Dredging fundamentally reshapes the ecological significance of 3D terrain features for fish in estuarine seascapes

Context Landscape modification alters the condition of ecosystems and the structure of terrain, with widespread impacts on biodiversity and ecosystem functioning. Seafloor dredging impacts a diversity of flora and fauna in many coastal landscapes, and these processes also transform three-dimensional terrain features. The potential ecological significance of these terrain changes in urban seascapes has, however, not been investigated. Objectives We examined the effects of terrain variation on fish assemblages in 29 estuaries in eastern Australia, and tested whether dredging changes how fish associate with terrain features. Methods We surveyed fish assemblages with baited remote underwater video stations and quantified terrain variation with nine complementary metrics (e.g. depth, aspect, curvature, slope, roughness), extracted from bathymetry maps created with multi-beam sonar. Results Fish diversity and abundance were strongly linked to seafloor terrain in both natural and dredged estuaries, and were highest in shallow waters and near features with high curvature. Dredging, however, significantly altered the terrain of dredged estuaries and transformed the significance of terrain features for fish assemblages. Abundance and diversity switched from being correlated with lower roughness and steeper slopes in natural estuaries to being linked to features with higher roughness and gentler slopes in dredged estuaries. Conclusions Contrasting fish-terrain relationships highlight previously unrecognised ecological impacts of dredging, but indicate that plasticity in terrain use might be characteristic of assemblages in urban landscapes. Incorporating terrain features into spatial conservation planning might help to improve management outcomes, but we suggest that different approaches would be needed in natural and modified landscapes.

An Improved Coordinate Registration for Over-the-Horizon Radar Using Reference Sources

In the target localization of skywave over-the-horizon radar (OTHR), the error of the ionospheric parameters is one main error source. To reduce the error of ionospheric parameters, a method using both the information of reference sources (e.g., terrain features, ADS-B) in ground coordinates and the corresponding OTHR measurements is proposed to estimate the ionospheric parameters. Describing the ionospheric electron density profile by the quasi-parabolic model, the estimation of the ionospheric parameters is formulated as an inverse problem, and is solved by a Markov chain Monte Carlo method due to the complicated ray path equations. Simulation results show that, comparing with using the a prior value of the ionospheric parameters, using the estimated ionospheric parameters based on four airliners in OTHR coordinate registration process, the ground range RMSE of interested targets is reduced from 2.86 to 1.13 km and the corresponding improvement ratio is up to 60.39%. This illustrates that the proposed method using reference sources is able to significantly improve the accuracy of target localization.

Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring.

An insect-scale robot reveals the effects of different body dynamics regimes during open-loop running in feature-laden terrain

The transition from the lab to natural environments is an archetypal challenge in robotics. While larger robots can manage complex limb-ground interactions using sensing and control, such strategies are difficult to implement on small platforms where space and power are limited. The Harvard Ambulatory Microrobot (HAMR) is an insect-scale quadruped capable of effective open-loop running on featureless, hard substrates. Inspired by the predominantly feedforward strategy of rapidly-running cockroaches on uneven terrain [Sponberg, 2007], we used HAMR to explore open-loop running on two 3D printed heterogeneous terrains generated using fractional Brownian motion. The ``pocked'' terrain had foot-scale features throughout while the ``jagged'' terrain features increased in height in the direction of travel. We measured the performance of trot and pronk gaits while varying limb amplitude and stride frequency. The frequencies tested encompassed different dynamics regimes: body resonance (10-25~Hz) and kinematic running (30-40~Hz), with dynamics typical of biological running and walking, respectively, and limb-transmission resonance (45-60~Hz). On the featureless and pocked terrains, low mechanical cost-of-transport (mCoT) kinematic running combinations performed best without systematic differences between trot and pronk; indicating that if terrain features are not too tall, a robot can transition from homo- to heterogeneous environments in open-loop. Pronk bypassed taller features than trot on the jagged terrain, and higher mCoT, lower frequency running was more often effective. While increasing input power to the robot improved performance in general, lower frequency pronking on jagged terrain allowed the robot to bypass taller features compared with the same input power at higher frequencies. This was correlated with the increased variation in center-of-mass orientation occurring at frequencies near body resonance. This study established that appropriate choice of robot dynamics, as mediated by gait, frequency, and limb amplitude, can expand the terrains accessible to microrobots without the addition of sensing or closed-loop control.

