Tropical forest loss currently exceeds forest gain, leading to a net greenhouse gas emission that exacerbates global climate change. This has sparked scientific debate on how to achieve natural climate solutions. Central to this debate is whether sustainably managing forests and protected areas will deliver global climate mitigation benefits, while ensuring local peoples’ health and well-being. Here, we evaluate the 10-y impact of a human-centered solution to achieve natural climate mitigation through reductions in illegal logging in rural Borneo: an intervention aimed at expanding health care access and use for communities living near a national park, with clinic discounts offsetting costs historically met through illegal logging. Conservation, education, and alternative livelihood programs were also offered. We hypothesized that this would lead to improved health and well-being, while also alleviating illegal logging activity within the protected forest. We estimated that 27.4 km2 of deforestation was averted in the national park over a decade (∼70% reduction in deforestation compared to a synthetic control, permuted P = 0.038). Concurrently, the intervention provided health care access to more than 28,400 unique patients, with clinic usage and patient visitation frequency highest in communities participating in the intervention. Finally, we observed a dose–response in forest change rate to intervention engagement (person-contacts with intervention activities) across communities bordering the park: The greatest logging reductions were adjacent to the most highly engaged villages. Results suggest that this community-derived solution simultaneously improved health care access for local and indigenous communities and sustainably conserved carbon stocks in a protected tropical forest.

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.

IMPORTANCE Understanding opioid prescribing patterns in community health centers (CHCs) that disproportionately serve low-income patients may help to guide strategies to reduce opioidrelated harms. OBJECTIVE To assess opioid prescribing patterns between January 1, 2009, and December 31, 2018, in a network of safety-net clinics serving high-risk patients. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of 3 227 459 opioid prescriptions abstracted from the electronic health records of 2 129 097 unique primary care patients treated from 2009 through 2018 at a network of CHCs that included 449 clinic sites in 17 states. All age groups were included in the analysis. MAIN OUTCOMES AND MEASURES The following measures were described at the population level for each study year: (1) percentage of patients with at least 1 prescription for an opioid by age and sex, (2) number of opioid prescriptions per 100 patients, (3) number of long-acting opioid prescriptions per 100 patients, (4) mean annual morphine milligram equivalents (MMEs) per patient, (5) mean MME per prescription, (6) number of chronic opioid users, and (7) mean of high-dose opioid users. RESULTS The study population included 2 129 097 patients (1 158 413 women [54.4%]) with a mean (SD) age of 32.2 (21.1) years and a total of 3 227 459 opioid prescriptions. The percentage of patients receiving at least 1 opioid prescription in a calendar year declined 67.4% from 15.9% in 2009 to 5.2% in 2018. Over the 10-year study period, a greater percentage of women received a prescription (13.1%) compared with men (10.9%), and a greater percentage of non-Hispanic White patients (18.1%) received an opioid prescription compared with non-Hispanic Black patients (9.5%), non-Hispanic patients who self-identified as other races (8.0%), and Hispanic patients (6.9%). The number of opioid prescriptions for every 100 patients decreased 73.7% from 110.8 in 2009 to 29.1 in 2018. The number of long-acting opioids for every 100 patients decreased 85.5% during the same period, from 22.0 to 3.2. The MMEs per patient decreased from 1682.7 in 2009 to 243.1 in 2018, a decline of 85.6%. CONCLUSIONS AND RELEVANCE In this cross-sectional study, the opioid prescribing rate in 2009 in the CHC network was higher than national population estimates but began to decline earlier and more precipitously. This finding likely reflects harm mitigation policies and efforts at federal, state, and clinic levels and strong clinical quality improvement strategies within the CHCs.

Riparian vegetation restoration efforts demand cost effective, accurate, and replicable impact assessments. In this thesis a method is presented using an Unmanned Aerial Vehicle (UAV) equipped with a GoPro digital camera to collect photogrammetric data of a 2.02-acre riparian restoration. A three-dimensional point cloud was created from the photos using Structure from Motion (SfM) techniques. The point cloud was analyzed and compared to traditional, ground-based monitoring techniques. Ground truth data collected using the status-quo approach was collected on 6.3% of the study site and averaged across the entire site to report stem heights in stems/acre in three height classes, 0-3 feet, 3-7 feet, and greater than 7 feet. The project site was divided into four analysis sections, one for derivation of parameters used in the UAV data analysis, and the remaining three sections reserved for method validation. The most conservative of several methods tested comparing the ground truth data to the UAV generated data produced an overall error of 21.6% and indicated an r 2 value of 0.98. A Bland Altman analysis indicated a 99% probability that the UAV stems/plot result will be within 159 stems/plot of the ground truth data. The ground truth data is reported with an 80% confidence interval of +/-844 stems/plot, thus the UAV was able to estimate stems well within this confidence interval. Further research is required to validate this method longitudinally at this same site and across varying ecologies. These results suggest that UAV derived environmental impact assessments at riparian restoration sites may offer competitive performance and value.

Increasingly, satellite-derived rainfall data is used for climate research and action in Africa. In this study, we use six years of rain gauge data from 596 stations operated by the Trans-African Hydro-Meteorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products – CHIRPS, TAMSAT and GSMaP_wGauge – and one satellite-only rainfall product – GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, Southern Africa, and West Africa at daily, pentadal, and monthly timescales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5 % (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7 %). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29 %; 26.3 mm wet bias), TAMSAT in Southern Africa (13 %; 10.4 mm dry bias) and GSMaP in West Africa (23 %; 19.6 mm wet bias). Considerable biases in seasonal rainfall are observed in all sub-regions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1 km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple timescales relative to CHIRPS and TAMSAT.