A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States

The complex feedback relationship between climate variability and vegetation dynamics is a subject of intense investigation for its implications in furthering our understanding of the global biogeochemical cycle. We address an important question in this context: “How does topography influence the vegetation's response to natural climate fluctuations?” We explore this issue through the analysis of inter-annual vegetation variability over a very large area (continental United States) using long-term (13-year period of 1989-2001), monthly averaged, biweekly maximum value composite normalized difference vegetation index (NDVI) data. These data are obtained from satellite remote sensing at 1-km resolution. Through the novel implementation of data mining techniques, we show that the Northern Pacific climate oscillation and the ENSO phenomena influence the year-to-year vegetation variability over an extensive geographical domain. Further, the vegetation response to these fluctuations depends on a variety of topographic attributes such as elevation, slope, aspect, and proximity to moisture convergence zones, although the first two are the predominant controls. Therefore, the dynamic response of terrestrial vegetation to climate fluctuations, which shows tremendous spatial heterogeneity, is closely linked to the variability induced by the topography. These findings suggest that the representation of vegetation dynamics in existing climate models, which do not incorporate such dependencies, may be inadequate. Therefore, climate models that are regularly employed to guide policy decisions need to better incorporate these dependencies for the assessment of terrestrial carbon sequestration under evolving climate scenarios.     

On the variation of the tropospheric ozone over Indian region in relation to the meteorological parameters

Using monthly mean satellite measurements of TOMS/SBUV tropospheric ozone residual (TOR) data and meteorological parameters (tropopause height (TPH), 200 hPa geopotential height (GPH) and outgoing longwave radiation (OLR)) during 1979–2001, seasonal variability of TOR data and their association with meteorological parameters are outlined over the Indian region. Prominent higher values of TOR (44–48 DU, which is higher than the globally averaged 31.5 DU) are observed over the northern parts of the country during the summer monsoon season (June–September). Similar to the TOR variation, meteorological parameters (tropopause height, 200 hPa geopotential height and outgoing longwave radiation) also show higher values during the summer monsoon season, suggesting an in phase relationship and strong association between them because of deep convection present during summer monsoon time. The monthly trends in TOR values are found to be positive over the region. TOR has significant positive correlations (5% level) with GPH, and negative correlations with OLR and TPH for the month of September. The oxidation chains initiated by CH₄ and CO show the enhanced photochemical production of ozone that would certainly become hazardous to the ecological system. Interestingly, greenhouse gases (GHG) emissions were found to have continuously increased over the Indian region during the period 1990–2000, indicating more anthropogenic production of ozone precursor gases causing higher level of tropospheric ozone during this period.     

Satellite-measured temporal variability of the Columbia River plume

Six years (1998-2003) of SeaWiFS multispectral satellite data are used to document the seasonal and interannual variability of the Columbia River plume on the North American west coast. A supervised classification scheme using 5 channels of normalized water-leaving radiance (nLw at 412, 443, 490, 510 and 555 nm), with training pixels adjusted temporally to optimize the signature of plume core characteristics, quantifies the climatological seasonal location of 4 spectrally defined classes of surface water and provides estimates of variability in position as a probability. Winter plume orientation was northward and close to the shore, with infrequent adjustments to the south. Summer plume orientation was offshore and to the south, dissociated from the coast, with more frequent (> 20%) occurrences of plume water and peripheral plume water (> 50%) to the north. An effective characterization of interannual variability in plume dynamics is provided by time series of temporally averaged nLw at 555 nm, used as an estimate of suspended particulate material. Monthly means during maxima and minima in annual river discharge show the plume to be weakest both in spatial extent as well as absolute nLw values in 2001, a year of minimum river discharge. Time series of both (a) nLw 555 values at the river mouth and (b) Mode 2 of an empirical orthogonal function decomposition of the 6-year nLw 555 time series variance that isolates winter patterns are strongly correlated with river discharge. Interannual differences in monthly mean wind forcing are evident as changes in plume position during the winter, but at the 8-day and longer time scales examined here, summer interannual differences are dominated by differences in discharge volume.     

