Global vegetation dynamics: satellite observations over Asia

Abstract

The weekly global vegetation index (GVI) derived from the NOAA AVHRR instrument has been analysed for the 1982-1985 period over a wide range of vegetation formations of Asia. Temporal development curves of the index are presented for environments ranging from the desert of central Asia to the tropical forest of Borneo. The paper shows that, despite the coarse resolution of the GVI product, a large set of useful information on ecosystem dynamics and cropping practices can be consistently derived from time series of such data. In addition, it is shown that the impact of the 1982-1983 El Nino Southern Oscillation-related drought can be detected in the GVI data through an analysis of anomalies in the development of selected vegetation formations. The relevance of such analysis for global vegetation monitoring and change detection is then underlined.     

Vegetation classification based on advanced very high resolution radiometer (AVHRR) satellite imagery

Many studies have investigated the potential of Landsat data for vegetation analyses, but there have been few attempts to make similar use of meteorological satellite data. The feasibility of utilizing AVHRR imagery for vegetation classification was tested using a vegetation gradient model based on an experimental climatological variable and AVHRR data along an east-west transect across Texas. The normalized difference vegetation index of AVHRR data, when plotted against vegetation characteristics and moisture values, suggested that a multivariate gradient model incorporating satellite spectral and meteorological data has promise as a technique for vegetation stratification and monitoring.     

在植物生长周期内县级区域植被指数的时空演变特征分析

采用１９９１至１９９２年晴空时的ＮＯＡＡ卫星ＡＶＨＲＲ资料，计算甘肃省河东地区６０个县（市）作物和牧草生长周期内标准化差植被指数（ＮＤＶＩ）的平均值和标准差，并逐县绘制其时间演变曲线和直方图。选取以农作物、草地和森林草地混合为主的三类县作对比分析，研究县级区域植被指数时空变化与作物和牧草生育期的关系。分析１９９１至１９９２年度冬小麦生长周期内遇到严重干旱的情况，为干旱监测、估产和区分土地使用类型选择最佳时相提供依据     

Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado: An analysis within the Large-Scale Biosphere-Atmosphere Experiment in Amazônia (LBA)

The Brazilian Cerrado biome comprises a vertically structured mosaic of grassland, shrubland, and woodland physiognomies with distinct phenology patterns. In this study, we investigated the utility of spectral vegetation indices in differentiating these physiognomies and in monitoring their seasonal dynamics. We obtained high spectral resolution reflectances, during the 2000 wet and dry seasons, over the major Cerrado types at Brasilia National Park (BNP) using the light aircraft-based Modland Quick Airborne Looks (MQUALS) package, consisting of a spectroradiometer and digital camera. Site-intensive biophysical and canopy structural measurements were made simultaneously at each of the Cerrado types including Cerrado grassland, shrub Cerrado, wooded Cerrado, Cerrado woodland, and gallery forest. We analyzed the spectral reflectance signatures, their first derivative analogs, and convolved spectral vegetation indices (VI) over all the Cerrado physiognomies. The high spectral resolution data were convolved to the MODIS, AVHRR, and ETM⁺ bandpasses and converted to the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) to simulate their respective sensors. Dry and wet season comparisons of the measured biophysical attributes were made with the reflectance and VI data for the different Cerrado physiognomies. We found that three major domains of Cerrado could be distinguished with the dry and wet season spectral signatures and vegetation indices. The EVI showed a higher sensitivity to seasonality than the NDVI; however, both indices displayed seasonal variations that were approximately one-half that found with the measured landscape green cover dynamics. Inter-sensor comparisons of seasonal dynamics, based on spectral bandpass properties, revealed the ETM⁺-simulated VIs had the best seasonal discrimination capability, followed by MODIS and AVHRR. Differences between sensor bandpass-derived VI values, however, varied with Cerrado type and between dry and wet seasons, indicating the need for inter-sensor VI translation equations for effective multi-sensor applications.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology

