Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction.

Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, non-destructive and cost-efficient method for soil parameter estimations. Microelectromechanical system devices (MEMS) have become available that are suitable for many application fields due to their low cost as well as their small size and weight. We investigated the performance of MEMS spectrometers in the visual and NIR range to estimate forest soil samples total C and N content of Ah and Oh horizons at the lab. The results were compared to a full-range device using PLSR and Cubist regression models at local (2.3 ha, n: Ah = 60, Oh = 50) and regional scale (State of Saxony, Germany, 184,000 km2, n: Ah = 186 and Oh = 176). For each sample, spectral reflectance was collected using MEMS spectrometer in the visual (Hamamatsu C12880MA) and NIR (NeoSpetrac SWS62231) range and using a conventional full range device (Veris Spectrophotometer). Both data sets were split into a calibration (70%) and a validation set (30%) to evaluate prediction power. Models were calibrated for Oh and Ah horizon separately for both data sets. Using the regional data, we also used a combination of both horizons. Our results show that MEMS devices are suitable for C and N prediction of forest topsoil on regional scale. On local scale, only models for the Ah horizon yielded sufficient results. We found moderate and good model results using MEMS devices for Ah horizons at local scale (R2≥ 0.71, RPIQ ≥ 2.41) using Cubist regression. At regional scale, we achieved moderate results for C and N content using data from MEMS devices in Oh (R2≥ 0.57, RPIQ ≥ 2.42) and Ah horizon (R2≥ 0.54, RPIQ ≥2.15). When combining Oh and Ah horizons, we achieved good prediction results using the MEMS sensors and Cubist (R2≥ 0.85, RPIQ ≥ 4.69). For the regional data, models using data derived by the Hamamatsu device in the visual range only were least precise. Combining visual and NIR data derived from MEMS spectrometers did in most cases improve the prediction accuracy. We directly compared our results to models based on data from a conventional full range device. Our results showed that the combination of both MEMS devices can compete with models based on full range spectrometers. MEMS approaches reached between 68% and 105% of the corresponding full ranges devices R2 values. Local models tended to be more accurate than regional approaches for the Ah horizon. Our results suggest that MEMS spectrometers are suitable for forest soil C and N content estimation. They can contribute to improved monitoring in the future as their small size and weight could make in situ measurements feasible.

Drivers of seedling establishment success in dryland restoration efforts.

Restoration of degraded drylands is urgently needed to mitigate climate change, reverse desertification and secure livelihoods for the two billion people who live in these areas. Bold global targets have been set for dryland restoration to restore millions of hectares of degraded land. These targets have been questioned as overly ambitious, but without a global evaluation of successes and failures it is impossible to gauge feasibility. Here we examine restoration seeding outcomes across 174 sites on six continents, encompassing 594,065 observations of 671 plant species. Our findings suggest reasons for optimism. Seeding had a positive impact on species presence: in almost a third of all treatments, 100% of species seeded were growing at first monitoring. However, dryland restoration is risky: 17% of projects failed, with no establishment of any seeded species, and consistent declines were found in seeded species as projects matured. Across projects, higher seeding rates and larger seed sizes resulted in a greater probability of recruitment, with further influences on species success including site aridity, taxonomic identity and species life form. Our findings suggest that investigations examining these predictive factors will yield more effective and informed restoration decision-making.