Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data

Santos, João Roberto dos; Lacruz, María Silvia Pardi; Araujo, L. S. de; Keil, Mark

doi:10.1080/01431160110092867

Cited by 110 publications

(67 citation statements)

References 9 publications

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“…Those areas of primary forest present σ 0 hh = -7.51 dB± 3.34, σ 0 hv = -12.25dB ±3.15 and σ 0 vv = -7.53dB ± 3.24; secondary succession areas, which represent intermediate and advanced phases (characterized by the old regeneration age and also by the vertical structure) present σ 0 hh = -7.38 dB± 2.34, σ 0 hv = -11.95dB ± 2.27 and σ 0 vv = -7.06dB ± 2.29. The values found in this study are coherent with those obtained by Hoekman and Quiñones (2000), SAATCHI et al (1997), SANTOS et al (2002), whose variability can be attributed to general differences in the horizontal and vertical structure of the strata from the forest typologies. This also shows that the sensor SAR-R99B presents an adequate radiometric response.…”

Section: Methodssupporting

confidence: 79%

“…There are several interaction mechanisms of the radar signal with forest targets, such as: multiple backscatter within the canopy (volumetric scattering), direct scattering from the tree trunks, scattering from the interaction canopy-soil, scattering from the interaction trunk-soil (double bounce), whose intensities depend on the SAR wavelengths, on the polarization, on the angle of incidence and on the terrain parameters. (CLOUDE and POTTIER, 1997;FREEMAN and DURDEN, 1998) According several studies (SAATCHI et al, 1997;HOEKMAN and QUINÕNES, 2000;SANTOS et al, 2002), SAR data are also used to model forest volume and biomass estimation, using the polarimetric backscatter attributes. Depending on the frequency of the sensor, there is a saturation of radar signal for those areas with high biomass concentration.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the frequency of the sensor, there is a saturation of radar signal for those areas with high biomass concentration. Due to that, estimation models are being improved with SAR interferometric variables (BALTZER, 2001;TREUHAFT et al, 2006;HAJNSEK et al, 2009). Studying the values of backscatter features (σ 0 ), the penetration capacity and the predominant scattering mechanisms on vegetation as well as biomass evaluation from multi-frequency and multi-polarized AIRSAR data, it was verified that bands P HV and L HV are more sensitive to the total biomass, presenting furthermore a strong relation with some structural forest parameters such as height (H), basal area (G), and diameter (DBH) (MOUGIN et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Análise dos parâmetros estruturais de tipologias florestais utilizando dados SAR em banda L

2010

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The main objective of this work is the investigation of the relationship between the polarimetric L-band backscatter (σº) from different incidence angles (collected by the airborne sensor R99-B/SIPAM) and the structural parameters of the primary and secondary forest typologies. The area under study is located at the Tapajós National Forest and surroundings (Pará State, Brazil). Using the Freeman-Durden target decomposition technique, the basic backscatter mechanisms are presented to verify the contributions of the physiognomic-structural components of these forest stands in the L-band signal responses. As a conclusion, it is possible to verify that the variable "tree height" has better relations with the backscatter values, when compared to other structural variables, especially when the model also includes variations of the incidence angle of the stripes imaged. The Freeman-Durden decomposition technique indicates that the volumetric scattering component has the strongest influence on the L-band at primary and secondary tropical forests at incidence angles between 52 and 70 degrees, mainly due to the high incidence angle and, consequently the low depth vertical penetration. Keywords: Forest Inventory; L-Band; SAR Data; Mapping; Tropical Forest; Amazon.Bol. Ciênc. Geod., sec. Artigos, Curitiba, v. 16, n o 3, p.475-489, jul-set, 2010.Analysis of structural parameters of forest typologies using ... 4 7 6 RESUMO O objetivo principal desse trabalho é investigar a relação entre o retroespalhamento (σ°) de dados SAR polarimétricos de banda L, em diferentes ângulos de incidência (coletado pelo sensor aerotransportado R99-B/SIPAM) e os parâmetros estruturais de sítios de floresta primária e sucessão secundária. A área selecionada para esse estudo está localizada na região da Floresta Nacional do Tapajós (Estado do Pará, Brasil) e áreas circunvizinhas. É utilizada a técnica de decomposição de alvos de Freeman-Durden na avaliação dos mecanismos básicos de espalhamento, para verificar a contribuições das componentes fisionômico-estruturais dos alvos florestais na resposta-radar de banda L. Como conclusão, é possível verificar que a variável "altura das árvores" teve melhor relação com os valores de retroespalhamento, quando comparado com outras variáveis biofísicas, especialmente quando o modelo também incluiu variações do ângulo de incidência na direção em range. A técnica de decomposição de Freeman-Durden indicou que a componente volumétrica de espalhamento tem uma forte influência na resposta em banda L para florestas tropicais primárias e secundárias,em ângulos de incidência entre 52 e 70 graus, devido principalmente ao elevado ângulo de incidência e, consequentemente a baixa profundidade de penetração vertical da onda incidente.

