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Dear Colleagues,

Advancements in imaging technology in Earth and natural sciences have provided invaluable spatially-explicit data for relating observed spatial patterns with biological processes and drivers. In the field of landscape ecology, satellite and drone (UAV) imagery, LiDAR, and novel tracking methods have allowed researchers to measure the legacy of disturbances in a landscape and predict their future spread, to estimate the probability of native and invasive species to disperse through heterogeneous landscapes, and to better connect drivers such as climate, nutrient cycles, and species competition to the patterns observed in these data. Researchers make use of spatially-explicit models, such as cellular automata and agent-based models, as well as spatial statistics for hypothesis testing and predictive simulations.

We invite papers on the use of spatial pattern recognition and analysis to study landscape-scale processes. We also welcome review and perspective papers which discuss how similar analytical tools can be used for pattern recognition for imagery across many different types of biological systems, and interdisciplinary papers which examine patterns in human-scaled landscapes (such as cities or regions).

Dr. Audrey L. Mayer
Guest Editor

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Keywords

landscape ecology
ecosystem processes
remote sensing
imaging
spatial data

Published Papers (1 paper)

Research

16 pages, 5916 KiB

Open AccessArticle

Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China

by Qiong Wu, Fengxiang Guo and Hongqing Li

Remote Sens. 2018, 10(10), 1653; https://doi.org/10.3390/rs10101653 - 17 Oct 2018

Cited by 5 | Viewed by 3629

Abstract

Landscapes display overlapping sets of correlations in different regions at different spatial scales, and these correlations can be delineated by pattern analysis. This study identified the correlations between landscape pattern and topography at various scales and locations in urban-rural profiles from Jilin City, China, using Pearson correlation analysis and wavelet method. Two profiles, 30 km (A) and 35 km (B) in length with 0.1-km sampling intervals, were selected. The results indicated that profile A was more sensitive to the characterization of the land use pattern as influenced by topography due to its more varied terrain, and three scales (small, medium, and large) could be defined based on the variation in the standard deviation of the wavelet coherency in profile A. Correlations between landscape metrics and elevation were similar at large scales (over 8 km), while complex correlations were discovered at other scale intervals. The medium scale of cohesion and Shannon’s diversity index was 1–8 km, while those of perimeter-area fractal dimension and edge density index were 1.5–8 km and 2–8 km, respectively. At small scales, the correlations were weak as a whole and scattered due to the micro-topography and landform elements, such as valleys and hillsides. At medium scales, the correlations were most affected by local topography, and the land use pattern was significantly correlated with topography at several locations. At large spatial scales, significant correlation existed throughout the study area due to alternating mountains and plains. In general, the strength of correlation between landscape metrics and topography increased gradually with increasing spatial scale, although this tendency had some fluctuations in several locations. Despite a complex calculating process and ecological interpretation, the wavelet method is still an effective tool to identify multi-scale characteristics in landscape ecology. Full article

(This article belongs to the Special Issue Recent Advances in Pattern Recognition and Analysis in Landscape Ecology)

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