Focusing on the role of abiotic and biotic drivers on cross-taxon congruence

Bazzato, Erika; Lallai, Erik; CARIA, MICHELE; Schifani, Enrico; Cillo, Davide; ANCONA, CESARE; Pantini, Paolo; Maccherini, Simona; Bacaro, Giovanni; Marignani, Michela

doi:10.1016/j.ecolind.2023.110323

Cited by 7 publications

(2 citation statements)

References 134 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies indicate that seasonal differences in leaf cover can significantly impact the consistency of canopy height measurements, particularly for trees with thick, crooked, and spreading branches [8,10,49]. Natural processes such as growing, regeneration, fragmentation, and disturbance can provoke variable canopy heights and structure over time, particularly through forest gaps [82,83], which affects the accuracy of forest maps [41,52]. In our studied forests, human-induced disturbance likely affected only coniferous forests, as broadleaf sites were managed for conservation purposes.…”

Section: Comparison Of Predicted Canopy Height Maps With Reference Ai...mentioning

confidence: 99%

“…The results reveal that the CH-lang2020 map better fits the ALS-based CHM than CH-Potapov2019, with the best R-squared achieved in the coniferous forest sites. Accordingly, the comparison of canopy height maps from CH-Potapov 2019 and CH-Lang2020 to ALS-based CHMs can be sensitive to noise, lack, and registration errors [82]. In addition to most hindering factors affecting the mapping (Section 4.2), secondary factors, such as the following, may have influenced the comparison pixel-by-pixel approach: (1) ALS point density (60 vs. 300 points/m 2 ); (2) number of GEDI shots (acquired from late 2018 to 2019 vs. 2020); (3) mismatch between GEDI shot dates (2019 vs. 2020) and ALS acquisition date (2016 vs. 2021); (4) forest management plan (anthropic forestry interventions >50 past years vs. 2022); (5) tree species composition (mixed-species vs. unique); (6).…”

Section: Comparison Of Predicted Canopy Height Maps With Other Existi...mentioning

confidence: 99%

See 1 more Smart Citation

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

Alvites,

O’Sullivan,

Francini

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).

show abstract

Section: Comparison Of Predicted Canopy Height Maps With Reference Ai...mentioning

confidence: 99%

Section: Comparison Of Predicted Canopy Height Maps With Other Existi...mentioning

confidence: 99%

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

Alvites,

O’Sullivan,

Francini

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

Concordance among lacustrine communities are low and inconsistent in the conterminous United States

Liborio,

Bini

2024

Oecologia

View full text Add to dashboard Cite

Palms predict the distributions of birds in southwestern Amazonia and are potential surrogates for land-use planning by citizen scientists

Menger,

Santorelli Junior,

Emilio

et al. 2024

Biodivers Conserv

View full text Add to dashboard Cite

In the absence of high-quality biodiversity data, land-use planners and conservationists often rely on biodiversity surrogates. Many studies have proposed surrogates based on assumptions about the environmental niches of species. However, the use of such assumptions is not always useful because biological processes and ecological interactions can operate at different scales due to the non-uniform geographical distribution of environmental conditions. In such cases, compositional heterogeneity across the same region can be expected but is often hidden by broad-scale environmental data. Furthermore, these assumptions may obscure important relationships between species and their environment. To elucidate this issue, we asked whether biotic interactions between two taxonomic groups are more important than other factors in reflecting the distribution of unsampled species. To do this, we compared the relationship between the distribution of bird species and the distribution of the five most-abundant palm species which are often considered indicators of bird communities. These species include Lepidocaryum tenue, Oenocarpus bataua, Oenocarpus bacaba, Mauritiella aculeata, and Attalea speciosa. Additionally, we considered environmental factors (precipitation, water-table levels, sand and clay contents) and the ecoregions along the Purus-Madeira interfluve as drivers of bird species composition. Our results show that bird-assemblage composition was strongly correlated with changes in palm-species abundance. The presence-absence data for bird-species showed that palm-species alone explained 25% and 19% of composition of all birds and only canopy birds, respectively. These palm species are abundant and can be easily identified and monitored by non-specialists, such as citizen scientists. Citizens are often involved in data acquisition but may not have the experience to sample large assemblages consisting of hundreds of species; thus, these five most-abundant palms species could serve as a cost-effective and efficient biodiversity surrogate for birds.

show abstract

Focusing on the role of abiotic and biotic drivers on cross-taxon congruence

Cited by 7 publications

References 134 publications

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

Concordance among lacustrine communities are low and inconsistent in the conterminous United States

Palms predict the distributions of birds in southwestern Amazonia and are potential surrogates for land-use planning by citizen scientists

Contact Info

Product

Resources

About