Quantifying rangeland fractional cover in the Northern Great Basin sagebrush steppe communities using high-resolution unoccupied aerial systems (UAS) imagery DOI Creative Commons
Tao Huang, Peter J. Olsoy, Nancy F. Glenn

et al.

Landscape Ecology, Journal Year: 2024, Volume and Issue: 39(11)

Published: Nov. 14, 2024

Satellite products of fractional vegetation cover are often used to manage rangelands. However, they frequently miss the details heterogeneous landscapes. The use unoccupied aerial systems (UAS) produce high spatial resolution rangeland maps could fill that gap at local scales. We evaluated capabilities UAS imagery for mapping in sagebrush steppe communities Northern Great Basin, USA. applied segmentation and machine learning models image classification, established regression functions with field-measured herbaceous multiple spectral indices quantify fraction bare/herbaceous mixed polygons. Finally, we conducted a correlation analysis compare UAS-derived satellite-derived products. Overall classification accuracies were (89–98%). Modified Soil Adjusted Vegetation Index was most important index predicting photosynthetic classes including Brightness approach improved shadows bare ground. Regression effectively estimated fractions within polygons accuracy (R2 = 0.71–0.88). estimates captured within-site variability, while did not, specifically litter. This study demonstrated workflow using intensive ground sampling estimating communities. found disagreement between two Basin. recommend application when

Language: Английский

Assessing the potential distribution of Myracrodruon urundeuva Allemão (Aroeira) in the Caatinga under climate change scenarios DOI Creative Commons

Douglas Batista da Costa,

Róbson Borges de Lima, Rinaldo Luíz Caraciolo Ferreira

et al.

Frontiers in Forests and Global Change, Journal Year: 2025, Volume and Issue: 8

Published: March 19, 2025

The Caatinga, a seasonally dry tropical forest in northeastern Brazil, is notable for its biodiversity and high proportion of endemic plants adapted to semi-arid environment. Among prominent tree species, Myracrodruon urundeuva (Aroeira) stands out due extensive distribution economic value. Despite significance, little known about the environmental factors influencing distribution. This study uses species modeling (SDM) assess current potential M. habitat suitability under various climate change scenarios. Utilizing models like GLM, GAM, BRT, MaxEnt, research analyzes georeferenced occurrence data bioclimatic variables (selected by variance inflation factor) from precipitation temperature metrics. Our findings indicate that projected experience relative stability or slight expansion suitable habitats future scenarios, including pessimistic SSP585 scenario. However, localized losses may occur, particularly certain regions timeframes, highlighting complex regionally variable impacts change. emphasizes need regional action plans mitigate on ’s habitats. Conservation efforts should target areas identified as stable, ensuring species’ resilience against escalating threats, thereby preserving one critical within Caatinga.

Language: Английский

Citations

0
Exploring the resilience of global vegetation ecosystem: Nonlinearity, driving forces, and management DOI

Xuan Lv,

Guo Chen,

Qiang Wang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124634 - 124634

Published: Feb. 22, 2025

Language: Английский

Citations

0
Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles DOI Creative Commons
José Manuel Fernández‐Guisuraga, Leonor Calvo, Josh Enterkine

et al.

Landscape Ecology, Journal Year: 2024, Volume and Issue: 39(10)

Published: Oct. 14, 2024

Abstract Context The invasion of annual grasses in western U.S. rangelands promotes high litter accumulation throughout the landscape that perpetuates a grass-fire cycle threatening biodiversity. Objectives To provide novel evidence on potential fine spatial and structural resolution remote sensing data derived from Unmanned Aerial Vehicles (UAVs) to separately estimate biomass vegetation fractions sagebrush ecosystems. Methods We calculated several plot-level metrics with ecological relevance representative fraction distribution by strata UAV Light Detection Ranging (LiDAR) Structure-from-Motion (SfM) datasets regressed those predictors against vegetation, litter, total harvested field. also tested hybrid approach which we used digital terrain models (DTMs) computed LiDAR height-normalize SfM-derived point clouds (UAV SfM-LiDAR). Results had highest predictive ability terms (R 2 = 0.74) 0.59) biomass, while SfM-LiDAR provided performance for 0.77 versus R 0.72 LiDAR). In turn, SfM indicated pronounced decrease estimation biomass. Conclusions Our results demonstrate high-density are essential consistently estimating all through more accurate characterization (i) vertical structure plant community beneath top-of-canopy surface (ii) microtopography thick dense layers than achieved products.

Language: Английский

Citations

2
Quantifying rangeland fractional cover in the Northern Great Basin sagebrush steppe communities using high-resolution unoccupied aerial systems (UAS) imagery DOI Creative Commons
Tao Huang, Peter J. Olsoy, Nancy F. Glenn

et al.

Landscape Ecology, Journal Year: 2024, Volume and Issue: 39(11)

Published: Nov. 14, 2024

Satellite products of fractional vegetation cover are often used to manage rangelands. However, they frequently miss the details heterogeneous landscapes. The use unoccupied aerial systems (UAS) produce high spatial resolution rangeland maps could fill that gap at local scales. We evaluated capabilities UAS imagery for mapping in sagebrush steppe communities Northern Great Basin, USA. applied segmentation and machine learning models image classification, established regression functions with field-measured herbaceous multiple spectral indices quantify fraction bare/herbaceous mixed polygons. Finally, we conducted a correlation analysis compare UAS-derived satellite-derived products. Overall classification accuracies were (89–98%). Modified Soil Adjusted Vegetation Index was most important index predicting photosynthetic classes including Brightness approach improved shadows bare ground. Regression effectively estimated fractions within polygons accuracy (R2 = 0.71–0.88). estimates captured within-site variability, while did not, specifically litter. This study demonstrated workflow using intensive ground sampling estimating communities. found disagreement between two Basin. recommend application when

Language: Английский

Citations

0