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Section: Methodssupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Análise dos parâmetros estruturais de tipologias florestais utilizando dados SAR em banda L

2010

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“…While ground conditions also affect backscatter for low biomass levels, the majority of studies have observed that JERS backscatter increases, with few exceptions, with increasing biomass over tropical (Castel, Guerra, Caraglio, & Houllier, 2002;Kuplich, Salvatori, & Curran, 2000;Luckman, Baker, Honsák, & Lucas, 1998;Santos, Pardi Lacruz, Araujo, & Keil, 2002) and boreal forests (Fransson & Israelsson, 1999;). However, the backscatter level and sensitivity vary with tree species, non-forest vegetation and environmental conditions.…”

Section: Ers and Jers Sar In Forestry Applicationsmentioning

confidence: 99%

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Wagner

Luckman

Vietmeier

et al. 2003

Remote Sensing of Environment

127

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Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km 2 in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m 3 /ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m. D

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“…A number of studies have investigated the role of synthetic aperture radar (SAR) data to estimate aboveground biomass (Austin et al 2003;Harrell et al 1997;Luckman et al 1998;Santos et al 2002). SAR data are sensitive to geometric properties of the forest; thus, they are more directly related to measurements of aboveground biomass than optical remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

Banskota

Wynne

Johnson

et al. 2011

Annals of Forest Science

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Abstract• Introduction Accurate estimation of aboveground biomass is essential to better understand the carbon cycle in forest ecosystems.• Methods The objective of this study was to determine whether biomass in temperate hardwood forests is better estimated using very high-frequency radar data (from BioSAR) alone or in combination with small-footprint discrete-return lidar data (both profiling and scanning). The study area was in the Appomattox-Buckingham State Forest, Virginia, USA (78°41′W, 37°25′N). Aboveground biomass for 28 stands was estimated using 131 basal area factor 10 point samples. The resulting stand biomass estimates were used as the dependent variable in a multiple linear regression. Descriptors of the lidar distributions (both profiling and scanning) and averaged normalized radar cross-sections in each of these stands were used as independent variables.• Results Regression results revealed the following: (1) neither BioSAR nor scanning lidar data alone are good predictors of stand biomass (R 2 =0.57, root mean squared error (RMSE)= 31.0 tonnes/ha and R 2 =0.64, RMSE=28.5 tonnes/ha, respectively); (2) BioSAR data combined with small-footprint discrete lidar data (either profiling or scanning) are the best predictors of stand biomass (R 2 =0.80, RMSE=21.3 tonnes/ ha and R 2 =0.76, RMSE=24.2 tonnes/ha, respectively); and (3) when used with BioSAR data for stand biomass estimation, less costly profiling lidar data convey the same information as more costly scanning lidar data.• Conclusion Useful synergy can be realized by considering lidar and radar measurements jointly in estimating aboveground biomass in hardwood and mixed forests.

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Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data

Cited by 110 publications

References 9 publications

Análise dos parâmetros estruturais de tipologias florestais utilizando dados SAR em banda L

Análise dos parâmetros estruturais de tipologias florestais utilizando dados SAR em banda L

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

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