Comparative evaluation of the sensitivity of multi‐polarized multi‐frequency SAR backscatter to plant density

Patel, Parul; Srivastava, H. S.; Panigrahy, Sushma; Parihar, J. S.

doi:10.1080/01431160500214050

Cited by 90 publications

(58 citation statements)

References 10 publications

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“…From the figure, it can be seen that the values of the backscattering coefficient change ~6 dB from normal condition (green) to burned condition (red). This increase in the observed σ 0 values with increasing vegetation density is not unexpected, and it was also observed for natural vegetation in [17] and for crops in [18]. Since the images ENL is ~21, this change cannot be attributed to speckle fluctuations, and should be related to changes in the phenological/hydrological condition of the marsh.…”

Section: Estimation Of the Reduction Of Junco Plant Densitysupporting

confidence: 66%

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

et al. 2009

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Abstract:The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented.

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Section: Estimation Of the Reduction Of Junco Plant Densitysupporting

confidence: 66%

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

et al. 2009

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“…This points to the possibility to classify shrub vegetation in other sub-Arctic regions, but further testing would be necessary, especially in environments where wetlands are more prevalent, as this type of land cover is very complex and caused the most problems in the current study. Furthermore, looking at the results shown in [22][23][24][25], it seems that polarimetric SAR data could be used to classify shrub vegetation cover in arid and semi-arid environments using a similar method. In these cases, however, the effects of seasonality might not be as apparent as the current study, and the temporal contrasts might be more related to rainfall and soil moisture cycles.…”

Section: Discussionmentioning

confidence: 99%

“…However, these methods have certain limitations as the field sampling methods can be very costly and do not provide a high spatial coverage, while satellite imagery in the visible and infrared spectrum is affected by the presence of clouds, which can be persistent in northern regions [16,21]. Previous studies have shown that SAR imagery can be a suitable tool to detect, quantify and map shrub vegetation, but mostly in arid or semi-arid environments [22][23][24][25]. More recently, it has been demonstrated that C-and X-band SAR backscattering is sensitive to shrub height in sub-Arctic environments [26].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data

et al. 2016

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Abstract:The expansion of shrub vegetation in Arctic and sub-Arctic environments observed in the past decades can have significant effects on northern ecosystems. There is a need for efficient tools to monitor those changes, not only in terms of the spatial coverage of shrubs, but also their vertical growth. The objective of the current paper is to evaluate the performance of polarimetric C-band SAR datasets for land cover classification in sub-Arctic environments. A series of RADARSAT-2 quad-pol images were acquired between October 2011 and April 2012. The Support Vector Machine (SVM) classification scheme was used on three sets of features: the elements of the polarimetric coherency matrix [T], the parameters extracted from a polarimetric decomposition based on the eigenvalues and eigenvectors of [T] and the parameters extracted from a model-based decomposition. Using a single image, the results show that the best classification accuracies (≈ 75%) are obtained using the [T] matrix with the October images. When adding a second image to the feature set, either from two different dates or two incidence angles, the classification accuracy is improved and reaches 90.1% with two images from October 2011 and April 2012 at 27 • incidence. The results show that C-band polarimetric SAR imagery is an adequate tool to map shrub vegetation in sub-Arctic environments.

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“…First, the numerator (i.e., VH − VV) reflects the depolarization ratio described in [56]. Due to its sensitivity to surface roughness, as well as vegetation structure and dry biomass [57,58], it has proven useful for discriminating between bare and forested surfaces [59], and as an important input parameter in soil moisture retrieval [60].…”

Section: Radar Datamentioning

confidence: 99%

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

et al. 2017

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Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km 2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives.

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Comparative evaluation of the sensitivity of multi‐polarized multi‐frequency SAR backscatter to plant density

Cited by 90 publications

References 10 publications

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

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