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Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event.

 

Emergent marsh and open water have been identified as alternate stable states in tidal marshes with large, relative differences in hydrogeomorphic conditions. In the Florida coastal Everglades, concern has been raised regarding the loss of non-tidal, coastal peat marsh via dieback of emergent vegetation and peat collapse. To aid in the identification of alternate stable states, our objective was to characterize the variability of hydrogeomorphic and biologic conditions using a field survey and long-term monitoring of hydrologic and geomorphic conditions across a range of vegetated (emergent, submerged) and unvegetated (open water) communities, which we refer to as “ecosystem states,” in a non-tidal, brackish peat marsh of the coastal Everglades. Results show (1) linear relationships among field-surveyed geomorphic, hydrologic, and biologic variables, with a 35-cm mean difference in soil surface elevation between emergent and open water states, (2) an overall decline in soil elevation in the submerged state that was related to cumulative dry days, and (3) a 2× increase in porewater salinity during the dry season in the emergent state that was also related to the number of dry days. Coupled with findings from previous experiments, we propose a conceptual model that describes how seasonal hydrologic variability may lead to ecosystem state transitions between emergent and open water alternate states. Since vegetative states are only moderately salt tolerant, as sea-level rise pushes the saltwater front inland, the importance of continued progress on Everglades restoration projects, with an aim to increase the volume of freshwater being delivered to coastal wetlands, is the primary management intervention available to mitigate salinization and slow ecosystem state shifts in non-tidal, brackish peat marshes.

 

Land-cover class definitions are scale-dependent. Up-scaling categorical data must account for that dependence, but most decision rules aggregating categorical data do not produce scale-specific class definitions. However, non-hierarchical, empirically derived classification systems common in phytosociology define scale-specific classes using species co-occurrence patterns.

Evaluate tradeoffs in class precision and representativeness when up-scaling categorical data across natural landscapes using the multi-dimensional grid-point (MDGP)-scaling algorithm, which generates scale-specific class definitions; and compare spectral detection accuracy of MDGP-scaled classes to ‘majority-rule’ aggregated classes.

Vegetation maps created from 2-m resolution WorldView-2 data for two Everglades wetland areas were scaled to the 30-m Landsat grid with the MDGP-scaling algorithm. A full-factorial analysis evaluated the effects of scaled class-label precision and class representativeness on compositional information loss and detection accuracy of scaled classes from multispectral Landsat data.

MDGP‐scaling retained between 3.8 and 27.9% more compositional information than the majority rule as class-label precision increased. Increasing class-label precision and information retention also increased spectral class detection accuracy from Landsat data between 1 and 8.6%. Rare class removal and increase in class-label similarity were controlled by the class representativeness threshold, leading to higher detection accuracy than the majority rule as class representativeness increased.

When up-scaling categorical data across natural landscapes, negotiating trade-offs in thematic precision, landscape-scale class representativeness and increased information retention in the scaled map results in greater class-detection accuracy from lower-resolution, multispectral, remotely sensed data. MDGP-scaling provides a framework to weigh tradeoffs and to make informed decisions on parameter selection.

 

Over the last century, direct human modification has been a major driver of coastal wetland degradation, resulting in widespread losses of wetland vegetation and a transition to open water. High-resolution satellite imagery is widely available for monitoring changes in present-day wetlands; however, understanding the rates of wetland vegetation loss over the last century depends on the use of historical panchromatic aerial photographs. In this study, we compared manual image thresholding and an automated machine learning (ML) method in detecting wetland vegetation and open water from historical panchromatic photographs in the Florida Everglades, a subtropical wetland landscape. We compared the same classes delineated in the historical photographs to 2012 multispectral satellite imagery and assessed the accuracy of detecting vegetation loss over a 72 year timescale (1940 to 2012) for a range of minimum mapping units (MMUs). Overall, classification accuracies were >95% across the historical photographs and satellite imagery, regardless of the classification method and MMUs. We detected a 2.3–2.7 ha increase in open water pixels across all change maps (overall accuracies > 95%). Our analysis demonstrated that ML classification methods can be used to delineate wetland vegetation from open water in low-quality, panchromatic aerial photographs and that a combination of images with different resolutions is compatible with change detection. The study also highlights how evaluating a range of MMUs can identify the effect of scale on detection accuracy and change class estimates as well as in determining the most relevant scale of analysis for the process of interest. 

