About

The Spatial Ecology & Conservation (SPEC) Lab at the University of Florida was founded in 2014 by the faculty members Eben Broadbent and Angelica Almeyda Zambrano. The lab focuses on forest ecology, ecotourism, human-environment interactions, canopy biology, plant phenology, conservation biology, land use and land cover change, water quality, ecosystem services, and uses remote sensing techniques including satellite sensors, airborne sensors, UAVs, and ground-based systems.

Research topics

• Geography
• Ecology
• Computer Science
• Environmental science
• Biology
• Remote sensing
• Agroforestry
• Archaeology
• Forestry
• Cartography
• Chemistry
• Engineering
• Civil engineering
• Environmental resource management
• Environmental protection
• Environmental chemistry
• Mathematics
• Physical geography

Selected publications

• DUST: A framework for quantifying dugong-seagrass interactions using low-cost UAVs

  Marine Environmental Research · 2026-04-24

  article
  PublisherDOI

• Supplementary material to "The global forest diameter spectrum using a machine learning approach"

  2026-05-22

  articleOpen access
  PublisherDOI

• The global forest diameter spectrum using a machine learning approach

  2026-05-22

  articleOpen access

  Abstract. Global forest assessments assist climate policy development, ecosystem science, and conservation planning, yet they rely on biomass and canopy data that do not explicitly represent the stand structural attributes derived from tree diameter measurements. This limits the ability to compare size-related structure and within-stand heterogeneity at large spatial scales. Here we present a global, spatially explicit dataset of stand-level tree diameter structure for forest cover in 2020 at 0.027° (~3 km) resolution, based on 1,203,524 georeferenced forest inventory plots comprising 54.6 million trees (≥10 cm DBH) integrated with more than 50 environmental and satellite-derived covariates into machine learning models. The dataset provides the first globally consistent maps of three complementary diameter-based metrics: arithmetic mean diameter (Dmean), quadratic mean diameter (Dqm), and the coefficient of variation of diameter (Dcv), representing average tree size, large-tree dominance, and within-stand size variability, respectively. Model performance of the ecozone-specific Random Forest framework ranged from R² = 0.41–0.82 (RMSE = 3.91–4.63 cm) for Dmean, R² = 0.43–0.83 (RMSE = 4.38–5.27 cm) for Dqm, and R² = 0.47–0.62 with (RMSE = 0.10–0.13) for Dcv across different forest ecozones. By jointly quantifying central tendency and variability in tree size, the dataset revealed spatial patterns of forest structural organization not captured by existing biomass or canopy-height products. It provides a consistent baseline for cross-biome comparison of forest structure, supporting parameterization and evaluation of vegetation and Earth system models, while offering an independent benchmark for remotely sensed structural proxies. Furthermore, it enables spatial assessment of stand structural attributes, including large-tree dominance and structural complexity, facilitating integration of diameter-based structure into global analyses of carbon dynamics and ecosystem functioning.

  PublisherDOI

• The Promise and Peril of Coastal Infrastructure: Use Life of a Tidal Fish Trap on the Northern Gulf Coast of Florida, circa AD 400–650

  American Antiquity · 2025-10-29 · 1 citations

  articleOpen access

  Abstract The potential of coastal regimes for supporting permanent human settlement is tempered by the vulnerability of fixed infrastructure to changes in sea levels. First-millennium AD civic-ceremonial centers on the northern Gulf coast of Florida involved the construction of permanent infrastructure in support of regional gatherings that challenged sustainable settlement in the context of regressive sea. Although rising sea was the more common challenge over millennia of coastal dwelling, marine regression from periods of cooling climate slowly diminished near-shore habitat for fish and shellfish and eventually stranded settlements from tidal water. The challenge was especially acute for a community that built a tidal fish trap for summer solstice feasts, whose utility depended on the reliability of tides to flood the trap. High-resolution lidar data from the Richards Island fish trap enable accurate modeling of the effectiveness of the trap under current and lowered sea levels. The use-life history of the Richards Island fish trap illustrates the limits to intensification of coastal economies inherent to nonportable infrastructure whose utility is tide dependent—in particular, when demands on production are out of sync with optimal tidal conditions.

  PublisherOA PDFDOI

• Aboveground Biomass and Tree Mortality Revealed Through Multi-Scale LiDAR Analysis

  Remote Sensing · 2025-02-25 · 1 citations

  articleOpen access

  Accurately monitoring aboveground biomass (AGB) and tree mortality is crucial for understanding forest health and carbon dynamics. LiDAR (Light Detection and Ranging) has emerged as a powerful tool for capturing forest structure across different spatial scales. However, the effectiveness of LiDAR for predicting AGB and tree mortality depends on the type of instrument, platform, and the resolution of the point cloud data. We evaluated the effectiveness of three distinct LiDAR-based approaches for predicting AGB and tree mortality in a 25.6 ha North American temperate forest. Specifically, we evaluated the following: GEDI-simulated waveforms from airborne laser scanning (ALS), grid-based structural metrics derived from unmanned aerial vehicle (UAV)-borne lidar data, and individual tree detection (ITD) from ALS data. Our results demonstrate varying levels of performance in the approaches, with ITD emerging as the most accurate for AGB modeling with a median R2 value of 0.52, followed by UAV (0.38) and GEDI (0.11). Our findings underscore the strengths of the ITD approach for fine-scale analysis, while grid-based forest metrics used to analyze the GEDI and UAV LiDAR showed promise for broader-scale monitoring, if more uncertainty is acceptable. Moreover, the complementary strengths across scales of each LiDAR method may offer valuable insights for forest management and conservation efforts, particularly in monitoring forest dynamics and informing strategic interventions aimed at preserving forest health and mitigating climate change impacts.

