Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and temporal characteristics of fire, or the classic fire regime concept, we need to critically define fire events from remote sensing data. Events, fundamentally a geographic concept with delineated spatial and temporal boundaries around a specific phenomenon that is homogenous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe Fire Events Delineation (FIRED), an event-delineation algorithm, that has been used to derive fire events (N = 51,871) from the MODIS MCD64 burned area product for the coterminous US (CONUS) from January 2001 to May 2019. The optimized spatial and temporal parameters to cluster burned area pixels into events were an 11-day window and a 5-pixel (2315 m) distance, when optimized against 13,741 wildfire perimeters in the CONUS from the Monitoring Trends in Burn Severity record. The linear relationship between the size of individual FIRED and Monitoring Trends in Burn Severity (MTBS) events for the CONUS was strong (R² = 0.92 for all events). Importantly, this algorithm is open-source and flexible, allowing the end user to modify the spatio-temporal threshold or even the underlying algorithm approach as they see fit. We expect the optimized criteria to vary across regions, based on regional distributions of fire event size and rate of spread. We describe the derived metrics provided in a new national database and how they can be used to better understand US fire regimes. The open, flexible FIRED algorithm could be utilized to derive events in any satellite product. We hope that this open science effort will help catalyze a community-driven, data-integration effort (termed OneFire) to build a more complete picture of fire.

Impervious surfaces are traditionally mapped from remotely sensed imagery using image classification algorithms. The surface type is complex in that it consists of many distinct materials, for which image classification and aggregation approaches are generally used to map it. This work explores the use of fully convolutional neural networks (FCNN), specifically, UNet, in mapping these complex features at the pixel level from high resolution satellite imagery. Initial results are promising in both qualitative and quantitative assessment when compared to automated products.

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.

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.

EarthPy makes commonly performed spatial data exploration tasks easier for scientist by building upon functions in the widely used packages: Rasterio and GeoPandas. EarthPy is designed for users who are new to Python and spatial data with a focus on scientific data. When a user is working with spatial data for research, there are a suite of data exploration activities that are often performed including: