Aprende a utilizar diferentes índices de la vegetación además del tan conocido índice NDVI. Te contamos cómo hacerlo desde Geoinnova.

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¿Qué es y como se calcula el NDVI (índice de vegetación de diferencia normalizada)? Te contamos además para qué sirve

Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L. trees affected by ash dieback, a major threat to common ash populations across Europe. In this study, both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery. The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR–NDVI image, whereas the coarse segmentation relied on the segment anything model (SAM). Both methods successfully delineated tree crown outlines, however, only the fine segmentation accurately captured internal canopy gaps. Despite these structural differences, mean NDVI values calculated per tree crown revealed no significant differences between the two approaches, indicating that coarse segmentation is sufficient for mean vegetation index assessments. Nevertheless, the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees, underscoring its value for detailed structural and health analyses. Furthermore, the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys. For applications focused on structural integrity and spatial variation in canopy health, the fine segmentation approach is recommended.

Urban greenspaces are associated with improved health and climate resiliency. Large scale health impact assessments of urban greenspace and mortality have been limited to American and European cities. We estimated changes in mortality associated with observed differences in population-weighted greenest season normalized difference vegetation index (NDVI) between 2014–2018 and 2019–2023 across 1041 global cities representing 174 countries. We used publicly available high-resolution satellite-derived estimates of NDVI and population, baseline disease rates from the Global Burden of Disease study, and a hazard ratio of the association between NDVI and all-cause mortality from an epidemiological meta-analysis. We found that urban greenspace varies substantially across cities (NDVI mean: 0.270, range: 0.072, 0.580) and by climate classification and geographic region. Despite modest global average changes in NDVI from 2014–2018 to 2019–2023, NDVI has changed by over ±20% in individual cities. Median regional changes were largest in South-eastern Asia (−0.022), Sub-Saharan Africa (−0.010) and Eastern Asia (+0.014) and most stable in arid climates (