Academic literature on the topic 'Mixed shrublands'
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Journal articles on the topic "Mixed shrublands"
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Streeks, Tamara J., M. Keith Owens, and Steve G. Whisenant. "Examining fire behavior in mesquite - acacia shrublands." International Journal of Wildland Fire 14, no. 2 (2005): 131. http://dx.doi.org/10.1071/wf03053.
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The vegetation of South Texas has changed from mesquite savanna to mixed mesquite–acacia (Prosopis–Acacia) shrubland over the last 150 years. Fire reduction, due to lack of fine fuel and suppression of naturally occurring fires, is cited as one of the primary causes for this vegetation shift. Fire behavior, primarily rate of spread and fire intensity, is poorly understood in these communities, so fire prescriptions have not been developed. We evaluated two current fire behavior systems (BEHAVE and the CSIRO fire spread and fire danger calculator) and three models developed for shrublands to determine how well they predicted rate of spread and flame length during three summer fires within mesquite–acacia shrublands. We also used geostatistical analyses to examine the spatial pattern of net heat, flame temperature and fuel characteristics. The CSIRO forest model under-predicted the rate of fire spread by an average of 5.43 m min−1 and over-predicted flame lengths by 0.2 m while the BEHAVE brush model under-predicted rate of spread by an average of 6.57 m min−1 and flame lengths by an average of 0.33 m. The three shrubland models did not consistently predict the rate of spread in these plant communities. Net heat and flame temperature were related to the amount of 10-h fuel on the site, but were not related to the cover of grasses, forbs, shrubs, or apparent continuity of fine fuel. Fuel loads were typical of South Texas shrublands, in that they were uneven and spatially inconsistent, which resulted in an unpredictable fire pattern.
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Schrader-Patton, Charlie C., and Emma C. Underwood. "New Biomass Estimates for Chaparral-Dominated Southern California Landscapes." Remote Sensing 13, no. 8 (April 19, 2021): 1581. http://dx.doi.org/10.3390/rs13081581.
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Chaparral shrublands are the dominant wildland vegetation type in Southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrublands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict aboveground live biomass in Southern California using remote sensing data (Landsat Normalized Difference Vegetation Index (NDVI)) and a suite of geophysical variables. By substituting the NDVI and precipitation predictors for any given year, we were able to apply the model to each year from 2000 to 2019. Using a total of 980 field plots, our model had a k-fold cross-validation R2 of 0.51 and an RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE = 9.7) for Sierran mixed-conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers, we calculated the mean year 2010 shrubland biomasses for the four national forests that ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomasses from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measurements in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.
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Fernández-Guisuraga, José Manuel, Leonor Calvo, Paulo M. Fernandes, and Susana Suárez-Seoane. "Short-Term Recovery of the Aboveground Carbon Stock in Iberian Shrublands at the Extremes of an Environmental Gradient and as a Function of Burn Severity." Forests 13, no. 2 (January 19, 2022): 145. http://dx.doi.org/10.3390/f13020145.
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The degree to which burn severity influences the recovery of aboveground carbon density (ACD) of live pools in shrublands remains unclear. Multitemporal LiDAR data was used to evaluate ACD recovery three years after fire in shrubland ecosystems as a function of burn severity immediately after fire across an environmental and productivity gradient in the western Mediterranean Basin. Two large mixed-severity wildfires were assessed: an Atlantic site, dominated by resprouter shrubs and located at the most productive extreme of the gradient, and a Mediterranean site, dominated by obligate seeders and located at the less productive extreme. Initial assessment of burn severity was performed using the differenced Normalized Burn Ratio index computed from Landsat imagery. Thresholds for low and high burn severity categories were established using the Composite Burn Index (CBI). LiDAR canopy metrics were calibrated with field measurements of mean shrub height and cover at plot level in a post-fire situation. Pre-fire and post-fire ACD estimates, and their ratio (ACDr) to calculate carbon stock recovery, were computed from the predictions of LiDAR grid metrics at landscape level using shrubland allometric relationships. Overall, ACDr decreased both with high burn severity and low productivity, although the burn severity impact was not homogeneous within the gradient. In the Atlantic site, ACDr was similar under low and high burn severity, whereas it decreased with burn severity in the Mediterranean site. These results suggest that carbon cycling models could be biased by not accounting for both fire severity and species composition of shrublands under different environmental conditions.
