Purpose of Review: The 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 Findings: Recent 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.Summary: The 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.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole‐system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre‐fire vegetation inventories, (2) active‐fire monitoring, (3) post‐fire assessment, and (4) multi‐scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co‐development of information products using cloud computing that benefits from open‐access Earth observation and other geospatial data layers.

As freely available remotely sensed data sources proliferate, the ability to combine imagery with high spatial and temporal resolutions enables applications aimed at near-term disturbance detection. In this case study, we present methods for synthesizing burned-area information from multiple sources to map the active phase of the Elephant Hill fire from the 2017 fire season in British Columbia. We used the Bayesian Updating of Land Cover (BULC) algorithm to merge burned-area classifications from a range of remote-sensing sources such as Landsat-8, Sentinel-2, and MODIS. We created provisional classifications by comparing the post-fire Normalized Burn Ratio against pre-fire image composite within the fire boundary provided by the Province of British Columbia. BULC fused the classifications in Google Earth Engine, producing a cohesive time-series stack with updated burned areas for 19 distinct days. The fire burned unevenly throughout its lifespan: a rapid burn phase of 53,097 ha in two weeks by late July, a steady burn phase to 60,000 ha until late August, an accelerated burn phase of 95,766 ha until mid-September, and containment at 203,560 ha in October. The highly automated methods presented herein can synthesize multi-source fire classifications for active phase monitoring both retrospectively and in near-real-time.