Tree and Forest Measurement

West, Philip W.

doi:10.1007/978-3-662-05436-9

Cited by 61 publications

(38 citation statements)

References 39 publications

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“…Ground elevation was derived to: (1) evaluate the GLAS altimetry information and to (2) to assess the agreement between the ground return location approaches and the true terrain. The elevation referring to the GLA14 ground return was used as first measure of terrain elevation (from now on GLA14 elevation).…”

Section: Calculation Of Waveform Parametersmentioning

confidence: 99%

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Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape

Hilbert

Schmullius

2012

Remote Sensing

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This study explores ICESat/GLAS waveform data in Thuringian Forest, a low mountain range located in central Germany. Lidar remote sensing has been proven to directly derive tree height as a key variable of forest structure. The GLAS signal is, however, very sensitive to surface topography because of the large footprint size. This study therefore focuses on forests in a mountainous area to assess the potential of GLAS data to derive terrain elevation and tree height. The work enhances the empirical knowledge about the interaction between GLAS waveform and landscape structure regarding a special temperate forest site with a complex terrain. An algorithm to retrieve tree height directly from GLA01 waveform data is proposed and compared to an approach using GLA14 Gaussian parameters. The results revealed that GLAS height estimates were accurate for areas with a slope up to 10° whereas waveforms of areas above 15° were problematic. Slopes between 10-15° have been found to be a critical crossover. Further, different waveform shape types and landscape structure classes were developed as a new possibility to explore the waveform in its whole structure. Based on the detailed analysis of some waveform examples, it could be demonstrated that the waveform shape can be regarded as a product of the complex interaction between surface and canopy structure. Consequently, there is a great variety of waveform shapes which in turn considerably hampers GLAS tree height extraction in areas with steep slopes and complex forest conditions.

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Section: Calculation Of Waveform Parametersmentioning

confidence: 99%

“…Plants store the greenhouse gas carbon dioxide in their above and belowground biomass. Forests can, thus, be considered as one of the most significant terrestrial carbon sinks [1,2]. Biomass estimations refer to the horizontal and vertical forest structure.…”

Section: Introductionmentioning

confidence: 99%

Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape

Hilbert

Schmullius

2012

Remote Sensing

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“…Basal area is frequently reported as a ratio of the area occupied by tree stems to a specific unit of area, such as a hectare. It is calculated for individual trees, but is often used by foresters to estimate tree volume on a stand level and is correlated with aboveground biomass [12][13][14][15]. Basal area is typically estimated at the stand level, where a stand is defined as a group of trees (comprised of a single tree species or a mixture of different species) with similar ages, composition and structure [10].…”

Section: In Situ Forest Sampling and Basal Areamentioning

confidence: 99%

“…Within the study area, in situ sampling was conducted randomly within a stratum of interest, a practice widely used by foresters [15,42]. A GIS-based site suitability model, described in detail in Section 3.1, was developed to map the stratum and to select a random set of field sample plots according to topography, vegetation type, proximity to a road and land ownership.…”

Section: In Situ Reference Datamentioning

confidence: 99%

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Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m 2 /ha RMSE when the average basal area across the study area was ~12 m 2 /ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

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Biomass

2013

Measurements for Terrestrial Vegetation

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Tree and Forest Measurement

Cited by 61 publications

References 39 publications

Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape

Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Biomass

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