Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska DOI Creative Commons
Dedi Yang, Wouter Hantson, Daniel J. Hayes

et al.

Environmental Research Ecology, Journal Year: 2024, Volume and Issue: 3(4), P. 045007 - 045007

Published: Dec. 1, 2024

Abstract The Arctic is warming at over twice the rate of rest Earth, resulting in significant changes vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding intricate interplay among climate, permafrost, generates high phenology variability across extensive tundra landscapes. This oversight has led to discrepancies phenological patterns observed experiments, long-term ecological observations, satellite modeling studies, undermining our ability understand forecast plant responses climate change Arctic. To address this problem, we assessed three low-Arctic landscapes on Seward Peninsula, Alaska, using combination in-situ phenocam observations high-resolution PlanetScope CubeSat data. We examined drivers diversity landscape by (1) quantifying dominant function types (PFTs) (2) interrelations between fine-scale features, such as topography, snowmelt, vegetation. Our findings reveal both spring fall varied significantly PFTs, accounting for about 25%–44% 34%–59% landscape-scale variation start [SOS] [SOF], respectively. Deciduous tall shrubs (e.g. alder willow) had later SOS (∼7 d behind mean other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared PFTs. modeled SOF Random Forest, which showed can be accurately captured suite variables related composition, topographic characteristics, snowmelt timing (variance explained: 53%–68% 59%–82% SOF). Notably, was determinant SOS, factor often neglected most models. study highlights impact snow seasonality, features heterogeneity. Improved understanding considerable intra-site associated proximate controls offers critical insights representation process models assessments with change.

Language: Английский

GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data DOI Creative Commons
X.-Z. Zhang, Shuai Xie, Yiping Zhang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(18), P. 3475 - 3475

Published: Sept. 19, 2024

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary (GPP) estimation remains challenging because high spatial seasonal variations savanna GPP. China’s ecosystems constitute only a small part world’s ecologically fragile. studies on GPP phenological changes, while closely related climate change, remain scarce. Therefore, we simulated via satellite-based vegetation photosynthesis model (VPM) fine-resolution harmonized Landsat Sentinel-2 (HLS) imagery derived phenophases from phenocam images. From 2015 2018, compared HLS VPM (GPPHLS-VPM) simulations that Moderate-Resolution Imaging Spectroradiometer (MODIS) (GPPMODIS-VPM) estimates an eddy covariance (EC) flux tower (GPPEC) Yuanjiang, China. Moreover, consistency was validated for conventional MODIS product (MOD17A2). This study clearly revealed potential estimating Compared VPM, yielded more lower root-mean-square errors (RMSEs) slopes closer 1:1. Specifically, annual RMSE values were 1.54 (2015), 2.65 (2016), 2.64 (2017), 1.80 (2018), whereas those 3.04, 3.10, 2.62, 2.49, respectively. The 1.12, 1.80, 1.65, 1.27, indicating agreement EC data than 2.04, 2.51, 2.14, 1.54, suitably indicated during all phenophases, especially autumn green-down period. As first simulates involving compares observations Chinese ecosystems, our enables exploration different effective management conservation worldwide.

Language: Английский

Citations

1
The Influence of Spatial Scale Effect on Rock Spectral Reflectance: A Case Study of Huangshan Copper–Nickel Ore District DOI Creative Commons
Ziwei Wang, Huijie Zhao, Guorui Jia

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4643 - 4643

Published: Dec. 11, 2024

The spectral reflectance measured in situ is often regarded as the “truth”. However, its limited coverage and large spatial heterogeneity make ground-based unable to represent remote sensing images. Since scale mismatch between ground-based, airborne, spaceborne measurements, applications of geological exploration, metallogenic prognosis mine monitoring are facing severe challenges. In order explore influence effect on rock spectra, with uncertainty caused by differences illumination view geometry introduced into Bayesian Maximum Entropy (BME) method. Then, spectra upscaled from point-scale meter-scale 10 m-scale, respectively. Finally, evaluated based value, shape, characteristic parameters. results indicate that BME model shows better upscaling accuracy stability than Ordinary Kriging Least Squares model. maximum Euclidean Distance resolution change 6.271, Spectral Angle Mapper can reach 0.370. absorption position, depth, index less affected effect. For area similar Huangshan Copper–Nickel Ore District, when image greater m, rock’s spectrum influenced resolution. Otherwise, should be considered applications. addition, this work puts forward a set processes evaluate study carry out upscaling.

Language: Английский

Citations

0
Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska DOI Creative Commons
Dedi Yang, Wouter Hantson, Daniel J. Hayes

et al.

Environmental Research Ecology, Journal Year: 2024, Volume and Issue: 3(4), P. 045007 - 045007

Published: Dec. 1, 2024

Abstract The Arctic is warming at over twice the rate of rest Earth, resulting in significant changes vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding intricate interplay among climate, permafrost, generates high phenology variability across extensive tundra landscapes. This oversight has led to discrepancies phenological patterns observed experiments, long-term ecological observations, satellite modeling studies, undermining our ability understand forecast plant responses climate change Arctic. To address this problem, we assessed three low-Arctic landscapes on Seward Peninsula, Alaska, using combination in-situ phenocam observations high-resolution PlanetScope CubeSat data. We examined drivers diversity landscape by (1) quantifying dominant function types (PFTs) (2) interrelations between fine-scale features, such as topography, snowmelt, vegetation. Our findings reveal both spring fall varied significantly PFTs, accounting for about 25%–44% 34%–59% landscape-scale variation start [SOS] [SOF], respectively. Deciduous tall shrubs (e.g. alder willow) had later SOS (∼7 d behind mean other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared PFTs. modeled SOF Random Forest, which showed can be accurately captured suite variables related composition, topographic characteristics, snowmelt timing (variance explained: 53%–68% 59%–82% SOF). Notably, was determinant SOS, factor often neglected most models. study highlights impact snow seasonality, features heterogeneity. Improved understanding considerable intra-site associated proximate controls offers critical insights representation process models assessments with change.

Language: Английский

Citations

0