In this study, we improved the method and results of our first-generation Canadian wetland inventory map at 10-m resolution. Iin order to increase wetland classification accuracy, the main contributions of this new study are adding more
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Temporal Filters for Mapping Phragmites with C-HH SAR Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-18 Lori White; Brian Brisco; Kevin Murnaghan; Jon Pasher; Jason Duffe
We compared traditional spatial filters and multi-temporal filters to remove speckle from synthetic aperture radar (SAR) data for mapping Phragmites australis. SAR constellations, with more rapid revisit capability, allow one to generate stacks of SAR data and to use multi-temporal filters for speckle reduction. GAMMA software offers multi-temporal filters for SAR processing, two of which we compared
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Extending Estimates of Tree and Tree Species Presence-Absence through Space and Time Using Landsat Composites Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-09-08 Guy E. I. Strickland; Joan E. Luther; Joanne C. White; Michael A. Wulder
Abstract We developed a methodology for extending estimates of the presence-absence of trees and several tree species contained in the Canadian National Forest Inventory using nationally consistent Landsat data products. For a prototype boreal forest region of Newfoundland and Labrador, Canada, we modeled and assessed changes in the presence-absence of trees and tree species distributions over a 25-year
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Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-31 Dianjun Zhang; Jie Zhan; Zhi Qiao; Robert Župan
Abstract As an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM on a regional scale. In this study, the performance of the normalized land surface temperature-vegetation
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A Comprehensive Survey of Optical Remote Sensing Image Segmentation Methods Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-20 Yongzhi Wang; Hua Lv; Rui Deng; Shengbing Zhuang
Many papers have reviewed remote sensing image segmentation (RSIS) algorithms currently. Those existing surveys are insufficiently exhaustive to sort out the various RSIS methods, it is impossible to comprehensively compare characteristics of different RSIS methods. In addition, the segmentation efficiency and accuracy of the RSIS methods cannot always meet the subsequent image analysis requirements
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A Comprehensive Survey of Optical Remote Sensing Image Segmentation Methods Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-20 Yongzhi Wang; Hua Lv; Rui Deng; Shengbing Zhuang
Abstract Many papers have reviewed remote sensing image segmentation (RSIS) algorithms currently. Those existing surveys are insufficiently exhaustive to sort out the various RSIS methods, it is impossible to comprehensively compare characteristics of different RSIS methods. In addition, the segmentation efficiency and accuracy of the RSIS methods cannot always meet the subsequent image analysis requirements
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Temporal Filters for Mapping Phragmites with C-HH SAR Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-08-18 Lori White; Brian Brisco; Kevin Murnaghan; Jon Pasher; Jason Duffe
Abstract We compared traditional spatial filters and multi-temporal filters to remove speckle from synthetic aperture radar (SAR) data for mapping Phragmites australis. SAR constellations, with more rapid revisit capability, allow one to generate stacks of SAR data and to use multi-temporal filters for speckle reduction. GAMMA software offers multi-temporal filters for SAR processing, two of which
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A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-22 Mehrdad Eslami; Mohammad Saadatseresht
Today, both point cloud and imagery datasets processed for mapping aims. The precise fusion of both datasets is a major issue that leads to the fine registration problem. This article proposes a fine registration method based on a novel concept of tie plane. The assumption of our solution is that the laser scanner point cloud is much more accurate than the image interior and exterior geometric accuracy
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Incertitudes des niveaux d’eau dérivés de l’altimétrie satellitaire pour des étendues d’eau soumises à l’action de la glace Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-19 Jawad Ziyad; Kalifa Goïta; Ramata Magagi
RÉSUMÉ La présence de cibles hétérogènes, comme la glace, reste un défi majeur pour l’utilisation des données altimétriques au-dessus des plans d’eau continentaux. Les satellites Jason-2 et SARAL/Altika utilisent des algorithmes de retraitement conçus pour traiter les formes d’onde non continentales afin d’obtenir des estimations améliorées. Dans cette étude, nous analysons le potentiel des produits
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Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-14 Hwang Lee; Jinfei Wang; Brigitte Leblon
Abstract Crop nitrogen (N) needs to be accurately predicted to allow farmers to effectively match the N supply to the crop N demand during crop growth in order to minimize environmental impacts as excess N could seep into the water supplies around the field. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral MicaSense imagery validated with ground hyperspectral measurements
