A Collection of Novel Algorithms for Wetland Classification with SAR and Optical Data

Salehi, Bahram; Mahdianpari, Masoud; Amani, Meisam; M.Manesh, Fariba; Granger, Jean; Mahdavi, Sahel; Brisco, Brian

doi:10.5772/intechopen.80688

Cited by 10 publications

(8 citation statements)

References 92 publications

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“…The high accuracy of our final wetland maps might be attributed to the simplified classification scheme of forested and non-forested wetlands. Unlike a few other studies [33,[41][42][43], our classification scheme does not provide details on wetland classes, such as bog, fen, swamp, and marsh. However, the objective of our study was to accurately identify wetlands and existing stress factors, rather than identify more-detailed wetland classes.…”

Section: Discussionmentioning

confidence: 99%

“…However, as Landsat cannot accurately capture signals within dense foliage and soil, wetlands that are covered by dense forests or with water just below the surface cannot be accurately identified using Landsat imagery alone. To increase wetland classification accuracy, several studies have utilized SAR data, often by combining it with optical data, as it can penetrate into dense foliage and soil, leading to better classification of dynamic wetlands [33][34][35][36][37][38][39][40][41][42][43]. To capture conditions existing outside Delaware that may still be affecting Delaware wetlands, a 300-meter buffer around the state boundary was used to clip all of the satellite images used for classification.…”

Section: Land-cover Classificationmentioning

confidence: 99%

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A Rapidly Assessed Wetland Stress Index (RAWSI) Using Landsat 8 and Sentinel-1 Radar Data

Walter

Mondal

2019

Remote Sensing

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Wetland ecosystems are important resources, providing great economic benefits for surrounding communities. In this study, we developed a new stress indicator called “Rapidly Assessed Wetlands Stress Index” (RAWSI) by combining several natural and anthropogenic stressors of wetlands in Delaware, in the United States. We compared two machine-learning algorithms, support vector machine (SVM) and random forest (RF), to quantify wetland stress by classifying satellite images from Landsat 8 and Sentinel-1 Synthetic Aperture Radar (SAR). An accuracy assessment showed that the combination of Landsat 8 and Sentinel SAR data had the highest overall accuracy (93.7%) when used with an RF classifier. In addition to the land-cover classification, a trend analysis of the normalized difference vegetation index (NDVI) calculated from Landsat images during 2004–2018 was used to assess changes in healthy vegetation. We also calculated the stream sinuosity to assess human alterations to hydrology. We then used these three metrics to develop RAWSI, and to quantify and map wetland stress due to human alteration of the landscape. Hot-spot analysis using Global Moran’s I and Getis-Ord Gi* identified several statistically significant hot spots (high stress) in forested wetlands and cold spots (low values) in non-forested wetlands. This information can be utilized to identify wetland areas in need of further regulation, with implications in environmental planning and policy decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Land-cover Classificationmentioning

confidence: 99%

A Rapidly Assessed Wetland Stress Index (RAWSI) Using Landsat 8 and Sentinel-1 Radar Data

Walter

Mondal

2019

Remote Sensing

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“…Furthermore, some sensors record spectral or backscattered/reflected responses within a range or spectral resolution appropriate for differentiating classes and features or attributes of interest. For example, SAR easily differentiates inundated marshes from other wetland types [234][235][236], but fen and bog are not easily differentiated with single-date imagery due to inseparable backscatter from similar physical characteristics (e.g., tree species composition) [37,[237][238][239]. This often results in lower classification accuracies.…”

Section: Results: Feasibility Of Remote Sensing For Wetland Applicationsmentioning

confidence: 99%

“…Accuracy of wetland extent, class/form, and surface characteristics determined using remote sensing has improved greatly since 2009 due to the proliferation of high spatial resolution optical, lidar, and SAR data ( Figure 3). Further, new methods for data fusion and conflation using machine learning [66,239,263], decision tree approaches [264], and object-based image analysis [265][266][267] have greatly expanded statistical approaches. Multi-spectral and hyperspectral remote sensing are primarily used to determine species distribution, whereas lidar and SAR are used to determine terrain and vegetation structural attributes, which may be related to woody species type.…”

Section: Wetland Extent For Baseline Inventory and Long-term Monitoringmentioning

confidence: 99%

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

et al. 2020

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Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.

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“…As a result, the created objects bear more resemblance to real-world features than pixel-based classifiers. Additionally, the salt and pepper noise that exists in the pixel-based image classification is eliminated in OBIA classifiers (Frohn et al, 2011;Salehi et al, 2018).…”

Section: Object-based Machine Learning Classifiersmentioning

confidence: 99%

Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms

Adeli

Salehi

Mahidanpari

et al. 2021

Earth and Space Science

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Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands’ ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA‐ISRO SAR (NISAR) mission. In this study, the capability of L‐band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana, is investigated using two object‐based machine learning approaches: Support vector machine (SVM) and random forest (RF). L‐band Unmanned Aerial Vehicle SAR (UAVSAR) data are exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean‐shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L‐band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman‐Durden, and Aghababaee features have the highest contribution to the overall accuracy.

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A Collection of Novel Algorithms for Wetland Classification with SAR and Optical Data

Cited by 10 publications

References 92 publications

A Rapidly Assessed Wetland Stress Index (RAWSI) Using Landsat 8 and Sentinel-1 Radar Data

A Rapidly Assessed Wetland Stress Index (RAWSI) Using Landsat 8 and Sentinel-1 Radar Data

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms

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