Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign

Hajnsek, Irena; Kugler, Florian; Lee, Seung-Kuk; Papathanassiou, Konstantinos

doi:10.1109/tgrs.2008.2009437

Cited by 241 publications

(197 citation statements)

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“…These patches have the same spatial dimensions as the typical crown size of emergent trees in our observation area (20 m×20 m). This also implies that the spatial resolution possible with FORMIND2.0 is similar to that of remote sensing estimates (Frolking et al, 2009;Hajnsek et al, 2009). Finer spatial-scale features are not covered by the model.…”

Section: Description Of the Modelmentioning

confidence: 88%

“…Accuracy and uncertainties in the calculation of the canopy height during remote sensing are not discussed in detail here, but see Hajnsek et al (2009). We show from simulations covering the range of disturbances typically found in tropical rain forests in Borneo, how accurate AGB (and thus the carbon content of the vegetation) and LAI can be determined, once the canopy height is known.…”

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confidence: 99%

“…Remote sensing by LIDAR or radar interferometry nowadays accurately describes, for example, the height of the canopy in various different forest types in temperate and tropical regions (e.g. Lefsky et al, 2002Lefsky et al, , 2005Balzter et al, 2007;Hajnsek et al, 2009). To be able to calculate other parameters of the forest (e.g.…”

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confidence: 99%

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Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests

Köhler

Huth

2010

Biogeosciences

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Abstract. The canopy height h of forests is a key variable which can be obtained using air-or spaceborne remote sensing techniques such as radar interferometry or LIDAR. If new allometric relationships between canopy height and the biomass stored in the vegetation can be established this would offer the possibility for a global monitoring of the above-ground carbon content on land. In the absence of adequate field data we use simulation results of a tropical rain forest growth model to propose what degree of information might be generated from canopy height and thus to enable ground-truthing of potential future satellite observations. We here analyse the correlation between canopy height in a tropical rain forest with other structural characteristics, such as above-ground life biomass (AGB) (and thus carbon content of vegetation) and leaf area index (LAI) and identify how correlation and uncertainty vary for two different spatial scales. The process-based forest growth model FORMIND2.0 was applied to simulate (a) undisturbed forest growth and (b) a wide range of possible disturbance regimes typically for local tree logging conditions for a tropical rain forest site on Borneo (Sabah, Malaysia) in South-East Asia. In both undisturbed and disturbed forests AGB can be expressed as a power-law function of canopy height h (AGB = a ·h b ) with an r 2 ∼ 60% if data are analysed in a spatial resolution of 20 m × 20 m (0.04 ha, also called plot size). The correlation coefficient of the regression is becoming significant better in the disturbed forest sites (r 2 = 91%) if data are analysed hectare wide. There seems to exist no functional dependency between LAI and canopy height, but there is also a linear correlation (r 2 ∼ 60%) between AGB and the area fraction ofCorrespondence to: P. Köhler (peter.koehler@awi.de) gaps in which the canopy is highly disturbed. A reasonable agreement of our results with observations is obtained from a comparison of the simulations with permanent sampling plot (PSP) data from the same region and with the large-scale forest inventory in Lambir. We conclude that the spaceborne remote sensing techniques such as LIDAR and radar interferometry have the potential to quantify the carbon contained in the vegetation, although this calculation contains due to the heterogeneity of the forest landscape structural uncertainties which restrict future applications to spatial averages of about one hectare in size. The uncertainties in AGB for a given canopy height are here 20−40% (95% confidence level) corresponding to a standard deviation of less than ± 10%. This uncertainty on the 1 ha-scale is much smaller than in the analysis of 0.04 ha-scale data. At this small scale (0.04 ha) AGB can only be calculated out of canopy height with an uncertainty which is at least of the magnitude of the signal itself due to the natural spatial heterogeneity of these forests.

