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Influencia De Los Parámetros Climáticos Sobre La Variación De La Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE Influencia de los parámetros climáticos sobre la variación de la cobertura vegetal en la laguna Conococha, utilizando técnicas de percepción remota Daniel Colonia Ortiz 1 Edwin Loarte Cadenas 1 Judith Torres Castillo 1 Alexzander Santiago Martel 1 1Autoridad Nacional del Agua – ANA Unidad de Glaciología y Recursos Hídricos, Av. Confraternidad Internacional Oeste Nº167 - Huaraz, Perú {dcolonia, eloarte, jtorresc, asantiago }@ana.gob.pe Abstract. This study showed the effects of climate change in Peru's Andean ecosystems. In this regard, the vegetation cover was determined as an indicator, through the NDVI (Normalized Difference Vegetation Index) from Landsat TM and +ETM and application of remote sensing techniques, between 2001 and 2011. Furthermore, statistical analysis was performed by determining the degree of correlation between climatic parameters (surface and ENSO temperatures) and NDVI variations in the lake Conococha, located in the Cordillera Blanca of the Andes in northwest Peru. The results obtained in the sample of the lake Conococha explained that if there is direct relationship between the values of NDVI and climatic parameters such as temperature at 2 meters and temperatures in the areas of Niño 3 and Niño 3.4. This is confirmed by the Pearson linear correlation above 0.6, classified as good correlation. These parameters show that climate change directly affects the vegetation in the lake Conococha, because the temperature is an important factor in the production of chlorophyll and greater proliferation of emergent and subemergent vegetation, which generates the eutrophication of the lake Conococha. Palabras clave: NDVI, climate change, remote sensing,vegetation, lake. I. Introducción En las últimas décadas, la Cordillera Blanca han sufrido cambios significativos a causa del cambio climático, por las variaciones de los parámetros climáticos simples (Temperatura y precipitación) y episodios ENSO con tendencias a incrementar la temperatura en los ecosistemas de alta montaña según Ames Alcides y Francou (1995), Vuille et al. (2008) y Loarte Edwin (2012). El estudio permite confirmar, si los cambios de la vegetación en la laguna Conococha, se debe al cambio climático a partir de las parámetros climáticos (temperaturas de superficie y ENSO) durante el periodo 2001-2011. Por lo tanto, los objetivos planteados son: establecer la clasificación de la cobertura vegetal que permita indicar las variaciones de la vegetación y determinar el grado de correlación entre los cambios de temperatura y variación de la vegetación que demuestran los efectos del cambio climático en el periodo de análisis. La importancia radica en que la laguna Conococha es la naciente del Río Santa (cabecera de cuenca) y las zonas aledañas representan un servicio ambiental (disponibilidad de recursos) importante, porque alberga una gran diversidad de especies de flora y fauna silvestre. Según Knight Piésold (1998) y ANTAMINA S.A (1998) la laguna esta caracterizado por juncos (Scirpus sp), totoras (Scirpus rigidus) y bofedales y vegetación como Carex sp., Distichia muscoides y Calamagrostis. También especies subemergentes como Roripa nasturtium aquaticum, Elodea potamogeton y Myriophyllum sp. Asimismo, permite la subsistencia de las familias asentadas en sus cercanías que aprovechan los múltiples recursos según Antamina S.A. (2005). La laguna Conococha es parte de las lagunas de origen glaciar más grandes de la 2 cuenca del Río santa, siendo sus medidas morfométricas: superficie de 2’119,158 m , ancho máximo de 1,801 m y longitud máxima de 2,932 m, UGRH (2009). El área de estudio está ubicada en la Cordillera Blanca de los Andes al Noroeste del Perú, departamento de Ancash, provincia de Recuay, distrito de Catac, a 327 km aproximadamente 8653 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE de la ciudad de Lima y 90 km de la ciudad de Huaraz. Hidrológicamente se localiza en la divisoria de guas de la cuenca del río Fortaleza y forma parte de la cabecera de la cuenca del río Santa, a 4,300 msnm. Geográficamente se encuentra (Figura 1) en las coordenadas 10°07'40.97"S 77°16'44.92"O según UGRH (2009), sobre una ligera hendidura al Sudoeste de la planicie denominada Pampas Lampa Alto flanqueada por los cerros Quimahuanca y Shiushapunta, recibe aguas de las quebradas Tuco Ragra, Sacas o Shacsha y Ñupupata. Es de poca profundidad llegando hasta los 4.5 m en temporada de lluvia según Antamina S.A. (2005). Figura 1. Mapa de ubicación del área de estudio. II. Metodología de Trabajo En el estudio se ha utilizado 23 imágenes Satelitales de resolución media entre Landsat 5 TM y 7 +ETM ortorectificadas, de la página web USGS, las cuales fueron analizadas para que cumplan las siguientes características: Escenas tomadas en los meses de