  PublisherOA PDFDOI

• Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data

  Sensors · 2025-01-07 · 3 citations

  articleOpen access

  Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R2 = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data.

  PublisherOA PDFDOI

• Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S

  Remote Sensing · 2025-01-12

  articleOpen access

  Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal area in three tree diameter classes that are relevant to timber operations and wildlife habitat planning in southern United States pine forests. Specifically, linear modeling, ensemble linear modeling (ELM) and ensemble general additive modeling (EGAM) were compared. The results showed that the EGAM method provided the highest r-squared values and the lowest RMSE, and the ELM method provided good interpretability and 30 times faster processing than the EGAM method. Both ensemble methods produced a spatially explicit standard error estimate output without additional steps, unlike the single linear model. In general, the estimation results of this study were comparable or improved over prior studies’ estimates of basal area by tree diameter class.

  PublisherOA PDFDOI

• When can we detect lianas from space? Toward a mechanistic understanding of liana‐infested forest optics

  Ecology · 2025-04-01 · 6 citations

  articleOpen access

  Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals-reflectance across wavelengths-from liana-infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering 775 ha of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short-wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy-reflectance signal are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

  PublisherDOI

• Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States

  Remote Sensing · 2025-08-08 · 1 citations

  articleOpen access

  Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North Kaibab (NK) Plateau in Arizona and Monroe Mountain (MM) in Utah. We used random forest models to predict vegetation attributes, evaluating the performance of full models and transferred models using R2, RMSE, and bias. The MLS consistently outperformed the TLS system, particularly for canopy-related attributes and woody biomass components. However, the TLS system showed potential for capturing canopy structure attributes, while offering advantages like operational simplicity, low equipment demands, and ease of deployment in the field, making it a cost-effective alternative for managers without access to more complex and expensive mobile or airborne systems. Our results show that model transferability between NK and MM is highly variable depending on the fuel attributes. Attributes related to canopy biomass showed better transferability, with small losses in predictive accuracy when models were transferred between the two sites. Conversely, surface fuel attributes showed more significant challenges for model transferability, given the difficulty of laser penetration in the lower vegetation layers. In general, models trained in NK and validated in MM consistently outperformed those trained in MM and transferred to NK. This may suggest that the NK plots captured a broader complexity of vegetation structure and environmental conditions from which models learned better and were able to generalize to MM. This study highlights the potential of ground-based LiDAR technologies in providing detailed information and important insights into fire risk and forest structure.

  PublisherOA PDFDOI

• Optimizing UAV-LiDAR Point Density for Eucalyptus Height Estimation in Agroforestry

  Forests · 2025-11-19

  articleOpen access

  The demand for forest materials necessitates advancements in forest management and inventory practices. We explore the integration of Unmanned Aerial Vehicles (UAVs) equipped with LiDAR sensors as a cost-effective alternative for precise forest monitoring. It evaluates the impact of varying point cloud densities on the accuracy of individual tree height estimation in Eucalyptus benthamii within Crop–Livestock–Forestry systems (15.9 ha and 357 individuals·ha−1). We use a DJI M600 Pro UAV with a Velodyne 32c Ultra Puck LiDAR sensor at the Center for Technological Innovation in Agriculture (NITA) in Brazil. The resulting point clouds were processed to generate Digital Terrain Models and Canopy Height Models at densities ranging from 5 to 2000 points per square meter (pts·m−2). Statistical analyses, including Pearson correlation, root mean square error, and bias, were conducted to compare UAV-LiDAR-derived heights with field measurements. We found that reduced point densities, particularly around 100 pts·m−2, maintained high accuracy in height estimation (RMSE = 17.129%, BIAS = −7.889%), with more than 90% in trees’ detection. UAV-LiDAR systems with optimized point cloud densities offer a viable solution for forest monitoring. 100 pts·m−2 is an optimal density, promoting faster data collection, lower battery consumption, and reduced computational costs on trees’ height estimates.

  PublisherOA PDFDOI

Frequent coauthors

• Angélica M. Almeyda Zambrano

  University of Florida

  161 shared

• British Airways

  Institute of Aviation Medicine

  64 shared

• P Hearne

  Institute of Aviation Medicine

  64 shared

• P. Stow

  64 shared

• Gareth R. Howell

  Tufts University

  64 shared

• Gavin M Ratcliffe

  Oberlin College

  64 shared

• R Doganis

  Civil Aviation Authority

  64 shared

• David W. McLean

  Society for Maternal-Fetal Medicine

  64 shared

Labs

• SPATIAL ECOLOGY & CONSERVATION (SPEC) LABPI

  Angelica M. Almeyda Zambrano , PhD, is Research Faculty in the Center for Latin American Studies at the University of Florida. Where she is a Core Faculty of the Tropical Conservation and Development...

Education

• PhD, Biology

  Stanford University

  2012

• MS, School of Forest Resources and Conservation

  University of Florida

  2005

• BA, Botany

  University of Vermont

  2000