Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible

Monitoring gully change: a comparison of airborne and terrestrial laser scanning using a case study from Aratula, Queensland

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) technologies capture spatially detailed estimates of surface topography and when collected multi-temporally can be used to assess geomorphic change. The sensitivity and repeatability of ALS measurements to characterise geomorphic change in topographically complex environments such as gullies; however, remains an area lacking quantitative research. In this study, we captured coincident ALS and TLS datasets to assess their ability and synergies to detect geomorphic change for a gully located in Aratula, southeast Queensland, Australia. We initially used the higher spatial density and ranging accuracy of TLS to provide an assessment of the Digital Elevation Models (DEM) derived from ALS within a gully environment. Results indicated mean residual errors of 0.13 and 0.09\ua0m along with standard deviation (SD) of residual errors of 0.20 and 0.16\ua0m using pixel sizes of 0.5 and 1.0\ua0m, respectively. The positive mean residual errors confirm that TLS data consistently detected deeper sections of the gully than ALS. We also compared the repeatability of ALS and TLS for characterising gully morphology. This indicated that the sensitivity to detect change using ALS is substantially lower than TLS, as expected, and that the ALS survey characteristics influence the ability to detect change. Notably, we found that using one ALS transect (mean density of 5 points\ua0/\ua0m) as opposed to three transects increased the SD of residual error by approximately 30%. The supplied classification of ALS ground points was also demonstrated to misclassify gully features as non-ground, with minimum elevation filtering found to provide a more accurate DEM of the gully. The number and placement of terrestrial laser scans were also found to influence the derived DEMs. Furthermore, we applied change detection using two ALS data captures over a four year period and four TLS field surveys over an eight month period. This demonstrated that ALS can detect large scale erosional changes with head cutting of gully branches migrating approximately 10\ua0m upslope. In comparison, TLS captured smaller scale intra-annual erosional patterns largely undetectable by the ALS dataset with a large rainfall event coinciding with the highest volumetric change (net change\ua0>\ua046\ua0m). Overall, these findings reaffirm the importance of quantifying DEM errors and demonstrate that ALS is unlikely to detect subtle geomorphic changes

Analysis of multi-date MISR measurements for forest and woodland communities, Queensland, Australia

Many theoretical and applied studies have demonstrated that the anisotropic reflectance of the land surface is determined by the structural and optical properties of the land surface. The Multi-angle Imaging SpectroRadiometer (MISR) measures the anisotropic reflectance of the land surface thus has the potential to improve the operational mapping of canopy attributes in Queensland, one of the largest states in Australia. The aim of this study was to assess the relationship between foliage projective cover (FPC) and the shape of the bidirectional reflectance distribution function (BRDF) for a mixed grassland/woodland ecosystem, the Southern Brigalow Belt Biogeographic Region. A practical approach for deriving the surface bidirectional reflectance factor (BRF) from MISR "Local" mode data using existing MISR products is presented. The BRDF typology of the land surface was determined using shape parameters derived from inversion of the linear Ross-Thick Li-Sparse Reciprocal and the non-linear Rahman-Pinty-Verstracte (RPV) models against a time series of MISR "Local" mode surface BRF data. Following an evaluation of the models inversion error, the RPV model was examined for correlation with FPC, a canopy attribute routinely derived from Landsat data in Queensland. Our empirical analyses showed the MISR derived RPV model parameters were qualitatively related to spatial and temporal variations in vegetation structure in Queensland and the k parameter contained information independent of FPC. These results are consistent with published findings from other regions. (c) 2006 Elsevier Inc. All rights reserved

Relationship of MISR RPV parameters and MODIS BRDF shape indicators to surface vegetation patterns in an Australian tropical savanna

