Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations—A Brief Review DOI Creative Commons
Mateo Gašparović, Ivan Pilaš, Dorijan Radočaj

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 12020 - 12020

Published: Dec. 22, 2024

Monitoring and predicting land surface phenology (LSP) are essential for understanding ecosystem dynamics, climate change impacts, forest agricultural productivity. Satellite Earth observation (EO) missions have played a crucial role in the advancement of LSP research, enabling global continuous monitoring vegetation cycles. This review provides brief overview key EO satellite missions, including advanced very-high resolution radiometer (AVHRR), moderate imaging spectroradiometer (MODIS), Landsat program, which an important capturing dynamics at various spatial temporal scales. Recent advancements machine learning techniques further enhanced prediction capabilities, offering promising approaches short-term cropland suitability assessment. Data cubes, organize multidimensional data, provide innovative framework enhancing analyses by integrating diverse data sources simplifying access processing. highlights potential satellite-based monitoring, models, cube infrastructure advancing research insights into current trends, challenges, future directions.

Language: Английский

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems DOI Creative Commons
Fan Yang, Chao Liu, Qianqian Chen

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(9), P. 1580 - 1580

Published: April 29, 2024

Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon capacity. However, our knowledge the seasonal responses productivity to phenological physiological changes in alpine ecosystems still weak. In this study, we quantified spatio-temporal variations gross primary (GPP) across source region Yellow River (SRYR) analyzing MODIS-derived GPP from 2001 2019, explored how maximum capacity (GPPmax) affected over region. Our results showed that SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) peak (POS) growing season 2019. Spring (GPPspr) had a increasing trend 0.01), earlier SOS obvious positive effects on GPPspr. Summer (GPPsum) was negatively correlated POS 0.05). addition, GPPmax significant correlation with GPPsum GPPann respectively. It found an spring-summer higher enhanced efficiency spring summer altered patterns under warming wetting climates. This study indicated not only autumn but also should be regarded as crucial indicators regulating process ecosystems. research provides important information about affect global climate warming.

Language: Английский

Citations

3
Optimizing natural boundary definition and functional zoning in protected areas: An integrated framework encompassing species, landscapes and ecosystems DOI Creative Commons
Shiyuan Wang, Wutao Yao,

Yong Ma

et al.

Global Ecology and Conservation, Journal Year: 2023, Volume and Issue: 49, P. e02781 - e02781

Published: Dec. 21, 2023

To promote the harmonized development of economic construction and ecological protection, our study introduces an integrated framework that employs various methodologies to delineate natural reserve boundaries spatial zoning. These aim address issues such as insufficient protected area, excessive human-induced influences, inadequate protection endangered animals within nature boundaries. Leveraging comprehensive data from diverse sources, including ground surveys remote sensing detection, we conducted a survey using Chebaling National Nature Reserve in China its environs case study. Models maximum entropy model (MaxEnt), Fragstats, Integrated Valuation Ecosystem Services Trade-offs (InVEST) were employed identify areas with highly suitable habitats, significant landscape diversity, superior ecosystem quality for 16 key species. Subsequently, irreplaceable value research area was calculated Marxan model, leading establishment novel boundary plan. We propose expanding original 1344 km², dividing it into core (321 23.88%) general control (1023 76.12%). Additionally, recommend further division several functional zones facilitate integration diversity protection. This contributes more scientifically informed rational management approach Reserve. Moreover, this offers valuable insights assessing identifying animal habitats globally spatially zoning other reserves.

Language: Английский

Citations

7
Internal physiological drivers of leaf development in trees: Understanding the relationship between non‐structural carbohydrates and leaf phenology DOI Creative Commons
Yunpeng Luo, Constantin M. Zohner, Thomas W. Crowther

et al.

Functional Ecology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 11, 2024

Abstract Plant phenology is crucial for understanding plant growth and climate feedback. It affects canopy structure, surface albedo, carbon water fluxes. While the influence of environmental factors on well‐documented, role intrinsic factors, particularly internal physiological processes their interaction with external conditions, has received less attention. Non‐structural carbohydrates (NSC), which include sugars starch essential growth, metabolism osmotic regulation, serve as indicators availability in plants. NSC levels reflect balance between photosynthesis (source activity) demands respiration (sink activity), making them key traits that potentially during critical periods such spring leaf‐out autumn leaf senescence. However, connections concentrations various organs phenological events are poorly understood. This review synthesizes current research relationship dynamics. We qualitatively delineate seasonal variations deciduous evergreen trees propose testable hypotheses about how may interact stages bud break also discuss levels, align existing conceptual models allocation. Accurate characterization simulation dynamics should be incorporated into allocation models. By comparing reviewing development models, we highlight shortcomings methodologies recommend directions to address these gaps future research. Understanding NSC, source–sink relationships, poses challenges due difficulty characterizing high temporal resolution. advocate a multi‐scale approach combines methods, deepening our mechanistic through manipulative experiments, integrating sink source data from multiple observational networks better characterize dynamics, quantifying spatial pattern trends NSC‐phenology using remote sensing modelling. will enhance comprehension impact across different scales environments. Read free Plain Language Summary this article Journal blog.

Language: Английский

Citations

2
Modeling time series of vegetation indices in tallgrass prairie using machine and deep learning algorithms DOI Creative Commons
Pradeep Wagle, Gopichandh Danala, Catherine Donner

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102917 - 102917

Published: Nov. 1, 2024

Language: Английский

Citations

2
Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations—A Brief Review DOI Creative Commons
Mateo Gašparović, Ivan Pilaš, Dorijan Radočaj

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 12020 - 12020

Published: Dec. 22, 2024

Monitoring and predicting land surface phenology (LSP) are essential for understanding ecosystem dynamics, climate change impacts, forest agricultural productivity. Satellite Earth observation (EO) missions have played a crucial role in the advancement of LSP research, enabling global continuous monitoring vegetation cycles. This review provides brief overview key EO satellite missions, including advanced very-high resolution radiometer (AVHRR), moderate imaging spectroradiometer (MODIS), Landsat program, which an important capturing dynamics at various spatial temporal scales. Recent advancements machine learning techniques further enhanced prediction capabilities, offering promising approaches short-term cropland suitability assessment. Data cubes, organize multidimensional data, provide innovative framework enhancing analyses by integrating diverse data sources simplifying access processing. highlights potential satellite-based monitoring, models, cube infrastructure advancing research insights into current trends, challenges, future directions.

Language: Английский

Citations

2