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Pearce, H. G., W. R. Anderson, L. G. Fogarty, C. L. Todoroki, and S. A. J. Anderson. "Linear mixed-effects models for estimating biomass and fuel loads in shrublands." Canadian Journal of Forest Research 40, no. 10 (October 2010): 2015–26. http://dx.doi.org/10.1139/x10-139.
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Shrubland biomass is important for fire management programmes and for carbon estimates. Aboveground biomass and the combustible portion of biomass, the fuel load, in the past have been measured using destructive techniques. These techniques are detailed, highly labour intensive, and costly; hence, an alternative approach was sought. The new approach used linear mixed-effects models to estimate biomass and fuel loads from easily measured field variables: shrub overstorey height and cover, and understorey height and cover. Site was regarded as a random effect. Sampling sites were located throughout New Zealand and included a range of shrubland vegetation types: manuka ( Leptospermum scoparium J.R. Forst. et G. Forst.) and kanuka ( Kunzea ericoides (A. Rich.) J. Thomps.) scrub and heath, pakihi (mixed low heath, fern, and rushes), and gorse ( Ulex europaeus L.). The approach was extended and confidence intervals were constructed for the regression models. Statistical analysis showed that understorey height and overstorey cover were significant (at the 5% level) in some cases. Overstorey height was highly significant in all cases (p < 0.0001), allowing development of models useful to the operational user. The models allow rapid estimation of average fuel loads or biomass on new sites, and double sampling theory can be applied to calculate the error in the resultant biomass estimate.
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Costa-Saura, José M., Ángel Balaguer-Beser, Luis A. Ruiz, Josep E. Pardo-Pascual, and José L. Soriano-Sancho. "Empirical Models for Spatio-Temporal Live Fuel Moisture Content Estimation in Mixed Mediterranean Vegetation Areas Using Sentinel-2 Indices and Meteorological Data." Remote Sensing 13, no. 18 (September 17, 2021): 3726. http://dx.doi.org/10.3390/rs13183726.
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Live fuel moisture content (LFMC) is an input factor in fire behavior simulation models highly contributing to fire ignition and propagation. Developing models capable of accurately estimating spatio-temporal changes of LFMC in different forest species is needed for wildfire risk assessment. In this paper, an empirical model based on multivariate linear regression was constructed for the forest cover classified as shrublands in the central part of the Valencian region in the Eastern Mediterranean of Spain in the fire season. A sample of 15 non-monospecific shrubland sites was used to obtain a spatial representation of this type of forest cover in that area. A prediction model was created by combining spectral indices and meteorological variables. This study demonstrates that the Normalized Difference Moisture Index (NDMI) extracted from Sentinel-2 images and meteorological variables (mean surface temperature and mean wind speed) are a promising combination to derive cost-effective LFMC estimation models. The relationships between LFMC and spectral indices for all sites improved after using an additive site-specific index based on satellite information, reaching a R2adj = 0.70, RMSE = 8.13%, and MAE = 6.33% when predicting the average of LFMC weighted by the canopy cover fraction of each species of all shrub species present in each sampling plot.
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Poulos, Helen M., Michael R. Freiburger, Andrew M. Barton, and Alan H. Taylor. "Mixed-Severity Wildfire as a Driver of Vegetation Change in an Arizona Madrean Sky Island System, USA." Fire 4, no. 4 (October 20, 2021): 78. http://dx.doi.org/10.3390/fire4040078.
