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Article

NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area

by
Dimitri Charrière
1,
Loïc Francon
2,3 and
Gregory Giuliani
1,4,*
1
EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
2
Climate Change Impacts and Risks in the Anthropocene, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
3
Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany
4
GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(20), 3894; https://doi.org/10.3390/rs16203894
Submission received: 28 August 2024 / Revised: 10 October 2024 / Accepted: 12 October 2024 / Published: 19 October 2024

Abstract

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments.
Keywords: NDVI; PPI; grassland; shrubland; mountains; Sentinel-2; phenology NDVI; PPI; grassland; shrubland; mountains; Sentinel-2; phenology

Share and Cite

MDPI and ACS Style

Charrière, D.; Francon, L.; Giuliani, G. NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area. Remote Sens. 2024, 16, 3894. https://doi.org/10.3390/rs16203894

AMA Style

Charrière D, Francon L, Giuliani G. NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area. Remote Sensing. 2024; 16(20):3894. https://doi.org/10.3390/rs16203894

Chicago/Turabian Style

Charrière, Dimitri, Loïc Francon, and Gregory Giuliani. 2024. "NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area" Remote Sensing 16, no. 20: 3894. https://doi.org/10.3390/rs16203894

APA Style

Charrière, D., Francon, L., & Giuliani, G. (2024). NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area. Remote Sensing, 16(20), 3894. https://doi.org/10.3390/rs16203894

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