temporada seca (Mayo a Agosto), poca nubosidad y disponibilidad de imágenes en el periodo 2001-2011. Según Chuvieco Emilio (2006), las imágenes tienen una resolución radiométrica de 8 bits lo que permite diferenciar varias coberturas, porque brinda valores de niveles digitales de 0-255. Según Chander et al. (2005), la misión Landsat tiene una órbita sincrónica, donde produce escenas de 185x185 km, con resolución temporal de 16 días. Los sensores contienen bandas del visible e infrarrojo que cubre el rango espectral de 0.45-1.75 µm (bandas del 1 a 5) y la banda del infrarrojo térmico entre 10.4 a 12.5 µm (banda 6). Las bandas térmicas tienen resolución espacial de 120 y 60 m para el sensor TM y +ETM respectivamente, 15 m la banda pancromática para el sensor +ETM (banda 8) y las otras bandas tienen 30 m. Por otro lado, se han utilizado datos climáticos que corresponden a los siguientes parámetros: temperatura de reanálisis a nivel de 2 m y anomalías de la temperatura superficial del NIÑO 1+2, NIÑO 3, NIÑO 4 y NIÑO 3.4. Los datos de temperatura de reanálisis fueron obtenidos del proyecto ERA-interim a nivel mensual de la página web de European Climate Assessment & Data, en el cuadrante de 10.09º a 10.17º Latitud Sur y 77.23º a 77.32º longitud Oeste, para el periodo de estudio. Los datos de temperatura superficial del mar se obtuvo de la página web de National Weather Service- Climate Prediction Center-NOAA en los sectores: NIÑO 1+2, NIÑO 3, NIÑO 4 y NIÑO 3.4 y sus anomalías que permiten diferenciar entre eventos Niño y Niña, según Espinosa Leticia y Valdez (2007). 8654 Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE 2.1.Procesamiento de imágenes Las imágenes Landsat 5 TM y 7 +ETM fueron procesadas en dos etapas que comprenden la corrección radiométrica y geométrica. Según Chuvieco Emilio (2006) en la corrección radiométrica se adecuó los valores de los niveles digitales (ND) de las imágenes medidos por el sensor en cantidades físicas (radiación electromagnética) que permiten representar las coberturas en estudio, convirtiendo los ND a valores de reflectancia al tope de la atmosfera, aplicando las fórmulas de calibración de valores ND a radianza y a reflectancia según APN- SIB (2005) y Colonia Daniel y Torres (2011). Mientras que la corrección geométrica comprendió el posicionamiento de la imágenes Landsat a la zona de estudio, lográndose un error de ajuste menor a 1 píxel (30 m), tomando como referencia la cartografía base (curvas de nivel, ríos y lagunas) a escala 1:100, 000 del Instituto geográfico Nacional (IGN), usándose la proyección cilíndrica Transversal Mercator, el sistema de coordenadas UTM Zona 18 Sur y el Datum WGS84 según UGRH (2009), compatible con la resolución espacial de 30 m de las imágenes Landsat. 2.2.Aplicación de índice de vegetación Para evidenciar los cambios de vegetación y espejo de agua en la laguna Conococha, se eligió un sector de mayor cambio denominado muestra, cuya área aproximada es de 749,800 m2 que representa el 38% del área total (1’957,560 m2). La muestra ha demostrado cambios resaltantes para realizar el análisis de comparación estadística con los datos climáticos mensuales correspondientes a las fechas de toma de las imágenes satelitales. Para determinar las variaciones espectrales de la vegetación se ha calculado el NDVI (Normalized Difference Vegetation Index) (Ecuación 1) cuyos valores están entre -1 a 1. Además, se ha estimado las superficies con la aplicación del método de umbrales para clasificar los pixeles de acuerdo a la respuesta espectral, Parra et al. (2006), las cuales fueron clasificadas en dos rangos (tabla 1), permitiendo discriminar la vegetación de otras coberturas. Los umbrales se determinaron según los valores del NDVI, prueba error para ajustar el umbral e interpretación de las coberturas en el área nuestra, corroboradas en campo, con la finalidad de extraer las superficies de las coberturas para cada imagen con el uso de herramientas de geo-procesamiento. Tabla 1. Criterios de umbrales. Cobertura Umbrales Espejo de agua y suelo pobre de vegetación -1≤NDVI≤0.2 Vegetación 0.2 <NDVI<≤1 2.2.1. Normalized Difference Vegetation Index (NDVI). ρ −ρ NDVI = irc vis (1) ρirc +ρvis El ρvis y ρirc son valores de reflectancia al tope de la atmosfera (TOA) que corresponden a las bandas del visible e infrarrojo cercano respectivamente, donde la mayor reflectancia de la vegetación está en la región de la banda 4 y con poca reflectancia en la región de la banda 3. 2.3.Correlación de variables Se ha aplicado la correlación de Pearson entre las variables climáticas (Eventos ENSO y datos de reanálisis) y el NDVI y los cambios de cobertura, para determinar si existe o no una relación directamente proporcional. El coeficiente de correlación de Pearson (r) es aplicado para variables cuantitativas a escala mínima de intervalo (Gráfico 1) como es el caso del presente estudio.
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