Martin van Leeuwen

Retrieval of forest structural parameters using LiDAR remote sensing

In this paper, a literature overview is presented on the use of laser rangefinder techniques for the retrieval of forest inventory parameters and structural characteristics. The existing techniques are ordered with respect to their scale of application (i.e. spaceborne, airborne, and terrestrial laser scanning) and a discussion is provided on the efficiency, precision, and accuracy with which the retrieval of structural parameters at the respective scales has been attained. The paper further elaborates on the potential of LiDAR (Light Detection and Ranging) data to be fused with other types of remote sensing data and it concludes with recommendations for future research and potential gains in the application of LiDAR for the characterization of forests.

Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation.

Canopy surface reconstruction from a LiDAR point cloud using Hough transform

Tree height and canopy volume are critical forestry parameters that are used to derive estimates of growth, carbon sequestration, standing timber volume, and biomass. Through the use of light detection and ranging, these attributes can be estimated rapidly over large areas. At the stand level, estimates of these attributes have been derived successfully from canopy height models. However, a number of challenges identified in using canopy height models remain, such as correcting for height underestimation and canopy surface irregularities, such as data pits and holes that may result from acquisition and/or post-processing, and consistent delineation of tree crowns – all of which can limit the accurate retrieval of individual tree and crown attributes. In this letter, a novel canopy model is proposed in which individual tree crowns are represented as objects for which delineations can be derived through geometric operations. The technique is based on fitting simple geometric shapes to the raw light detection and ranging point cloud and thereby compensates for this underestimation, reduces data size, and allows effective and efficient modelling at the individual tree level.

Bias in lidar-based canopy gap fraction estimates

Leaf area index and canopy gap fraction (GF) provide important information to forest managers regarding the ecological functioning and productivity of forest resources. Traditional measurements such as those obtained from hemispherical photography (HP) measure solar irradiation, penetrating the forest canopy, but do not provide information regarding the three-dimensional canopy structure. Terrestrial laser scanning (TLS) is an active sensor technology able to describe structural forest attributes by measuring interceptions of emitted laser pulses with the canopy and is able to record the spatial distribution of the foliage in three dimensions. However, due to the beam area of the laser, interceptions are detected more frequently than using conventional HP, and GF is typically underestimated. This study investigates the effects of laser beam area on the retrieval of GF by using morphological image processing to describe estimation bias as a function of canopy perimeters. The results show that, using canopy perimeter, improvements in correlation between HP and TLS can be achieved with an increase in the coefficient of determination R 2 up to 28% (from an original R 2 of 0.66 to an adjusted R 2 of 0.85).

Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity

Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at spatial scales much finer than the eddy-covariance footprint. Scaling these contributions to the stand level requires a consideration of the heterogeneity in the canopy radiation field. This paper presents a stochastic ray tracing approach to predict the probabilities of light absorption from over a thousand hemispherical directions by thousands of individual scene elements. Once a look-up table of absorption probabilities is computed, dynamic illumination conditions can be simulated in a computationally realistic time, from which stand-level gross primary productivity can be obtained by integrating photosynthetic assimilation over the scene. We demonstrate the method by inverting a leaf-level photosynthesis model with eddy-covariance and meteorological data. Optimized leaf photosynthesis parameters and canopy structure were able to explain 75% of variation in eddy-covariance gross primary productivity estimates, and commonly used parameters, including photosynthetic capacity and quantum yield, fell within reported ranges. Remaining challenges are discussed including the need to address the distribution of radiation within shoots and needles.