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Fire is a powerful natural disturbance influencing vegetation patterns across landscapes. Recent transitions from mixed-species forests to post-fire shrublands after severe wildfire is an increasingly prevalent phenomenon in pine-oak and conifer forest ecosystems in southwestern North America. However, we know little about how variation in fire severity influences other common forest types in the region. In this study, we evaluated fire-induced changes in woody plant community composition and forest structure in Chiricahua Mountains in southeastern Arizona in the United States that hosts a diverse set of vegetation types. Cluster analysis of the pre-fire vegetation data identified three dominant pre-fire vegetation types including juniper woodland, piñon forest, and pine-oak forest. All vegetation types experienced significant tree mortality across a wide range of size classes and species, from forests to shrublands. The magnitude of change within sample plots varied with fire severity, which was mediated by topography. Significant shifts in dominance away from coniferous obligate seeder trees to resprouting hardwoods and other shrubs occurred across all vegetation types in response to the fire. Regeneration from seed can be episodic, but projected increases in aridity and fire frequency may promote continued dominance by hardwoods and fire- and drought-resistant shrub communities, which is a regional forest management concern as wildfire size and severity continue to increase throughout the southwestern USA.
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Gartzia, Maite, Concepción L. Alados, and Fernando Pérez-Cabello. "Assessment of the effects of biophysical and anthropogenic factors on woody plant encroachment in dense and sparse mountain grasslands based on remote sensing data." Progress in Physical Geography: Earth and Environment 38, no. 2 (April 2014): 201–17. http://dx.doi.org/10.1177/0309133314524429.
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Land abandonment exacerbated by climate change has led to increased woody plant encroachment of mountain grasslands in many regions of the world. The present study assessed woody plant encroachment below potential tree line in the Central Pyrenees of Spain and the association of this encroachment with changes in land use. Remote sensing data from Landsat-5 Thematic Mapper (TM) from the mid-1980s and mid-2000s were analyzed by supervised classification for identification of land cover types. The transition matrix indicated that shrublands were the most dynamic plant communities. Consequently, 21% of cultivated areas, 19% of dense grasslands, and 24% of sparse grasslands became shrublands during the period analyzed, and 35% of shrublands became forest. Generalized additive mixed models (GAMMs) were used to identify biophysical and anthropogenic factors that were significantly correlated with woody plant encroachment of dense and sparse grasslands. Distance to the nearest woody plant habitat (shrub or forest) was the most strongly correlated factor with woody plant encroachment of both types of grassland. This factor explained 69% and 71% of the variance in models of dense and sparse grasslands, respectively. Besides this factor, anthropogenic factors had larger effects on woody plant encroachment of dense grasslands, regions that were more productive and accessible. However, biophysical and especially topographic factors had slightly greater effects on woody plant encroachment of sparse grasslands, regions that were less productive and accessible. The changes in land cover that we observed indicated that land cover has become more homogeneous. There have been reductions in the variety, functions, and services of grasslands, particularly in areas below the potential tree line that are vulnerable to the development of woody plant habitats.
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García, Miguel, Hassane Moutahir, Grant Casady, Susana Bautista, and Francisco Rodríguez. "Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain." Remote Sensing 11, no. 5 (March 2, 2019): 507. http://dx.doi.org/10.3390/rs11050507.
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Land Surface Phenology (LSP) metrics are increasingly being used as indicators of climate change impacts in ecosystems. For this purpose, it is necessary to use methods that can be applied to large areas with different types of vegetation, including vulnerable semiarid ecosystems that exhibit high spatial variability and low signal-to-noise ratio in seasonality. In this work, we evaluated the use of hidden Markov models (HMM) to extract phenological parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI). We analyzed NDVI time-series data for the period 2000–2018 across a range of land cover types in Southeast Spain, including rice croplands, shrublands, mixed pine forests, and semiarid steppes. Start of Season (SOS) and End of Season (EOS) metrics derived from HMM were compared with those obtained using well-established smoothing methods. When a clear and consistent seasonal variation was present, as was the case in the rice croplands, and when adjusting average curves, the smoothing methods performed as well as expected, with HMM providing consistent results. When spatial variability was high and seasonality was less clearly defined, as in the semiarid shrublands and steppe, the performance of the smoothing methods degraded. In these cases, the results from HMM were also less consistent, yet they were able to provide pixel-wise estimations of the metrics even when comparison methods did not.
