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Abstract AimAs part of the Gaborone Declaration for Sustainability in Africa, Liberia has pledged to include the value of nature in national decision making through natural capital accounting. Surveying species of concern, such as the western chimpanzee (Pan troglodytes verus), which was recently reclassified as “critically endangered” by the International Union for Conservation of Nature, and identifying protection priority areas are critical first steps towards achieving Liberia's goal to conserve 30% of its remaining forests and supporting the wave of conservation projects taking place in the country. LocationLiberia, Africa. MethodsWe modelled western chimpanzee habitat suitability, focusing on determining relevant environmental predictors and the most appropriate scale for modelling species–habitat relationships. We built models at six resolutions (30–960 m) to identify scale domains where relationships remain constant. We include several habitat variables that have not been included in prior modelling efforts. We then used the suitability map as the conductance input into a connectivity analysis using Circuitscape. ResultsThe amount of forest within 1–3 km was the most important predictor of chimpanzee occurrence. Variable ranks and importance shifted considerably between modelling scales, supporting the need for multiscale investigations, but scale domains were present. Several important corridors for chimpanzee habitat and movement overlap considerably with existing timber and palm oil concessions and overlap mining and rubber concessions to a lesser degree. Main conclusionsThe proportion of primary forest within 1–3 km is critically important for chimpanzee habitat. Ongoing conservation projects and efforts taking place in Liberia including the Good Growth Partnership and the Gaborone Declaration for Sustainability in Africa can utilize the spatial findings on connectivity provided by this study to inform future conservation decisions, particularly expanding exiting protected areas. 

With the ability to capture daily imagery of Earth at very high spatial resolutions, commercial smallsats are emerging as a key resource for the remote sensing community. Planet (Planet Labs, Inc., San Francisco, CA, USA) operates the largest constellation of Earth imaging smallsats, which have been capturing multispectral imagery for consumer use since 2016. Use of these images is growing in the remote sensing community, but the variation in radiometric and geometric quality compared to traditional platforms (i.e., Landsat, MODIS, etc.) means the images are not always ‘analysis ready’ upon download. Neglecting these variations can impact derived products and analyses. Users also must contend with constantly evolving technology, which improves products but can create discrepancies across sensor generations. This communication provides a technical review of Planet’s PlanetScope smallsat data streams and extant literature to provide practical considerations to the remote sensing community for utilizing these images in remote sensing research. Radiometric and geometric issues for researchers to consider are highlighted alongside a review of processing completed by Planet and innovations being developed by the user community to foster the adoption and use of these images for scientific applications. 

Mapping invasive vegetation species in arid regions is a critical task for managing water resources and understanding threats to ecosystem services. Traditional remote sensing platforms, such as Landsat and MODIS, are ill-suited for distinguishing native and non-native vegetation species in arid regions due to their large pixels compared to plant sizes. Unmanned aircraft systems, or UAS, offer the potential to capture the high spatial resolution imagery needed to differentiate species. However, in order to extract the most benefits from these platforms, there is a need to develop more efficient and effective workflows. This paper presents an integrated spectral–structural workflow for classifying invasive vegetation species in the Lower Salt River region of Arizona, which has been the site of fires and flooding, leading to a proliferation of invasive vegetation species. Visible (RGB) and multispectral images were captured and processed following a typical structure from motion workflow, and the derived datasets were used as inputs in two machine learning classifications—one incorporating only spectral information and one utilizing both spectral data and structural layers (e.g., digital terrain model (DTM) and canopy height model (CHM)). Results show that including structural layers in the classification improved overall accuracy from 80% to 93% compared to the spectral-only model. The most important features for classification were the CHM and DTM, with the blue band and two spectral indices (normalized difference water index (NDWI) and normalized difference salinity index (NDSI)) contributing important spectral information to both models.