Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors

Obuchowicz, Claire; Poussin, Charlotte; Giuliani, Gregory

doi:10.1080/20964471.2023.2268322

Cited by 9 publications

(2 citation statements)

References 76 publications

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“…Importantly, this aggregation facilitates comparative analyses of different municipalities over time, without incurring the high storage costs associated with pixel-level data storage. Furthermore, recent studies such as (Obuchowicz et al, 2023;Poussin et al, 2021) have successfully demonstrated the wealth of information that can be obtained at a national scale. These studies highlight the significance of capturing local environmental changes for informing policy decisions.…”

Section: Advantages and Limitations Of Aggregating Pixel Eo Datamentioning

confidence: 99%

Towards semantic enrichment of Earth Observation data: The LEODS framework

Milon-Flores,

Bernard,

Gensel

et al. 2024

AGILE GIScience Ser.

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Abstract. The Earth’s ecosystem is facing serious threats due to the depletion of natural resources and environmental pollution. To promote sustainable practices and formulate effective policies that address these issues, both experts and non-expert stakeholders require access to meaningful Open Data. Current Earth monitoring programs provide a large volume of open Earth Observation (EO) data typically organized and managed in EO Data Cubes (EODCs). From these datasets, satellite-derived indices can be calculated for assessing various environmental aspects in areas of interest over time. However, current EOs lack semantics and are isolated from significant Web resources, greatly hindering their comprehension and limiting their use to specialized users. To enhance EO data with semantic richness and ensure their understanding by a wider audience, it is pertinent to adopt a Linked Open Data (LOD) approach. In this paper, we present the Linked Earth Observation Data Series (LEODS) framework designed to publish aggregated EO data in the LOD Cloud. LEODS provides a processing chain that converts EO data into EO-RDF data cubes based on a spatio-temporal modeling approach that ensures integration and future semantic enrichment of EO data while preserving the advantages of traditional EODCs and following the FAIR principles (i.e., findable, accessible, interoperable, and reusable). To highlight the advantages of our proposal, we explore through SPARQL queries and visualizations, the results of implementing LEODS with study areas located in Switzerland and France.

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Section: Advantages and Limitations Of Aggregating Pixel Eo Datamentioning

confidence: 99%

Towards semantic enrichment of Earth Observation data: The LEODS framework

Milon-Flores,

Bernard,

Gensel

et al. 2024

AGILE GIScience Ser.

View full text Add to dashboard Cite

show abstract

“…Their findings suggest that indices combining temperature and precipitation have a more significant positive impact on local vegetation compared to bioclimatic indices that rely solely on precipitation parameters [17]. Obuchowicz et al's study on vegetation in Switzerland found that NDVI had a low correlation with precipitation, while temperature was the main driving factor [18].…”

Section: Introductionmentioning

confidence: 98%

Spatiotemporal Dynamics of Vegetation Productivity and Its Response to Meteorological Factors in China

Gong,

Ma,

Wang

et al. 2024

Atmosphere

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Climate is one of the key factors driving changes in vegetation, and the response of the vegetation to climate often occurs with a time delay. However, research on the cumulative lagged response of the vegetation to meteorological factors in large-scale regions is limited. Therefore, this study first evaluated the performance of the Gross Primary Productivity (GPP) products provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman–Monteith–Leuning (PML) over the past 20 years in China and then determined the lagged relationships between the GPP and major meteorological factors in different regions and land-use types in China based on a partial correlation analysis. The results indicate that (1) GPP_PML outperforms GPP_MODIS products in the regional context of China; (2) China’s regional GPP has shown a fluctuating upward trend over the past 20 years, with a stepwise increase in the multi-year average from the northwest inland to the southeast coastal regions, and a higher contribution from the southern regions than the northern ones; (3) unlike the recent upward trend in regional temperatures, both precipitation and radiation have decreased, with these two factors showing completely opposite multi-year trends in most regions; and (4) the proportion of regions with lagged effects of the GPP on meteorological factors is higher than those with cumulative effects in China. Among these, GPP exhibits a higher proportion of a 3-month lagged response to precipitation, which is particularly pronounced at altitudes between 500 and 2500 m and above 5500 m. the proportion of the areas with no lag cumulative response to temperature and radiation with GPP in China is the highest due to the influence of more barren land and grassland in the northwest interior. Simultaneously, grassland and barren land have a higher proportion of the non-lagged cumulative responses to temperature and precipitation. This study contributes to our understanding of vegetation dynamics in the context of global climate change and provides a theoretical foundation for regional ecological conservation and high-quality coordinated development.

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Global 24 solar terms phenological MODIS normalized difference vegetation index dataset in 2001–2022

Yang,

Wu,

Sun

et al. 2024

Geoscience Data Journal

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Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology.

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Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors

Cited by 9 publications

References 76 publications

Towards semantic enrichment of Earth Observation data: The LEODS framework

Towards semantic enrichment of Earth Observation data: The LEODS framework

Spatiotemporal Dynamics of Vegetation Productivity and Its Response to Meteorological Factors in China

Global 24 solar terms phenological MODIS normalized difference vegetation index dataset in 2001–2022

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