Measuring landscape pattern in three dimensional space

Wu, Qiong; Guo, Fengxiang; Li, Hongqing; Kang, Jingyu

doi:10.1016/j.landurbplan.2017.05.022

Cited by 53 publications

(20 citation statements)

References 36 publications

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“…This result was considered to be caused by the tiny fluctuations in the DEMs data [68]. Some scholars have pointed out that this difference will increase as DEMs fluctuations increase, especially in mountainous landscapes with large topography undulations [44]. This research provides supporting evidence for this viewpoint.…”

Section: Sensitivity Of the 3d Landscape Pattern Indices To Topographysupporting

confidence: 62%

“…The traditional landscape pattern indices only consider the two-dimensional structure of the landscape, and its research objects are derived from the bird's eye view of the landscape, thus ignoring the topographic characteristics and the differences in the vertical direction of the research objects [43]. Especially in mountainous areas with complex terrain, the patch area and circumference obtained using 2D plane information are much smaller than the actual patch surface area and surface circumference, leading to inaccurate calculation results and weakening the differences between the research objects [44]. As a vital attribute of landform patterns, topographic features must be considered when quantifying landform differences.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Loess Landforms from Three-Dimensional Landscape Pattern Perspective by Using DEMs

Wei

et al. 2021

IJGI

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Quantitative analysis of the differences and the exploration of the evolution models of different loess landform types are greatly important to the in-depth understanding of the evolution process and mechanism of the loess landforms. In this research, several typical loess landform areas in the Chinese Loess Plateau were selected, and the object-oriented image analysis (OBIA) method was employed to identify the basic loess landform types. Three-dimensional (3D) landscape pattern indices were introduced on this foundation to measure the morphological and structural features of individual loess landform objects in more detail. Compared with the traditional two-dimensional (2D) landscape pattern indices, the indices consider the topographic features, thereby providing more vertical topographic information. Furthermore, the evolution modes between different loess landform types were discussed. Results show that the OBIA method achieved satisfying classification results with an overall accuracy of 88.12%. There are evident differences in quantitative morphological indicators among loess landform types, especially in indicators such as total length of edge, mean patch size, landscape shape index, and edge dimension index. Meanwhile, significant differences are also found in the combination of loess landform types corresponding to different landform development stages. The degree of surface erosion became increasingly significant as loess landforms developed, loess tableland area rapidly reduced or even vanished, and the dominant loess landform types changed to loess ridge and loess hill. Hence, in the reconstruction and management of the Loess Plateau, the loess tableland should be the key protected loess landform type. These preliminary results are helpful to further understand the development process of loess landforms and provide a certain reference for regional soil and water conservation.

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Section: Sensitivity Of the 3d Landscape Pattern Indices To Topographysupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Quantification of Loess Landforms from Three-Dimensional Landscape Pattern Perspective by Using DEMs

Wei

et al. 2021

IJGI

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“…Class/Landscape LSI is calculated by the square amended total patch edge length divided by the total landscape area. LSI describes the complexity of the patch shape and the shape characteristics and possible evolutionary trends of the landscape spatial structure [42]. It is the most effective measure of overall shape complexity [43].…”

Section: Complexity Landscape Shape Index (Lsi)mentioning

confidence: 99%

“…CONTAG indicates the degree of reunion or extension of different patch types in the landscape. The probability of adjacent patches belonged to a class is calculated by the number of patches [42].…”

Section: Contagion Index (Contag) Landscapementioning

confidence: 99%

Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

Fang

Gong

et al. 2021

IJGI

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Urbanization is changing the world’s surface pattern more and more drastically, which brings many social and ecological problems. Quantifying the changes in the landscape pattern and 3D structure of the city is important to understand these issues. This research study used Melbourne, a compact city, as a case study, and focused on landscape patterns and vertical urban volume (volume mean (VM), volume standard deviation (VSD)) and investigate the correlation between them from the scope of different scales and functions by Remote Sensing (RS) and Geographic Information System (GIS) techniques. We found: (1) From 2000 to 2012, the landscape pattern had a trend of decreasing fragmentation and increasing patch aggregation. The growth of VM and VSD was more severe than that of landscape metrics, and presented a “high–low” situation from the city center to the surroundings, maintaining the structure of “large east and small west”. (2) Landscape pattern was found closely associated with the urban volume. In the entire study area, landscape pattern patches with low fragmentation and high aggregation were directly proportional to VM with high value, which represented high urbanization, and patches with high connectivity and fragmentation had a positive relationship with high VSD, which represented strong spatial recognition. (3) The urban volumes of different urban functional areas were affected by different landscape patterns, and the analysis based on the local development situation can explain the internal mechanism of the interaction between the landscape pattern and the urban volume.

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“…The 2D categorical maps can be made with a series of developed metrics that can effectively characterize landscape structures, but they can be misleading when correlated with people's 3D visual landscape preferences [14] because of the lack of quantitative information in the vertical direction [15]. The availability of high-resolution Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and LiDAR point clouds allowed for the generation of 3D models [16][17][18] and the implementation of the third dimension to the analysis workflow in environmental modeling [19][20][21][22]. Although it greatly improved the quality of landscape classifications, it does not solve the biggest issue of landscape mapping, which is the potential discrepancy between landscape classification and the actual view from the observer location.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

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The study presents a new method for quantitative landscape assessment. The method uses LiDAR data and combines the potential of GIS (ArcGIS) and 3D graphics software (Blender). The developed method allows one to create Classified Digital Surface Models (CDSM), which are then used to create 360° panoramic images from the point of view of the observer. In order to quantify the landscape, 360° panoramic images were transformed to the Interrupted Sinusoidal Projection using G.Projector software. A quantitative landscape assessment is carried out automatically with the following landscape classes: ground, low, medium, and high vegetation, buildings, water, and sky according to the LiDAR 1.2 standard. The results of the analysis are presented quantitatively—the percentage distribution of landscape classes in the 360° field of view. In order to fully describe the landscape around the observer, graphs of little planets have been proposed to interpret the obtained results. The usefulness of the developed methodology, together with examples of its application and the way of presenting the results, is described. The proposed Quantitative Landscape Assessment method (QLA360) allows quantitative landscape assessment to be performed in the 360° field of view without the need to carry out field surveys. The QLA360 uses LiDAR American Society of Photogrammetry and Remote Sensing (ASPRS) classification standards, which allows one to avoid differences resulting from the use of different algorithms for classifying images in semantic segmentation. The most important advantages of the method are as follows: observer-independent, 360° field of view which simulates human perspective, automatic operation, scalability, and easy presentation and interpretation of results.

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Measuring landscape pattern in three dimensional space

Cited by 53 publications

References 36 publications

Quantification of Loess Landforms from Three-Dimensional Landscape Pattern Perspective by Using DEMs

Quantification of Loess Landforms from Three-Dimensional Landscape Pattern Perspective by Using DEMs

Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

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