Remote sensing provides spatially and temporally continuous measures of forest reflectance, and vegetation indices calculated from satellite data can be useful for monitoring climate change impacts on forest tree phenology. Monitoring of evergreen coniferous forest is more difficult than monitoring of deciduous forest, as the new buds only account for a small proportion of the green biomass, and the shoot elongation process is relatively slow. In this study, we have analyzed data from 186 coniferous monitoring sites in Sweden covering boreal, southern-boreal, and boreo-nemoral conditions. Our objective was to examine the possibility to track seasonal changes in coniferous forests by time-series of MODIS eight-day vegetation indices, testing the coherence between satellite monitored vegetation indices (VI) and temperature dependent phenology. The relationships between two vegetation indices (NDVI and WDRVI) and four phenological indicators (length of snow season, modeled onset of vegetation period, tree cold hardiness level and timing of budburst) were analyzed.The annual curves produced by two curve fitting methods for smoothening of seasonal changes in NDVI and WDRVI were to a large extent characterized by the occurrence of snow, producing stable seasonal oscillations in the northern part and irregular curves with less pronounced annual amplitude in the southern part of the country. Measures based on threshold values of the VI-curves, commonly used for determining the timing of different phenological phases, were not applicable for Swedish coniferous forests. Evergreen vegetation does not have a sharp increase in greenness during spring, and the melting of snow can influence the vegetation indices at the timing of budburst in boreal forests. However, the interannual variation in VI-values for specific eight-day periods was correlated with the phenological indicators. This relation can be used for satellite monitoring of potential climate change impacts on northern coniferous spring phenology.     

Mapping of Siberian forest landscapes along the Yenisey transect with AVHRR

In this paper NOAA AVHRR data acquired at the Sukachev Institute of Forest in Siberia, Russia is evaluated for forest management mapping applications. First a classification of the entire 1000km 2 3000km transect was performed, but was found to be too general to be of value. More useful interpretation procedures require a landscape-ecological approach. This means that computer classification should be made separately for segments of territory based ecologically distinct regions. This segmentation of the transect into ecological regions was found to improve the level of detail available in the classification. Using this approach AVHRR data were found to be adequate for small scale mapping at the level of vegetation types or plant formations. A limited study using AVHRR data for classification of mountainous regions showed that AVHRR-derived maps were more detailed than existing landscape maps. AVHRR derived classifications also compared favourably to larger scale forest management maps of softwood and hardwood forests. Current forest management in Siberia relies on very small-scale inventory maps. Thus, there is a potential role for AVHRR (or Terra) data for northern Siberian forest monitoring. The southern forests of the Yenisey meridian (below the 57th parallel ) are less uniform due to considerable human activity, and NOAA/AVHRR data will play a subordinate role in its monitoring.     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

Linear mixture modelling applied to AVHRR data for crop area estimation

Abstract

A technique for estimating crop coverage using linear mixture modelling of multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data is presented for a study area in northern Greece. This paper identifies some of the problems associated with using satellite sensor data with coarse spatial resolution for crop area estimation. Using satellite sensor imagery with a high spatial resolution to extrapolate ground measurements to AVHRR scales, the paper shows how the mixture model can be applied to AVHRR data in a mixed agricultural system. Crop areas are estimated to an average accuracy of 89 percent on regional scale using this technique. The results show that this linear mixture modelling has potential for operational crop area monitoring on a regional basis.     

Monitoring vegetation phenology using MODIS

Accurate measurements of regional to 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. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.     

Monitoring global vegetation using Nimbus-7 37 GHz Data Some empirical relations

Abstract

The difference of the vertically and horizontally polarized brightness temperatures observed by the 37 GHz channel of the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite are correlated temporally with three indicators of vegetation density, namely the temporal variation of the atmospheric CO₂ concentration at Mauna Loa (Hawaii), rainfall over the Sahel and the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA-7 satellite. We find the SMMR 37GHz and AVHRR provide complementary data sets for monitoring global vegetation, the 37 GHz data being more suitable for arid and semi-arid regions as these data are more sensitive to changes in sparse vegetation. The 37 GHz data might be useful for understanding desertification and indexing CO₂ exchange between the biosphere and the atmosphere.     