基于GIMMS、VGT和MODIS的中国东部植被指数对比分析

GIMMS NDVI、VGT NDVI和MODIS NDVI/EVI是目前在植被变化有关研究中经常使用的植被遥感数据,它们之间的差异也得到了广泛关注。然而,在分析这些数据之间的差异时,较少有研究注意到植被本身固有的季节循环可能夸大了各数据间的相关关系。应用2000~2006年GIMMS NDVI、VGT NDVI、MODIS NDVI/EVI等不同植被遥感数据,对比了基于这些数据集的中国东部植被年际变化的差异,探讨了植被季节循环对不同遥感数据之间相关性的影响。结果表明:由不同遥感数据提取的植被年际变化特征具有明显的一致性,然而,植被本身固有的季节循环特征掩盖了不同数据集的差异。季节循环去除前,各数据集之间具有显著的相关性;季节循环去除后,各数据集的相关性明显降低,但不同数据集在北部区域依然具有较好的一致性,其差异主要出现在南部区域,差异最明显的是GIMMS与MODIS数据,二者在淮河以南的区域几乎不存在显著相关。     

Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa

The response of photosynthetic activity to interannual rainfall variations in Africa South of the Sahara is examined using 20 years (1981-2000) of Normalised Difference Vegetation Index (NDVI) AVHRR data. Linear correlations and regressions were computed between annual NDVI and annual rainfall at a 0.5° latitude/longitude resolution, based on two gridded precipitation datasets (Climate Prediction Center Merged Analysis of Precipitation [CMAP] and Climatic Research Unit [CRU]). The spatial patterns were then examined to detect how they relate to the mean annual rainfall amounts, land-cover types as from the Global Land Cover 2000 data set, soil properties and soil types. Yearly means were computed starting from the beginning of the vegetative year (first month after the minimum of the NDVI mean regime), with a one-month lead for rainfall.One third of tropical Africa displays significant (95% c.l.) correlations between interannual NDVI variations and those of rainfall. At continental scale, soil types and soil properties are only minor factors in the overall distribution of the correlations. Mean annual rainfall amounts and land-cover types are much more discriminating. The largest correlations, mostly over 0.60, are distinctly found in semi-arid (200-600 mm annual rainfall) open grassland and cropland areas. The presence of one of these two determinants (semi-aridity, and favourable land-cover type, i.e. open grassland and cropland) in the absence of the other does not systematically result in a significant correlation between rainfall and NDVI. By contrast, NDVI variations are independent from those of rainfall in markedly arid environments and in most forest and woodland areas. This results from a low signal-to-noise ratio in the former, and the fact that precipitation is generally not a limiting factor in the latter.The marginal response of NDVI to a given increase/decrease in rainfall, as described by the slope of the regression, displays a similar pattern to that of the correlation, with maximum slopes in semi-arid regions, except that a weaker response is noted in more densely populated areas, suggesting an incidence of particular land-use and agricultural practises.One-year lag relationships between annual rainfall and NDVI in the next year were also considered. Ten percent of the grid-points show significant correlations, but the spatial patterns remain difficult to interpret.     

Spatio-temporal variability of surface chlorophyll-a in the Halmahera Sea and its relation to ENSO and the Indian Ocean Dipole