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Tarasova, L. V., and L. N. Smirnova. "Satellite-based analysis of classification algorithms applied to the riparian zone of the Malaya Kokshaga river." IOP Conference Series: Earth and Environmental Science 932, no. 1 (December 1, 2021): 012012. http://dx.doi.org/10.1088/1755-1315/932/1/012012.
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Abstract The paper comparatively analyses the accuracy of land cover classification in the riparian zone of the Malaya Kokshaga river in the Mari El Republic of Russia using Sentinel-2A satellite images with the algorithms of supervised classification: Maximum Likelihood (ML), Decision Tree (DT) and Neural Net (NN) in the ENVI-5.2 software package. Six main classes of land cover were identified based on field studies: coniferous, mixed (deciduous), shrublands, herbaceous, and water. The assessment of the area and the structure of land cover showed that forest covers 76% of the entire territory of the riparian area of the Malaya Kokshaga river. The analysis of the results of thematic mapping shows that the overall classification accuracy obtained by the ML algorithm is 96.09%, by NN - 94.51%, and by DT - 86.54%. The producer’s accuracy and user’s accuracy for most classes have the maximum value when the ML algorithm is used. For the NN algorithm, the maximum value of producer’s accuracy is observed for the mixed (deciduous) class, while for the DT algorithm – for the coniferous. When classified using all three algorithms the water and bare land classes were mixed, which requires more detailed work when estimating riparian forest ecosystems.
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Alt, Mio, David B. McWethy, Rick Everett, and Cathy Whitlock. "Millennial scale climate-fire-vegetation interactions in a mid-elevation mixed coniferous forest, Mission Range, northwestern Montana, USA." Quaternary Research 90, no. 1 (May 8, 2018): 66–82. http://dx.doi.org/10.1017/qua.2018.25.
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AbstractMixed coniferous forests are widespread at middle elevations in the Northern Rocky Mountains, yet relatively little is known about their long-term vegetation and fire history. Pollen and charcoal records from Twin Lakes, in the Mission Range of northwestern Montana provide information on mixed-coniferous forest development and fire activity over the last 15,000 years. These data suggest an open parkland and minimal fire activity before 13,500 cal yr BP, consistent with cold, dry conditions. Increases inPinuspollen after 13,500 cal yr BP indicate a transition to closed forests, and a slight rise in fire activity as conditions warmed and fuel biomass increased. High levels ofArtemisiapollen between 10,000 and 6000 cal yr BP suggest an open forest during the early Holocene when conditions were warmer and drier than present. Low-severity fires likely maintained open forest/shrublands but produced little charcoal during this interval. Present-day mixed-coniferous forests were established in the last 6 ka and included forest taxa characteristic of low- (Pseudotsuga-Larix/Pinus ponderosa)and high-severity fire regimes (Picea/Abies). Increased climate variability and anthropogenic burning may have helped maintain heterogeneous, mixed-coniferous forests during the last several millennia when climate conditions became cooler and wetter.
Dissertations / Theses on the topic "Mixed shrublands"
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Imran, Hosen M. "The Urban Heat Island of Melbourne during Heatwaves: Impacts of Future Urban Expansion and Effectiveness of Green Infrastructure as Mitigation Strategies." Thesis, 2018. https://vuir.vu.edu.au/43345/.
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The city of Melbourne in southeast Australia experiences an Urban Heat Island (UHI) effect, which is exacerbated during heatwaves, and the latter are becoming more frequent, intense and longer in southeast Australia. In addition, Melbourne is the fastest growing city in Australia. Therefore, it is urgent to understand the dynamics of UHI and impacts of future urban expansion on the UHI during heatwaves. Based on these issues, there is a crucial need to investigate the effectiveness of potential mitigation strategies to minimize UHI effects during heatwaves. The overarching aim of the thesis is to investigate the impacts of future urban expansion on the UHI during heatwave events in Melbourne, and examine the effectiveness of different Green Infrastructure (GI) scenarios such as green/cool roofs, mixed forest (MF), mixed forest and grassland (MFAG), and mixed shrublands and grasslands (MSAG) in mitigating UHI effects.
