Stand biomass estimation method by canopy coverage for application to remote sensing in an arid area of Western Australia

Suganuma, Hideki; Abe, Yukuo; Taniguchi, Masahiko; Tanaka, Hiroyuki; Utsugi, Hajime; Kojima, Toshinori; Yamada, Kôichi

doi:10.1016/j.foreco.2005.10.014

Cited by 64 publications

(53 citation statements)

References 35 publications

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“…The results for the R veg are consistent with that of Suganuma et al [25] who used remote sensing derived canopy coverage to estimate stand biomass in forest species (Acacia aneura and Eucalyptus camaldulensis) in arid Western Australia. Similarly, Sousa et al [27], working on Quercus rotundifolia in southern Portugal, found that AGB as a function of crown horizontal projection had the same trend for individual trees and plots, even though estimation for individual trees produced large individual errors.…”

Section: Indicators Of Carbon Stocks (C T )supporting

confidence: 88%

“…This is in contrast to many forest inventory studies where there is canopy closure and, thus, it is not possible to differentiate between individual trees and height has a large contribution to overall tree mass. For both this study and that of Suganuma et al [25], the canopies were separated, thus we can suggest that canopy coverage approaches may be applicable to carbon inventory in open woodlands as well as shrubby systems. Similar relationships between canopy coverage and biomass have also been reported in the semiarid savanna of Sudan [53], and in semi-arid Senegal [54].…”

Section: Indicators Of Carbon Stocks (C T )mentioning

confidence: 69%

“…Normalized Difference Vegetation Index NDVI = (NIR − red)/(NIR + red) [ [25,42] Note: NIR is the reflectance of the near-infrared band, red is the reflectance of the red band, green is the reflectance of the green band, and blue is the reflectance of the blue band. The radiometrically corrected DMSI images are used for vegetation index calculations.…”

Section: Vegetation Index Formula Referencementioning

confidence: 99%

“…Estimates of canopy coverage derived from remote sensing images have also been applied as a proxy for calculating individual tree and stand biomass [25,26]. For example, Sousa et al [27] found that the tree canopy horizontal projection derived from QuickBird satellite images produced highly accurate estimates of AGB of Quercus rotundifolia at both individual and plot scales.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

et al. 2017

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Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is required. This study investigated the utility of high-resolution airborne images (Digital Multi Spectral Imagery (DMSI)) obtained in two seasons to estimate carbon stocks at the plant-and stand-scale. Pixel-scale vegetation indices, sub-pixel fractional green vegetation cover for individual plants, and estimates of the fractional coverage of the grazing plants within entire plots, were extracted from the high-resolution images. Carbon stocks were correlated with both canopy coverage (R 2 : 0.76-0.89) and spectral-based vegetation indices (R 2 : 0.77-0.89) with or without the use of the near-infrared spectral band. Indices derived from the dry season image showed a stronger correlation with field measurements of carbon than those derived from the green season image. These results show that in semi-arid environments it is better to estimate saltbush biomass with remote sensing data in the dry season to exclude the effect of pasture, even without the refinement provided by a vegetation classification. The approach of using canopy cover to refine estimates of carbon yield has broader application in shrublands and woodlands.

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Section: Indicators Of Carbon Stocks (C T )supporting

confidence: 88%

Section: Indicators Of Carbon Stocks (C T )mentioning

confidence: 69%

Section: Vegetation Index Formula Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

et al. 2017

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“…According to our discussion above, several problems are, however, related to the use of optical RS data in combination with empirical approaches to retrieve structural variables. Particularly in closed dense forests with a complex and multi-layered canopy [25,120], spectral signals and eventually derived empirical models saturate. Further, calculated surface reflectances are typically affected by shadowing and BRDF effects, contributions of the soil background, and atmospheric conditions.…”

Section: Strategies For Improved Simultaneous Retrieval Of Biochemicamentioning

confidence: 99%

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

et al. 2015

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Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R 2 = 0.81, rRMSE = 10%), basal area (R 2 = 0.68, rRMSE = 20%), and timber volume (R 2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.

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Prediction intervals: Placing real bounds on regression‐based allometric estimates of biomass

Ward

2015

Biometrical J

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Biomass allometry studies routinely assume that regression models can be applied across species and sites, and that goodness of fit of a regression model to its derivation dataset indicates both the relevance of the model to a new dataset and the likely error. Assuming that a model is relevant for a new sample, a prediction interval is a useful error measure for stand mass. Prediction coverage tests whether the model and hence the interval are appropriate in the new sample. Data for three similar shrubby species from four similar sites were combined in various ways to test the impact of varying levels of biodiverse heterogeneity on the performance of the four models most commonly used in published biomass studies. No one model performed consistently well predicting new data, and validation checks were not good indicators of prediction coverage. The highly variable results suggest that the common models might contain insufficient variables. Euclidean distance was used to quantify the relative similarity of samples as a possible means of estimating prediction coverage; it proved unsuccessful with these data.

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Stand biomass estimation method by canopy coverage for application to remote sensing in an arid area of Western Australia

Cited by 64 publications

References 35 publications

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

Prediction intervals: Placing real bounds on regression‐based allometric estimates of biomass

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