Interannual variability of NDVI in northwest Mexico. Associated climatic mechanisms and ecological implications

Salinas-Zavala, César Augusto; Douglas, Arthur V.; Díaz, Henry F.

doi:10.1016/s0034-4257(02)00057-3

Cited by 70 publications

(62 citation statements)

References 15 publications

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“…Following previous regional studies [25][26][27], we focus on the North American Monsoon region that can be defined as broadly existing from 90°W to 120°W and 15°N to 35°N. To include more land cover types and climate regimes in our NDVI intercomparison, we expand the domain to cover the western and central United States and Mexico, extending from 85°W to 125°W and 15°N to 50°N.…”

Section: Ndvi Preprocessingmentioning

confidence: 99%

“…It should be pointed out that areas not covered by vegetation may still show variations in NDVI due to atmospheric variations such as water vapor and aerosols, soil conditions as well as sensor characteristics [18]. NDVI has been used to study large-scale vegetation change at the global scale [19][20][21][22][23], Northern Hemisphere [24] as well as across the US and Mexico [25][26][27]. Furthermore, there have been several studies to explore large-scale interannual and seasonal dependence of satellite-based vegetation on climate [28][29][30][31] as well as how vegetation feeds back to the atmosphere [26,32].…”

Section: Introductionmentioning

confidence: 99%

“…Following previous regional studies [25][26][27], we focus on the western and central United States and Mexico that cover many biomes including tropical forests, arid and semi-arid regions, and temperate deciduous and evergreen forests. This study attempts to answer two questions: How consistent are the NDVI data from various groups?…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Seven NDVI Products over the United States and Mexico

et al. 2014

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Abstract:Satellites have provided large-scale monitoring of vegetation for over three decades, and several satellite-based Normalized Difference Vegetation Index (NDVI) datasets have been produced. Here we intercompare four long-term NDVI datasets based largely on the AVHRR sensor (NDVIg, NDVI3g, STAR, VIP) and three datasets based on newer sensors (SPOT, Terra, Aqua) and evaluate the effectiveness of homogenizing the datasets using the green vegetation fraction (GVF) and the impact it has on phenology trends. Results show that all NDVI datasets are highly correlated with each other. However, there are significant differences in the regression slopes that vary spatially and temporally. There is a general trend towards higher maximum annual NDVI over much of the temperate forests of the US and a longer greening period due mostly to a delayed end of the season. These trends are less well-defined over rainfall dependent ecosystems in Mexico and the southwest US Compared with the NDVI datasets, the derived GVF datasets show more one-to-one relationships, have reduced interannual variation, preserve their relationships better over the entire time period and are characterized by weaker trends. Finally, weak agreement between the trends in the datasets stresses the importance of using multiple datasets to evaluate changes in vegetation and its phenology.

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Section: Ndvi Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Seven NDVI Products over the United States and Mexico

et al. 2014

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“…A diverse set of analytical methods that exploit phenological information in time-series of multispectral images has been developed for distinguishing and mapping vegetation cover types [20][21][22][23][24][25], estimating and mapping biomass and vegetation fuel loads [26][27][28][29][30] and for investigating how vegetation and related ecosystem processes respond to management, disturbance, weather, drought and climate change [31][32][33][34][35][36][37]. These methods examine the seasonal growth dynamics of the vegetation through quantification and analysis of various metrics of the time signal observed within one or more years.…”

Section: Multispectral Image-based Approaches To Mapping Of Semiaridmentioning

confidence: 99%

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

et al. 2016

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Abstract:We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C 4 ) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI 2 ) data from Landsat to develop the NDPI, and MSAVI 2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI 2 and summer maximum MSAVI 2 ), and two simple, conventional VI metrics (summer average MSAVI 2 , summer maximum MSAVI 2 ). The NDPI in a scaled form (NDPI s ) performed best in predicting variation in perennial C 4 grass cover as estimated from landscape photographs at 92 sites (R 2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R 2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R 2 = 0.62 and 0.56, p < 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C 4 grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.

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“…Por ejemplo, Salinas-Zavala et al (2002) estudiaron el efecto macro-regional del fenómeno de El Niño (ENSO) sobre indicadores como el Índice de Vegetación Normalizado (NDVI), y aportaron elementos para entender la variabilidad interanual de procesos como el incremento de la actividad vegetal a escalas geográficas amplias. Autores posteriores como Franklin et al (2006), Romo (2006), y Bravo y Castellanos (2013) utilizaron el mismo índice para monitorizar el comportamiento de la Producción Primaria Aérea (PPA) en zonas naturales y explotadas por la ganadería, discriminando el efecto de las actividades humanas y los ciclos naturales de la vegetación en esta zona del país.…”

Section: Introductionunclassified