Satellite-observed biological variability in the equatorial Pacific during the 2009–2011 ENSO cycle

The event of 2009–2011 El Niño Southern Oscillation (ENSO) provides an opportunity to gain insight into the biological variability of the equatorial Pacific Ocean for an entire ENSO cycle with satellite and in situ observations. Even though El Niño and La Niña in general led to respectively weakened and enhanced chlorophyll-a concentration and net primary production (NPP) along the equatorial Pacific Ocean during the 2009–2011 ENSO cycle, biological responses were highly disparate along the equator and attributed to different driving mechanisms. In the eastern equatorial Pacific east of 150°E, the El Niño-La Niña biological change was in general small except for the transition period even though sea surface temperature (SST) showed over ∼5 °C drop from El Niño to La Niña. In the central-eastern (170°W–140°W) equatorial Pacific, moderate change of biological activity is attributed to the changes of thermocline driven by the eastward propagating equatorial Kelvin waves and changes of zonal currents and undercurrents. Highest biological response in this ENSO cycle was located in the central (170°E–170°W) and central-western (150°E–170°E) equatorial Pacific with quadruple chlorophyll-a concentration and over ∼400 mg C m⁻² d⁻¹ increase of NPP from El Niño in 2009 to La Niña in 2010. However, spatial pattern of ENSO biological variability as represented with NPP is not exactly the same as chlorophyll-a variability. Wind-driving mixing of nutrients and eastward advection of the oligotrophic warm pool waters are attributed to this significant biological variability in this region.     

A study of forest vegetation dynamics in the south of the Krasnoyarskii Krai in spring

Remote sensing applications have greatly enhanced ability to monitor and manage in the areas of forestry. Accurate measurements of regional and global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Study of vegetation phenology is required for understanding of variability in ecosystem. In this paper, monitoring of vegetation dynamics using time series of satellite data is presented. Vegetation variability (vegetation rate) in different topoclimatic areas is investigated. Original software using IDL interactive language for processing of satellite long-term data series was developed. To investigate growth dynamics vegetation rate inferred from remote sensing was used. All estimations based on annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Vegetation rate for Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) was calculated using MODIS data. The time series covers spring seasons of each of 9 years, from 2000 to 2008. Comparison of EVI and NDVI derived growth rates has shown that NDVI derived rates reveal spatial structure better. Using long-term data of vegetation rates variance was estimated that helps to reveal areas with anomalous growth rate. Such estimation shows sensitivity degree of different areas to different topoclimatic conditions. Woods of heights depend on spatial topoclimatic variability unlike woods of lowlands. Principal components analysis shows vegetation with different rate conditions. Also it reveals vegetation of same type in areas with different conditions. It was demonstrated that using of methods for estimating the dynamic state of vegetation based on remote sensing data enables successful monitoring of vegetation phenology.     

Analysis of the variability of temperature gradient in the ocean frontal zones based on satellite data

AVHRR MCSST data for the periods 1982–2000 (mean weekly data) were used to calculate mean gradient fields in the ocean for different periods of time. Three-month averaged sea surface temperature gradients (SSTG) and their mean seasonal variations have been studied for 25 points in the large-scale oceanic fronts zones. Major oceanic fronts in the Atlantic and Pacific have been identified and compared in literature. In the North Atlantic and Pacific, the areas under study were the North Polar Front and Subpolar Fronts. In the South Atlantic and Pacific we studied the region of the Antarctic Circumpolar Current (ACC) and the fronts formed by this current, known as the South Polar Front, and the Subantarctic Front. SSTG were also calculated for El Niño (Southeast Pacific) and Benguela Current (Southeast Atlantic).     

Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China

Vegetation typically elicits dynamics at the seasonal and annual level. Time-series of normalized difference vegetation index (NDVI) datasets, such as the pathfinder AVHRR land (PAL) NDVI dataset, have proven to be appropriate for the detection of long-term vegetation cover changes. It has been applied in modelling experiments for terrestrial ecosystems at the global, continental, and regional scales. But some PAL NDVI time series remain significant residual effects and noise levels. A simple method, the mean-value iteration filter (MVI), has been developed to reduce the noise and to enable the reconstruction of high quality NDVI time-series. A comparison between the newly developed method and other existing methods (the modified BISE algorithm and a fast Fourier transform algorithm) indicates that the newly developed method is an effective tool for reconstructing high-quality time series of PAL NDVI time series.     