ABSTRACT

Long-term satellite data are used to investigate the variability of ocean surface chlorophyll-a (chl-a) concentration in the Halmahera Sea (HS) under influence of the Australian-Indonesian Monsoon (AIM), the El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). In this study, we first analysed the seasonal variability of chl-a, and then examine the relationship between surface chl-a, sea surface temperature (SST), and sea surface wind stress in the area. Our results suggest that prevailing southeasterly winds play a fundamental role in generating chl-a blooms in the HS. Particularly on a seasonal timescale, through the mechanism of Ekman mass transport, strengthening of southeasterly wind stress during the Southeast Monsoon season (June – August) produces enhanced chl-a concentrations associated with ocean surface cooling in the area of study. On the other hand, the chl-a bloom completely diminishes during the Northwest Monsoon season (December – February) due to weakening of wind stress and Ekman transport. On an interannual timescale, sea level pressure and wind stress are coherent with ENSO and IOD phases. During El Niño and positive IOD events (La Niña and negative IOD events), both sea level pressure and wind stress greatly increase (decrease) over the HS. These conditions cause an anomaly in southerly (northerly) wind stress, which is favourable to an enhancement (reduction) of the chl-a concentration in the region. This study demonstrates that sea level pressure and wind stress are the critical factors in determining the magnitude of chl-a bloom in the HS.     

KVM over IP技术在气象业务网络中的应用

本文主题的KVM over IP就是采用OVER IP技术的一个热门例子。本文介绍了KVM的基本概念和发展历程,并通过KVM over IP技术在市气象局机房和雷达站机房的应用实例,指出KVM over IP的应用可以大大提升气象业务网络的管理效率。     

SODAR pattern classification and its dependence on meteorological parameters over a semiarid region of India

The variability of the atmospheric boundary layer together with meteorological parameters has been investigated over the semi-arid region Delhi. Two sources of the dataset have been used: sound detection and ranging (SODAR) and automatic weather station during the period from December 2013 to November 2014. A Laboratory Virtual Instrument Engineering Workbench (LabVIEW)-based programme has been developed to plot the stability class from A to F directly from the mixing height dataset. Based on the SODAR echograms and mixing height, temporal and seasonal variability of stability classes has been estimated. It is observed that the convective boundary layer height advances and decreases during the daytime depending on the increase and decrease of surface temperature due to solar heating of the ground. From seasonal classification of the stability class, it is observed that the class A and class E are dominated in convection and nocturnal periods in all seasons, whereas class F is not found during the winter and pre-monsoon seasons. Impact of meteorological parameters, that is, wind speed, temperature, and relative humidity on mixing height during different seasons has also been studied.     

Use of vegetation index and meteorological parameters for the prediction of crop yield in India

Monsoon rainfall distribution over the Indian sub‐continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop yield, normalized difference vegetation index, soil moisture, surface temperature and rainfall data for 16 years (from 1984 to 1999) have been carried out. A non‐linear iterative multivariate optimization approach (quasi‐Newton method with least square loss function) has been used to derive an empirical piecewise linear crop yield prediction equation (with a break point). The derived empirical equation (based on 1984 to 1998 data) has been used to predict 1999 crop yield with R²>0.90. The model has been validated for the three years 1997, 1998 and 1999. A crop yield prediction equation has been obtained for each province in India (for wheat and rice) that accounts for>90% of the variance in the dataset.     

Retrieval of bare soil and vegetation parameters from wind scatterometer measurements over three different climatic regions

Medium to low resolution (1-50 km) active microwave sensors such as spaceborne scatterometers and wide-swath mode synthetic aperture radars have great potential as tools for long term monitoring over land and ice. To optimise the use of this kind of data, the heterogeneity of the target and its effects on the radar measurements need to be investigated and modelled, particularly in the view of retrieving geophysical parameters. In this paper, wind scatterometer measurements over three different test sites, the NOPEX region in Sweden, the HAPEX-Sahel site in Niger and the Niger delta area in Nigeria, are analysed. For these regions, a forward model is developed by considering the backscatter contributions of the bare surface, the seasonal and evergreen vegetation and the open water areas. Colocated high spatial resolution SAR data and ground information are used to characterise the target scene. The model is then inverted to retrieve monthly soil roughness, dielectric properties and vegetation parameters. It is shown that the measurements contain enough information to characterise these three different regions and to monitor their temporal evolution. The retrieved values obtained for the bare surface and the vegetation parameters are consistent with ground measurements collected in these areas. Further improvements are achieved by incorporating the time scale variability of the variables investigated into the retrieval scheme.     

Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model

In order to prioritize the measurement requirements and accuracies of the two new lidar missions, a physical model is required for a fundamental understanding of the impact of surface topography, footprint size and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval. In this study, we extended a well developed Geometric Optical and Radiative Transfer (GORT) vegetation lidar model to take into account for the impacts of surface topography and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval and applied this extended model to assess the aforementioned impacts on vegetation lidar waveforms and height retrieval.Model simulation shows that surface topography and off-nadir pointing angle stretch waveforms and the stretching effect magnifies with footprint size, slope and off-nadir pointing angle. For an off-nadir pointing laser penetrating vegetation over a slope terrain, the waveform is either stretched or compressed based on the relative angle. The stretching effect also results in a disappearing ground peak return when slope or off-nadir pointing angle is larger than the “critical slope angle”, which is closely related to various vegetation structures and footprint size. Model simulation indicates that waveform shapes are affected by surface topography, off-nadir pointing angle and vegetation structure and it is difficult to remove topography effects from waveform extent based only on the shapes of waveform without knowing any surface topography information.Height error without correction of surface topography and off-nadir pointing angle is the smallest when the laser beams at the toward-slope direction and the largest from the opposite direction. Further simulation reveals within 20° of slope and off-nadir pointing angle, given the canopy height as roughly 25 m and the footprint size as 25 m, the error for vegetation height (RH100) ranges from − 2 m to greater than 12 m, and the error for the height at the medium energy return (RH50) from − 1 m to 4 m. The RH100 error caused by unknown surface topography and without correction of off-nadir pointing effect can be explained by an analytical formula as a function of vegetation height, surface topography, off-nadir pointing angle and footprint size as a first order approximation. RH50 is not much affected by topography, off-nadir pointing and footprint size. This forward model simulation can provide scientific guidance on prioritizing future lidar mission measurement requirements and accuracies.     

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989-2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI-SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.     

Effects of spatial and temporal climatic variability on terrestrial carbon and water fluxes in the Pacific Northwest,USA

The Pacific Northwest (PNW) of the conterminous United States is characterized by large variations in climate and topography, and provides an ideal geographic domain for studying interactions between regional climate and vegetation dynamics. We examined vegetation carbon (C) and water dynamics along PNW climate and topographic gradients using a process-based biogeochemical model, BIOME-BGC, the algorithms of which form bases for a fully-prognostic treatment of carbon and nitrogen cycles in Land Community Model (CLM). Simulation experiments were used to (1) analyze spatial and temporal variability of terrestrial carbon (C) stocks and flux, (2) investigate primary climatic variables controlling the variability, and (3) predict effects of future climate projections on vegetation productivity and water flux variables including evapotranspiration and water supply. The model experiments focused on two 18-year (1980–1997 and 2088–2105) simulations using future climate predictions for A2 (+4.2 °C, −7% precipitation) and B2 (1.6 °C, +11% precipitation) emissions scenarios through year 2100. Our results show large west to east spatial variations in C and water fluxes and C stocks associated with regional topography and distance from coastal areas. Interannual variability of net primary productivity (NPP) and evapotranspiration (ET) are 57% and 33%, respectively, of the 18-year mean annual fluxes for 1980–1997. The annual NPP and ET are positively correlated with precipitation but inversely proportional to vapor pressure deficit; this suggests that modeled NPP and ET are predominantly water limited in the PNW. The A2 scenario results in higher NPP and ET of 23% and 10%, respectively, and 15% lower water outflow. The B2 scenario results in higher NPP and ET of 12% and 15%, respectively, and 2% lower water outflow, despite projected increases in precipitation. Simulation experiments indicate that most PNW ecosystems are water limited, and that annual water outflow will decrease under both drier (A2) and wetter (B2) scenarios. However, higher elevations with high snowpacks of long duration may buffer the loss of water resources in some areas, even if precipitation is lower.     

