The Use of Polarimetric SAR Data for Forest Disturbance Monitoring

Trisasongko, Bambang H.

doi:10.1007/s11220-010-0048-8

Cited by 21 publications

(11 citation statements)

References 17 publications

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“…Further investigation is also required on the preprocessing steps (e.g., filtering noise method) and on the derived texture indicators. To the extent of our knowledge, degraded forest structure and status remain understudied, with a lot of technical aspects (frequency of the time series, spatial resolution, polarizations, incident angles and frequency bands) that need to be tackled [80][81][82].…”

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confidence: 99%

The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest

Bourgoin

Blanc

Bailly

et al. 2018

Forests

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In the agricultural frontiers of Brazil, the distinction between forested and deforested lands traditionally used to map the state of the Amazon does not reflect the reality of the forest situation. A whole gradient exists for these forests, spanning from well conserved to severely degraded. For decision makers, there is an urgent need to better characterize the status of the forest resource at the regional scale. Until now, few studies have been carried out on the potential of multisource, freely accessible remote sensing for modelling and mapping degraded forest structural parameters such as aboveground biomass (AGB). The aim of this article is to address that gap and to evaluate the potential of optical (Landsat, MODIS) and radar (ALOS-1 PALSAR, Sentinel-1) remote sensing sources in modelling and mapping forest AGB in the old pioneer front of Paragominas municipality (Para state). We derived a wide range of vegetation and textural indices and combined them with in situ collected AGB data into a random forest regression model to predict AGB at a resolution of 20 m. The model explained 28% of the variance with a root mean square error of 97.1 Mg·ha −1 and captured all spatial variability. We identified Landsat spectral unmixing and mid-infrared indicators to be the most robust indicators with the highest explanatory power. AGB mapping reveals that 87% of forest is degraded, with illegal logging activities, impacted forest edges and other spatial distribution of AGB that are not captured with pantropical datasets. We validated this map with a field-based forest degradation typology built on canopy height and structure observations. We conclude that the modelling framework developed here combined with high-resolution vegetation status indicators can help improve the management of degraded forests at the regional scale. IntroductionDeforestation and forest degradation are major sources of greenhouse gas emissions [1,2], contributing to forest carbon losses [3], global climate change, affecting biodiversity [4] and the entire forest ecosystem. While deforestation refers to the rapid conversion from forest to non-forest areas, degradation implies changes in the forest structure with no change in land use [5,6]. In Amazonia, over the last decades, deforestation and forest degradation have shaped the rural landscape, resulting in a complex mosaic of fragmented forests associated with agricultural lands [7]. A total area of 766,448.5 km 2 was cleared in 2015 [8], representing 20% of Amazonia [9,10].Since 2005, deforestation in Brazil has drastically decreased thanks to coercive measures taken by the Brazilian government associated with private initiatives (soy and beef moratoria), among other factors [11]. However, these measures are not effective for reducing forest degradation [12,13]. Most of the remaining forested lands are degraded due to the accumulation over time and space of severe degradation processes mainly triggered by anthropogenic impacts through unsustainable logging practices, fire, shifting cultivation ...

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confidence: 99%

The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest

Bourgoin

Blanc

Bailly

et al. 2018

Forests

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“…In order to better describe the stochastic characteristics of media, Cloude and Pottier [10,23] gave the definitions as follows:…”

Section: Feature Extraction and Combinationmentioning

confidence: 99%

Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

2015

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Abstract:A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST OPEN ACCESSRemote Sens. 2015, 7 1381 classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule.

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“…Anisotropy measures the relative importance of the second and third eigenvalues of eigen decomposition. It can be used as a source of discrimination, when H 4 0.7 (Fang et al 2006, Trisasongko 2010.…”

Section: Geocarto Internationalmentioning

confidence: 99%

Characterization of forests and deforestation in Cambodia using ALOS/PALSAR observation

Avtar

Sawada

Takeuchi

et al. 2012

Geocarto International

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In this study, we have demonstrated the capability of full polarimetric ALOS/Phased Array L-band Synthetic Aperture Radar data for the characterization of the forests and deforestation in Cambodia, to support climate change mitigation policies of Reducing Emission from Deforestation and Forest Degradation (REDD). We have observed mean backscattering coefficient (s8), entropy (H), alpha angle (a), anisotropy (A), pedestal height (PH), Radar Vegetation Index (RVI) and Freeman-Durden three-component decomposition parameters. The observations show that the forest types and deforested area are showing variable polarimetric and backscattering properties because of the structural difference. Evergreen forest is characterized by a high value of s8 HV (712.96 dB) as compared with the deforested area (s8 HV¼722.2 dB). The value of polarimetric parameters such as entropy (0.93), RVI (0.91), PH (0.41) and Freeman-Durden volume scattering (0.43) is high for evergreen forest, whereas deforested area is characterized by the low values of entropy (0.36) and RVI (0.17). Based on these parameters, it is found that s8 HV, entropy, RVI and PH provide best results among other parameters.

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The Use of Polarimetric SAR Data for Forest Disturbance Monitoring

Cited by 21 publications

References 17 publications

The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest

The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest

Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

Characterization of forests and deforestation in Cambodia using ALOS/PALSAR observation

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