Co-varying effects of vegetation structure and terrain attributes are responsible for soil respiration spatial patterns in a sandy forest–steppe transition zone

Forest–steppe habitats in central Hungary have contrasting canopy structure with strong influence on the spatiotemporal variability of ecosystem functions. Canopy differences also co-vary with terrain feature effects, hampering the detection of key drivers of carbon cycling in this threatened habitat. We carried out seasonal measurements of ecosystem functions (soil respiration and leaf area index), microclimate and soil variables as well as terrain features along transects for 3 years in poplar groves and the surrounding grasslands. We found that the terrain features and the canopy differences co-varyingly affected the abiotic and biotic factors of this habitat. Topography had an effect on the spatial distribution of soil organic carbon content. Canopy structure had a strong modifying effect through allocation patterns and microclimatic conditions, both affecting soil respiration rates. Due to the vegetation structure difference between the groves and grasslands, spatial functional diversity was observed. We found notably different conditions under the groves with high soil respiration, soil water content and leaf area index; in contrast, on the grasslands (especially in E–SE–S directions from the trees) soil temperature and vapor pressure deficit showed high values. Processes of aridification due to climate change threaten these habitats and may cause reduction in the amount and extent of forest patches and decrease in landscape diversity. Owing to habitat loss, reduction in carbon stock may occur, which in turn has a significant impact on the local and global carbon cycles.

Satellite and aerial imagery geo-referencing using ground features

The method of geo-referencing satellite- and aerial imagery using reference points, linear and non-linear features, and segments of geodetic tracks as elements of a plan-altitude basis is discussed in this article. The method can be used for any mathematical model of satellite- and aerial imagery. The parametric description of the features, that can be used for the geo-referencing and various mathematical models of the above-mentioned imagery are presented. The mathematical formulation of the matter of satellite and aerial imagery geo-referencing using terrain objects and reference points is presented. A list of linear and non-linear features that can be included in a high-raised basis along with reference points is made. A generalized algorithm for geo-referencing satellite and aerial imagery using reference points and terrain features is given. The algorithm includes making a nonlinear system of equations for reference points and items, linearizing the system and solving by the sequential approximation technique according to the least squares method. An example of clarifying the satellite RPC-model and aerial imagery using reference points, linear and non-linear features is given. The advantages of the proposed method of using features created according to the measurements on satellite and aerial imagery compared with method, when the model of the feature is created according to the measurements on the ground are described.

Fast Geo-Location Method Based on Panoramic Skyline in Hilly Area

Localization method based on skyline for visual geo-location is an important auxiliary localization method that does not use a satellite positioning system. Due to the computational complexity, existing panoramic skyline localization methods determine a small area using prior knowledge or auxiliary sensors. After correcting the camera orientation using inertial navigation sensors, a fine position is achieved via the skyline. In this paper, a new panoramic skyline localization method is proposed that involves the following. By clustering the sampling points in the location area and improving the existing retrieval method, the computing efficiency of the panoramic skyline localization is increased by fourfold. Furthermore, the camera orientation is estimated accurately from the terrain features in the image. Experimental results show that the proposed method achieves higher localization accuracy and requires less computation for a large area without the aid of external sensors.

A Survey of Remote Sensing Image Classification Techniques Using AI Techniques

Subsequently Terrain Classification is one of the most liberal aspects of Remote Sensing in the field of Artificial Intelligence. Remote sensing image classification is the wide range area which first sense the part of image that is going to be classified afterwards classification takes place. The purpose of remote sensing is to get information out from the object without being coming in a direct contact with the object. The purpose of the classification process is the categorization of all the pixels in an image into several land cover classes, as well as the themes. The data that is to be categorized is then used to produce maps that are thematic of the land cover present in an image. Image classification enables the grouping of pixels to represent the coverage features of land (can be urban, forested, and agricultural and may also include other varieties of Terrain features). Image classification make use the reflectance statistics for determining the pixels and responds to Terrain features as well. There are several classification techniques which would use to manipulate the persistence for uncertainty, imprecision and cost effective solutions.

Analysis of Landslide Occurrence using DTM-Based Weighted Overlay: A Case Study in Tropical Mountainous Forest of Cameron Highlands, Malaysia

Landslides are massive natural disasters all around the world. In general, our society is only concerned with the landslides that can cause economic distress and impact human life. Landslides in remote areas such as mountainous forests have often been neglected. Referring to the historical disaster event, forest landslides have vast potential to cause unexpected ecological and social damage. This study reveals the terrain characteristics of the complex mountainous forest area of Cameron Highlands (CH), Malaysia, and demonstrates an approach to evaluate the terrain sensitivity of CH. Terrain assessment can be a powerful tool to prevent or reduce the risk of landslides. In this study, terrain features; elevation, slope gradient, aspect, topography wetness index (TWI), and length-slope factor (LS Factor) were extracted using a Digital Terrain Model (DTM) at 10 m resolution. The selected terrain features were incorporated using weighted overlay analysis to derive a terrain sensitivity map (TSM) using SAGA GIS software. The map identified five types of terrain sensitivity classified as very high sensitivity, high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity; these areas have a coverage of 0.78 km2, 114.31 km2, 107.50 km2, 102.99 km2, and 0.65 km2, respectively. The findings suggest that the sensitive areas are scattered throughout all of the mountainous forests of CH; thus, this enhanced the risk of landslide. Results showed 79.25% accuracy, which is satisfactory to be a guideline for forest management planning and assist decision making in the respective region.