Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS/PALSAR Data

Chen, Wei; Zheng, Qihui; Xiang, Hai; Chen, Xu; Sakai, Tetsuro

doi:10.3390/rs13020174

Cited by 28 publications

(16 citation statements)

References 38 publications

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“…The results suggest that further pathways, to overcome some weaknesses shown, will aim to: (i) increase the EUNIS habitat categories to be mapped (i.e., grasslands, shrubs, wetlands, and coastal area); (ii) amplify the geographical extent to a wider area (i.e., from national to continental scale), obtain homogeneous classification within single biome unit (i.e., Mediterranean Basin); (iii) test different semi-supervised machine learning algorithms (e.g., Convolutional Neural Network, [118]) to obtain a more suitable habitat classification; and, (iv) also consider C-band Synthetic Aperture Radar (SAR) satellite data as candidate predictors for habitat classification purposes (e.g., forest canopy structure [119]).…”

Section: Discussionmentioning

confidence: 99%

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

et al. 2021

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In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forest (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.

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Section: Discussionmentioning

confidence: 99%

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

et al. 2021

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“…The variation in interaction of backscatter depending on target characteristics further complicate the information content of RADAR data. Such complexity might render the data suitable for continuous-scale statistical assessment of species diversity [ 141 ]. Exploring the efficacy of image intensity for species diversity estimation, [ 142 ] used images of different polarisations including HV, (HH, VV, and VH derived from Sentinel-1 C-band and Advanced Land Observation System Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) for species diversity estimation in a Montane savanna (6 km 2 ).…”

Section: Remote Sensing Of Savanna Woody Plant Species Diversity Usin...mentioning

confidence: 99%

“…Linear regression analysis correlating the coherence data with plot-level field surveys resulted in an R 2 of 0.5. Recently, [ 141 ] recorded tree height information to modelling species diversity (Shannon diversity index) from 5-m 2 plots in a Montane savanna covering 1 102 km 2 . They, then, regressed the index against coherence data derived from ALOS/PALSAR imagery and found R 2 of 0.67.…”

Section: Remote Sensing Of Savanna Woody Plant Species Diversity Usin...mentioning

confidence: 99%

Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods

2022

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Despite savannas being known for their relatively sparse vegetation coverage compared to other vegetation ecosystems, they harbour functionally diverse vegetation forms. Savannas are affected by climate variability and anthropogenic factors, resulting in changes in woody plant species compositions. Monitoring woody plant species diversity is therefore important to inform sustainable biodiversity management. Remote sensing techniques are used as an alternative approach to labour-intensive field-based inventories, to assess savanna biodiversity. The aim of this paper is to review studies that applied remote sensing to assess woody plant species diversity in savanna environments. The paper first provides a brief account of the spatial distribution of savanna environments around the globe. Thereafter, it briefly defines categorical classification and continuous-scale species diversity assessment approaches for savanna woody plant estimation. The core review section divides previous remote sensing studies into categorical classification and continuous-scale assessment approaches. Within each division, optical, Radio Detection And Ranging (RADAR) and Light Detection and Ranging (LiDAR) remote sensing as applied to savanna woody species diversity is reviewed. This is followed by a discussion on multi-sensor applications to estimate woody plant species diversity in savanna. We recommend that future research efforts should focus strongly on routine application of optical, RADAR and LiDAR remote sensing of physiologically similar woody plant species in savannas, as well as on extending these methodological approaches to other vegetation environments.

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“…In [32,33] the Gaussian vertical backscatter (GVB) model was applied to forest height estimation in the P band, with the authors finding that this new model could match well with the forest's vertical structure in the long wavelength in terms of the estimation results. In [34,35] the sloped random volume over ground (S-RVoG) method was introduced for height estimations using both P-band and L-band data. This model was able to alleviate the model estimation errors caused by topographic fluctuations.…”

Section: Introductionmentioning

confidence: 99%

“…Evidently, most of researchers have conducted this research under the framework of long-wavelength acquisitions, i.e., L-band and P-band data [35,37,38]. Few researchers have considered the feasibility of forest height estimation in shortwaves [19][20][21]39].…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Forest Height Estimation Using Airborne X-Band PolInSAR Data

Xie

Zhuang

et al. 2022

Remote Sensing

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Because the penetration depth of electromagnetic waves in forests is large in the longer wavelength band, most traditional forest height estimation methods are carried out using polarimetric interferometry synthetic aperture radar (PolInSAR) data of the L or P band, and the estimation method is a three-stage method based on the random volume over ground (RVoG) model. For X−band electromagnetic waves, the penetration depth of radar waves in forests is limited, so the traditional forest height estimation method is no longer applicable. In view of the above problems, in this paper we propose a new forest height estimation strategy for airborne X−band PolInSAR data. Firstly, the sub-view interferometric SAR pairs obtained via frequency segmentation (FS) in the Doppler domain are used to extend the polarimetric interferometry coherence coefficient (PolInCC) range of the original SAR image under different polarization states, so as to obtain the accurate ground phase. For the determination of the effective volume coherence coefficient (VCC), part of the fitting line of the extended-range PolInCC distribution that is intercepted by the fixed extinction coherence coefficient curve (FECCC) of the fixed range is averaged to obtain the accurate effective VCC. Finally, the high-precision forest canopy height in the X−band is estimated using the effective VCC with the ground phase removed in the look-up table (LUT). The effectiveness of the proposed method was verified using airborne-measured data obtained in Shaanxi Province, China. The comparison was carried out using different strategies, in which we substituted one step of the process with the conventional method. The results indicated that our new strategy could reduce the root mean square error (RMSE) of the predicted canopy height vastly to 1.02 m, with a lower estimation height error of 12.86%.

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Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS/PALSAR Data

Cited by 28 publications

References 38 publications

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods

A New Strategy for Forest Height Estimation Using Airborne X-Band PolInSAR Data

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