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A Rule-Based Classification Method for Mapping Saltmarsh Land-Cover in South-Eastern Bangladesh from Landsat-8 OLI Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-07-11 Sheikh Mohammed Rabiul Alam; Mohammad Shawkat Hossain
Wetland vegetation classification often treated the saltmarsh as a single type of land-cover (LCT). Mapping the dynamic and spatially complex coastal zones using optical remote sensing is still challenging. This study firstly analyzed the spectral properties of target objects generated by Landsat 8 (OLI), formulated new spectral indices and then proposes a rule-based approach to mapping five vegetated
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Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-04-17 Xiang Zhang; Xinming Tang; Xiaoming Gao; Hui Zhao
The main objective of this study is to develop a robust soil moisture retrieval approach using multi-sensor remote sensing data. Firstly, the water cloud model was employed to eliminate the vegetation effects on SAR observations over vegetated areas, thus to obtain the bare soil backscatter associated with soil moisture. Then, against the underdetermined system for soil moisture retrieval, the advanced
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Forest Inventory and Aboveground Biomass Estimation with Terrestrial LiDAR in the Tropical Forest of Malaysia Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-05-13 Solomon M. Beyene; Yousif A. Hussin; Henk E. Kloosterman; Mohd Hasmadi Ismail
An accurate forest inventory is crucial for forest monitoring and quantifying forest aboveground biomass (AGB). This study aimed to investigate the feasibility of Terrestrial Laser Scanning (TLS) in forest inventory and AGB estimation in the tropical forest of Malaysia. Individual trees were detected using manual and automatic detection methods. An average tree detection rate of 99.55% and 93.75% were
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Improving Spatial-Spectral Classification of Hyperspectral Imagery by Using Extended Minimum Spanning Forest Algorithm Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-05-06 Davood Akbari
Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. Recently, an effective approach for spatial-spectral classification has been proposed using Minimum Spanning Forest (MSF) algorithm. Our goal is to improve this approach to the classification of hyperspectral images in urban areas. In the proposed method two spatial/texture
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Calibrating PhenoCam Data with Phenological Observations of a Black Spruce Stand Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-05-12 Shaokang Zhang; Valentina Buttò; Siddhartha Khare; Annie Deslauriers; Hubert Morin; Jian-Guo Huang; Hai Ren; Sergio Rossi
Bud and leaf development are important phenological events and help in defining the growing period of trees. Canopy greenness derived from PhenoCam has been used to investigate leaf phenology. Questions remain on how much the continuous records of canopy greenness represent bud developmental phases, and how growing period boundaries are related to canopy greenness and bud phenology. In this study,
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Forest Variable Estimations Using TanDEM-X Data in Hyrcanian Forests Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-05-20 Mozhgan Zahriban Hesari; Shaban Shataee; Yasser Maghsoudi; Jahangir Mohammadi; Johan E. S. Fransson; Henrik J. Persson
The objective of this study was to estimate forest variables using TanDEM-X interferometric synthetic aperture radar (InSAR) data acquired over the Shastkalate forest of Gorgan in northern Iran. Inventory variables, including diameter at breast height, tree height (Lorey’s mean tree height), basal area and volume, were collected from 112 circular sample plots with a size of 0.1 ha. Interferometric
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Object-Oriented Automatic Identification of Forest Gaps Using Digital Orthophoto Maps and LiDAR Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-06-01 Mao Xuegang; Zhu Liang; Wenyi Fan
Identification of forest gaps is a prerequisite for quantification of their size, shape, and dynamics, and for clarification of both complex structural forest species regeneration and understory species diversity. Although airborne LiDAR and digital orthophoto maps (DOM) have been used separately to identify forest gaps, few studies have considered integration of the two data sources for forest gap
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Multiple Spatial Features Extraction and Fusion for Hyperspectral Images Classification Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-06-22 Jianshang Liao; Liguo Wang
In recent decades, spatial feature extraction has greatly improved the performance of hyperspectral image (HSI) classification. This paper presents an HSI classification method based on multiple spatial features extraction and fusion (MSFs-EF). The method consists of five sequential steps. 1- Principal component analysis is applied for HSI dimensionality reduction. 2- A mean curvature filter is used
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Transferability of ALS-Derived Forest Resource Inventory Attributes Between an Eastern and Western Canadian Boreal Forest Mixedwood Site Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-06-04 Karin van Ewijk; Piotr Tompalski; Paul Treitz; Nicholas C. Coops; Murray Woods (ret.); Douglas Pitt (ret.)
