On the influence of canopy structure on the radar backscattering of mangrove forests

Proisy, Christophe; Mougin, E.; Fromard, François; Trichon, Valérie; Karam, M.A.

doi:10.1080/01431160110107725

Cited by 49 publications

(48 citation statements)

References 3 publications

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“…Because of persistent cloud cover in the tropical and subtropical regions, radar imagery is an appropriate option compared with optical remotely sensed data. Radar data deliver information that is useful for characterizing the cover extent of mangrove surfaces [146], structural parameters [9,[81][82][83]85,87,102,147,148], flooding boundaries [84], health status [88,149], deforestation status [150], and the amount of total biomass [81][82][83]85]. Studies were performed at different locations in various countries based on different radar data (Mexico: RADARSAT-1 SAR, [149] Several investigations were carried out to examine and describe the effects and relationships among mangrove canopy, stand structures, and the backscattering response of a SAR system, exemplified by the NASA/JPL airborne SAR (AIRSAR) system at different frequencies (C-, L-, P-band) and polarization modes (HH, VV, HV).…”

Section: Overview Of Mangrove-mapping Studies and Methods Based On Ramentioning

confidence: 99%

“…and external components (e.g., size, geometry, and orientation of leaves, trunks, branches, and aerial or stilt roots) result in a specific backscatter signal (see Figure 4). Mougin et al [81] and Proisy et al [82,83] investigated the relationships between airborne SAR data, for various polarization and multifrequency modes, and the structural components of mangroves for a study area in French Guiana. The following table (see Table 1) describes these interactions and the general relationships found by Wang and Imhoff [84], Aschbacher et al [9], Kasischke et al [80], Mougin et al [81], Proisy et al [82,83,85], Lucas et al [86,87], and Kovacs et al [88].…”

Section: Expression Of Mangrove Backscatter In Radar Datamentioning

confidence: 99%

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Remote Sensing of Mangrove Ecosystems: A Review

et al. 2011

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Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high-and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the OPEN ACCESSRemote Sens. 2011, 3 879 services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations.

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Section: Overview Of Mangrove-mapping Studies and Methods Based On Ramentioning

confidence: 99%

Section: Expression Of Mangrove Backscatter In Radar Datamentioning

confidence: 99%

Remote Sensing of Mangrove Ecosystems: A Review

et al. 2011

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“…Several studies have related radar backscattering with the structural parameters of mangrove vegetation such as homogeneous forest canopies to estimate above-ground biomass (AGB) [25][26][27][28][29]. Recently, Kovacs et al [30,31] estimated structural attributes of degraded mangrove forests on the Pacific coast of México using multi-polarized C-band (Radarsat-2) and L-band images (ALOS PALSAR).…”

Section: Introductionmentioning

confidence: 99%

Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests

et al. 2015

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Abstract:The aim of this study is to understand the relationship between radar backscattering (σ˝, β˝and γ) of a multi-polarized Radarsat-2 C-band image with the structural attributes of regenerating mangrove vegetation located at the mouth of the Amazon River. CBH (circumference at breast height), height and species data were collected to characterize vegetation structure and above-ground biomass (AGB) at 17 plots with a total of 3090 measured individuals. Significant relationships between the linear σ˝in VH (vertical transmit, horizontal receive) cross-polarization produced r 2 values of 0.63 for the average height, 0.53 for the DBH, 0.46 for the basal area (BA) and 0.52 for the AGB. Using co-polarized HH (horizontal transmit, horizontal receive) and VV (vertical transmit, vertical receive), r 2 values increased to 0.81, 0.79, 0.67 and 0.79, respectively. Vegetation attribute maps of average canopy height, DBH and AGB were generated for the study area. We conclude that multi-polarized Radarsat-2 images were adequate for characterization of vegetation attributes in areas of mangrove regeneration.

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“…For this purpose, the values for these two variables specified in Section 3.3 (Table 1) These results indicate that in the first case (LAI true ) the olive grove canopy is very thin with a very low attenuation, while in the second case (LAI eff ) these values are higher, which appear to be more realistic and according to this vegetation cover. Moreover, as the coefficient of agreement is better for effective LAI, the characterization of this type of canopy by means of this biophysical variable is proven to be more acceptable, and might also imply that the grouping effect of the vegetation constituents (or their geometrical properties), is better taken into account by this type of microwave scattering models, as it has been suggested in [51,53,54]. Finally, these statements are also confirmed by means of the corresponding regression fits between simulated and measured backscattering coefficients depicted in Figure 15a and 15b, where it is noticed that the simulated 0 vv σ of the canopy using LAI eff exhibits a higher dependency with the measured 0 vv σ (Figure 15(b)).…”

Section: Rt Vegetation Scattering Models-modified Xu and Steven Modelmentioning

confidence: 99%

Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

Molina

Morillo

García‐Meléndez

et al. 2011

Sensors

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Abstract:One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (σ 0 ).The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r 2 = 0.88). In turn, when effective LAI OPEN ACCESSSensors 2011, 11 7477(LAI eff ) has been taken into account, the parameters of the scattering canopy model are better estimated (r 2 = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values.

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On the influence of canopy structure on the radar backscattering of mangrove forests

Cited by 49 publications

References 3 publications

Remote Sensing of Mangrove Ecosystems: A Review

Remote Sensing of Mangrove Ecosystems: A Review

Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests

Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

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