The hyper-arid Atacama Desert is one of the most extreme environments for life and only few species have evolved to survive its aridness. One such species is the tree Prosopis tamarugo Phil. Because Tamarugo completely depends on groundwater, it is being threatened by the high water demand from the Chilean mining industry and the human consumption. In this paper, we identified the most important biophysical variables to assess the water status of Tamarugo trees and tested the potential of WorldView2 satellite images to retrieve these variables. We propose green canopy fraction (GCF) and green drip line leaf area index (DLLAI green) as best variables and a value of 0.25 GCF as a critical threshold for Tamarugo survival. Using the WorldView2 spectral bands and an object-based image analysis, we showed that the NDVI and the Red-edge Chlorophyll Index (CI Red-edge) have good potential to retrieve GCF and DLLAI green. The NDVI performed best for DLLAI green (RMSE = 0.4) while the CI Red-edge was best for GCF (RMSE = 0.1). However, both indices were affected by Tamarugo leaf movements (leaves avoid facing direct solar radiation at the hottest time of the day). Thus, monitoring systems based on these indices should consider the time of the day and the season of the year at which the satellite images are acquired.
References
[1]
Ezcurra, E. Global Deserts Outlook; United Nations Environment Programme: Nairobi, Kenya, 2006.
[2]
Blaschke, T. Object based image analysis for remote sensing. ISPRS J Photogram. Remote Sens 2010, 65, 2–16.
[3]
Laliberte, A.S.; Rango, A.; Havstad, K.M.; Paris, J.F.; Beck, R.F.; McNeely, R.; Gonzalez, A.L. Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sens. Environ 2004, 93, 198–210.
[4]
Laliberte, A.S.; Fredrickson, E.L.; Rango, A. Combining decision trees with hierarchical object-oriented image analysis for mapping arid rangelands. Photogram. Eng. Remote Sens 2007, 73, 197–207.
[5]
Gibbes, C.; Adhikari, S.; Rostant, L.; Southworth, J.; Qiu, Y. Application of object based classification and high resolution satellite imagery for savanna ecosystem analysis. Remote Sens 2010, 2, 2748–2772.
[6]
Asner, G.P.; Wessman, C.A.; Bateson, C.A.; Privette, J.L. Impact of tissue, canopy, and landscape factors on the hyperspectral reflectance variability of arid ecosystems. Remote Sens. Environ 2000, 74, 69–84.
[7]
Borzuchowski, J.; Schulz, K. Retrieval of leaf area index (LAI) and soil water content (WC) using hyperspectral remote sensing under controlled glass house conditions for spring barley and sugar beet. Remote Sens 2010, 2, 1702–1721.
[8]
Chávez, R.O.; Clevers, J.G.P.W.; Herold, M.; Ortiz, M.; Acevedo, E. Modelling the spectral response of the desert tree Prosopis tamarugo to water stress. Int. J. Appl. Earth Obs. Geoinf 2013, 21, 53–65.
[9]
Taiz, L.; Zeiger, E. Plant Physiology; Sinauer Associates: Sunderland, MA, USA, 2010.
[10]
Kimes, D.S.; Kirchner, J.A. Diurnal variations of vegetation canopy structure. Int. J. Remote Sens 1983, 4, 257–271.
[11]
Moran, M.S.; Pinter, P.J., Jr; Clothier, B.E.; Allen, S.G. Effect of water stress on the canopy architecture and spectral indices of irrigated alfalfa. Remote Sens. Environ 1989, 29, 251–261.
[12]
Verhoef, W.; Bach, H. Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sens. Environ 2007, 109, 166–182.
[13]
Rojas, R.; Dassargues, A. Groundwater flow modelling of the regional aquifer of the Pampa del Tamarugal, Northern Chile. Hydrogeol. J 2007, 15, 537–551.
[14]
Romero, H.; Méndez, M.; Smith, P. Mining development and environmental injustice in the Atacama Desert of Northern Chile. Environ. Justice 2012, 5, 70–76.