The ability to expand the use of predictive Airborne Laser Scanning (ALS)-derived Forest Resource Inventory (FRI) models to broader regional scales is crucial for supporting large scale sustainable forest management. This research examined the transferability of ALS-based FRI attributes between two forest estates located in the eastern and western boreal forest regions of Canada. The sites were structurally
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Clay Mineral Alteration in Oil and Gas Fields: Integrated Analyses of Surface Expression, Soil Spectra, and X-Ray Diffraction Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-05-30 Tri Muji Susantoro; Asep Saepuloh; Fitriani Agustin; Ketut Wikantika; Agus Handoyo Harsolumakso
Subsurface hydrocarbon occurrences can be detected by clay mineral (CM) alteration at the surface as a consequence of hydrocarbon migration. This study analyzed CM alteration in an oil and gas (O&G) field in the West Tugu field, located in the northwest Java Basin of Indonesia. Landsat 8 OLI data acquired on 25 September 2015 and soil spectral reflectance (SSR) data recorded using analytical spectral
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Efficacy of Remote Sensing in Early Forest Fire Detection: A Thermal Sensor Comparison Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-06-12 Isabelle-Gabriele Hendel; Gregory M. Ross
Abstract The objective of this research was to determine what technologies could be used to improve current forest fire detection methodology. Although human observation provides a wealth of information on the presence and size of fires, remote sensing technologies can provide increasingly more detailed and accurate information. Thus, the cost efficiency, thermal accuracy, spatial accuracy, range and
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Potato Late Blight Detection at the Leaf and Canopy Level Using Hyperspectral Data Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-06-02 Claudio I. Fernández; Brigitte Leblon; Ata Haddadi; Jinfei Wang; Keri Wang
Abstract This study aims to assess which spectral variables and at which time late blight can be detected over potato crops. Two experiments were done in a walk-in chamber under controlled environments. To determine the time, the reflectance spectra were plotted as a function of the day post inoculation (DPI), then a Principal Component Analysis (PCA) was applied and the Jeffries–Matusita distance
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Delineation of Bare Soil Field Areas from Unmanned Aircraft System Imagery with the Mean Shift Unsupervised Clustering and the Random Forest Supervised Classification Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-05-13 Odysseas Vlachopoulos; Brigitte Leblon; Jinfei Wang; Ataollah Haddadi; Armand LaRocque; Greg Patterson
Abstract The use of aerial remote sensing platforms such as Unmanned Aircraft Systems (UAS) has been proven as a cost and time effective way to perform tasks related to precision agriculture and decision making. Two machine learning (ML) algorithms have been implemented on UAS blue and red band imagery to delineate field areas and extents of various bare soil fields: the Random Forest non-parametric
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Forest Inventory and Aboveground Biomass Estimation with Terrestrial LiDAR in the Tropical Forest of Malaysia Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-05-13 Solomon M. Beyene; Yousif A. Hussin; Henk E. Kloosterman; Mohd Hasmadi Ismail
An accurate forest inventory is crucial for forest monitoring and quantifying forest aboveground biomass (AGB). This study aimed to investigate the feasibility of Terrestrial Laser Scanning (TLS) in forest inventory and AGB estimation in the tropical forest of Malaysia. Individual trees were detected using manual and automatic detection methods. An average tree detection rate of 99.55% and 93.75% were
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An Improved Approach for Selecting and Validating Burn Severity Indices in Forested Landscapes Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-03-11 Michael R. Gallagher; Nicholas S. Skowronski; Richard G. Lathrop; Timothy McWilliams; Edwin J. Green