A simplified data assimilation method for reconstructing time-series MODIS NDVI data

The Normalized Difference Vegetation Index (NDVI) is an important vegetation index, widely applied in research on global environmental and climatic change. However, noise induced by cloud contamination and atmospheric variability impedes the analysis and application of NDVI data. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. We extracted 16-Day L3 Global 1 km SIN Grid NDVI data sets for western China from MODIS vegetation index (VI) products (MOD13A2) for the period 2003–2006. NDVI data in the first three years (2003–2005) were used to generate the background field of NDVI based on a simple three-point smoothing technique, which captures annual features of vegetation change. NDVI data for 2006 were used to test our method. For every time step, the quality assurance (QA) flags of the MODIS VI products were adopted to empirically determine the weight between the background field and NDVI observations. Ultimately, more reliable NDVI data can be produced. The results indicate that the newly developed method is robust and effective in reconstructing high-quality MODIS NDVI time-series.     

Decadal variability of tropical Pacific temperature in relation to solar cycles

We use the 8-year long satellite temperature data (2002–2010) from Atmospheric InfraRed Sounder (AIRS) and Atmospheric Microwave Sounding Unit (AMSU) on the Aqua satellite to identify temperature trends in the troposphere and low stratosphere over the Niño 3.4 region of the Tropical Pacific Ocean in the most recent 11-year solar cycle. Employing more extended sea surface temperature (SST) data for five solar cycles (1950–2009) in this region we show that the satellite trends reflect a typical decrease of the sea surface temperature (SST) in the Niño 3.4 region in the declining phase of the solar cycle. The magnitude of the SST decrease depends on the solar cycle and ranges between 0.07 K/yr and 0.27 K/yr for the last five solar cycles.     

Rio de la Plata estuarine system: Relationship between river flow and frontal variability

This paper deals with the application of SeaWIFS images to characterize spatial and temporal variability of fronts in the Rio de la Plata estuarine system over the period 2000–2003. We aim to depict the relationship between river outflow and variability of fronts’ loci on monthly to ENSO-related timescales and the influence of the winds along Rio de la Plata (axial winds) on the abrupt changes in frontal dynamics over synoptic timescales. During the studied period both La Niña (July 1999–June 2000) and El Niño (April 2002–May 2003) events induced significant displacements of fronts. Three distinct fronts were analyzed between river, estuarine, coastal and marine waters of the Rio de la Plata: Main Turbidity Front, Main Marine Front, and Secondary Marine Front. Their number, location and separation seem to be mainly related to river outflow and second, to fresh (>8 m/s) axial winds. During low discharge periods (i.e. summer time and/or La Niña events) these winds induce abrupt changes in the location of fronts (100–200 km) and greater separation between them over synoptic timescale, whereas during high river discharge or ENSO years some of the variability of fronts location is explained by the river’s outflow fluctuations, especially by the high variability of the River Uruguay discharge.     

Experiment for mapping land cover and it’s change in southeastern Sri Lanka utilizing 250 m resolution MODIS imageries

Utilizing freely available MODIS NDVI and Natural color imageries of 250 m spatial resolution produced by NASA, an experiment was made to map land-cover and its change with an emphasis on vegetation cover in southeastern Sri Lanka, which plays a vital role for control of green house gas. For the change detection purpose, 1987 land cover map made by present authors from Landsat MSS image and extensive ground truth survey data was used as the base map. The result of the experiment shows that MODIS data are useful to make a land cover map of 250 m spatial resolution for tropical areas with high cloud coverage like Sri Lanka. It was found that the forest cover decrease amounted as large as 21% in 19 years time span in southeastern Sri Lanka, the prominent forest region of the country. On the other hand homestead/vegetation and mixed vegetation/scrub dominant categories increased by 13.7% and 7.1%, respectively. These changes are considered due to a large clearance of forest areas for agriculture and building houses to accommodate increasing inhabitants.     

