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Historical and ongoing land use patterns in the Hawaiian Islands have degraded the Islands' drylands, causing erosion and detrimentally affecting adjacent coastal marine ecosystems. Biological soil crust (biocrust) communities have been shown to increase soil stability in drylands worldwide, but their efficacy in mitigating soil erosion in Hawaiian drylands is largely unknown. Using a combination of field data and imagery collected by small unmanned aerial systems (sUAS), we mapped biocrusts and examined their influence on soil stability in the Kawaihae watershed, an erosion-prone dryland on leeward Hawai'i Island. We created classified maps of biocrust cover from imagery collected at three spatial resolutions (1.2, 2.1 and 2.8 cm/pixel) using the pixel-based Support Vector Machine (SVM) classifier and investigated the impacts of spatial resolution and biocrust level of development on classification accuracy. Our medium (2.1 cm) resolution image produced the highest overall classification accuracy when biocrust was treated as a single class (82.1%). We explored the spatial impacts of biocrusts on soil loss via sUAS-derived measurements of elevation change over a four-year time span. We found differences in soil loss among land cover types, but robustly quantifying these was a challenge, as much of the change fell below statistically significant limits of detection. We investigated the relationship between biocrust development and soil stability by conducting soil aggregate stability testing at the three biocrust levels of development (LODs) present at the study site. We found a significant increase in soil stability from soils without surface biocrusts (LOD score of 0) to those with biocrusts at any development level (LOD 1–3). Our research adds to the body of biocrust knowledge by presenting new information about biocrust distribution and soil stabilization capabilities in Hawaiian drylands. We also provide insights into the trade-offs between spatial resolution and classification accuracy for biocrust classification and land cover analysis. [ABSTRACT FROM AUTHOR]

The early detection of plant pathogens at the landscape scale holds great promise for better managing forest ecosystem threats. In Hawai'i, two recently described fungal species are responsible for increasingly widespread mortality in 'ōhi'a Metrosideros polymorpha, a foundational tree species in Hawaiian native forests. In this study, we share work from repeat laboratory and field measurements to determine if visible near-infrared and optical remote sensing can detect pre-symptomatic trees infected with these pathogens. After generating a dense time series of laboratory spectral reflectance data and red green blue (RGB) images for inoculated 'ōhi'a seedlings, seedlings subjected to extreme drought, and control plants, we found few obvious spectral indicators that could be used for reliable pre-symptomatic detection in the inoculated seedlings, which quickly experienced complete and total wilting following stress onset. In the field, we found similar results when we collected repeat multispectral and RGB imagery over inoculated mature trees (sudden onset of symptoms with little advance warning). We found selected vegetation indices to be reliable indicators for detecting non-specific stress in 'ōhi'a trees, but never providing more than five days prior warning relative to visual detection in the laboratory trials. Finally, we generated a sequence of linear support vector machine classification models from the laboratory data at time steps ranging from pre-treatment to late-stage stress. Overall classification accuracies increased with stress stage maturity, but poor model performance prior to stress onset and the sudden onset of symptoms in infected trees suggest that early detection of rapid 'ōhi'a death over timescales helpful for land managers remains a challenge. [ABSTRACT FROM AUTHOR]