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Context: To monitor wetlands at regional scales, it is pivotal to identify metrics that show rapid and predictable responses to restoration interventions. Remote sensing can monitor such metrics at high frequency and low cost but remains underutilized in practice. Objectives: This study sought to find a set of landscape metrics most responsive to restoration and vegetation dynamics across 11 years and 20 restored wetlands in the Sacramento-San Joaquin Delta of California, USA. Methods: Breakpoint analysis was used to detect phases in the development of vegetated pixels as estimated from the enhanced vegetation index (EVI) derived from Landsat (2004–2017). Landscape metrics were then generated from land cover classifications based on high resolution aerial images from the National Agricultural Inventory Program (NAIP). Using hierarchical clustering, we grouped phases showing similar temporal characteristics. We selected a subset of landscape metrics that best described the spatial structure of vegetation and its dynamics in each phase type. Results: We identified four phases in vegetation development: (1A) rapid increase; (1B) decrease; (2A) low change; (2B) low change with fluctuations. Landscape metrics showed a significant response to vegetation dynamics in our sample, suggesting their potential to expand current monitoring practices at low cost. Young sites and sites experiencing a rapid increase in greenness were characterized by a lower density of small patches, while older sites, reference sites, and low variability sites were characterized by large, clustered patches. Conclusions: Our study demonstrates that open source remote sensing can detect patterns in wetland response to restoration and help identify factors promoting their recovery. [ABSTRACT FROM AUTHOR]

Significant wetland losses and continuing threats to remnant habitats have motivated extensive restoration efforts in the San Francisco Bay-Delta estuary of California, the largest in the western United States. Consistent monitoring of ecological outcomes from this restoration effort would help managers learn from past projects to improve the design of future endeavors. However, budget constraints and challenging field conditions can limit the scope of current monitoring programs. Geospatial tools and remote sensing data sets could help complement field efforts for a low-cost, longer, and broader monitoring of wetland resources. To understand where geospatial tools could best complement current field monitoring practices, we reviewed the metrics and monitoring methods used by 42 wetland restoration projects implemented in the estuary. Monitoring strategies within our sample of monitoring plans relied predominantly on field surveys to assess key aspects of vegetation recovery while geospatial data sets were used sparingly. Drawing on recent publications that focus on the estuary and other wetland systems, we propose additional geospatial applications to help monitor the progress made toward site-specific and regional goals. These include the use of ecological niche models to target on-the-ground monitoring efforts, the up-scaling of field measurements into regional estimates using remote sensing data, and the analysis of time-series to detect ecosystem shifts. We discuss challenges and limitations to the broadscale application of remote sensing data in wetland monitoring. These notably include the need to find a venue to store and share computationally intensive data sets, the often cumbersome pre-processing effort needed for long-term analyses, and multiple confounding factors that can obscure the signal of remote sensing data sets. [ABSTRACT FROM AUTHOR]

• Remotely sensed proxies of wetland phenology are sensitive to spatial heterogeneity • Site schedules of remotely sensed greenness resonate with ecosystem productivity • Heterogeneity and spatial resolution affect phenological agreement among sensors • Higher spatial resolution may increase error and complexity in phenological fitting • Satellite-based phenology may elucidate links among canopy structure and function Seasonal phenological dynamics of vegetation hold important clues on ecosystem performance towards management goals, such as carbon uptake, and thus should be considered in projections of their targeted services. However, in wetlands spatio-temporal heterogeneity due to mixing of open water, soil, green and dead vegetation makes it difficult to generalize ecosystem functioning across different regions. Remote sensing observations can provide spatially-explicit, cost-effective phenology indicators; however, little is known about their capacity to indicate the links between wetland ecosystem structure and function. Here we assessed this potential by comparing one-year Enhanced Vegetation Index (EVI) from satellite products at high (5m; RapidEye) and low (30m; Landsat) spatial resolutions with eddy covariance time series of net carbon exchange, field digital camera (phenocam) greenness and water temperature among three floristically similar restored wetlands in California, USA. Phenological timing differed by wetland site: depending on satellite, the range in site-median start of greening was up to 28 days, end of greening – up to 73 days, start of senescence – up to 79 days, and end of senescence – up to 10 days. Key transition dates from satellite inputs agreed with seasonal changes in net carbon exchange, phenocam greenness and water temperatures, suggesting that phenological contrasts could result in part from site differences in vegetation configuration and litter affecting the exposure of canopy, soil and water to sunlight and thus sub-canopy microclimate and ecosystem functioning. Yet, the agreement between satellite inputs was non-systematic, with the greatest disparities at the more heterogeneous, less vegetated site. Phenological model fitting uncertainty increased with greater spatial resolution, highlighting the tradeoff between the accuracy of representing vegetation and the complexity of local seasonal variation. These findings highlight the sensitivity of satellite-derived phenology to structural and functional heterogeneity of ecosystems and call for more rigorous spatially-explicit analyses to inform assessments of restoration and management outcomes. [ABSTRACT FROM AUTHOR]