Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

Journal of Geophysical Research Biogeosciences, Journal Year: 2022, Volume and Issue: 127(2)

Published: Feb. 1, 2022

Abstract Observing the environment in vast regions of Earth through remote sensing platforms provides tools to measure ecological dynamics. The Arctic tundra biome, one largest inaccessible terrestrial biomes on Earth, requires across multiple spatial and temporal scales, from towers satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale using IS derived advances our understanding vegetation communities their interaction with environment. To best leverage ongoing forthcoming resources, including National Aeronautics Space Administration’s Surface Biology Geology mission, we identify series opportunities challenges based intrinsic spectral dimensionality analysis review current data literature that illustrates unique attributes biome. These include thematic mapping, complicated by low‐stature plants very fine‐scale surface composition heterogeneity; development scalable algorithms retrieval canopy leaf traits; nuanced variation growth complicates detection long‐term trends; rapid phenological changes brief growing seasons may go undetected due low revisit frequency or be obscured snow cover clouds. recommend improvements future field campaigns satellite missions, advocating research combines multi‐scale spectroscopy, lab studies satellites enable frequent continuous monitoring, inform statistical biophysical approaches model

Language: Английский

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The value of hyperspectral UAV imagery in characterizing tundra vegetation

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 308, P. 114175 - 114175

Published: May 15, 2024

The fine-scale spatial heterogeneity of low-growth Arctic tundra landscapes necessitates the use high-spatial-resolution remote sensing data for accurate detection vegetation patterns. While multispectral satellite and aerial imaging, including uncrewed vehicles (UAVs), are common approaches, hyperspectral UAV imaging has not been thoroughly explored in these ecosystems. Here, we assess added value relative to modelling plant communities oroarctic heaths Saariselkä, northern Finland. We compare three different spectral compositions: 4-channel broadband images, 5-channel images 112-channel narrowband images. Based on field plot data, estimate vascular aboveground biomass, leaf area index, species richness, Shannon's diversity community composition. topographic information compile 12 explanatory datasets random forest regression classification. For biomass highest R2 values were 0.60 0.65, respectively, variables most important. In best models biodiversity metrics richness index 0.53 0.46, with hyperspectral, topographic, having high importance. 4 floristically determined clusters, both classifications fuzzy cluster membership regressions conducted. Overall accuracy (OA) classification was 0.67 at best, while estimated an 0.29–0.53. Variable importance heavily dependent composition, but multispectral, all selected composition models. Hyperspectral generally outperformed ones when excluded. With this difference diminished, performance improvements from limited 0–10 percentage point increases R2, largest occurring lowest R2. These results suggest that can outperform mostly sufficient practical applications heaths.

Language: Английский

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Improving the Accuracy of Forest Structure Analysis by Consumer-Grade UAV Photogrammetry Through an Innovative Approach to Mitigate Lens Distortion Effects

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 383 - 383

Published: Jan. 23, 2025

The generation of aerial and unmanned vehicle (UAV)-based 3D point clouds in forests their subsequent structural analysis, including tree delineation modeling, pose multiple technical challenges that are partly raised by the calibration non-metric cameras mounted on UAVs. We present a novel method to deal with this problem for forest structure analysis photogrammetric particularly areas complex textures varying levels canopy cover. Our proposed selects various subsets camera’s interior orientation parameters (IOPs), generates dense cloud each, then synthesizes these models form combined model. hypothesize model can provide superior representation than calibrated an optimal subset IOPs alone. effectiveness our methodology was evaluated sites across semi-arid ecosystem, known diverse crown structures varied density due traditional pruning as pollarding. results demonstrate enhanced outperformed standard 23% 37% both site- tree-based metrics, respectively, therefore be suggested further applications based consumer-grade UAV data.