Detection of volcanic,CO2, and ENSO signals in surface air temperature

The Russian surface air temperature data set (Northern Hemisphere, gridded, monthly average, 1891–1981) is analyzed to separate signals from large volcanic eruptions, ENSO (El Niño/Southern Oscillation) events, and signals in conjunction with hemispheric temperature changes. Characteristic patterns of zonal average temperature as a function of month and latitude as well as monthly average maps are produced for each forcing. The largest response to volcanic forcing appears at a lag of 2 and 3 years following volcanic eruptions. The zonal average response shows cooling at all latitudes and months, except in the Arctic in summer. The largest cooling is in the high latitudes in the winter. This agrees very well with energy balance model calculations of the response pattern to large volcanic eruptions, and can be explained by sea ice/thermal inertia and, to a lesser extent, snow/albedo feedbacks. The spatial pattern shows large areas of cooling at all longitudes. In response to ENSO oscillations the largest response is synchronous or with the temperature lagging ENSO oscillations by 6 months. The zonal average response is virtually zero, but the spatial patterns show large variations of opposite sign at different longitudes. Patterns produced by weighting temperature anomalies with hemispheric mean temperature indicate patterns to be expected following a CO₂ warming.     

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

Assessing ecohydrological factors variations and their relationships at different spatio-temporal scales in semiarid area,northwestern China

As a typical semiarid farming-pastoral ecotone sensitive to the environmennt, the Plain of West Liaohe Basin (WLBP) is currently experiencing drastic environmental changes. To identify how environmental change affect vegetation in the WLBP, we analysed spatiotemporal variation characteristics of Ecological environment factors based on monthly and annual air temperature (T), precipitation (P) and Normalized Difference Vegetation Index (NDVI) from 1982 to 2015. And the correlations between them were investigated by correlation analysis (Simple correlation, partial correlation and complex correlation) at temporal and spatial scale. The results showed that: (1) the vegetation growth of the WLBP showed ameliorated trend, with a change rate of 0.004/yr.; (2) P was more sensitive to NDVI than T; (3) and the influence of hydrothermal changes on vegetation growth was more significant than that of the change of single climate factors at time scales; (4) the effects of anthropogenic factors on vegetation change were 75.07% (1982–1993) and 98.08% (1994–2015), respectively. At the temp-special scales, P&T and land use type change (LUCC) were the main climatic and anthropogenic factors that affect vegetation changes, respectively.     

Is the phenology-based algorithm for mapping deciduous rubber plantations applicable in an emerging region of northern Laos?

Updated information of rubber plantations is essential for assessing socioeconomic and environmental impacts, especially in the emerging region of northern tropics. Here, a phenological method was modified to detect rubber plantations using Landsat Operational Land Imager (OLI) imagery in Phongsaly Province of northern Laos, where it begun a rubber boom in the mid-2000s due to geo-economic cooperation. It highlighted the landscape and pixel differences of deciduous rubber plantations in the tri-temporal phases (i.e., pre-defoliation, defoliation, and foliation) during the dry season due to phenological changes. Six commonly used vegetation indices (VIs), including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Burn Ratio (NBR and NBR2) derived from OLI imagery during 2013–2016 were compared to determine the most suitable VI for discriminating the phenological differences of rubber plantations from natural forests. Then, the Differences of Normalized Burn Ratio (DNBR) was applied to generate the 30 m map of rubber plantations in 2016, by combining two masks of Landsat-derived forest and suitable elevation for rubber trees cultivation. The resultant map of rubber plantations had a classification accuracy of 93.7% and the Kappa coefficient of 0.848. Our study demonstrated the usefulness of the Landsat-derived tri-temporal phenological DNBR approach in an emerging region of northern Laos, despite requiring more scenes compared with single- and double temporal window methods.     

Fire risk assessment using satellite data

Forest fires are one of the most important sources of land degradation that lead to deforestation and desertification processes. Risk indexes obtained by means of satellite measurements have widely improved the forecasting and monitoring of fire and its impact over the global ecosystem in an operational manner. Using Advanced Very High Resolution Radiometer (AVHRR) data and the Normalized Difference Vegetation Index (NDVI) as a key variable, we have defined a new risk index in which a static map of fire probability is modulated with the NDVI values. Its usefulness to study the dynamic of fire risk over a test area that has been affected by fire in the past years is tested. The potential application of these tools for fire management and its suitability to be provided as an end product to the forest services and cooperators aiding in that sense is also assessed.     

