Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non-forest ecosystems.

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha-1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

Using ion-exchange resins to monitor nitrate fluxes in remote semiarid stream beds.

Monitoring in remote areas can represent a real challenge in environmental studies. Numerous techniques have been developed over the last decades to monitor nutrients and other elements in different systems. However, not all of them are suitable for field applications, particularly when the locations are difficult to access or its accessibility depends on seasonal climate conditions. This study was aimed to test the applicability and efficiency of resin samplers and resin bags to monitor nitrates fluxes (NO3-N) in two small semi-arid catchments in Northwestern Mexico. Resin samplers were installed in the hyporheic zone below the river bed in order to monitor the vertical fluxes of NO3-N and remained there for 5 months (during the summer rains). Resin bags were anchored in rock outcrops upstream of the resin samplers before the onset of the summer rainfall season and replaced every 2 weeks during 4 months to capture pulses of NO3-N in ephemeral streams. NO3-N pulses in the stream are a potential source of NO3-N that can infiltrate into the soil. Results of the resin samplers found a difference of up to 12 kg ha-1 season-1 between the two catchments. The resin bags showed a higher accumulation of NO3-N in the catchment with lower vegetation cover (160.3 mg L-1 season-1) compared to the one with higher vegetation (67.8 mg L-1 season-1). Measured nitrate fluxes at both sites responded to rainfall pulses recorded during the monitoring period. Resin samplers and resin bags can be used together, to assess nutrient fluxes on the surface and in the soil and can be tested in any type of ecosystem. In this particular case, these methods demonstrated an efficient way of determining spatio-temporal nitrate fluxes in semi-arid ecosystems in remote areas that are difficult to access, monitor, and collect data.

Image dataset acquired from an unmanned aerial vehicle over an experimental site within El Soldado estuary in Guaymas, Sonora, México.

It is well known that remote sensing is a series of procedures which detects physical characteristics of the earth surface by remotely-measuring its reflected and emitted radiation using cameras or sensors. Lately, the increasing use of unmanned aerial vehicles (UAVs) as remote sensing platforms and the development of small-size sensors have resulted in the expansion of continuous monitoring of earth surface at smaller spatial scales. For this reason, the integration of UAV- and consumer-grade cameras can be useful to acquire surface characteristics at plot or footprint scale. This dataset contains 314 aerial images covering an area of aproximately 18,800 m2 within the footprint of an Eddy covariance and meterorological station. The monitoring site was deployed at "El Soldado" estuary (27°57'14.4″ N and 110°58'19.2″ W) located in the southern coast of the Mexican State of Sonora. UAV flight path was programmed to flight in autonomous mode with an altitude of 30 m, a velocity of 5 m/s and a frontal and side overlap of 85 and 75% respectively. This dataset was created to support mapping surveys for surface classification and site description. This dataset is aimed to support researchers, stakeholders and general public interested in coastal areas, natural resources management and ecosystem conservation. Finally, this dataset could be also used for those interested in digital photogrammetry and 3D reconstruction as benchmark example to develop high resolution orthomosaics.