The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters

NDVI (Normalized Difference Vegetation Index) has been widely used to monitor vegetation changes since the early eighties. On the other hand, little use has been made of land surface temperatures (LST), due to their sensitivity to the orbital drift which affects the NOAA (National Oceanic and Atmospheric Administration) platforms flying AVHRR sensor. This study presents a new method for monitoring vegetation by using NDVI and LST data, based on an orbital drift corrected dataset derived from data provided by the GIMMS (Global Inventory Modeling and Mapping Studies) group. This method, named Yearly Land Cover Dynamics (YLCD), characterizes NDVI and LST behavior on a yearly basis, through the retrieval of 3 parameters obtained by linear regression between NDVI and normalized LST data. These 3 parameters are the angle between regression line and abscissa axis, the extent of the data projected on the regression line, and the regression coefficient. Such parameters characterize respectively the vegetation type, the annual vegetation cycle length and the difference between real vegetation and ideal cases. Worldwide repartition of these three parameters is shown, and a map integrating these 3 parameters is presented. This map differentiates vegetation in function of climatic constraints, and shows that the presented method has good potential for vegetation monitoring, under the condition of a good filtering of the outliers in the data.     

Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Rapid changes of land use and land cover (LULC) in urban areas have become a major environmental concern due to environmental impacts, such as the reduction of green spaces and development of urban heat islands (UHI). Monitoring and management plans are required to solve this problem effectively. The Tabriz metropolitan area in Iran, selected as a case study for this research, is an example of a fast growing city. Multi-temporal images acquired by Landsat 4, 5 TM and Landsat 7 ETM+ sensors on 30 June 1989, 18 August 1998, and 2 August 2001 respectively, were corrected for radiometric and geometric errors, and processed to extract LULC classes and land surface temperature (LST). The relationship between temporal dynamics of LST and LULC was then examined. The temperature vegetation index (TVX) space was constructed in order to study the temporal variability of thermal data and vegetation cover. Temporal trajectory of pixels in the TVX space showed that most changes due to urbanization were observable as the pixels migrated from the low temperature-dense vegetation condition to the high temperature-sparse vegetation condition in the TVX space. The uncertainty analysis revealed that the trajectory analysis in the TVX space involved a class-dependant noise component. This emphasized the need for multiple LULC control points in the TVX space. In addition, this research suggests that the use of multi-temporal satellite data together with the examination of changes in the TVX space is effective and useful in urban LULC change monitoring and analysis of urban surface temperature conditions as long as the uncertainty is addressed.     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry

The challenge to retrieve canopy height from large-footprint satellite lidar waveforms over mountainous areas is formidable given the complex interaction of terrain and vegetation. This study explores the potential of GLAS (Geoscience Laser Altimeter System) for retrieving maximum canopy height over mountainous areas in the Pacific Coast region, including two conifers sites of tall and closed canopy and one broadleaf woodland site of shorter and sparse canopy. Both direct methods and statistical models are developed and tested using spatially extensive coincident airborne lidar data. The major findings include: 1) the direct methods tend to overestimate the canopy height and are complicated by the identification of waveform signal start and terrain ground elevation, 2) the exploratory data analysis indicates that the edge-extent linear regression models have better generalizability than the edge-extent nonlinear models at the inter-site level, 3) the inter-site level test with mixed-effects models reveals that the edge-extent linear models have statistically-justified generalizability between the two conifer sites but not between the conifer and woodland sites, 4) the intra-site level test indicates that the edge-extent linear models have statistically-justified generalizability across different vegetation community types within any given site; this, combined with 3), unveils that the statistical modeling of maximum canopy height over large areas with edge-extent linear models only need to consider broad vegetation differences (such as woodlands versus conifer forests instead of different vegetation communities within woodlands or conifer forests), and 5) the simulations indicate that the errors and uncertainty in canopy height estimation can be significantly reduced by decreasing the footprint size. It is recommended that the footprint size of the next-generation satellite lidar systems be at least 10 m or so if we want to achieve meter-level accuracy of maximum canopy height estimation using direct and statistical methods.     

