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ecology & evolution 20, no. 9 (2005): 503-510. Pettorelli, Nathalie. The normalized difference vegetation index . Oxford University Press, 2013. Raport de evaluare detaliată a biodiversității pentru ROSCI0267 Valea Roşie (Detailed report of biodiversity assessment for ROSCI0267 Red Valley). The project the „Impact of the ecosystems from the protected areas in the custody of the Bihor County Council and the Crisuri Country Museum over the main economic sectors”

Ground-Based Sensors for the Urban Heat Island Analysis in the City of Rome , 2010 Gillies R. R., Carlson T. N., Cui J., A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index, NDVI and surface temperature , International Journal of Remote Sensing, 1997 Goetz S. J., Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site , 1997 Imhoff M. L., Zhang, P., Wolfe R. E., Bounoua L., Remote sensing of the urban

References Calcagno, G, Mendicino, G, Monacelli, G, Senatore, A & Versace, P 2007, ‘Distributed estimation of actual evapotranspiration through remote sensing techniques’, Methods and Tools for Drought Analysis and Management, vol. 62, pp. 125-147. Ha, W, Gowda, PH & Howell, TA 2013, ‘A review of downscaling methods for remote sensing-based irrigation management: part I, Irrigation science, vol. 31, no. 4, pp. 831-850. Haynes, JV & Senay, GB 2012, ‘Evaluation of the relation between evapotranspiration and normalized difference vegetation index for downscaling the

References [1] Y. Wang (Ed.), Remote Sensing of Coastal Environments (Remote Sensing Applications Series), CRC Press, 2009, p. 41. [2] E. Sahebjalal and K. Dashtekian, “Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods,” African Journal of Agricultural Research, vol. 8, no. 37, pp. 4614-4622, Sept. 2013. [3] A. Stepchenko and J. Chizhov, “Applying Markov Chains for NDVI Time Series Forecasting of Latvian

rainforests. PNAS 104(12), 4820-4823. DOI: 10.1073/pnas.0611338104 Neil, K., Wu, J. 2006. Effects of urbanisation on plant flowering phenology. Urban Ecosystem 9(3), 243-257. DOI: 10.1007/s11252-006-9354-2 Nicholson, S., Davenport, M, Malo, A. 1990. A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR. Climatic Change 17(2), 209-241. DOI: 10.1007/bf00138369 Rizzi, R., Rudorff, B., Shimabukuro Y., Doraiswami, P. 2006. Assessment of MODIS LAI retrievals over soybean crop in southern

photosynthetically active radiation absorbed by corn canopies. International Journal of Remote Sensing 34(24): 8789-8802. TEAL, R. K. - TUBANA, B. - GIRMA, K. - FREEMAN, K. W. - ARNALL, D. B. - WALSH, O. - RAUN, W. R. (2006): In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal 98(6): 1488-1494. VOGELMANN, J. E. - ROCK, B. N. - MOSS, D. M. (1993): Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14: 1563-1575. ZARCO-TEJADA, P. J. - BERJÓN, A. - MILLER, J. R. (2004): Stress

, Valentín A, Plasència A, Nieuwenhuijsen MJ. Normalized difference vegetation index (NDVI) as a marker of surrounding greenness in epidemiological studies: The case of Barcelona city. Urban For Urban Green 2016;19:88–94. doi: 10.1016/j.ufug.2016.07.001 37. Gascon M, Sánchez-Benavides G, Dadvand P, Martínez D, Gramunt N, Gotsens X, Cirach M, Vert C, Molinuevo JL, Crous-Bou M, Nieuwenhuijsen M. Long-term exposure to residential green and blue spaces and anxiety and depression in adults: A cross-sectional study. Environ Res 2018;162:231–9. doi: 10.1016/j.envres.2018

volume via the emission of a laser beam ( Bietresato et al., 2016 ; Vidoni et al., 2017 ), while active light sensors are able to provides information about the status of the crop (Mazzetto et al., 2010; D’Auria et al., 2016 ). Active optical sensors, such as OptRx™, are able to generate a light at three known wavelengths (670, 730, and 780 nm) and record the light reflected by the target. Given the value of the recorded reflectance, the OptRx™ can compute two vegetation indices (VIs): the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference

). 2.2.7 Rainfall The most important triggering factor influencing the slope stability is the rainfall [ 39 ]. The annual average rainfall map was obtained from the inverse distance weight (IDW) interpolation of data from five hydroclimatic stations (Constantine, Hamma Bouziane, Aïn El Bey, Fourchi and Bir Drimil) over a 32-year period. The study areas get precipitations from 500 to 650 mm of rainfall ( Fig. 4h ). 2.2.8 Normalized Difference Vegetation Index (NDVI) The NDVI is considered an influencing factor in landslide susceptibility assessment as it estimates the

., López-Moreno, J. I., 2010: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23:1696–1718. Vicente-Serrano, S. M., Camarero, J. J., Olano, J. M., Martín-Hernández, N., Peña-Gallardo, M., Tomás-Burguera, M. et al., 2016: Diverse relationships between forest growth and the Normalized Difference Vegetation Index at a global scale. Remote Sensing of Environment, 187:14–29. Wigley, T. M. L., Briffa, K. R., Jones, P. D., 1984: On the average value of correlated time-series, with applications