[15]
Gajardo, R. La Vegetación Natural de Chile Clasificación y Distribución Geográfica; Editorial Universitaria: Santiago, Chile, 1994.
[16]
CONAMA. Biodiversidad de Chile, Patrimonio Y Desafíos; Ocho Libros Editores: Santiago, Chile, 2008.
[17]
CONAF. Plan de Manejo Reserva Nacional Pampa del Tamarugal. Corporación Nacional Forestal (CONAF); Ministerio de Agricultura: Gobierno, Chile, 1997; p. 110.
[18]
Estades, C.F. Natural history and conservation status of the Tamarugo Conebill in northern Chile. Wilson Bull 1996, 108, 268–279.
[19]
Ramírez-Leyton, G.; Pincheira-Donoso, D. Fauna del Altiplano y Desierto de Atacama. Vertebrados de la Provncia de El Loa; Phrynosaura Ediciones: Calama, Chile, 2005.
[20]
Oyarzún, J.; Oyarzún, R. Sustainable development threats, inter-sector conflicts and environmental policy requirements in the arid, mining rich, Northern Chile territory. Sustain. Dev 2011, 19, 263–274.
[21]
Burkart, A. A monograph of the genus Prosopis (Leguminosae subfam. Mimosoideae). J. Arnold. Arbor 1976, 57, 219–249.
[22]
Altamirano, H. Prosopis tamarugo Phil. Tamarugo. In Las especies arbóreas de los bosques templados de Chile y Argentina. Autoecología; Donoso, C., Ed.; Marisa Cuneo Ediciones: Valdivia, Chile, 2006; pp. 534–540.
[23]
Riedemann, P.; Aldunate, G.; Teillier, S. Flora nativa de valor ornamental. Chile, Zona Norte. Identificación y propagación; Productora Gráfica Andros Ltda: Santiago, Chile, 2006; p. 404.
[24]
Acevedo, E.; Ortiz, M.; Franck, N.; Sanguineti, P. Relaciones hídricas de Prosopis tamarugo Phil. Uso de isótopos estables; Universidad de Chile: Santiago, Chile, 2007; p. 82.
[25]
Sudzuki, F. Environmental Influence on Foliar Anatomy of Prosopis Tamarugo Phil. In The Current State of Knowledge on Prosopis Tamarugo; Habit, M., Ed.; FAO: Rome, Italy, 1985.
[26]
DICTUC. Anexo VIII.2 Modelación de la Evolución del Nivel de la Napa en la Pampa del Tamarugal. In EIA proyecto Pampa Hermosa; Dirección de Investigaciones Científicas y Tecnológicas Universidad Católica de Chile: Santiago, Chile, 2008; p. 169.
[27]
Geohidrología-SQM. Informe Semestral 2. In Plan de Segumiento Ambiental Hidrogeológico Proyecto Pampa Hermosa; Geohidrología: Santiago, Chile, 2012; p. 118.
[28]
Meyer, W.S.; Ritchie, J.T. Resistance to water flow in the Sorghum plant. Plant. Physiol 1980, 65, 33–39.
[29]
Scholander, P.F.; Hammel, H.T.; Bradstreet, E.D.; Hemmingsen, E.A. Sap pressure in vascular plants. Science 1965, 148, 339–346.
[30]
Lichtenthaler, H.K.; Wellburn, A.R. Determination of total carotenoids and chlorophyll a and b of leaf extract in different solvents. Biochem. Soc. Trans. 1983, 591–592.
Jonckheere, I.; Fleck, S.; Nackaerts, K.; Muys, B.; Coppin, P.; Weiss, M.; Baret, F. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agric. For. Meteorol 2004, 121, 19–35.
[33]
Peper, P.J.; McPherson, E.G. Evaluation of four methods for estimating leaf area of isolated trees. Urban. For. Urban. Green 2003, 2, 19–29.