The Weather Research and Forecasting (WRF) model coupled with the Single Layer Urban canopy Model (SLUCM) was used in simulating the UHI and heatwaves. Since the WRF model is known to be sensitivity to the choice of physical parameterisation options, an initial sensitivity analysis of the model was conducted and the best-possible WRF configuration to simulate the UHI during heatwaves in Melbourne was determined, among a 27-member physics ensemble. This configuration was used throughout the rest of the thesis.
Urban expansion increased near surface UHI by 0.75 to 2.80 °C during the night but no substantial impacts during the day. Urban surfaces absorbed more solar heat during the day as compared to vegetated surfaces, and the absorbed heat was released slowly from evening to early morning. The storage heat in urban surfaces was the key driver in increasing UHI during the night. Urban expansion did not substantially affect human health (HTC) comfort in existing and expanded urban areas.
Green roofs showed good performance in reducing roof surface UHI (1 to 3.8 °C) and near surface UHI (0.3 to 1.1 °C) during the day but not during the night, while cool roofs showed higher reductions at the roof surface UHI (2.2 to 5.2 °C) and near surface UHI (0.5 to 1.6 °C) during the day. Green roofs increased evapotranspiration and provided shading, and consequently, increased Latent Heat (LH) and substantially decreased storage heat and sensible heat, and as a result, reduced the UHI. Cool roofs reflected a major portion of incoming solar radiation due to higher albedo, and reduced the sensible heat flux and storage heat, and these were the key drivers in reducing UHI during the day. In addition, both green and cool roofs showed good potential in improving HTC from extreme to very strong during the day.
Other GI scenarios such as MF, MFAG and MSAG were effective in reducing UHI effects and improving HTC during the night but no substantial reductions were occurred during the day. By increasing GI fractions from 20 to 50 %, the UHI was reduced by 0.6 to 3.4 °C for MF, 0.4 to 3.0 °C for MSAG and 0.6 to 3.7 °C for MFAG. The night time cooling was driven by reductions in storage heat as 20 to 50 % urban areas were replaced by GI, which would have led to even less radiation reaching the ground surface during the day due to their higher LAI and shade factor, and leading to lower storage heat.
As the green and cool roofs showed potential in reducing UHI effects during the day while urban vegetated patches showed effectiveness during the night, therefore, a combination of green/cool roofs and urban vegetated patches could be an optimal mitigation strategy in reducing UHI effects and improving HTC during both day and night.
Reports on the topic "Mixed shrublands"
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Evans, Julie, Kendra Sikes, and Jamie Ratchford. Vegetation classification at Lake Mead National Recreation Area, Mojave National Preserve, Castle Mountains National Monument, and Death Valley National Park: Final report (Revised with Cost Estimate). National Park Service, October 2020. http://dx.doi.org/10.36967/nrr-2279201.
