Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing

Amani, Meisam; Brisco, Brian; Mahdavi, Sahel; Ghorbanian, Arsalan; Moghimi, Armin; DeLancey, Evan R.; Merchant, Michael; Jahncke, Raymond; Fedorchuk, Lee; Mui, Amy; Fisette, Thierry; Kakooei, Mohammad; Ahmadi, Seyed Ali; Leblon, Brigitte; LaRocque, Armand

doi:10.1109/jstars.2020.3036802

Cited by 25 publications

(14 citation statements)

References 34 publications

(73 reference statements)

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“…In fact, the condition of emergent Marsh was closely related to local weather patterns, which can make these areas to be flooded at the time of imaging, such that the classifier was unable to discriminate Marsh from Shallow Water. On the other hand, meadow Marsh can also bring difficulties for the classifier to separate these areas from Bog and Fen [57]. Finally, it was observed that the confusion between wetland classes is less significant in the case of Shallow Water as this class is less similar to the other wetland classes.…”

Section: ) Accuracy Levelsmentioning

confidence: 99%

Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery

Amani¹,

Mahdavi²,

Kakooei

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this study, wetland trends in Alberta were investigated in the past four decades using Landsat satellite imagery to produce updated information about wetland changes and to prevent further degradation of these valuable natural resources. All the processing steps and analyses were conducted in Google Earth Engine (GEE) to produce 16 wetland maps from 1984 to 2020. A comprehensive change analysis showed (1) approximately 18% of the province was subjected to change; (2) in terms of wetland classes, there was a decreasing trend for the Shallow Water and Swamp classes and an increasing trend for the Fen and Marsh classes; (3) in terms of non-wetland classes, there was a considerable decreasing trend for the Forest class and increasing trend for the Grassland/Shrubland class; (4) wetland loss was approximately 22,000 km 2 , which was mainly due to the conversion of wetlands to Forest and Grassland/Shrubland; (5) wetland gain was approximately 24,000 km 2 , which was mainly due to the conversion from the Forest class to wetlands, especially the Swamp and Fen classes; (6) The highest class transition was from Cropland to Grassland/Shrubland and vice versa (29,000 km 2 ), from Forest to different wetland classes (18,000 km 2 ), and from Fen to Forest (6,000 km 2 ). In summary, the results of this study provided the first comprehensive information on wetland trends in Alberta over the past 37 years and will assist policymakers to adjust the required/established policies to mitigate the potential wetland changes due to anthropogenic activities and climate-related events.

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Section: ) Accuracy Levelsmentioning

confidence: 99%

Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery

Amani¹,

Mahdavi²,

Kakooei

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The training of a wetland classification model in the PPR is also troublesome. Datasets that are normally considered to be high-quality training for landcover classifications, such as the Alberta Biodiversity Monitoring Institute's (ABMI) photoplots [18] used in wetland/landcover studies such as in Castilla et al [12], Hird et al [15], and others [19][20][21][22][23], do not appear to be very useful in the PPR. These data contain over 100,000 spatially explicit, photo-interpreted, attributed wetland polygons which are normally ideal for wetland class, form, and type classifications, but ultimately, these data are derived from a static interpretation of the landscape during late summer or fall.…”

Section: Classmentioning

confidence: 99%

Creating a Detailed Wetland Inventory with Sentinel-2 Time-Series Data and Google Earth Engine in the Prairie Pothole Region of Canada

et al. 2022

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Wetlands in the Prairie Pothole Region (PPR) of Canada and the United States represent a unique mapping challenge. They are dynamic both seasonally and year-to-year, are very small, and frequently altered by human activity. Many efforts have been made to estimate the loss of these important habitats but a high-quality inventory of pothole wetlands is needed for data-driven conservation and management of these resources. Typical landcover classifications using one or two image dates from optical or Synthetic Aperture Radar (SAR) Earth Observation (EO) systems often produce reasonable wetland inventories for less dynamic, forested landscapes, but will miss many of the temporary and seasonal wetlands in the PPR. Past studies have attempted to capture PPR wetland dynamics by using dense image stacks of optical or SAR data. We build upon previous work, using 2017–2020 Sentinel-2 imagery processed through the Google Earth Engine (GEE) cloud computing platform to capture seasonal flooding dynamics of wetlands in a prairie pothole wetland landscape in Alberta, Canada. Using 36 different image dates, wetland flood frequency (hydroperiod) was calculated by classifying water/flooding in each image date. This product along with the Global Ecosystem Dynamics Investigation (GEDI) Canopy Height Model (CHM) was then used to generate a seven-class wetland inventory with wetlands classified as areas with seasonal but not permanent water/flooding. Overall accuracies of the resulting inventory were between 95% and 96% based on comparisons with local photo-interpreted inventories at the Canadian Wetland Classification System class level, while wetlands themselves were classified with approximately 70% accuracy. The high overall accuracy is due, in part, to a dominance of uplands in the PPR. This relatively simple method of classifying water through time generates reliable wetland maps but is only applicable to ecosystems with open/non-complex wetland types and may be highly sensitive to the timing of cloud-free optical imagery that captures peak wetland flooding (usually post snow melt). Based on this work, we suggest that expensive field or photo-interpretation training data may not be needed to map wetlands in the PPR as self-labeling of flooded and non-flooded areas in a few Sentinel-2 images is sufficient to classify water through time. Our approach demonstrates a framework for the operational mapping of small, dynamic PPR wetlands that relies on open-access EO data and does not require costly, independent training data. It is an important step towards the effective conservation and management of PPR wetlands, providing an efficient method for baseline and ongoing mapping in these dynamic environments.

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“…In other words, PLC task is designed to find the corresponding category for every pixel in given images. Some PLC benchmarks, such as Houston2013 1 , Houston2018 1 https://hyperspectral.ee.uh.edu/?page\ id=459 2 and CWI [33] , are proposed for various purposes. For ILC tasks, labels are annotated at image level, and ILC can be further subdivided into Single-label Classification and Multi-Label classification problems.…”

Section: Rs Image Classificationmentioning

confidence: 99%

Multimodal Information Fusion for Weather Systems and Clouds Identification From Satellite Images

Bai

Zhao

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Seeing the cloud and then understanding the weather is one of the important means for people to forecast weather. There has been a certain progress in the use of deep learning technology for weather forecasting, especially in the automatic understanding of disaster weather from satellite image, which can be seen as the image classification problem. Publicly available satellite image benchmark database tries to link weather directly with satellite images. However, single image modal is far from enough to correctly identify weather systems and clouds. Thus, we integrate images with Meteorological Elements, in which five kinds of meteorological elements , such as season, month, date stamp and geographic longitude & latitude are labeled. To effectively use such various modalities for clouds and weather systems identification through satellite image classification tasks, we propose a new satellite image classification framework: Multi modal auxiliary network (MANET). MANET consists of three parts: image feature extraction module based on convolutional neural network, meteorological information feature extraction module based on perceptron and layer-level multimodal fusion. MANET successfully integrates the multi modal information including meteorological elements and satellite images. The experimental results show that MANET can achieve better weather systems, clouds and land cover classification results based on satellite images.

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Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing

Cited by 25 publications

References 34 publications

Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery

Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery

Creating a Detailed Wetland Inventory with Sentinel-2 Time-Series Data and Google Earth Engine in the Prairie Pothole Region of Canada

Multimodal Information Fusion for Weather Systems and Clouds Identification From Satellite Images

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