Extreme droughts, heat waves, fires, hurricanes, floods, and landslides cause the largest losses in the United States, and globally, from natural hazards linked to weather and climate. There is evidence that the frequency of such extremes is increasing, particularly for heat waves, large fires, and intense precipitation, making better understanding of the probability and consequences of these events imperative. Further, these events are not isolated, but rather interact with each other and with other social and biophysical drivers and conditions, to amplify impacts. Less is known about the nature and strength of these interactions. Natural and social science subfields frame extreme events with different definitions and analytical approaches, often neglecting interactions and the subsequent novel extremes that can arise. Here we propose a framework for social‐environmental extremes, defined as extraordinary events that emerge from interactions among biophysical and social systems. We argue that this definition is critical because it constrains the focus to major events that are capturing societal and scientific attention because of their extreme biophysical drivers and/or the extreme social outcomes. We review how different fields approach extremes as interacting phenomena and propose a synthetic framework that allows analytical separation of the multiple drivers and responses that yield extreme events and extreme effects. We conclude with a future research agenda for understanding the extreme events that matter to society. This agenda will help to identify where, when, and why communities may have high exposure and vulnerability to social‐environmental extremes—informing future mitigation and adaptation strategies.

Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.

Context: Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. Objectives: The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. Methods: We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. Results: We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. Conclusion: We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.

Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that “learns” how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts.

Forest biodiversity is critical for many ecosystem functions and services. Yet, it remains uncertain how plant functional diversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We integrated remote sensing and terrestrial biosphere modeling to explore functional diversity–productivity relationships at multiple spatial scales for a heterogeneous forest ecosystem in Switzerland. We initialized forest structure and composition in the ecosystem demography model (ED2) through a combination of ground‐based surveys, airborne laser scanning and imaging spectroscopy for forest patches at 10 × 10‐m spatial grain. We derived morphological and physiological forest traits and productivity from model simulations at patch‐level to relate morphological and physiological aspects of functional diversity to the average productivity from 2006 to 2015 at 20 × 20 to 100 × 100‐m spatial extent. We did this for model simulations under observed and experimental conditions (mono‐soils, mono‐cultures and mono‐structures). Functional diversity increased productivity significantly (p < 0.001) across all simulations at 20 × 20 to 30 × 30 m scale, but at 100 × 100‐m scale positive relationships disappeared under homogeneous soil conditions potentially due to the low beta diversity of this forest and the saturation of functional richness represented in the model. Although local functional diversity was an important driver of productivity, environmental context underpinned the variation of productivity (and functional diversity) at larger spatial scales. In this study, we could show that the integration of remotely sensed information on forest composition and structure into terrestrial biosphere models is important to fill knowledge gaps about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas.

Context Forest traits and characteristics are challenging to measure across ecosystems with traditional field methods. There is a ripe opportunity for unoccupied aerial systems (UAS) to contribute to landscape ecology through mapping forest traits and characteristics and linking scales between ground surveys and airborne/spaceborne remote sensing. Objectives We consider the unique perspective of UAS in forests and the considerations that come with working with an emerging technology. Methods We performed a literature review of which forest traits and characteristics have been derived from UAS and dive into a case example of how researchers derive a particular trait, aboveground carbon stock, from UAS-based data. Results UAS are most useful and cost-effective in contexts where high resolution data are required across a limited spatial extent. Due to the high spatial resolution and ability to fly close to top-of-canopy, UAS excel at measuring morphological and physiological characteristics, like canopy structure and foliar chemical traits. Combining spectral and structural information can be done particularly easily with UAS data and enhances aboveground carbon estimation from UAS. UAS-based lidar is best for measuring forest structural attributes, but RGB imagery with post-processing is an acceptable alternative for a tight budget. Conclusions UAS contribute to landscape ecology through measuring forest traits and characteristics in novel ways. We need better metadata and validation reporting and method standardization to improve reproducibility and comparison across UAS forest studies. This review is written for ecologists interested in measuring forests at a landscape scale, and particularly for researchers interested in adding UAS to their toolkit.