Forest Structural Estimates Derived Using a Practical, Open-Source Lidar-Processing Workflow

Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used the lidR R software package, a custom forest metrics function in R, and a distributed cloud computing environment to process 11 TB of airborne lidar data covering ~22,900 km2 into 28 height, cover, and density metrics. We combined these lidar outputs with field plot data to model basal area, trees per acre, and quadratic mean diameter. We compared lidar-only models with models informed by spectral imagery only, and lidar and spectral imagery together. We found that lidar models outperformed spectral imagery models for all three metrics, and combination models performed slightly better than lidar models in two of the three metrics. One lidar variable, the relative density of low midstory canopy, was selected in all lidar and combination models, demonstrating the importance of midstory forest structure in the study area. In general, this open-source lidar-processing workflow provides a practical, scalable option for estimating structure over large, forested landscapes. The methodology and systems used for this study offered us the capability to process large quantities of lidar data into useful forest structure metrics in compressed timeframes.

The Three-Dimensional Structure of Mediterranean Shallow Rocky Reefs: Use of Photogrammetry-Based Descriptors to Assess Its Influence on Associated Teleost Assemblages

In the Mediterranean Sea, shallow rocky reefs and the associated three-dimensional (3D) structure support rich and abundant communities; they are therefore of functional importance, in particular for the renewal of fish stocks. However, these habitats and their functions are likely to be altered by anthropogenic pressures inducing habitat transformations. It is therefore necessary to assess their 3D structure, their transformations and relationship to communities, especially for management and conservation purposes. In this article we aimed (i) to compare two methods that quantify the metrics of the 3D structure (rugosity) of shallow rocky reefs (chain-and-tape method and photogrammetry), and (ii) to quantify the possible links between this habitat structure and the fish assemblages. We found that photogrammetry and the chain-and-tape method yielded a similar estimate of rugosity, but photogrammetry was the most efficient method in terms of measurement quality and time (when considering in-water acquisition). This method also displayed the best repeatability. The 3D habitat descriptors (mean surface rugosity, variation of surface rugosity, and depth) differed significantly between the studied sites and were therefore included as covariables. Total fish abundance and species richness increased with higher mean surface rugosity. In addition, the composition of fish assemblages was significantly influenced by surface rugosity, although this effect was modulated by depth. When focusing on specific taxa, neither density patterns nor size class distributions displayed clear patterns in relation to rugosity metrics. However, this study demonstrated that spatial variability of teleost fish assemblages can be explained by habitat rugosity which probably increases the number of shelters and food resources, and therefore improves chances of survival. In addition, our study has shown that photogrammetry is an appropriate method to assess 3D structure metrics in a temperate rocky reef.

Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning

We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applications, ranging from computations of crucial aspects of the effective field theory of string compactifications such as the canonical normalizations for Yukawa couplings, and the massive string spectrum which plays a crucial role in swampland conjectures, to mirror symmetry and the SYZ conjecture. In the case of SU(3) structure, our machine learning approach allows us to engineer metrics with certain torsion properties. Our methods are demonstrated for Calabi-Yau and SU(3)-structure manifolds based on a one-parameter family of quintic hypersurfaces in ℙ4.

Quantum gravity and Riemannian geometry on the fuzzy sphere

We study the quantum geometry of the fuzzy sphere defined as the angular momentum algebra $$[x_i,x_j]=2\imath \lambda _p \epsilon _{ijk}x_k$$ [ x i , x j ] = 2 ı λ p ϵ ijk x k modulo setting $$\sum _i x_i^2$$ ∑ i x i 2 to a constant, using a recently introduced 3D rotationally invariant differential structure. Metrics are given by symmetric $$3 \times 3$$ 3 × 3 matrices g and we show that for each metric there is a unique quantum Levi-Civita connection with constant coefficients, with scalar curvature $$ \frac{1}{2}(\mathrm{Tr}(g^2)-\frac{1}{2}\mathrm{Tr}(g)^2)/\det (g)$$ 1 2 ( Tr ( g 2 ) - 1 2 Tr ( g ) 2 ) / det ( g ) . As an application, we construct Euclidean quantum gravity on the fuzzy unit sphere. We also calculate the charge 1 monopole for the 3D differential structure.

