Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon, Brazil

Liesenberg, Veraldo; Gloaguen, Richard

doi:10.1016/j.jag.2012.08.016

Cited by 50 publications

(47 citation statements)

References 60 publications

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“…The study found that these processes are necessary and must be done on any SAR images before further analyses are carried out. Studies [9,11,33] have demonstrated that PALSAR polarization data actually has a logarithmic relationship with AGB. Therefore, instead of employing linear regression only, the study also attempted to correlate the AGB with the polarizations in a non-linear form.…”

Section: Satellite Datasetsmentioning

confidence: 99%

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

2017

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Space borne synthetic aperture radar (SAR) data have become one of the primary sources for aboveground biomass (AGB) estimation of forests. However, studies have indicated that limitations occur when a single sensor system is employed, especially in tropical forests. Hence, there is potential for improving estimates if two or more different sensor systems are used. Studies on integrating multiple sensor systems for estimation of AGB over Malaysia's tropical forests are scarce. This study investigated the use of PALSAR-2 L-band and Sentinel-1A C-band SAR polarizations to estimates the AGB over 5.25 million ha of the lowland, hill, and upper hill forests in Peninsular Malaysia. Polarized images, i.e., HH-HV from PALSAR-2 and VV-VH from Sentinel-1A have been utilized to produce several variables for predictions of the AGB. Simple linear and multiple linear regression analysis was performed to identify the best predictor. The study concluded that although limitations exist in the estimates, the combination of all polarizations from both PALSAR-2 and Sentiel-1A SAR data able to increase the accuracy and reduced the root means square error (RMSE) up to 14 Mg ha −1 compared to the estimation resulted from single polarization. A spatially distributed map of AGB reported the total AGB within the study area was about 1.82 trillion Mg of the year 2016.

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Section: Satellite Datasetsmentioning

confidence: 99%

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

2017

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“…Furthermore, besides their ability in providing vegetation structure information, as discussed in next paragraphs, active data have been increasingly used in mapping applications. Phased Array type L-band SAR (PALSAR) and RADARSAT-2 data have provided high potential in LC and forest characterization, used either individually (Walker et al 2010;Maghsoudi et al 2012;Longépé et al 2011) or in synergy with optical data (Liesenberg and Gloaguen 2013;Vaglio Laurin et al 2013). In a study by Vaglio Laurin et al (2013), PALSAR coupled with AVNIR-2 identified eight LC classes in a tropical rainforest and fragmented area with accuracy 97.5%, outperforming PALSAR synergy with Landsat TM.…”

Section: Terrestrial Mappingmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…Figure 5a shows the backscattering in HH and HV polarized bands, and Figure 5b shows the distribution of alpha angle and entropy on the Cloude-Pottier diagram. The results may be affected by the use of dual-polarization data rather than quad-polarization data [13] that were not available for the area. The backscattering values in both HH and HV polarized bands (Figure 5a) tend to decrease from NF to the both managed forest classes due to a more clear forest floor.…”

Section: Alos/palsar Processingmentioning

confidence: 99%

“…Active remote sensing data like SAR overcome some limitations of optical data. They have the advantage to be weather and daylight independent [9,13]. SAR backscatter correlates with forest biomass, particularly in low-medium biomass forest at lower frequencies like P and L-bands [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Attarchi

Gloaguen

2014

Remote Sensing

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Abstract:The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R 2 = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R 2 = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest's physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions. OPEN ACCESSRemote Sens. 2014, 6 3694

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Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon, Brazil

Cited by 50 publications

References 60 publications

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

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