Long-term satellite NDVI data sets: evaluating their ability to detect ecosystem functional changes in South America

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR "Normalized Difference Vegetation Index" (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMIMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the "Eastern Paraguay" and "Uruguay River margins" focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative "Land ecosystem change utility for South America", which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.Fil: Baldi, Germán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Aragón, Myriam Roxana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Aversa, Fernando. Universidad Nacional de San Luis; ArgentinaFil: Paruelo, José. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentin

Vegetation monitoring through retrieval of NDVI and LST time series from historical databases.

The PhD dissertation presented here falls into the Earth Observation field, specifically vegetation monitoring. This work consists in the extensive exploitation of historical databases of satellite images for vegetation monitoring through two parameters, which are the land surface temperature (LST) and a vegetation index (NDVI). Up to now, vegetation monitoring has been limited to the use of vegetation indices, so the addition of the land surface temperature parameter represents the main innovative character of this PhD study. This dissertation is divided into 5 chapters. The first chapter begins by introducing the theoretical aspects of NDVI and LST parameters, addressing the means for retrieving them from remotely sensed observations, as well as their main limitations. Then, an introduction to vegetal physiology is developed, which allows for understanding how NDVI and LST parameters are linked to plants. A bibliographical study is then presented, which stresses out the gaps in the exploitation of historical databases. The second describes the data used in this PhD. The instrument providing most of these data is embarked on the NOAA (National Oceanic and Atmospheric Administration) satellite series. This instrument is the AVHRR (Advanced Very High Resolution Radiometer). The AVHRR databases used in this work are the PAL (Pathfinder AVHRR Land) and GIMMS (Global Inventory Modeling and Mapping Studies) databases. Additional data used punctually are also described briefly. The third chapter describes the operations applied to the data to prepare their temporal analysis. These operations start with the calculations of vegetation index and land surface temperature parameters. The AVHRR data used in this work are contaminated by the orbital drift of NOAA satellites, so an important part of this doctorate consisted in developing a technique for correcting this effect. We chose to develop our own technique, which we validated by direct comparison with data retrieved by geostationary satellites. In the fourth chapter, the different methods used for data temporal analysis are presented. Those methods consist of trend detection, harmonic analysis, and fitting the temporal series to annual NDVI evolution curves. Then, a phenological analysis is presented, which allows for retrieval of trends in spring and autumn dates for most of the globe. These trends are validated by comparison with previous studies. The trend analysis for spring dates is then extended to the 1948-2006 period using air temperature data. The long-term observation of different NDVI indicators also allows for the detection of land vegetation changes, even in our case of coarse spatial resolution. Finally, two methods for NDVI temporal analysis are compared. In the fifth chapter, a quick presentation of simultaneous study of NDVI and LST is developed through a revision of previous results, followed by the observations carried out from the orbital drift corrected data. These observations allowed for the determination of indicators of NDVI and LST, thus enabling for the characterization of the vegetation at global scale. A harmonic analysis of NDVI and LST at European scale is also presented. The application of the developed indicators for simultaneous monitoring of NDVI and LST shows promising results. As a conclusion, the main results described above are summarized, and plans for a close future are presented. This PhD has also demonstrated that such work could be carried out in a small structure with limited resources. __________________________________________________________________________________________________ RESUMEN El trabajo de tesis doctoral aquí presentado consiste en el uso extensivo de bases de datos históricas de imágenes de satélite para el seguimiento de la vegetación terrestre, a través de dos parámetros; la temperatura de la superficie terrestre (LST por sus siglas en inglés) y el índice de vegetación NDVI. El primer capítulo de la memoria introduce las nociones de NDVI y LST desde una perspectiva teórica, así como sus principales limitaciones y sus vínculos con la fisiología vegetal. Un estudio bibliográfico permite poner el acento sobre las lagunas en el uso de las bases de datos históricas. El segundo capítulo describe los datos utilizados en este trabajo, proporcionados en su mayoría por el instrumento AVHRR (Advanced Very High Resolution Radiometer) a bordo de la serie de satélites de la NOAA (National Oceanic and Atmospheric Administration) a través de las bases de datos PAL (Pathfinder AVHRR Land) y GIMMS (Global Inventory Modeling and Mapping Studies). También se presentan datos adicionales que se usaron puntualmente. El tercer capítulo describe el proceso para obtener las series temporales de NDVI y LST, las cuales están contaminadas por la deriva orbital de los satélites NOAA. Hemos propuesto una técnica propia para su corrección, validada por comparación directa con datos obtenidos por satélites geoestacionarios. En el cuarto capítulo se introducen diferentes métodos utilizados para el análisis temporal de los datos. Se obtuvieron tendencias acerca de parámetros vinculados a la evolución anual de NDVI para la mayor parte del globo, validadas por comparación con estudios previos. En el quinto capítulo se presenta un análisis conjunto del NDVI y de la LST, seguido por la elaboración de indicadores de la evolución anual de estos dos parámetros. A continuación se presenta un análisis armónico del NDVI y de la LST para Europa. El uso de los indicadores desarrollados para el seguimiento simultáneo del NDVI y de la LST revela resultados prometedores. Por último se presentan las conclusiones más relevantes del trabajo realizado, así como planes de trabajo para un futuro próximo

