Abstract
Purpose of ReviewThe purpose of this article is to review landscape ecology research from the past 5 years to identify past and future contributions from remote sensing to landscape ecology.Recent FindingsRecent studies in landscape ecology have employed advances made in remote sensing. These include the use of reliable and open datasets derived from remote sensing, the availability of new sources for freely available satellite imagery, and machine-learning image classification techniques for classifying land cover types. Remote sensing data sources and methods have been used in landscape ecology to examine landscape structure. Additionally, these data sources and methods have been used to analyze landscape function including the effects of landscape structure and landscape change on biodiversity and population dynamics. Lastly, remote sensing data sources and methods have been used to analyze historical landscape changes and to simulate future landscape changes.SummaryThe ongoing integration of remote sensing analyses in landscape ecology will depend on continued accessibility of free imagery from satellite sources and open-access data-analysis software, analyses spanning multiple spatial and temporal scales, and novel land cover classification techniques that produce accurate and reliable land cover data. Continuing advances in remote sensing can help to address new landscape ecology research questions, enabling analyses that incorporate information that ranges from ground-based field samples of organisms to satellite-collected remote sensing data.
Highlights
In the last 5 years, landscape ecologists have continued their seminal focus on the relationships of pattern and process [1], addressing questions of landscape structure, landscape function, and landscape change [2]
We examine recently published manuscripts from landscape ecology that have been made possible through advances in remote sensing
Habitat classifications identifying population preferences and vulnerability related to landscape change, and primary productivity related to spatial distributions of species have been assessed using object-based classifications and random forest machine-learning algorithms of satellite data, vegetation structural observations provided by lidar data, and gross primary productivity values derived from the enhanced vegetation index [139, 143, 145, 166, 167]
Summary
In the last 5 years, landscape ecologists have continued their seminal focus on the relationships of pattern and process [1], addressing questions of landscape structure, landscape function, and landscape change [2]. Since the launch of the satellite Landsat-1 MSS in 1972, a variety of remote sensing platforms (e.g. satellite, aerial) have collected. Researchers are able to choose their remote sensing sources based on their research questions, whether they use sources such as unmanned aerial vehicles (UAVs), active sensors like light detection and ranging (lidar), field-based spectroscopy, cross-boundary satellites [38–41]. By comparing imagery and ground-based measurements, users can classify land cover types (e.g., forests, wetlands, development) to analyze the landscape structure [45–48]. Available remote sensing data from satellite sensors with large spatial coverage has become available in the last 10 years [49–53, 54]. With increasing data availability for large-area coverage and medium spatial resolution sensors like Landsat and Sentinel, there has been a dramatic increase in research using satellite data in the last 5 years [52]
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