Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs

Poussin, Charlotte; Massot, Alexandrine; Ginzler, Christian; Weber, Dominique; Chatenoux, Bruno; Lacroix, Pierre; Piller, Thomas; Nguyen, Liliane; Giuliani, Gaston

doi:10.1080/20964471.2021.1974681

Cited by 16 publications

(6 citation statements)

References 81 publications

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“…is inconsistent withPoussin et al (2021)'s observation of a slight decline in NDWI values in the Alps region of Switzerland during the period from 1984 to 2019. Several factors could be influencing these discrepancies, such as the different temporal scales used for trend calculation, as noted by de Jong and de Bruin (2012), or the increased dependence on gap-filling due to limited available high-quality EO data in these regions.…”

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confidence: 99%

Time-series of Landsat-based spectral indices for continental Europe for 2000--2022 to support soil health monitoring

Tian,

Hengl,

Consoli

et al. 2024

Preprint

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As the importance of soil health in supporting European ecosystems and agriculture becomes increasingly critical, effective monitoring is essential for its protection and restoration. This paper describes production and quality assessment of a data cube (17TB in size) derived from Landsat Analysis Ready Data version 2 (ARD V2) dataset and tailored for soil health monitoring, featuring multiple spectral indices, long time span, high resolution, and analysis readiness. The focus was on indices of importance for monitoring soil health: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Normalized Difference Snow Index (NDSI), Normalized Difference Water Index (NDWI), Normalized Difference Tillage Index (NDTI), minimum Normalized Difference Tillage Index (minNDTI), Bare Soil Fraction (BSF), Number of Seasons (NOS) and Crop Duration Ratio (CDR). The data set is available with a resolution of 30~m, with bimonthly (i.e. one image available per two months), annual, and long-term analysis throughout continental Europe, including Ukraine, the UK, and Turkey, covering 2000 to 2022. The results of the quality assessment, including both visual examination and plausibility check with ground survey data, show that these indices can effectively capture environmental processes and offer insight into soil health through aspects such as vegetation, crop status, tillage practices, and soil exposure directly or serve as covariates in digital soil mapping. In particular, the BSF shows a strong negative correlation of -0.73 with crop coverage data from 2006 to 2017, suggesting an effective detection of soil exposure. The minNDTI shows a moderate positive correlation of 0.57 with the Eurostat tillage practices survey data, indicating that it provides valuable information on tillage intensity, although not definitively. This data set is analysis-ready and cloud-optimized, making it suitable for applications in soil property mapping and the development of comprehensive soil health assessment approaches throughout Europe.

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confidence: 99%

Time-series of Landsat-based spectral indices for continental Europe for 2000--2022 to support soil health monitoring

Tian,

Hengl,

Consoli

et al. 2024

Preprint

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“…Disturbance and stresses that cause vegetation changes exceeding normal annual or interannual changes and successional patterns creates challenges for sustainable forest management. With less than 10 years to achieve SDGs, there is an urgent need to develop long-term indicators that can be used to monitor and evaluate changes in vegetation conditions caused by different driving forces from weather and climate conditions to disasters [2,3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characteristics of Vegetation Change Based on Reconstructed Heterogenous NDVI in Seismic Regions

Ustin

et al. 2023

Remote Sensing

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The need to protect forests and enhance the capacity of mountain ecosystems is highlighted in the U.N.’s Sustainable Development Goal (SDG) 15. The worst-hit areas of the 2008 Wenchuan Earthquake in southwest China were mountainous regions with high biodiversity and the impacted area is typical of other montane regions, with the need for detecting vegetation changes following the impacts of catastrophes. While the widely used remotely sensed vegetation indicator NDVI is available from various satellite data sources, these satellites are available for different monitoring periods and durations. Combining these datasets proved challenging to make a continuous characterization of vegetation change over an extended time period. In this study, compared with linear regression, multiple linear regression, and random forest, Convolutional Neural Networks (CNNs) performed best with an average R2 of 0.819 (leave-one-out cross-validation). Thus, the CNNs model was selected to establish the map of the overlapping periods of two remote-sensing products: SPOT-VGT NDVI and PROBA-V NDVI, to reconstruct a SPOT-VGT NDVI for the period from June 2014 to December 2018 in the worst-hit areas of the Wenchuan earthquake. We analyzed the original and reconstructed SPOT-VGT NDVI in the hard-hit areas of the Wenchuan earthquake from 1999 to 2018, and we concluded that NDVI showed an overall upward trend throughout the study period, but experienced a sharp decline in 2008 and reached its lowest value a year later (2009). Vegetation recovery was rapid from 2009 until 2011 after which, it returned to a pattern of slower natural growth (2012–2018). The Longmenshan fault zone experienced the greatest vegetation damage and initiation of recovery there has caused the overall regional average recovery to lag by 1–2 years. In areas where the land was denuded of vegetation (i.e., effectively all vegetation was stripped from the surface) after the earthquake, the damage exceeded what was experienced anywhere else in the entire study area, and by 2018 it remained unrestored. In the 15 years since the earthquake, the areas that were denuded were expected to recover to the level of restoration equivalent with the NDVI of 2007, as was the case in other earthquake-damaged regions. In addition to the earthquake and the immediate loss of vegetation, the Chinese government’s Grain for Green Policy, the elevation ranges within the region, the forest’s phenological conditions, and human activities all had an impact on vegetation recovery and restoration. The reconstructed NDVI provides a long-term continuous record, which contributes to the identifying changes that are improving predictive forest recovery models and to better vegetation management following catastrophic disturbances, such as earthquakes.

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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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Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs

Cited by 16 publications

References 81 publications

Time-series of Landsat-based spectral indices for continental Europe for 2000--2022 to support soil health monitoring

Time-series of Landsat-based spectral indices for continental Europe for 2000--2022 to support soil health monitoring

Dynamic Characteristics of Vegetation Change Based on Reconstructed Heterogenous NDVI in Seismic Regions

Towards semantic enrichment of Earth Observation data: The LEODS framework

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