Operational near-real-time (NRT) monitoring of Earth's surface deformation using interferometric synthetic aperture radar (InSAR) requires processing algorithms that efficiently incorporate new acquisitions without reprocessing historical archives. We present a sequential phase linking approach using compressed single-look-complex (SLC) images capable of producing surface displacement estimates within hours of the time of a new acquisition. Our key algorithmic contribution is a mini-stack reference scheme that maintains phase consistency across processing batches without adjusting or reestimating previous time steps, enabling straightforward operational deployment. We introduce online methods for persistent and distributed scatterer (DS) identification that adapt to temporal changes in surface properties through incremental amplitude statistics updates. The processing chain incorporates multiple complementary metrics for pixel quality that are reliable for small SLC stack sizes, as well as an $L_{1}$ -norm network inversion to limit propagation of unwrapping errors across the time series. We use our algorithm to produce the Observational Products for End-Users from Remote Sensing Analysis (OPERA) Surface Displacement from Sentinel-1 (DISP-S1) product, the first continental-scale surface displacement product over North America. Validation against GPS measurements and InSAR residual analysis demonstrates millimeter-level agreement in velocity estimates in varying environmental conditions. We also demonstrate our algorithm's capabilities with a successful recovery of meter-scale co-eruptive displacement at Kilauea volcano during the 2018 eruption, as well as detection of subtle uplift at Three Sisters volcano, Oregon-a challenging environment for C-band InSAR due to dense vegetation and seasonal snow. We have made all software available as open source libraries, providing a significant advancement to the open scientific community's ability to process large InSAR datasets in a cloud environment.
Tropical forests can recover diversity, structure, and function rapidly after disturbance. However, plant compositional recovery remains incomplete even after many decades, and the reasons for this lag are poorly understood. We investigated the roles of source, dispersal, and establishment limitation in affecting compositional recovery in Costa Rican forests using long-term data on species composition of trees, seeds, seedlings, and saplings, along with measurements of herbivory and leaf pathogen damage in seedlings and saplings from old-growth (OG) and second-growth (SG) forest plots. We classified tree species into successional groups, including: generalist (similar relative abundance in OG and SG), old-growth specialist (higher relative abundance in OG), old-growth exclusive (detected only in OG), and too rare to classify conclusively. Infrequent species drove community dissimilarity between old-growth and second-growth plots, and 40% of species in old-growth plots were absent as trees in second-growth forests up to 55 y old. Old-growth exclusive species (as trees and seedlings and saplings in OG plots and as seeds in seed traps in OG and SG) have extremely low abundance (11 trees/ha; 1 seedling or sapling/ha; 8 seeds/ha), indicating strong source limitation. Old-growth exclusive species shared seed dispersal traits with old-growth specialists established in SG, providing no evidence of dispersal limitation. Old-growth specialist seedlings and saplings had higher survival in SG than in OG, providing no evidence of establishment limitation. These findings suggest source limitation as the primary driver of delayed compositional recovery in mid-to-late stages of forest succession and underscore the potential for targeted enrichment plantings to accelerate full compositional recovery.
This study aimed to analyze the preferences and purchasing behaviors of urban Honduran consumers toward fresh market tomatoes, with a focus on quality attributes, purchasing behavior, and sociodemographic segmentation. A total of 2611 face-to-face surveys were conducted in municipal markets and supermarkets across seven cities in Honduras. The survey instrument collected demographic data, consumption patterns, and the importance assigned to various tomato attributes using a 4-point Likert scale. Consumer segmentation was performed using Gower dissimilarity and Partitioning Around Medoids (PAM) clustering. Data visualization was carried out through Factor Analysis of Mixed Data (FAMD). Kruskal-Wallis tests, Dunn's post hoc comparisons, and multinomial logistic regression were employed for statistical analysis. Tomato consumption was found to be frequent, with 83% of respondents consuming fresh market tomatoes at least three times per week. Consumers placed the highest value on hygiene (2.89), firmness (2.80), and color (2.80), and expressed a preference for pear-type (1.85), organically grown (2.62) tomatoes sold in municipal markets (2.04). Eight distinct consumer segments were identified, primarily differing in purchase channels, preferred tomato types, and storage methods. Segment membership was significantly associated with city, education, and income (p < 0.001), but not with gender or age. A majority (78.4%) indicated a willingness to pay more for higher quality products. While quality attributes were universally important, clear consumer subgroups emerged based on local context and purchasing habits. These findings provide actionable insights for producers, retailers, and policymakers aiming to improve tomato supply chains, food quality, and equitable access in low-income urban areas of Honduras.
