Land cover mapping with ultra‐high‐resolution aerial imagery

There has been an increase in the use of UAVs and other low altitude aerial imaging platforms for mapping land cover. Creating accurate land cover maps from these ultra-high (sub-decimeter) resolution images is difficult due to many factors, including high intra-object variability and variable illumination and reflection geometry within objects. Unfortunately, traditional image processing methods are not well suited to this classification task. On top of this already complicated situation is the reality that, in many cases, the imagery is acquired under lessthan-ideal weather conditions using consumer-grade UAVs and cameras. Along with an increase in the use of consumer drones for mapping, there has also been an increase in the performance of machine-learning-based image processing software and hardware required to run many of these algorithms. Machine learning algorithms, including those categorized as deep learning, are designed to identify patterns in data without being programmed to identify specific patterns using preconceived rules or statistical relationships. Much work is going into improving the state of the art and the state of the practice to improve our ability to map land cover objects using ultra-high-resolution imagery. In this special issue of Remote Sensing in Ecology and Conservation, five papers present research to map terrestrial and marine environments using imagery acquired from UAVs and other platforms. A range of image processing techniques are presented, each tailored to respond to specific mapping challenges. Working in the marine realm, Ridge et al. (2020) developed a novel deep learning tool called OysterNet that uses a convolutional neural network (CNN) to automate the detection and delineation of oyster reefs. Their approach has the potential to greatly reduce the level of effort required to perform this time-consuming manual task, thereby opening the possibility of mapping large areas which is impracticable using traditional methods. The work presented provides a foundation for future research to monitor oyster reefs and other biogenically structured coastal objects. Moving to terrestrial landscapes, Havrilla et al. (2020) present research on mapping and monitoring biological soil crust (biocrust) condition and extent. Mapping biocrusts is not new, but this research extends previous capabilities by developing methods to map fine-scale patterns using sub-centimeter resolution aerial imagery acquired using an UAV. Their approach uses object-based image analysis (OBIA) with an image segmentation step followed by classification into biocrust categories with a support vector machine algorithm. The resulting classification was then used to calculate various landscape metrics. Their results include lessons learned and guidance for researchers and practitioners. These fine-scale mapping methods are important tools to support land management as well as improving our ability to reliably scale biocrust mapping to satellite imagery. The remaining three papers in this special issue focus on mapping different vegetation types. R€as€anen et al. (2020) studied peatland vegetation patterns in the northern boreal vegetation zone of Finland. Their research project incorporated a variety of image products ranging from 0.02 m resolution UAV imagery to 4 m resolution satellite imagery as well as airborne lidar. They show how these different data sets complement each other in this compositionally and spatially complex environment. Instead of classifying vegetation into discrete categories, they used random forest regression to create fractional cover maps of plant functional type and other metrics. Their results highlight that different data sets and processing methods need to be considered and tested when mapping any unique cover type to maximize the quality of the output product. This paper provides general guidance that will be of benefit to people working in peatlands and other complex environments. The study by Kattenborn et al. (2020) uses UAV imagery to predict fractional cover for three landscapes with varied cover types such as trees, other woody vegetation, grasses and forbs, and moss. This research project developed a novel workflow for mapping species and vegetation type using CNN-based regression to output continuous cover maps instead of classified images typically output from CNNs. Their work provides insight into Expansion of the acronym “UAV” has several interpretations. The most common is unmanned aircraft vehicle, but some public and private organizations replace “unmanned” with gender neutral terms such as unoccupied, uncrewed, unpiloted and uninhabited.