QUANTIFICATION OF ERROR IN AVHRR NDVI DATA

Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. However temporal aggregation improved the precision of the data to a limited extent only. The research presented in this dissertation indicated that the NDVI change of ~0.03 to ~0.08 NDVI units in 10 to 20 years, frequently reported in recent literature, can be significant in some cases. However, unless spatially explicit uncertainty metrics are quantified for the specific spatiotemporal aggregation schemes used by these studies, the significance of observed differences between sites and temporal trends in NDVI will remain unknown

Caracterização da área queimada à escala global (1982-1999) e análise de alguns dos seus impactos climáticos e ecológicos

Doutoramento em Engenharia Florestal - Instituto Superior de AgronomiaThe recent awareness to what would be the climate change consequences, namely in the observed changes in the fire regimes, highlight the urgency for piro-climatic studies. The lack of an historical burnt area dataset to complement recent products, covering the global extent and its full variability,makes it difficult to detect significant trends. Due to the lack of global information before year-2000, a burned area product based on the NOAA Pathfinder AVHRR land (8km) dataset was developed covering the period from 1981 to 1999. As reference we screened the World Fire Atlas dataset, by removing all the non wildfires and false alarm information and we addressed the expected detectable area made by the coarser sensor. Data pre-processing was prefomed by removing the satellite orbital drift trends with empirical mode decomposition and a burned area classification was done based on the Random Forest classification algorithm. Although the developed methodologies allowed to overcome many of the technical difficulties and the results showed high flexibility to cover the full extent of possible fire occurrences, burned areas were characterised by large underestimation. This is mainly explained by the PAL re-sampling procedure and by the limitations of the coarser sensor to detect certain burned scar spatial patterns

Change and Persistence in Land Surface Phenologies of the Don and Dnieper River Basins

The formal collapse of the Soviet Union at the end of 1991 produced major socio-economic and institutional dislocations across the agricultural sector. The picture of broad scale patterns produced by these transformations continues to be discovered. We examine here the patterns of land surface phenology (LSP) within two key river basins—Don and Dnieper—using AVHRR (Advanced Very High Resolution Radiometer) data from 1982 to 2000 and MODIS (Moderate Resolution Imaging Spectroradiometer) data from 2001 to 2007. We report on the temporal persistence and change of LSPs as summarized by seasonal integration of NDVI (normalized difference vegetation index) time series using accumulated growing degree-days (GDDI NDVI). Three land cover super-classes—forest lands, agricultural lands, and shrub lands—constitute 96% of the land area within the basins. All three in both basins exhibit unidirectional increases in AVHRR GDDI NDVI between the Soviet and post-Soviet epochs. During the MODIS era (2001–2007), different socio-economic trajectories in Ukraine and Russia appear to have led to divergences in the LSPs of the agricultural lands in the two basins. Interannual variation in the shrub lands of the Don river basin has increased since 2000. This is due in part to the better signal-to-noise ratio of the MODIS sensor, but may also be due to a regional drought affecting the Don basin more than the Dnieper basin

Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions

Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being a candidate choice for the development of monitoring systems. This paper reviews the statistical and ecological frameworks of assessing land degradation and desertification using vegetation index data. The development of multi-temporal analysis as a desertification assessment technique is reviewed, with a focus on how current practice has been shaped by controversy and dispute within the literature. The statistical techniques commonly employed are examined from both a statistical as well as ecological point of view, and recommendations are made for future research directions. The scientific requirements for degradation and desertification monitoring systems identified here are: (I) the validation of methodologies in a robust and comparable manner; and (II) the detection of degradation at minor intensities and magnitudes. It is also established that the multi-temporal analysis of vegetation index data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 30 years, considerable progress has been made in the respective research

Analysis of monotonic greening and browning trends from global NDVI time-series

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends

Assessing Long-Term Trends In Vegetation Productivity Change Over the Bani River Basin in Mali (West Africa)

Using time series of Normalized Difference Vegetation Index (NDVI) and rainfall data, we investigated historical vegetation productivity trends from 1982 to 2011 over the Bani River Basin in Mali. Statistical agreements between long-term trends in vegetation productivty, corresponding rainfall and rate of land cover change from Landsat time-series imagery was used to discern climate versus human-induced vegetation cover change. Spearman correlation was used to investigate the relationship between metrics of vegetation, rainfall trends and land cover change categories. The results show there is a positive correlation between increases in rainfall and some land cover classes, while some classes such as settlements were negatively correlated with vegetation productivity trends. Croplands and Natural Vegetation were positively correlated (r=0.89) with rainfall while settlements have a negative correlation with NDVI time series trend (r=-057). Despite the fact that rainfall is the major determinant of vegetation cover dynamics in the study area, it appears that other human-induced factors such as urbanization have negatively influenced the change in vegetation cover in the study area. The results show that a combined analysis of NDVI, rainfall and spatially explicit land cover change provides a comprehensive insight into the drivers of vegetation cover change in semi-arid Africa

A Multitemporal Analysis of Georgia\u27s Coastal Vegetation, 1990-2005

Land and vegetation changes are part of the continuous and dynamic cycle of earth system variation. This research examines vegetation changes in the 21-county eco-region along coastal Georgia. The Advanced Very High Resolution Radiometer (AVHRR) with Normalized Difference Vegetation Index (NDVI) data is used in tandem with a Principal Component Analysis (PCA) and climatic variables to determine where, and to what extent vegetation and land cover change is occurring. This research is designed around a 16 year time-series from 1990-2005. Findings were that mean NDVI values were either steady or slightly improved, and that PC1 (Healthiness) and PC2 (Time-Change) explained nearly 99 percent of the total mean variance. Healthiness declines are primarily the result of expanding urban districts and decreased soil moisture while increases are the results of restoration, and increased soil moisture. This research aims to use this analysis for the assessment of land changes as the conduit for future environmental research

Evapotranspiration in the Nile Basin: Identifying Dynamics, Trends, and Drivers 2002-2011

Analysis of the relationship between evapotranspiration (ET) and its natural and anthropogenic drivers is critical in water-limited basins such as the Nile. The spatiotemporal relationships of ET with rainfall and vegetation dynamics in the Nile Basin during 2002–2011 were analyzed using satellite-derived data. Non-parametric statistics were used to quantify ET-rainfall interactions and trends across land cover types and subbasins. We found that 65% of the study area (2.5 million km2) showed significant (p \u3c 0.05) positive correlations between monthly ET and rainfall, whereas 7% showed significant negative correlations. As expected, positive ET-rainfall correlations were observed over natural vegetation, mixed croplands/natural vegetation, and croplands, with a few subbasin-specific exceptions. In particular, irrigated croplands, wetlands and some forests exhibited negative correlations. Trend tests revealed spatial clusters of statistically significant trends in ET (6% of study area was negative; 12% positive), vegetation greenness (24% negative; 12% positive) and rainfall (11% negative; 1% positive) during 2002–2011. The Nile Delta, Ethiopian highlands and central Uganda regions showed decline in ET while central parts of Sudan, South Sudan, southwestern Ethiopia and northeastern Uganda showed increases. Except for a decline in ET in central Uganda, the detected changes in ET (both positive and negative) were not associated with corresponding changes in rainfall. Detected declines in ET in the Nile delta and Ethiopian highlands were found to be attributable to anthropogenic land degradation, while the ET decline in central Uganda is likely caused by rainfall reduction