Derivating forest structure from Sentinel-1 time series to assist forest fire risk assessments

<p>The assessment of forest fire risk has recently gained interest in countries of Central Europe and the alpine region since the occurrence of forest fires is expected to increase with a changing climate. Information on forest fuel structure, which is related to forest structure, is a key component in such assessments. Forest structure information can be derived from airborne laser scanning (ALS) data, whose value for the derivation of respective metrics at a high accuracy level has been demonstrated in numerous studies over the last years.</p><p>Yet, the temporal resolution of ALS data is low as flight missions are typically carried out in time intervals of five to ten years in Central Europe. ALS-derived forest structure descriptors for fire risk assessments, therefore, are often outdated. Open access earth observation data offer the potential to fill these information gaps. Data provided by synthetic aperture radar (SAR) sensors, in particular, are of interest in this context since this technology has a known sensitivity to the vegetation structure and acquires data independent of weather or daylight conditions.</p><p>In our study, we investigate the potential to derive forest structure descriptors from time series of Sentinel-1 (S-1) SAR data for a deciduous forest site in the Eastern part of Austria. We focus on forest stand height and fractional cover, which is a measure for forest density, as both of these components impact forest fire propagation and ignition. The two structure metrics are estimated using a random forest (RF) model, which takes a total of 36 predictors as input, which we compute from the S-1 time series. The model is trained using ALS-derived structure metrics acquired during the same year as the S-1 data.</p><p>We estimated stand height with a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m at 100 m resolution, while the RMSE for the fractional cover estimation is 0.08 with a bias of zero at the same resolution. The spatial comparison of the structure predictions with the ALS reference further shows that the general structure is well reproduced. Yet, fine scale variations cannot be completely reproduced by the S1-derived structure products, and the height of tall stands and very dense canopy parts are underestimated. Due to the high correlation of the predicted values to the reference (Pearson’s R of 0.88 and 0.94 for the stand height and the fractional cover, respectively), we consider S-1 time series in combination with ALS data with low temporal resolution and machine learning techniques to be a reliable data source and workflow for regularly (e.g. < yearly) updating ALS structure information in an operational way.</p>

Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.

UAV-BASED SORGHUM GROWTH MONITORING: A COMPARATIVE ANALYSIS OF LIDAR AND PHOTOGRAMMETRY

Canopy height (CH) and leaf area index (LAI) provide key information about crop growth and productivity. A rapid and accurate retrieval of CH and LAI is critical for a variety of agricultural applications. LiDAR and RGB photogrammetry have been increasingly used in plant phenotyping in recent years thanks to the developments in Unmanned Aerial Vehicle (UAV) and sensor technology. The goal of this study is to investigate the potential of UAV LiDAR and RGB photogrammetry in estimating crop CH and LAI. To this end, a high resolution 32 channel LiDAR and RGB cameras mounted on DJI Matrice 600 Pro UAV were employed to collect data at sorghum fields near Maricopa, Arizona, USA. A series of canopy structure metrics were extracted using LiDAR and RGB photogrammetry-based point clouds. Random Forest Regression (RFR) models were established based on the UAV-LiDAR and photogrammetry-derived metrics and field-measured LAI. The results show that both UAV-LiDAR and RGB photogrammetry demonstrated promising accuracies in CH extraction and LAI estimation. Overall, UAV-LiDAR yielded superior performance than RGB photogrammetry in both low and high canopy density sorghum fields. In addition, Pearson’s correlation coefficient, as well as RFR-based variable importance analysis demonstrated that height-based metrics from both LiDAR and photogrammetric point clouds were more useful than density-based metrics in LAI estimation. This study proved that UAV-based LiDAR and photogrammetry are important tool in sustainable field management and high-throughput phenotyping, but LiDAR is more accurate than RGB photogrammetry due to its greater canopy penetration capability.