Lidar remote sensing of biophysical properties of tolerant northern hardwood forests

Lim, Kevin; Treitz, Paul; Baldwin, Ken; Morrison, I. K.; Green, Jim

doi:10.5589/m03-025

Cited by 223 publications

(139 citation statements)

References 18 publications

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“…height and volume) in the TLW was studied previously by Lim et al (2003a ). While the diameter at breast height (DBH) and airborne laser scanner data are used again in this study, the objective of this paper and the research carried out differ from previous efforts reported, as do the methods used and laser height metrics examined.…”

Section: Study Sitementioning

confidence: 52%

“…This type of model has been used successfully by others to model various forest biophysical properties (e.g. Means et al 1999, Naesset 2002, Lim et al 2003a. The natural logarithm transformations required in eq.…”

Section: Resultsmentioning

confidence: 99%

“…and yellow birch (Betula alleghaniensis Britton), was explored by Lim et al (2003a ). Above ground biomass was estimated using linearly transformed multiplicative models with the height corresponding to the maximum laser return (r 2 0/0.82), the mean height of all laser returns (r 2 0/0.78) and the mean height of all laser returns that exceeded an arbitrary intensity value threshold (r 2 0/0.85) within sample plots as individual predictor variables (Lim et al 2003a). Additional studies reporting on the capabilities of discrete return airborne laser scanning for the estimation of above ground biomass in conifer and mixed forests are required.…”

Section: Previous Researchmentioning

confidence: 99%

“…(1) full waveform laser scanning (Means et al 1999, Drake et al 2002a, 2002b; (2) discrete return laser profiling (Nelson et al 1988a(Nelson et al , 1988b(Nelson et al , 2004; and (3) discrete return laser scanning (Lim et al 2003a ).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Lim

Treitz

2004

Scandinavian Journal of Forest Research

185

127

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). Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Received Nov. 11, 2003. Accepted Aug. 10, 2004. Scand. J. For. Res. 19: 558 Á/570, 2004 A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r 2 ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in rootmean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha(1 , respectively.

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Section: Study Sitementioning

confidence: 52%

Section: Resultsmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Lim

Treitz

2004

Scandinavian Journal of Forest Research

185

127

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“…All z-values above 0.1 m were dropped. We further used 'lasthin.exe' to simulate the diameter of the laser beam at 135 mm in 450 m distance from the scan position [58]. To replicate each point eight times in a discrete circle, we used a radius of 0.075 m around every input point, and then increased the spatial resolution of the point clouds two-fold to 0.03 m. Noise and unclassified points were filtered from ground points.…”

Section: Terrestrial Laser Scanner (Tls) Canopy Height Model (Chm)mentioning

confidence: 99%

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

Nickless

Berger

et al. 2016

Forests

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Abstract:The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity to clouds addresses some of these problems, but many space borne studies are still limited by low quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date, few studies have explored the strength of TLS for vegetation structural mapping, and particularly few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR) within a 9 km 2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height, and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area. The results from model validation showed a significant linear relationship between TLS-derived predictors with field biomass, p < 0.05 and adjusted R 2 ranging between 0.56 for SAR to 0.93 for the TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics compared with non-transformed AGB. An assessment of the backscatter based on root mean square error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can be used in the detection of small changes in savanna vegetation over time.

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Responses of Multi‐Frequency Remote Sensing to Forest Biomass

Sinha

Santra

Sharma

et al. 2021

Advances in Remote Sensing for Natural Resource Monitoring

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Lidar remote sensing of biophysical properties of tolerant northern hardwood forests

Cited by 223 publications

References 18 publications

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Responses of Multi‐Frequency Remote Sensing to Forest Biomass

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