A review of short-term climate variability mechanisms

Climate varies from seasonal to centennial time scales. In the last two decades progress has been made to better understand several aspects of short-term variability of the Earth’s climate system. Tropical Pacific is dominated by a single mode of interannual climate variability, which reflects the coupling of the Ocean and the atmosphere and is expressed by the El Niño/Southern Oscillation (ENSO) phenomenon. The Pacific climate contains another mode of variability similar to the ENSO, but varying on a decadal scale, the Pacific Decadal Oscillation (PDO) mode. Variations of the sea surface temperature (SST) in the Tropical Atlantic also play an important role in modulating the climate variability. The SST variability in this basin might be explained by at least five modes: separate modes in the Tropical North Atlantic and Tropical South Atlantic, equatorial mode, dipole mode and cross-equatorial SST anomaly gradient mode. Among these modes, the equatorial mode contains strong interannual variability, while the dipole and the cross-equatorial SST anomaly gradient modes contain strong decadal variability. Some aspects of the climate variability, with emphasis on the ENSO, PDO modes in the Pacific and the Atlantic SST modes on the interannual and decadal time scales, are discussed in the present paper.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

Using the MIR bands in vegetation indices for the estimation of grassland biophysical parameters from satellite remote sensing in the Alps region of Trentino (Italy)

Development of new methods for estimating biophysical parameters can be considered one of the most important targets for the improvement of grassland parameters estimation at full canopy cover. In fact, accurate assessment methods of biophysical characteristics of vegetation are needed in order to avoid the uncertainties of carbon terrestrial sinks.

Remote sensing is a valid tool for scaling up ecosystem measurements towards landscape levels serving a wide range of applications, many of them being related to carbon-cycle models. The aim of this study was to test the suitability of satellite platform sensors in estimating grassland biophysical parameters such as LAI, biomass, phytomass, and Green herbage ratio (GR). Also, we wanted to compare some of the most common NIR and red/green-based vegetation indices with ones that also make use of the MIR band, in relation to their ability to predict grassland biophysical parameters.

Ground-truth measurements were taken on July 2003 and 2004 on the Monte Bondone plateau (Italian Alps, Trento district) in grasslands varying in land use and management intensities. From satellite platforms, an IRS-1C-LISS III image (18/07/2003; 25 m resolution in the visible-NIR and 70 m resolution in the MIR) and a SPOT 5 image (27/07/2004, 10 m resolution in the visible-NIR and MIR) were used.

LAI, biomass, and phytomass measurements showed logarithmic relationships with the investigated NIR and red/green-based indices. GreenNDVI showed the highest R² values (0.59, IRS 2003; 0.60, SPOT 2004). Index saturation occurred above approximately 100–150 g m⁻² of biomass (LAI 1.5–2). On the other hand, GR relationships were shown to be linear. MIR-based indices performed better than NIR and red/green-based ones in estimating biophysical variables, with no saturation effect. Biomass showed a linear regression with Canopy Index (MIR/green ratio) and with the Normalised Canopy Index (NCI) calculated as a normalised difference between MIR and green bands (IRS: R² = 0.91 and 0.90, respectively. SPOT: R² = 0.63 and 0.64). Similar correlations could also be found for LAI and phytomass, and GR predictability was shown to be higher than NDVI and GreenNDVI. According to these results obtained in the investigated areas, phytomass, biomass, LAI, and GR are linearly correlated with the investigated MIR band indices and as a result, these parameters could be estimated from the adopted satellite platforms with limited saturation problems.     

Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea

Moderate Resolution Imaging Radiometer (MODIS) gross primary productivity (GPP) has been used widely to study the global carbon cycle associated with terrestrial ecosystems. The retrieval of the current MODIS productivity with a 1 × 1 km² resolution has limitations when presenting subgrid scale processes in terrestrial ecosystems, specifically when forests are located in mountainous areas, and shows heterogeneity in vegetation type due to intensive land use. Here, we evaluate MODIS GPP (MOD17) at Gwangneung deciduous forest KoFlux tower (deciduous forest; GDK) for 2006–2010 in Korea, where the forests comprise heterogeneous vegetation cover over complex terrain. The monthly MODIS GPP data overestimated the GDK measurements in a range of +15% to +34% and was fairly well correlated (R = 0.88) with the monthly variability at GDK during the growing season. In addition, the MODIS data partly represented the sharp GPP reduction during the Asian summer monsoon (June–September) when intensive precipitation considerably reduces solar radiation and disturbs the forest ecosystem. To examine the influence of subgrid scale heterogeneity on GPP estimates over the MODIS scale, the individual vegetation type and its area within a corresponding MODIS pixel were identified using a national forest type map (∼71-m spatial resolution), and the annual GPP in the same area as the MODIS pixel was estimated. This resulted in a slight reduction in the positive MODIS bias by ∼10%, with a high degree of uncertainty in the estimation. The MODIS discrepancy for GDK suggests further investigation is necessary to determine the MODIS errors associated with the site-specific aerodynamic and hydrological characteristics that are closely related to the mountainous topography. The accuracy of meteorological variables and the impact of the very cloudy conditions in East Asia also need to be assessed.     

SPOT 4 VEGETATION system: Association with high resolution data for multiscale studies

The SPOT 4 VEGETATION system is a joint program of the European Commission, France, Sweden, Belgium and Italy, to be launched in 1997. It will provide global and frequent measurements adapted to biosphere studies, especially for monitoring of the biophysical processes of the vegetation canopies. Information derived from its measurements will allow operational and long term studies related either to the production of agricultural areas or to the functioning of ecosystems, to their interaction with the atmosphere and climatic changes. The design of the entire system, from the instrument to the ground segment, was aimed at providing direct capability for multitemporal analysis of surface reflectance measurements at medium spatial resolution as well as for association with high spatial resolution data sets available from the SPOT High Resolution instrument or from other systems. Nature and quality of products delivered by the system were especially tailored taking into account the development of research done on previous systems and the needs for characterizing the temporal dynamic aspects of biosphere processes and relating regional or ecosystems' parameters to local parameters less affected by spatial heterogeneity.     

Dust storms detection over the Indo-Gangetic basin using multi sensor data

Dust storms are common during the summer season over the Indo-Gangetic basin and are considered to be a major health hazard to millions of people living in the basin. In countries like India, there is no early warning made for dust outbreaks and as a result the day-to-day life is affected by these dust events. In this paper, efforts have been made to utilize multi sensor data to study the characteristics of dust storms. Moderate Resolution Imaging Spectroradiometer and Multiangle Imaging SpectroRadiometer images clearly show dust storm events over the Indo-Gangetic basin. The Total Ozone Mapping Spectroradiometer (TOMS) Aerosol Index and the Advanced Microwave Sounding Unit (AMSU) data over the western end of the Indo-Gangetic basin have been analyzed. These data show characteristic behavior of brightness temperature and Aerosol Index due to dust, which change significantly as dust migrates towards east over Kanpur. The TOMS Aerosol Index and the AMSU brightness temperature (T_b) show a characteristic anti-correlation, which confirms the presence of a dust storm over Indo-Gangetic basin.     

Preparing for space: Increasing technical readiness of low-cost high-performance remote sensing using high-altitude ballooning

Two widely available, small size, weight and power camera systems were flown above 97 % of Earth’s atmosphere and showed utility in single filter vegetation and soil analysis in a space analogue environment. The experiment was conducted as a low-cost verification and test analogue to flying on vastly more expensive low Earth orbit missions. Normalised Difference Vegetation Index (NDVI) was used as the metric by which performance was analysed for ground calibration testing, low and near-space altitude remote sensing. Ground calibration testing with a laboratory-grade spectrometer revealed that both cameras were able to return consistent NDVI results, and high-altitude balloon flight allowed similar data capture from an environment similar to space. Although compressed captured imagery had been processed using gamma correction and pre-image processing, these were able to be corrected provided that access to radiometrically-calibrated data was available. The two hobbyist cameras were shown to return scientifically useful results, demonstrating performance, and additionally their utility for citizen science applications in the near-space environment.