Extreme rainfall, induced by severe weather events, such as hurricanes, impacts wetlands because rapid water-depth increases can lead to flora and fauna mortality. This study developed an innovative algorithm to detect significant water-depth increases (SWDI, defined as water-depth increases above a threshold) in wetlands, using Sentinel-1 SAR backscatter. We used Hurricane Irma as an example that made landfall in the south Florida Everglades wetlands in September 2017 and produced tremendous rainfall. The algorithm detects SWDI for during- and post-event SAR acquisition dates, using pre-event water-depth as a baseline. The algorithm calculates Normalized Difference Backscatter Index (NDBI), using pre-, during-, and post-event backscatter, at a 20-m SAR resolution, as an indicator of the likelihood of SWDI, and detects SWDI using all NDBI values in a 400-m resolution pixel. The algorithm successfully detected large SWDI areas for the during-event date and progressive expansion of non-SWDI areas (water-depth differences less than the threshold) for five post-event dates in the following two months. The algorithm achieved good performance in both ‘herbaceous dominant’ and ‘trees embedded within herbaceous matrix’ land covers, with an overall accuracy of 81%. This study provides a solution for accurate mapping of SWDI and can be used in global wetlands, vulnerable to extreme rainfall. 

Bamboo‐dominated forests (BDF) extend over large areas in the drought‐prone Southwestern Amazon, yet little is known about the dynamics of these ecosystems. Here, we investigate the hypothesis that bamboo modulates large‐scale ecosystem dynamics through competition with coexisting trees for water.

We examined spatio‐temporal patterns of remotely sensed metrics (Enhanced Vegetation Index [EVI], Normalized Difference Moisture Index [NDMI]) in >300 Landsat images as proxies for canopy leaf phenology and water content at two time scales: (1) a complete bamboo life cycle (~28 years), and (2) the seasonal cycle; and at two spatial scales: (a) comparing adjacent areas of BDF vs.Terra‐firmeforests (TFF) to investigate regional dynamics, and (b) comparing the vegetation classes of bamboo, trees in BDF, and trees in TFF to investigate the effects of bamboo on coexisting trees.

At the regional scale, BDF showed higher EVI (leaf area density) and lower NDMI (water content) than nearby TFF but these differences disappeared as bamboo died, suggesting a strong influence of bamboo life stage in the functioning of these forests. BDF seasonal cycle showed a bimodal EVI pattern as trees and bamboos had asynchronized leaf production peaks.

At the scale of vegetation classes, trees in BDF showed lower NDMI (i.e. water content) than trees in TFF except after bamboo mortality, indicating a release from competition with bamboo for water. Canopy water content of trees in BDF was also reduced during bamboo dry‐season greening (increased EVI ~ leaf production) due to increased water demands. Nevertheless, long‐term and seasonal phenology of trees in BDF did not differ from that of trees in TFF suggesting a potential selection for drought‐tolerant trees in BDF.

Synthesis. Bamboo‐dominated forests have received less attention than other Amazonian forests and their functional dynamics are commonly ignored or misinterpreted. Using remote sensing to characterize forest phenology and water content, we show the distinctive seasonal and long‐term dynamics of BDF and coexisting trees and the importance of bamboo competition for water in shaping this ecosystem. Our results suggest a potential selection for drought‐tolerant trees in BDF since they maintain the same EVI as trees in bamboo‐free forests but with lower water content. A better characterization of BDF and their cyclical dynamics is crucial for accurately interpreting Amazonian forests' responses to extreme climatic events such as high temperatures and droughts.

 

Madagascar's lemurs are threatened by forest loss, fragmentation, and degradation. Many species use flexible behaviors to survive in degraded habitat, but their ability to persist in very small areas may be limited. Insular lemurs, like those found on Nosy Be, an island off the northwestern coast of Madagascar, are at heightened risk of sudden population declines and extirpation. Nosy Be is home to two Critically Endangered species—the endemic Nosy Be sportive lemur (Lepilemur tymerlachsoni) and Claire's mouse lemur (Microcebus mamiratra)—as well as the Endangered black lemur (Eulemur macaco). Most of the remaining forest on Nosy Be is protected by the 862‐ha Lokobe National Park. To document how Nosy Be lemurs use their restricted habitat, we conducted vegetation and reconnaissance surveys on 53 transects in and around Lokobe. We collected data on tree size, canopy cover, understory visibility, and elevation for 248 lemur sightings. We used a spatially explicit, multi‐species occupancy model to investigate which forest‐structure variables are important to lemurs. Our results represent some of the first data on habitat use by insular lemurs. Black lemurs preferred significantly larger trees and areas with less dense understory. They also occurred significantly less outside of Lokobe National Park, even when accounting for sampling effort and geography. The distributions of the sportive and mouse lemurs were not related to the forest structure variables we documented, but they did negatively predict each other—perhaps because their habitat requirements differ. These results also underscore the importance of the national park to protecting the black lemur population on Nosy Be and raise questions about what factors do influence the distribution of Nosy Be's smaller lemurs. Close monitoring is needed to prevent these populations and the ecosystem services they provide from disappearing, as have other island lemurs.

 

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.