Evaluation of approaches to estimating aboveground biomass in Southern pine forests using SIR-C data

Harrell, Peter; Kasischke, Eric S.; Bourgeau-Chavez, L. L.; Haney, Eric M.; Christensen, Norman L.

doi:10.1016/s0034-4257(96)00155-1

Cited by 97 publications

(51 citation statements)

References 24 publications

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“…However, this study found that PALSAR backscatter measurements in co-and cross-polarization (HH, HV) were overall less correlated to AGCD than the Landsat derived canopy density estimates (Pearson correlation for different forest types of AGCD and HV: 0.22-0.54, HH: 0.18-0.47, VCF: 0.5-0.58) and hence, with few regional exceptions (Section 3.3), ranked lower in the randomForest importance ranking ( Figure 6). This was most likely a consequence of the sensitivity of the radar measurements to environmental factors, most prominently soil and canopy moisture variations at stand-to landscape-scales that have been shown to affect the form and strength of the relationship between the backscatter measurements and forest biophysical attributes [19,29,66,68,72,73,84]. Model investigations in Wang et al [85] indicated that also other factors (forest floor roughness, litter depth) could introduce significant variability in L-band observations of forest, in particular at HH polarization.…”

Section: Discussionmentioning

confidence: 99%

“…Model investigations in Wang et al [85] indicated that also other factors (forest floor roughness, litter depth) could introduce significant variability in L-band observations of forest, in particular at HH polarization. The retrieval of forest biophysical parameters based on L-band data generally performs best when the effect of moisture variations in soils and vegetation are minimized, for instance in arid regions or during extended dry periods [30,84]. As discussed in Lucas et al [29], the limited availability of multi-temporal observations from ALOS PALSAR hampers the possibility to generate large-area ALOS PALSAR L-band backscatter mosaics that consistently represent dry conditions.…”

Section: Discussionmentioning

confidence: 99%

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A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

et al. 2014

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

et al. 2014

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“…The retrieval instead improved in combination with L-band data [53,54]. Non-parametric models performed better than a multi-linear regression in [54], whereas using a physically based model, the retrieval performed better than using an empirical multivariate model [55]. Finally, adding InSAR height at C-band to a linear model expressing biomass as a function of multi-polarized AIRSAR data improved the retrieval [56]; furthermore, the performance was better for increasing the wavelength.…”

Section: Multi-frequency Retrieval Approachesmentioning

confidence: 98%

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Santoro

Cartus

2018

Remote Sensing

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Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evaluated primarily (i) L-band observations of SAR backscatter; and, (ii) single-image or multi-polarized retrieval schemes. The use of multi-temporal or multi-frequency data improved the biomass estimates when compared to single-image retrieval. Low frequency SAR backscatter contributed the most to the biomass estimates. Single-pass InSAR height was reported to be a more reliable predictor of biomass, overcoming the loss of sensitivity of SAR backscatter and coherence in high biomass forest. A variety of empirical and semi-empirical regression models relating biomass to the SAR observables were proposed. Semi-empirical models were mostly used for large-scale mapping because of the simple formulation and the robustness of the model parameters estimates to forest structure and environmental conditions. Non-parametric models were appraised for their capability to ingest multiple observations and perform accurate retrievals having a large number of training samples available. Some studies argued that estimating compartment biomass (in stems, branches, foliage) with different types of SAR observations would lead to an improved estimate of total biomass. Although promising, scientific evidence for such an assumption is still weak. The increased availability of free and open SAR observations from currently orbiting and forthcoming spaceborne SAR missions will foster studies on forest biomass retrieval. Approaches attempting to maximize the information content on biomass of individual data streams shall be pursued.

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“…This has enabled repetitiveness and cost-effectiveness. A large number of recent studies have explored the use of radar data for above-ground biomass estimation [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: Role Of Remote Sensing In Mapping Above-ground Biomassmentioning

confidence: 99%

Remote Sensing of Above-Ground Biomass

2017

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Accurate measurement and mapping of biomass is a critical component of carbon stock quantification, climate change impact assessment, suitability and location of bio-energy processing plants, assessing fuel for forest fires, and assessing merchandisable timber. While above-ground biomass includes both live and dead plant material, most of the recent research effort on biomass estimation has focussed on the 'live' component (live trees) due to the prominence of this component. Accurate estimates of biomass is a prerequisite for better understanding of the impacts of deforestation and environmental degradation on climate change.The Intergovernmental Panel on Climate Change (IPCC) [1] has listed five terrestrial ecosystem carbon pools involving biomass: above-ground biomass, below-ground biomass, litter, woody debris and soil organic matter. Of these five, above-ground biomass is the most visible, dominant, dynamic and important pool of the terrestrial ecosystem, constituting around 30% of the total terrestrial ecosystem carbon pool. Above-ground biomass estimation, and especially forest biomass, has received considerable attention over the last few decades because of increased awareness of climate warming and the role forest biomass plays in carbon sequestration and release of greenhouse gases due to deforestation.Above-ground biomass estimates are the central basis for carbon inventories and most international negotiations in carbon trading schemes. Carbon trading markets require long-term information on carbon stocks, particularly on the above-ground 'live' biomass component as this is the most dynamic, changing and manipulable component of all the biomass pools. This is the 'merchantable' component of biomass.Above-ground forest biomass accounts for between 70% to 90% of total forest biomass [2]. While soil organic matter holds two to three times more carbon than biomass on a global scale, much of the soil carbon is more protected and not easily oxidised [3]. On the other hand, above-ground biomass is in a continuous state of flux due to fire, logging, storms, landuse changes, etc., and thus contributes to atmospheric carbon fluxes to a much greater extent and so is of much greater interest. Due to this dynamism of above-ground biomass, it is necessary to monitor it continuously and not measure once and forget.While detailed estimations of biomass is necessary for accurate carbon accounting, reliable estimation methods are few. Accurate estimates of stored carbon (biomass as dry weight is 50% carbon [4] and understanding sources and sinks can improve the accuracy of carbon flux models and thus lead to better projections of climate change and impacts. Initiatives such as Reducing Emissions from Deforestation and Forest Degradation (REDD) and REDD+ also rely heavily on above-ground biomass estimates [5,6]. REDD+ includes financing schemes and incentives with the aim of mitigating climate change by reducing deforestation and forest degradation through sustainable forest management and conservation, and enhancement...

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Evaluation of approaches to estimating aboveground biomass in Southern pine forests using SIR-C data

Cited by 97 publications

References 24 publications

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Remote Sensing of Above-Ground Biomass

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