Burn severity maps based on remotely sensed reflectance data provide a useful way for land managers and researchers to represent and compare spatial variation in fire effects among wildfires and prescribed fires. A need exists for an objective and rigorous selection approach that ensures the best possible spatial predictions of burn severity. The aim of this study was to present and test a methodology
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Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-03-20 Omid Reisi Gahrouei; Heather McNairn; Mehdi Hosseini; Saeid Homayouni
Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models and predictor variables. These biophysical parameters are indicative of crop productivity, and thus, are of interest in applications such as crop yield forecasting and precision farming. This study evaluated the potential of leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning
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Land–Use and Land-Cover Change Detection Using Dynamic Time Warping–Based Time Series Clustering Method Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-03-18 Yanghua Zhang; Hu Zhao
Accurate and timely monitoring of urban land-use and land-cover (LULC) change is useful for understanding the various impacts of human activity on the urban environment. In order to demonstrate the advantage of time series imaging for urban LULC change detection, we selected time series Landsat images over a two-year period to detect inter-annual changes. A time series trajectory for each pixel was
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Exploring Polarimetric Phase of Microwave Backscatter from Typha Wetlands Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-02-20 Don Atwood; Michael Battaglia; Laura Bourgeau-Chavez; Frank Ahern; Kevin Murnaghan; Brian Brisco
Despite their natural and societal importance, wetlands are becoming increasingly threatened. The goal of this study is to investigate the potential of polarimetric synthetic aperture radar (SAR) for monitoring one important vegetation constituent of wetlands: Typha. An idealized cylindrical scattering model is developed to portray double bounce microwave scattering from Typha stalks. Then a thin cylinder
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Fast Unmixing of Noisy Hyperspectral Images Based on Vertex Component Analysis and Singular Spectrum Analysis Algorithms Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-03-25 Dongmei Song; Ning Sun; Mingming Xu; Bin Wang; Ling Zhang
Efficient denoising is of great significance to unmixing hyperspectral images. In the present study, a fast unmixing method for noisy hyperspectral images based on the combination of vertex component analysis and singular spectrum analysis is proposed. First, the noisy endmember spectra are extracted by using the vertex component analysis algorithm. Then the singular spectrum analysis is used to denoise
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Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-01-27 Masoud Mahdianpari; Bahram Salehi; Fariba Mohammadimanesh; Brian Brisco; Saeid Homayouni; Eric Gill; Evan R. DeLancey; Laura Bourgeau-Chavez
Detailed information on the spatial distribution of wetlands is crucial for sustainable management and resource assessment. Furthermore, regularly updated wetland inventories are of particular importance given that wetlands comprise a dynamic, rather than permanent, land condition. Accordingly, satellite-derived wetland maps are greatly beneficial, as they capture a synoptic and multi-temporal view
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A Modified Semi-Empirical Radar Scattering Model for Weathered Rock Surfaces Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-01-29 Byung-Hun Choe; Gordon R. Osinski; Catherine D. Neish; Livio L. Tornabene
This study presents a modified semi-empirical radar scattering model for weathered rough rock surfaces. Weathered rocks generally have dry surfaces except for a few hours after heavy rain due to their rapid drainage compared to bare soils. We find that the dielectric properties of the rocks themselves and the moisture content of a marginal amount of soil patches in and around the rock surfaces have
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Object-Based Thermal Remote-Sensing Analysis for Fault Detection in Mashhad County, Iran Can. J. Remote Sens. (IF 2.126) Pub Date : 2020-01-27 Bakhtiar Feizizadeh, Hejar Shahabi Sorman Abadi, Khalil Didehban, Thomas Blaschke, Franz Neubauer
