The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire

Anderson, Jeanne E.; Martin, Mary E.; Smith, Mike; Dubayah, Ralph; Hofton, M. A.; Hyde, Peter; Peterson, Birgit; Blair, J. B.; Knox, Robert G.

doi:10.1016/j.rse.2006.07.001

Cited by 109 publications

(54 citation statements)

References 29 publications

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“…Previous estimates of temperate forest biomass using purpose-built methods have produced a RMSE of 58.0 Mg/ha [53], 28.9 Mg/ha [54] and 31.6 Mg/ha [55]. For comparison, doubling our RMSE for ACD (since we take ACD to be half the biomass) gives a value of 42.8 Mg/ha, which suggests that our model can estimate ACD or biomass reasonably well from SDD predictions.…”

Section: Assessing Predictions Of Stand Attributesmentioning

confidence: 77%

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

et al. 2017

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Abstract:We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R 2 ≥ 0.75, and another 23% had 0.5 ≤ R 2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m 2 /ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution.

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Section: Assessing Predictions Of Stand Attributesmentioning

confidence: 77%

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

et al. 2017

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“…Waveform lidar instruments digitize the entire outgoing and return signal to provide waveforms, from which various parameters such as subcanopy topography, canopy height, foliage profiles and vertical heterogeneity may be derived (Blair et al, 1999;Dubayah et al, 2000). Waveform metrics from small and large footprint lidar have been used to predict biomass in tropical (Clark et al, 2004;Drake et al, 2002b) and temperate forests (Anderson et al, 2005;Hyde et al, 2005;Lefsky et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Clark et al, 2005;Martin et al, 1998). Studies have suggested that spectral attributes (Bergen et al, 2006;Ustin et al, 2004) and species composition (Anderson et al, 2005;Rosenqvist, et al, 2003) from hyperspectral data could improve biomass estimates in conjunction with lidar. However, it is still unclear as to how biophysical and biochemical attributes from hyperspectral data relate with structural attributes from lidar.…”

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confidence: 99%

Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion

Swatantran

Dubayah

Roberts

et al. 2011

Remote Sensing of Environment

197

117

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a b s t r a c t a r t i c l e i n f oIn this paper, we explored fusion of structural metrics from the Laser Vegetation Imaging Sensor (LVIS) and spectral characteristics from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) for biomass estimation in the Sierra Nevada. In addition, we combined the two sensors to map species-specific biomass and stress at landscape scale. Multiple endmember spectral mixture analysis (MESMA) was used to classify vegetation from AVIRIS images and obtain sub-pixel fractions of green vegetation, non-photosynthetic vegetation, soil, and shade. LVIS metrics, AVIRIS spectral indices, and MESMA fractions were compared with field measures of biomass using linear and stepwise regressions at stand (1 ha) level. AVIRIS metrics such as water band indices and shade fractions showed strong correlation with LVIS canopy height (r 2 = 0.69, RMSE = 5.2 m) and explained around 60% variability in biomass. LVIS variables were found to be consistently good predictors of total and species specific biomass (r 2 = 0.77, RMSE = 70.12 Mg/ha). Prediction by LVIS after species stratification of field data reduced errors by 12% (r 2 = 0.84, RMSE = 58.78 Mg/ha) over using LVIS metrics alone. Species-specific biomass maps and associated errors created from fusion were different from those produced without fusion, particularly for hardwoods and pines, although mean biomass differences between the two techniques were not statistically significant. A combined analysis of spatial maps from LVIS and AVIRIS showed increased water and chlorophyll stress in several high biomass stands in the study area. This study provides further evidence that lidar is better suited for biomass estimation, per se, while the best use of hyperspectral data may be to refine biomass predictions through a priori species stratification, while also providing information on canopy state, such as stress. Together, the two sensors have many potential applications in carbon dynamics, ecological and habitat studies.

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“…In recent years, Light Detection and Ranging (Lidar) has become one of the most promoted remote sensing techniques for the assessment of various aspects of forest ecosystems, for instance for investigating the linkages between forest structure and biodiversity or for quantifying forest biophysical parameters such as canopy height (CH), growing stock volume (GSV) and aboveground biomass [1][2][3][4][5][6]. As yet, Lidar is a great sampling tool that allows, with optimized sampling strategies, the estimation of regional means and variance of forest resources and structure [7,8].…”

Section: Introductionmentioning

confidence: 99%

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

et al. 2012

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Abstract:We have investigated for forest plantations in Chile the stand-level retrieval of canopy height (CH) and growing stock volume (GSV) using Airborne Laser Scanner (ALS), ALOS PALSAR and Landsat. In a two-stage up-scaling approach, ensemble regression tree models (randomForest) were used to relate a suite of ALS canopy structure indices to stand-level in situ measurements of CH and GSV for 319 stands. The retrieval of CH and GSV with ALS yielded high accuracies with R 2 s of 0.93 and 0.81, respectively. A second set of randomForest models was developed using multi-temporal ALOS PALSAR intensities and repeat-pass coherences in two polarizations as well as Landsat data as predictor and stand-level ALS based estimates of CH and GSV as response variables. At three test sites, the retrieval of CH and GSV with PALSAR/Landsat reached promising accuracies with R 2 s in the range of 0.7 to 0.85. We show that the combined use of multi-temporal PALSAR intensity, coherence and Landsat yields higher retrieval accuracies than the retrieval with any of the datasets alone. Potential limitations for the large-area application of the fusion approach included (1) the low sensitivity of ALS first/last return data to forest horizontal structure, affecting the retrieval of GSV in less managed types of forest, and (2) the dense ALS sampling required to achieve high retrieval accuracies at larger scale.

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The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire

Cited by 109 publications

References 29 publications

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

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