Finer topographic data improves distribution modeling of Picea crassifolia in the northern Qilian Mountains

The Qilian Mountains, a national key ecological function zone in Western China, play a pivotal role in ecosystem services. However, the distribution of its dominant tree species, Picea crassifolia (Qinghai spruce), has decreased dramatically in the past decades due to climate change and human activity, which may have influenced its ecological functions. To restore its ecological functions, reasonable reforestation is the key measure. Many previous efforts have predicted the potential distribution of Picea crassifolia, which provides guidance on regional reforestation policy. However, all of them were performed at low spatial resolution, thus ignoring the natural characteristics of the patchy distribution of Picea crassifolia. Here, we modeled the distribution of Picea crassifolia with species distribution models at high spatial resolutions. For many models, the area under the receiver operating characteristic curve (AUC) is larger than 0.9, suggesting their excellent precision. The AUC of models at 30 m is higher than that of models at 90 m, and the current potential distribution of Picea crassifolia is more closely aligned with its actual distribution at 30 m, demonstrating that finer data resolution improves model performance. Besides, for models at 90 m resolution, annual precipitation (Bio12) played the paramount influence on the distribution of Picea crassifolia, while the aspect became the most important one at 30 m, indicating the crucial role of finer topographic data in modeling species with patchy distribution. The current distribution of Picea crassifolia was concentrated in the northern and central parts of the study area, and this pattern will be maintained under future scenarios, although some habitat loss in the central parts and gain in the eastern regions is expected owing to increasing temperatures and precipitation. Our findings can guide protective and restoration strategies for the Qilian Mountains, which would benefit regional ecological balance.

Measuring user preferences and behaviour in a topographic immersive virtual environment(TopoIVE)of 2D and 3D urban topographic data

This paper investigates user preferences and behaviour associated with 2D and 3D modes of urban representation within a novel Topographic Immersive Virtual Environment(TopoIVE)created from official 1:10,000 mapping.Sixty participants were divided into two groups:the first were given a navigational task within a simulated city and the second were given the freedom to explore it.A Head-Mounted Display(HMD)Virtual Reality(VR)app allowed participants to switch between 2D and 3D representations of buildings with a remote controller and their use of these modes during the experiment was recorded.Participants performed mental rotation tests before entering the TopoIVE and were interviewed afterwards about their experiences using the app.The results indicate that participants preferred the 3D mode of representation overall,although preference for the 2D mode was slightly higher amongst those undertaking the navigational task,and reveal that different wayfinding solutions were adopted by participants according to their gender.Overall,the findings suggest that users exploit different aspects of 2D and 3D modes of visualization in their wayfinding strategy,regardless of their task.The potential to combine the functionality of 2D and 3D modes therefore offers substantial opportunities for the development of immersive virtual reality products derived from topographic datasets.

Fast modeling of gravity gradients from topographic surface data using GPU parallel algorithm

The gravity gradient is a secondary derivative of gravity potential,containing more high-frequency information of Earth’s gravity field.Gravity gradient observation data require deducting its prior and intrinsic parts to obtain more variational information.A model generated from a topographic surface database is more appropriate to represent gradiometric effects derived from near-surface mass,as other kinds of data can hardly reach the spatial resolution requirement.The rectangle prism method,namely an analytic integration of Newtonian potential integrals,is a reliable and commonly used approach to modeling gravity gradient,whereas its computing efficiency is extremely low.A modified rectangle prism method and a graphical processing unit(GPU)parallel algorithm were proposed to speed up the modeling process.The modified method avoided massive redundant computations by deforming formulas according to the symmetries of prisms’integral regions,and the proposed algorithm parallelized this method’s computing process.The parallel algorithm was compared with a conventional serial algorithm using 100 elevation data in two topographic areas(rough and moderate terrain).Modeling differences between the two algorithms were less than 0.1 E,which is attributed to precision differences between single-precision and double-precision float numbers.The parallel algorithm showed computational efficiency approximately 200 times higher than the serial algorithm in experiments,demonstrating its effective speeding up in the modeling process.Further analysis indicates that both the modified method and computational parallelism through GPU contributed to the proposed algorithm’s performances in experiments.

A new GIS-compatible methodology for visibility analysis in digital surface models of earth sites

As a GIS tool,visibility analysis is used in many areas to evaluate both visible and non-visible places.Visibility analysis builds on a digital surface model describing the terrain morphology,including the position and shapes of all objects that can sometimes act as visibility barriers.However,some barriers,for example vegetation,may be permeable to a certain degree.Despite extensive research and use of visibility analysis in different areas,standard GIS tools do not take permeability into account.This article presents a new method to calculate visibility through partly permeable obstacles.The method is based on a quasi-Monte Carlo simulation with 100 iterations of visibility calculation.Each iteration result represents 1%of vegetation permeability,which can thus range from 1%to 100%visibility behind vegetation obstacles.The main advantage of the method is greater accuracy of visibility results and easy implementation on any GIS software.The incorporation of the proposed method in GIS software would facilitate work in many fields,such as architecture,archaeology,radio communication,and the military.