Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions.

Estimates suggest that more than 70% of the world’s rangelands are degraded. The Normalized Difference Vegetation Index (NDVI) is commonly used by ecologists and agriculturalists to monitor vegetation and contribute to more sustainable rangeland management. This paper aims to explore the scaling character of NDVI and NDVI anomaly (NDVIa) time series by applying three fractal analyses: generalized structure function (GSF), multifractal detrended fluctuation analysis (MF-DFA), and Hurst index (HI). The study was conducted in four study areas in Southeastern Spain. Results suggest a multifractal character influenced by different land uses and spatial diversity. MF-DFA indicated an antipersistent character in study areas, while GSF and HI results indicated a persistent character. Different behaviors of generalized Hurst and scaling exponents were found between herbaceous and tree dominated areas. MF-DFA and surrogate and shuffle series allow us to study multifractal sources, reflecting the importance of long-range correlations in these areas. Two types of long-range correlation appear to be in place due to short-term memory reflecting seasonality and longer-term memory based on a time scale of a year or longer. The comparison of these series also provides us with a differentiating profile to distinguish among our four study areas that can improve land use and risk management in arid rangelands.

were assigned to 3 different species groups in Delforge (2006).Nowadays, the inclusion of Coeloglossum viride (L.) Hartm. in the genus Dactylorhiza is generally accepted. This species has a holarctic-boreal distribution, extending from North America to Japan (Delforge, 2006). Morphologically, it is distinguished from the rest of the genus Dactylorhiza by the spur, which is short and nectariferous, and the petals and sepals, which are joined

abstraCt. A number of automated species recognition systems have been developed recently to aid nonprofessionals in the identification of taxa. These systems have primarily used geometric morphometric based techniques, however issues surround their wider applicability due to the need for homologous landmarks. Here we investigate the use of color to discriminate species using the two horticulturally important slipper orchid genera of Paphiopedilum and Phragmipedium as model systems. The ability to differentiate the various taxonomic groups varied, depending on the size of the group, diversity of colors within the group, and the background of the image. In this study the image analysis was conducted with images of single flowers of the species, however since flowers are ephemeral, flowering for a relatively short period of time, such analysis should be extended to vegetative parts, particularly as this is the form in which they are most often traded internationally.rEsumEn. Una gran cantidad de sistemas de reconocimiento automático de especies se han desarrollado en los últimos años, como ayuda a aquellas personas que no son especialistas en la identificación de especies. Estos sistemas han utilizado sistemas de reconocimiento automático basados en geometría morfométrica, sin embargo existen límites debido a la necesidad de encontrar puntos de georreferenciación en los diferentes organismos. En este artículo investigamos el uso de los colores para diferenciar especies en los géneros Paphiopedilum y Phragmipedium, ambos con gran importancia en la horticultura. La capacidad de discriminación varía entre los grupos taxonómicos, dependiendo del tamaño del taxón, la variedad de colores entre las especies y el fondo de las imágenes. En este estudio el análisis de imágenes se ha llevado a cabo con fotografías de flores individuales. No obstante dado que las flores son órganos efímeros, en el futuro esta investigación incluirá partes vegetativas, ya que es en estado vegetativo la forma en la que se suele comerciar internacionalmente más a menudo.

<p>Soil-vegetation-atmosphere transfer (SVAT) schemes explicitly consider the role of vegetation in affecting water and energy balance by considering its physiological properties. However, most current SVAT schemes and hydrological models do not consider vegetation a dynamic component. The seasonal and monthly evolution of the physiological parameters is kept constant year after year. This fact is likely crucial in transient climate simulations for hydrological models used to study climate change impact. Therefore, the analysis of vegetation dynamics became crucial to study these scenarios.</p> <p>Vegetation dynamics, especially over large scales, can be monitored using remote sensing. The Normalised Difference Vegetation Index (NDVI) is still the most well-known and frequently used spectral indices derived from remote sensing, identifying vegetated areas and their condition. NDVI is based on plants' differential reflectance for different parts of the solar radiation spectrum.</p> <p>In this work, we present a classification of rangelands in Spain based on the NDVI time series using them, like the result of SVAT and defining metrics and the Hurst Exponent from detrended fluctuation analysis. These areas are located in different precipitation and temperature regimen but with a Mediterranean climate with different aridity grades: Huescar, Castuera and Lozoya. K-means and unsupervised random forest were used to cluster the pixels using time series metrics and Hurst exponents. The clustering results will be discussed by comparing them to climate and topographical data.</p> <p><strong>References</strong></p> <p>Sanz E, Sotoca JJM, Saa-Requejo A, Díaz-Ambrona CH, Ruiz-Ramos M, Rodríguez A, Tarquis AM. Clustering Arid Rangelands Based on NDVI Annual Patterns and Their Persistence. Remote Sensing. 2022; 14(19):4949. https://doi.org/10.3390/rs14194949</p> <p><strong>Acknowledgements</strong></p> <p>Financial support from the project "CLASIFICACIÓN DE PASTIZALES MEDIANTE MÉTODOS SUPERVISADOS - SANTO" code RP220220C024, by Universidad Politécnica de Madrid, is highly appreciated.</p>