[34]
Ortiz, M.; Silva, P.; Acevedo, E. Leaf Water Parameters in Prosopis Tamarugo Phil. Subject to a Lowering of the Water Table. In Nivel freático en la Pampa del Tamarugal y Crecimiento de Prosopis tamarugo Phil; Tesis para optar al Grado Académico de Doctor en Ciencias Silvoagrpecuarias y Veterinarias: Santiago, Chile, 2010; pp. 15–42.
[35]
Updike, T.; Comp, C. Radiometric Use of WorldView2 Imagery. Technical Note; Revision 1; DigitalGlobe, Inc.: Longmont, CO, USA, 2010; p. 17.
[36]
Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ 1979, 8, 127–150.
[37]
Gitelson, A.A.; Keydan, G.P.; Merzlyak, M.N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett 2006, 33, L11402.
[38]
Pastenes, C.; Pimentel, P.; Lillo, J. Leaf movements and photoinhibition in relation to water stress in field-grown beans. J. Exp. Bot 2005, 56, 425–433.
[39]
Liu, C.C.; Welham, C.V.J.; Zhang, X.Q.; Wang, R.Q. Leaflet movement of Robinia pseudoacacia in response to a changing light environment. J. Integr. Plant. Biol 2007, 49, 419–424.
[40]
Pastenes, C.; Porter, V.; Baginsky, C.; Norton, P.; González, J. Paraheliotropism can protect water-stressed bean (Phaseolus vulgaris L.) plants against photoinhibition. J. Plant. Physiol 2004, 161, 1315–1323.
[41]
Ortega, A.; Escobar, R.; Colle, S.; de Abreu, S.L. The state of solar energy resource assessment in Chile. Renewable Energy 2010, 35, 2514–2524.
[42]
Hirschmann, R.J. Records on solar radiation in Chile. Solar Energy 1973, 14, 129–138.
[43]
Schmidhalter, U. The gradient between pre-dawn rhizoplane and bulk soil matric potentials, and its relation to the pre-dawn root and leaf water potentials of four species. Plant. Cell. Environ 1997, 20, 953–960.
[44]
Veste, M.; Staudinger, M.; Küppers, M. Spatial and temporal variability of soil water in drylands: Plant water potential as a diagnostic tool. Forestry Studies China 2008, 10, 74–80.
[45]
Richter, H. Water relations of plants in the field: some comments on the measurement of selected parameters. J. Exp. Bot 1997, 48, 1–7.
[46]
Boochs, F.; Kupfer, G.; Dockter, K.; Kuhbauch, W. Shape of the red edge as vitality indicator for plants. Int. J. Remote Sens 1990, 11, 1741–1753.
[47]
Filella, I.; Penuelas, J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens 1994, 15, 1459–1470.
[48]
Horler, D.N.H.; Dockray, M.; Barber, J. The red edge of plant leaf reflectance. Int. J. Remote Sens 1983, 4, 273–288.
[49]
Carter, G.A.; Knapp, A.K. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot 2001, 88, 677–684.
[50]
Malenovsky, Z.; Bartholomeus, H.M.; Acerbi-Junior, F.W.; Schopfer, J.T.; Painter, T.H.; Epema, G.F.; Bregt, A.K. Scaling dimensions in spectroscopy of soil and vegetation. Int. J. Appl. Earth Obs. Geoinf 2007, 9, 137–164.
[51]
Moorthy, I.; Miller, J.R.; Noland, T.L. Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level. Remote Sens. Environ 2008, 112, 2824–2838.
[52]
Baret, F.; de Solan, B.; Lopez-Lozano, R.; Ma, K.; Weiss, M. GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops. Agric. For. Meteorol 2010, 150, 1393–1401.
[53]
Liu, J.; Pattey, E.; Admiral, S. Assessment of in situ crop LAI measurement using unidirectional view digital photography. Agric. For. Meteorol 2013, 169, 25–34.
