Retrieval of Effective Leaf Area Index in Heterogeneous Forests With Terrestrial Laser Scanning

Zheng, Guang; Moskal, L. Monika; Kim, Sung Hyun

doi:10.1109/tgrs.2012.2205003

Cited by 97 publications

(59 citation statements)

References 53 publications

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“…(1) and is actually an exponential distribution with respect to LAI L. Hence, the Poisson and exponential distributions are two manifestations of the same light-vegetation principle (Jonckheere et al, 2004); their associated random variables are contact number n and leaf area L, respectively. A tiny-footprint laser beam is obscured by its first interception and is unable to reliably detect the remaining contacts, if any (Zheng et al, 2013). The laser range to its first interception is a random variable compatible with cumulative leaf area, thus, making the exponential form of the Poisson model a natural choice for inference in our MLE method.…”

Section: Discussionmentioning

confidence: 99%

“…(11) and (12) is a reasonable approximation, an assumption that has been implicitly made in many early studies (Zheng et al, 2013). Using the observation probabilities p hit Â i , r i and p miss Â i , r i of Eqs.…”

Section: Maximum Likelihood Estimator (Mle)mentioning

confidence: 99%

“…The first group encompasses many empirical models previously calibrated to correlate lidar metrics with biophysical variables (Jakubowski et al, 2013); however, correlative models for one region or time are not always transferrable to another. In contrast, the second group of methods turns to physics, signal processing, and heuristic rules, such as radiative transfer theories (Ni-Meister et al, 2008) and computer geometry algorithms (Zheng et al, 2013). These methods manifest improved transferability, but their design and applicability are limited by the validity of assumptions involved and also by the availability of prior knowledge, computer algorithms, and physical models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Zhao

Garcı́a

Liu

et al. 2015

Agricultural and Forest Meteorology

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Section: Discussionmentioning

confidence: 99%

Section: Maximum Likelihood Estimator (Mle)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Zhao

Garcı́a

Liu

et al. 2015

Agricultural and Forest Meteorology

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“…Terrestrial Laser Scanning (TLS), on the other hand, has been established as an efficient tool for acquiring 3D data used for a range of fine-scale forest studies (Liang et al, 2016), including stem mapping (Liang et al, 2012), tree height measurement (Olofsson et al, 2014), diameter estimation (Wang et al, 2017), stem curve retrieval , biomass calculation (Kankare et al, 2013), and leaf area index (LAI) estimation (Zheng et al, 2013). To better retrieve forest ecological attributes, it is often necessary to separate wood and leaf components of trees (Tao et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Wang

Hollaus

Pfeifer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an Erytrophleum fordii and a Betula pendula (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

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“…With this technique, sediment disturbance can be limited to the scanner location while remotely measuring undisturbed areas. While TLS has mostly been applied to industrial and engineering studies [21], terrestrial ecological applications include tree allometry [22], measurement of leaf area index [23], and characterizing peatland morphology [24,25]. To date, there are few examples of TLS studies in intertidal areas, but it has been used to measure marsh morphology [26], and tidal stream channels [27].…”

Section: Introductionmentioning

confidence: 99%

Terrestrial Laser Scanning Reveals Seagrass Microhabitat Structure on a Tideflat

Hannam

Moskal

2015

Remote Sensing

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Local-scale environmental heterogeneity can provide microhabitats that influence the spatial distribution of competing species. Microhabitats may influence the distribution of seagrasses along elevation gradients, but difficulty measuring intertidal microtopography has hindered quantification. Using a terrestrial laser scanner (TLS), we mapped and monitored a 1.84 ha study site for three years to understand spatial and temporal patterns of sediment microtopography. We performed high-accuracy GPS surveys and vegetation surveys of a native and an invasive seagrass. TLS provided sub-decimeter scale precision in digital elevation models (DEMs) of the tideflat. The location and shape of microtopographic features were stable from year to year, but the magnitude of local relief varied. A simple index of topographic context predicted the shoot density of the native seagrass, Zostera marina and the invasive seagrass, Zostera japonica, but the shoot density of the invasive seagrass was better predicted by the shoot density of Z. marina than by topographic context. Microtopographic relief at this site appears to exert a strong influence on the meter-scale distribution of seagrass. We demonstrate the potential for TLS mapping of habitat-relevant microtopography in a soft sediment intertidal environment where TLS faces substantial challenges but promises unique insights.Remote Sens. 2015, 7 3038

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Retrieval of Effective Leaf Area Index in Heterogeneous Forests With Terrestrial Laser Scanning

Cited by 97 publications

References 53 publications

Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Terrestrial Laser Scanning Reveals Seagrass Microhabitat Structure on a Tideflat

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