Ghost is unavoidable in marine seismic data acquisition, limiting the bandwidth of useful information and reducing the resolution of imaging results. In the deghosting method, the 3D algorithms can better eliminate the 3D effect compared with the conventional 2D algorithm. However, the 3D algorithm requires dense sampling in the crossline direction, which means greater storage space and computational cost. In this paper, we propose a pseudo-3D deghosting method to get rid of the limitation of dense spatial sampling. We arrange the conventional 2D multi-shot gathers in the form of 3D data cube by time, offset and shot number to achieve simultaneous deghosting for multi-shot gathers; We also introduce a sparse transform based L1 constraint to avoid local minimum. The basic idea of our method is including the information of the common offset gathers (COGs) into the deghosting to improve the inversion accuracy. The proposed method is easy to implemented without any pre-processing, and field example demonstrates the effectiveness of the proposed method.

Energy harvesting from ambient energy sources has gained increased attention due to its advantage of less maintenance and for removing the dependency on batteries in IWSNs. However, due to the dynamic nature of ambient energy sources and the position of harvesting nodes, energy-harvesting is not always available, resulting in unbalanced energy-harvesting in IWSNs. Although, some battery operated nodes are used, the limited lifetime problem still exists due to the non-harvesting nodes. In this article, we propose a scheme that combines the advantages of energy-harvesting and sleep-scheduling in hybrid solar energy-harvesting IWSNs and non-harvesting nodes. We present a model of the harvesting-node using a three-state Markov chain. The proposed harvest-use-store type architecture aims to guarantee an energy-neutral condition to avoid energy harvesting nodes from early energy exhaustion. The proposed approach allows to wake up a few more non-harvesting nodes to handle network coverage and connectivity during less-energy-harvesting intervals. Similarly, non-harvesting nodes are allowed to sleep by increasing the default transmission range of the solar-harvesting nodes during higher energy harvesting intervals prolonging network lifetime.

The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources.

The causes of landslides are manifold and highly influenced by multiple interacting natural and anthropogenic factors. In particular human induced land cover changes, such as deforestation and afforestation are known to strongly influence slope stability. Thus, we investigate the understanding of differences between forested and non-forested conditions of an area is crucial in order to develop sustainable preventive countermeasures. One possibility to evaluate the influence of biomass changes on landslide activity is to apply physically based slope stability models where the dynamic influence of spatially and temporally variable vegetation areas on soil strength and hydrology is explicitly included. Some of these models also require detailed information on biomass related parameters (e.g. wood and crown volume, weight, Leaf Area Index) as well as surface and subsurface conditions. Newly developed algorithms allow deriving biomass parameters from highly resolved multi-temporal 3D Airborne Laser Scanning (ALS). This allows an improved parameterization of hydro-mechanical slope stability models since it accounts for the spatiotemporal variability in vegetation conditions. The BioSLIDE project aims to combine vegetation related parameters derived from ALS data with physically based slope stability modelling to allow a better understanding of geomorphic interdependencies at regional scale. The objective of this paper is to evaluate possibilities and potential limitations of an inclusion of ALS-derived biomass information within dynamic physically based hydro-mechanical slope stability modelling. Hereto both synthetic and real case study data will be used. This interdisciplinary approach is expected to improve spatio-temporal scenarios of anthropogenic effects and environmental changes on landslide activity.