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Vegetation inventory and mapping is a process to document the composition, distribution and abundance of vegetation types across the landscape. The National Park Service’s (NPS) Inventory and Monitoring (I&M) program has determined vegetation inventory and mapping to be an important resource for parks; it is one of 12 baseline inventories of natural resources to be completed for all 270 national parks within the NPS I&M program. The Mojave Desert Network Inventory & Monitoring (MOJN I&M) began its process of vegetation inventory in 2009 for four park units as follows: Lake Mead National Recreation Area (LAKE), Mojave National Preserve (MOJA), Castle Mountains National Monument (CAMO), and Death Valley National Park (DEVA). Mapping is a multi-step and multi-year process involving skills and interactions of several parties, including NPS, with a field ecology team, a classification team, and a mapping team. This process allows for compiling existing vegetation data, collecting new data to fill in gaps, and analyzing the data to develop a classification that then informs the mapping. The final products of this process include a vegetation classification, ecological descriptions and field keys of the vegetation types, and geospatial vegetation maps based on the classification. In this report, we present the narrative and results of the sampling and classification effort. In three other associated reports (Evens et al. 2020a, 2020b, 2020c) are the ecological descriptions and field keys. The resulting products of the vegetation mapping efforts are, or will be, presented in separate reports: mapping at LAKE was completed in 2016, mapping at MOJA and CAMO will be completed in 2020, and mapping at DEVA will occur in 2021. The California Native Plant Society (CNPS) and NatureServe, the classification team, have completed the vegetation classification for these four park units, with field keys and descriptions of the vegetation types developed at the alliance level per the U.S. National Vegetation Classification (USNVC). We have compiled approximately 9,000 existing and new vegetation data records into digital databases in Microsoft Access. The resulting classification and descriptions include approximately 105 alliances and landform types, and over 240 associations. CNPS also has assisted the mapping teams during map reconnaissance visits, follow-up on interpreting vegetation patterns, and general support for the geospatial vegetation maps being produced. A variety of alliances and associations occur in the four park units. Per park, the classification represents approximately 50 alliances at LAKE, 65 at MOJA and CAMO, and 85 at DEVA. Several riparian alliances or associations that are somewhat rare (ranked globally as G3) include shrublands of Pluchea sericea, meadow associations with Distichlis spicata and Juncus cooperi, and woodland associations of Salix laevigata and Prosopis pubescens along playas, streams, and springs. Other rare to somewhat rare types (G2 to G3) include shrubland stands with Eriogonum heermannii, Buddleja utahensis, Mortonia utahensis, and Salvia funerea on rocky calcareous slopes that occur sporadically in LAKE to MOJA and DEVA. Types that are globally rare (G1) include the associations of Swallenia alexandrae on sand dunes and Hecastocleis shockleyi on rocky calcareous slopes in DEVA. Two USNVC vegetation groups hold the highest number of alliances: 1) Warm Semi-Desert Shrub & Herb Dry Wash & Colluvial Slope Group (G541) has nine alliances, and 2) Mojave Mid-Elevation Mixed Desert Scrub Group (G296) has thirteen alliances. These two groups contribute significantly to the diversity of vegetation along alluvial washes and mid-elevation transition zones.
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Ruiz, Pablo, Craig Perry, Alejando Garcia, Magali Guichardot, Michael Foguer, Joseph Ingram, Michelle Prats, Carlos Pulido, Robert Shamblin, and Kevin Whelan. The Everglades National Park and Big Cypress National Preserve vegetation mapping project: Interim report—Northwest Coastal Everglades (Region 4), Everglades National Park (revised with costs). National Park Service, November 2020. http://dx.doi.org/10.36967/nrr-2279586.
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The Everglades National Park and Big Cypress National Preserve vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service’s (NPS) Vegetation Mapping Inventory Program (VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park and Big Cypress National Preserve prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within these two federally-managed units as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in park management, resource management, research, and monitoring. This mapping project covers an area of approximately 7,400 square kilometers (1.84 million acres [ac]) and consists of seven mapping regions: four regions in Everglades National Park, Regions 1–4, and three in Big Cypress National Preserve, Regions 5–7. The report focuses on the mapping effort associated with the Northwest Coastal Everglades (NWCE), Region 4 , in Everglades National Park. The NWCE encompasses a total area of 1,278 square kilometers (493.7 square miles [sq mi], or 315,955 ac) and is geographically located to the south of Big Cypress National Preserve, west of Shark River Slough (Region 1), and north of the Southwest Coastal Everglades (Region 3). Photo-interpretation was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed specifically for this project. Additionally, photo-interpreters categorized the absolute cover of cattail (Typha sp.) and any invasive species detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%). A total of 178 thematic classes were used to map the NWCE. The most common vegetation classes are Mixed Mangrove Forest-Mixed and Transitional Bayhead Shrubland. These two communities accounted for about 10%, each, of the mapping area. Other notable classes include Short Sawgrass Marsh-Dense (8.1% of the map area), Mixed Graminoid Freshwater Marsh (4.7% of the map area), and Black Mangrove Forest (4.5% of the map area). The NWCE vegetation map has a thematic class accuracy of 88.4% with a lower 90th Percentile Confidence Interval of 84.5%.