Land surface temperature (LST) and soil moisture are important factors in environmental hazard modeling. The main objective of this research is to derive the LST and a soil moisture index (SMI) from thermal satellite images. A split-window algorithm is applied to derive the spectral radiance and emissivity from two thermal infrared (TIR) bands of the Landsat 8 satellite in four consecutive years (2015–2018)
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Sensitivity of Ku- and X-Band Radar Observations to Seasonal Snow in Ontario, Canada Can. J. Remote Sens. (IF 2.126) Pub Date : 2019-12-24 Aaron Thompson, Richard Kelly, Joshua King
Radar scatterometer observations at 17.2 GHz and 9.6 GHz were made of the snow cover in mid-latitude agricultural fields, using the University of Waterloo scatterometer, to determine the sensitivity of the frequency-dependent radar response to snow water equivalent. Observations were made in alfalfa fields near Maryhill, Ontario during the 2013–2014 and 2014–2015 winter seasons. Additional observations
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Self-Correction of Soil Moisture Ocean Salinity (SMOS) Soil Moisture Dry Bias Can. J. Remote Sens. (IF 2.126) Pub Date : 2019-12-20 Ju Hyoung Lee, Michael Cosh, Patrick Starks, Zoltan Toth
Satellites produce global monitoring data, while field measurements are made at a local station over the land. Due to difference in scale, it has been a challenge how to define and correct the satellite retrieval biases. Although the relative approach of cumulative distribution functions (CDF) matching compares a long-term climatology of reference data with that of satellite data, it does not mitigate
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Target Detection Based on Spectral Derivation in HSI Shadow Region Classified by Convolutional Neural Networks Can. J. Remote Sens. (IF 2.126) Pub Date : 2019-12-17 Xuefeng Liu, Congcong Wang, Yue Meng, Hao Wang, Min Fu, Salah Bourennane
Because of the low reflection value of the shadow regions in hyperspectral image (HSI), these regions were deleted directly or ignored in target detection or classification. There are some studies on improving the reflectivity of shadow regions, but it is still difficult to determine the actual substances contained in shadow regions. In this paper, an improved target detection method in shadow regions
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A Novel PolSAR Image Classification Method Based on Optimal Polarimetric Features and Contextual Information Can. J. Remote Sens. (IF 2.126) Pub Date : 2019-12-10 Yan Duan, Na Chen, Yangbo Chen
Due to the severe speckle noise of a fully polarimetric synthetic aperture radar image and the complex backscattering mechanism at the junction of different land covers, some of the pixels are easily mislabeled, especially on the edge of the land covers. To address this issue, this study presents a novel scheme that selects polarimetric features step by step to participate in classification through
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The Synergistic Use of RADARSAT-2 Ascending and Descending Images to Improve Surface Water Detection Accuracy in Alberta, Canada Can. J. Remote Sens. (IF 2.126) Pub Date : 2019-11-26 Evan R. DeLancey, Brian Brisco, Francis Canisius, Kevin Murnaghan, Liam Beaudette, Jahan Kariyeva
Large, e.g., provincial or national, scale near-real-time surface water monitoring is an ambitious task, which can be accomplished by using Synthetic Aperture Radar (SAR) satellite data. SAR has demonstrated the ability to distinguish water and land, but there are many common errors of commission and omission that arise due to the side-looking nature of SAR and due to some landcover types with similar
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Regional Wheat Yield Estimation by Integration of Remotely Sensed Soil Moisture into a Crop Model Can. J. Remote Sens. (IF 2.126) Pub Date : 2019-11-21 Muhammad Fahad, Ishfaq Ahmad, Mariam Rehman, Muhammad Mohsin Waqas, Farhana Gul
A field study was conducted to estimate the regional wheat yield by integration of remotely sensed soil moisture index into CERES-Wheat model. The calibration and evaluation of model was performed using experimental data and then applied on the area of Faisalabad district for yield estimation. Area of Faisalabad district was divided into 7929 cells for independent simulations. The weather data of the
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