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Section: Description Of the Modelmentioning

confidence: 88%

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confidence: 99%

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Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests

Köhler

Huth

2010

Biogeosciences

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“…In a single-pass acquisition scenario, the association between InSAR observables and forest variables is expected to be maximized because temporal decorrelation can be assumed to be negligible. Experimental evidence on the suitability of single-pass InSAR to estimate forest variables at X-band (wavelength of approximately 3 cm) was provided by data acquired by airborne sensors [4][5][6], and during the Shuttle Radar Topography Mission (SRTM) [7].…”

Section: Introductionmentioning

confidence: 99%

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

et al. 2013

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Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to examine the potential offered by new sensors. To assess the contribution of the TanDEM-X mission, eighteen interferometric Synthetic Aperture Radar (SAR) image pairs acquired over the hemi-boreal test site of Remningstorp in Sweden were investigated. Three models were used for interpretation of TanDEM-X signatures and above-ground biomass retrieval: Interferometric Water Cloud Model (IWCM), Random Volume over Ground (RVoG) model, and a simple model based on penetration depth (PD). All use an allometric expression to relate above-ground biomass to forest height measured by TanDEM-X. The retrieval was assessed on 201 forest stands with a minimum size of 1 ha, and ranging from 6 to 267 Mg/ha (mean biomass of 105 Mg/ha) equally divided into a model training dataset and a validation test dataset. Biomass retrieved using the IWCM resulted in a Root Mean Square Error (RMSE) between 17% OPEN ACCESS Remote Sens. 2013, 5 5575 and 33%, depending on acquisition date and image acquisition geometry (angle of incidence, interferometric baseline, and orbit type). The RMSE in the case of the RVoG and the PD models were slightly higher. A multitemporal estimate of the above-ground biomass using all eighteen acquisitions resulted in an RMSE of 16% with R 2 = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone.

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“…L-band pol-inSAR has also shown promise to map structure in higher 525 density regions of the tropics (Hajnsek et al 2009). Pol-Insar data will be available with 526 the DESDynI mission.…”

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confidence: 99%

Characterizing 3D vegetation structure from space: Mission requirements

Hall¹,

Bergen²,

Blair³

et al. 2011

Remote Sensing of Environment

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18Human and natural forces are rapidly modifying the global distribution and 19 structure of terrestrial ecosystems on which all of life depends, altering the global carbon 20 cycle, affecting our climate now and for the foreseeable future, causing steep reductions 21 in species diversity, and endangering Earth's sustainability. 22To understand changes and trends in terrestrial ecosystems and their functioning 23 as carbon sources and sinks, and to characterize the impact of their changes on climate, 24 habitat and biodiversity, new space assets are urgently needed to produce high spatial 25 resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass 26 above ground, the carbon stored within and the implications for atmospheric green house 27 gas concentrations and climate. These needs were articulated in a 2007 National 28Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, 29DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a 30 multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives 31 of this paper are to articulate the importance of these new, multi-year, 3D vegetation 32 structure and biomass measurements, to briefly review the feasibility of radar and lidar 33 remote sensing technology to meet these requirements, to define the data products and 34 measurement requirements, and to consider implications of mission durations. The paper 35 addresses these objectives by synthesizing research results and other input from a broad 36 community of terrestrial ecology, carbon cycle, and remote sensing scientists and 37 working groups. We conclude that: 38(1) current global biomass and 3-D vegetation structure information is unsuitable 39 for both science and management and policy. The only existing global datasets of 40 biomass are approximations based on combining land cover type and representative 41 carbon values, instead of measurements of actual biomass. Current measurement attempts 42 based on radar and multispectral data have low explanatory power outside low biomass 43areas. There is no current capability for repeatable disturbance and regrowth estimates. 44(2) The science and policy needs for information on vegetation 3D structure can 45 be successfully addressed by a mission capable of producing (i) a first global inventory of 46 forest biomass with a spatial resolution 1km or finer and unprecedented accuracy (ii) 47 annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass 48 accumulation rates at resolutions of 1km or finer, and (iii) transects of vertical and 49 horizontal forest structure with 30 m along-transect measurements globally at 25 m 50 spatial resolution, essential for habitat characterization. 51 We also show from the literature that lidar profile samples together with wall-to-52 wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together 53 to satisfy these vegetation 3D structure and biomass measurement requirements. ...

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Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign

Cited by 241 publications

References 28 publications

Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests

Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

Characterizing 3D vegetation structure from space: Mission requirements

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