Annual and interannual (ENSO) variability of spatial scaling properties of a vegetation index (NDVI) in Amazonia

The space-time variability of the Normalized Difference Vegetation Index (NDVI) over the Amazon River basin is quantified through the bi-dimensional Fourier spectrum, and moment-scaling analysis of monthly imagery at 8 km resolution, for the period July 1981-November 2002. Monthly NDVI fields exhibit power law Fourier spectra, E(k)=ck^−β, with k denoting the wavenumber, c the prefactor, and β the scaling exponent. Fourier spectra exhibit two scaling regimes separated at approximately 29 km, above which NDVI exhibit long-range spatial correlations (0<β<2), and below which NDVI behaves like white noise in space (β?0). Series of monthly values of c(t) and β(t) exhibit high negative correlation (−0.88, P>0.99), which suggest their linkages in power laws, but also that E_t(k)=c(t)k^−β(t), with t the time index. Results show a significant negative simultaneous correlation (−0.82, P>0.95) between monthly series of average precipitation over the Amazon, 〈P(t)〉, and scaling exponents, β(t); and high positive lagged correlation (0.63, P>0.95), between 〈P(t)〉 and 〈NDVI(t+3)〉. Parameters also reflect the hydrological seasonal cycle over Amazonia: during the wet season (November-March), β(t) ranges between 0.9 and 1.15, while during the dry season (May-September), β(t)?1.30. These results reflect the more (less) coherent spatial effect of the dry (wet) season over Amazonia, which translates into longer (shorter)-range spatial correlations of the NDVI field, as witnessed by higher (lower) values of β(t). At interannual timescales, both phases of ENSO reflect on both parameters, as β(t) is higher during El Niño than during La Niña, due to the more coherent effects of El Niño-related dryness, whereas NDVI spatial variability is enhanced during La Niña, due to positive rainfall anomalies. Results from the moment-scale analysis indicate the existence of multi-scaling in the spatial variability of NDVI fields. Departures from single scaling exhibit also annual and interannual variability, which consistently reflect the effects from both phases of ENSO. Furthermore, departures from single scaling are independent of the order moment, q, as the PDF of departures scaled by the mean collapse to a unique distribution. These results point out that ideas of spatial scaling constitute a promising framework to synthesize important hydro-ecological processes of Amazonia.     

Effects of different physical workload parameters on mental workload and performance

The design and evaluation of an occupational task should include an assessment of mental workload, since excessive levels of mental workload can cause errors or delayed information processing. Physically demanding work that is performed concurrently with a cognitive task may impact mental workload by impairing mental processing or decreasing performance. The primary objective of this study was to determine whether there is a differential effect of various types of physical activity on both mental workload and cognitive performance. Objective and subjective assessment tools (heart rate variability and visual analog scale) were used as indicators of mental workload, while correct responses during an arithmetic task reflected levels of performance. Thirty participants (ages 18-24 years) performed a combination of tasks inducing both physical and mental workload. Type of physical effort, frequency of movement, and force exertion level were manipulated to alter the workload associated with the physical activity. Changes in subjective ratings generally corresponded to changes in both performance on the arithmetic task and objective mental workload assessment. Some discrepancies occurred at the highest physical force exertion level as participants perceived an increase in effort to maintain the same level of performance. Further research is needed to determine the force exertion threshold, beyond which the physical effort required interferes with mental workload and/or cognitive performance.

Relevance to industry

Technological advancements have increased the requirement for many workers to execute cognitive tasks concurrently with physical activity. When designing and evaluating such situations it is important to determine the interactive effects of these activities. A simple, uni-dimensional tool is suggested as a screening tool to identify situations requiring excessive or increased mental workload that many degrade performance or place additional stress on the individual.