Language: Английский

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Robustness and limitations of maximum entropy in plant community assembly

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103031 - 103031

Published: Feb. 1, 2025

Language: Английский

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Applications of unoccupied aerial systems (UAS) in landscape ecology: a review of recent research, challenges and emerging opportunities

Landscape Ecology, Journal Year: 2025, Volume and Issue: 40(2)

Published: Feb. 8, 2025

Language: Английский

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Repeated drone photogrammetry surveys demonstrate that reconstructed canopy heights are sensitive to wind speed but relatively insensitive to illumination conditions

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: July 26, 2024

Unoccupied aerial vehicle (UAV) based structure-from-motion (SfM) photogrammetry surveys are becoming a standard tool for ecologists to measure plant structure and biomass in non-forest ecosystems. The reproducibility of SfM survey results under different operational conditions, namely wind speed, sun elevation, cloud condition, is poorly understood. It also unclear what extent commonly applied point-to-grid interpolation derived point clouds affects inference vegetation structure. These knowledge gaps limit the use these methods measuring monitoring detection. We captured 61 UAV at same study area range wind/sun/cloud conditions over 24-day period during 2021 used generalized linear mixed effects models test how structural reconstructions varied with environmental conditions. Wind speed significantly influenced canopy height reconstructions, greater speeds reducing mean height. Different species exhibited varying sensitivities that likely related leaf attributes (size, structure, density), growth form canopy, physical properties such as limb flexibility. movement plants can reduce estimates from photogrammetric surveys, even relatively low speeds. Reconstructed heights were comparatively insensitive solar elevation variations. Cloud illumination by direct sunlight had weak, non-significant effect on reconstructed height, sunny (generating shadows) resulting measurable but marginal reduction heights. When comparing interpolated discontinuous highlighted this specific setting. recommend throughout where comparisons be made between drone-based either time or space. Care should taken ensure controlled so inferences valid.

Language: Английский

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To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests

Journal of Geophysical Research Biogeosciences, Journal Year: 2021, Volume and Issue: 126(12)

Published: Dec. 1, 2021

Abstract Tropical forests are complex multi‐layered systems, with the height and three‐dimensional (3D) structure of trees influencing carbon biodiversity they contain. Fine‐resolution 3D data on forest can be collected reliably Light Detection Ranging (LiDAR) sensors mounted aircraft or Unoccupied Aerial Vehicles (UAVs), however, remain expensive to collect process. Structure‐from‐Motion (SfM) Digital Photogrammetry (SfM‐DAP), which relies photographs taken same area from multiple angles, is a lower‐cost alternative LiDAR for generating structure. Here, we evaluate how SfM‐DAP compares acquired concurrently using fixed‐wing UAV, over two contrasting tropical in Gabon Peru. We show that cannot used isolation measure key aspects structure, including canopy (%Bias: 40%–50%), fractional cover, gap fraction, due difficulties measuring ground elevation, even under low tree cover. However, find forests, an effective means top‐of‐canopy surface heterogeneity, capable producing similar measurements vertical as LiDAR. Thus, areas where known, method important height, gaps, without data, more limited utility. Our results support growing evidence base pointing photogrammetry viable complement, alternative, LiDAR, providing much needed information mapping monitoring biomass biodiversity.

Language: Английский

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Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles

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

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Practical Guidelines for Performing UAV Mapping Flights with Snapshot Sensors

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 606 - 606

Published: Feb. 10, 2025

Uncrewed aerial vehicles (UAVs) have transformed remote sensing, offering unparalleled flexibility and spatial resolution across diverse applications. Many of these applications rely on mapping flights using snapshot imaging sensors for creating 3D models the area or generating orthomosaics from RGB, multispectral, hyperspectral, thermal cameras. Based a literature review, this paper provides comprehensive guidelines best practices executing such flights. It addresses critical aspects flight preparation execution. Key considerations in covered include sensor selection, height GSD, speed, overlap settings, pattern, direction, viewing angle; execution on-site preparations (GCPs, camera calibration, reference targets) as well conditions (weather conditions, time flights) to take into account. In all steps, high-resolution high-quality data acquisition needs be balanced with feasibility constraints time, volume, post-flight processing time. For reflectance measurements, BRDF issues also influence correct setting. The formulated are based consensus. However, identifies knowledge gaps particularly angle general. aim advance harmonization UAV practices, promoting reproducibility enhanced quality

Language: Английский

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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning

Sustainable Environment, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 25, 2025

Language: Английский

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