The global coverage of bidirectional reflectance distribution function (BRDF) products from the Multi-angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) has the potential to provide quantitative information on surface vegetation structure for input to process modelling and model–data assimilation schemes for regional and biome-scale assessment of carbon dynamics. The relationship of MISR Rahman–Pinty–Verstraete (RPV) model parameters, derived from inversion of MISR 275 m fine mode data, and BRDF shape indicators calculated from the latest MODIS 500 m MCD43 BRDF product to vegetation patterns in an Australian tropical savanna was examined for a time series covering the dry season period from April to October 2005. The bidirectional reflectance products were compared with geographical information system (GIS) data coverage combining floristic polygons with Landsat thematic mapper (TM) based estimates of canopy cover and height classes. The analysis showed that both the MISR RPV asymmetry parameter Θ and several MODIS BRDF shape indicators constructed using the red band were sensitive to local-scale anisotropic scattering and thus vegetation structure. The MISR RPV asymmetry parameter Θ showed consistent variation between grasslands, forest (closed canopies), and more open tree–grass mixtures over time. The MODIS indicators such as NDHD-R and ANIF-R produced distinctly different temporal profiles for major vegetation types such as rainforest, Melaleuca woodland, and Dichanthium grassland. These indices also showed evidence of consistent discrimination between eucalypt savanna types that varied in canopy cover and tree height. A clumping index calculated from NDHD-R for a single period (day 177 in a time series) showed good correspondence with savanna vegetation canopy properties but was insensitive to dense canopy rainforest vegetation. These results indicate there is potential for both MISR and MODIS BRDF products to provide a quantitative description of vegetation types in global tree–grass systems. However, there is a pressing need for further study to calibrate the responses with fine-scale structural data derived from both field measurement and light detection and ranging (lidar)

Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery

The detection of long term trends in woody vegetation in Queensland, Australia, from the Landsat-5 TM and Landsat-7 ETM+ sensors requires the automated prediction of overstorey foliage projective cover (FPC) from a large volume of Landsat imagery. This paper presents a comparison of parametric (Multiple Linear Regression, Generalized Linear Models) and machine learning (Random Forests, Support Vector Machines) regression models for predicting overstorey FPC from Landsat-5 TM and Landsat-7 ETM+ imagery. Estimates of overstorey FPC were derived from field measured stand basal area (RMSE 7.26%) for calibration of the regression models. Independent estimates of overstorey FPC were derived from field and airborne LiDAR (RMSE 5.34%) surveys for validation of model predictions. The airborne LiDAR-derived estimates of overstorey FPC enabled the bias and variance of model predictions to be quantified in regional areas. The results showed all the parametric and machine learning models had similar prediction errors (RMSE < 10%), but the machine learning models had less bias than the parametric models at greater than ~60% overstorey FPC. All models showed greater than 10% bias in plant communities with high herbaceous or understorey FPC. The results of this work indicate that use of overstorey FPC products derived from Landsat-5 TM or Landsat-7 ETM+ data in Queensland using any of the regression models requires the assumption of senescent or absent herbaceous foliage at the time of image acquisition

Aboveground Biomass Assessment Using GEDI Data across Diverse Forest Ecosystems in India

1042822325Swedish National Space AgencySwedish Research Council for Sustainable DevelopmentSwedish Kempe Foundatio

Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems

Leaf area index (LAI) is a primary descriptor of vegetation structure, function, and condition. It is a vegetation product commonly derived from earth observation data. Independently obtained ground-based LAI estimates are vital for global satellite product validation. Acceptable uncertainties of these estimates are guided by satellite product accuracy thresholds stipulated by the World Meteorological Organisation (WMO) and the Global Climate Observing System (GCOS). This study compared canopy openness, gap fraction and LAI estimates derived from ground-based instruments; the primary focus was to compare high- and low-resolution (HR and LR) digital hemispherical photography (DHP) to a terrestrial laser scanner (TLS), augmented with measurements using the LAI-2200 plant canopy analyser in a subset of plots. Additionally, three common DHP classification methods were evaluated including a manual supervised (S) classification, a global (G) binary automated threshold, and a two-corner (TC) automated threshold applied to mixed pixels only. Coincident measurements were collected across five diverse forest systems in Eastern Australia with LAI values ranging from 0.5 to 5.5. Canopy openness, gap fraction and LAI were estimated following standard operational field data collection and data processing protocols. A total of 75 method-to-method pairwise comparisons were conducted, out of which 37 had an RMSD≥0.5 LAI and 26 were significantly different (p0.75). Although TLS produced on average 55% higher openness and LAI than the HR-DHP (S) and (TC) classification methods, the strong coefficient of determination indicated the potential to calibrate these methods (R=0.88 and 0.79, respectively). Overall, results demonstrate a level of variability typically above the targeted uncertainty levels stipulated by the WMO and GCOS for satellite product validation. Further instrument calibration of TLS and improved DHP image capture and processing methods are expected to reduce these uncertainties

Erratum to: Terrestrial Laser Scanning for Plot-Scale Forest Measurement (Current Forestry Reports, (2015), 1, 4, (239-251), 10.1007/s40725-015-0025-5)

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