Forest cover assessment in north-east India--the potential of temporal wide swath satellite sensor data (IRS-1C WiFS)

There is a lack of scientific data on extent, type and the condition of tropical forests. In India, the forest cover assessment is being carried out by the Forest Survey of India (FSI) biennially satellite images. The assessment has not been able to present realistic details, especially for north-east India due to the nature of deforestation/degradation processes (primarily shifting cultivation). The present study suggests a methodology to monitor forest cover using IRS-1C Wide Field Sensor (WiFS) data. It avoids illumination differences and has a better temporal resolution. NOAA Advanced Very High Resolution Radiometer (AVHRR) data have also found considerable acceptance for land cover studies at the regional level. Many studies have found NOAA data deficient in presenting the regional status monitoring due to its coarse resolution. IRS-1C WiFS data with 188 m 2 188 m spatial resolution overcomes this deficiency. The study focuses on the approach of using temporal IRS-1C WiFS data for monitoring the phenological fluxes of forested landscape of north-east India. The Normalized Difference Vegetation Index (NDVI) is evaluated for monitoring seasonal changes in vegetation. Attempts are made to classify forest using the phenological characters as discriminant. A hybrid approach (unsupervised and supervised) of classification provided better results. The overall accuracy of different classes was found to be 82.15%. The Khat ( K hat ) significance coefficient was found to be 0.80. The present assessment of forest cover in the north-east region is 42.24% of the total geographical area. The estimate made by FSI is 64.31% of the geographical area. The estimate that is made based on visual interpretation has always been contested to be on the higher side. Comparison of the map and statistics reveals that inclusion of abandoned and current shifting cultivation areas in forest cover have led to the overestimation. The results indicate that IRS-1C WiFS data can be used to map and monitor vegetation cover at the regional scale. The stratification thus achieved can provide valuable input for land surface characterization for geosphere-biosphere studies.     

Development of a two-band enhanced vegetation index without a blue band

The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor β is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, β and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within ± 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied.     

Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.     

Global analysis of empirical relations between annual climate and seasonality of NDVI

This paper describes the use of satellite data to calibrate a new climate vegetation greenness relation for global change studies. We examined statistical relations between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our understanding of intra-annual patterns and global controls on natural vegetation dynamics. Multiple linear regression results using global 1 gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80% of the geographical variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from 1 grid cells mapped as greater than 25% inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.     

Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland,Australia

Time series analysis of satellite data can be used to monitor temporal dynamics of forested environments, thus providing spatial data for a range of forest science, monitoring and management issues. The moderate resolution imaging spectroradiometer (MODIS) vegetation index (MOD13Q1) product has potential for monitoring forest dynamics and disturbances. However, the suitability of the product to accurately measure temporal changes due to phenology and disturbances is questionable as the effects of variable solar and viewing geometry have not been removed from these data. This study aimed to examine the impact that viewing and illumination geometry differences had on MOD13Q1 vegetation index values, and their subsequent ability to map changes arising from phenology and disturbances in a number of forest communities in Queensland, Australia. MOD13Q1 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were compared to normalized NDVI and EVI (NDVI_normalized and EVI_normalized), which were derived from the reflectance modelled from a bidirectional reflectance distribution function (BRDF)/albedo parameters product (MCD43A1) using fixed viewing and illumination geometry. Time series plots of the vegetation index values from a number of pixels representing different forest types and known disturbances showed that the NDVI_normalized time series was more effective at capturing the changes in vegetation than the NDVI. MOD13Q1 NDVI showed higher seasonal amplitude, but was less accurate at capturing phenology and disturbances compared to the NDVI_normalized. The EVI was less affected by variable viewing and illumination geometry in terms of amplitude, but was affected in terms of time shift in periodicities providing erroneous information on phenology. More studies in different environments with appropriate vegetation phenology reference data will be needed to confirm these observations.     

Scale considerations in vegetation monitoring using AVHRR data

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) is currently the only operational remote sensing system capable of providing global daily data which can be used for vegetation monitoring. These data are available with resolution cell sizes ranging from around one to 20 km on a side, though the temporal and spatial extent of cover at each resolution is variable. In this paper Normalized Difference Vegetation Index temporal curves derived from AVHRR at different resolutions are compared over both agricultural and natural tropical vegetation types. For the agricultural regions the length of growing season and major breaks of slope associated with key crop development events are equally well shown at coarse and fine resolution. Detailed examination of the curves reveals differences thought to result from temporal changes in landscape structure. Temporal curves derived from AVHRR data at dilTerent spatial resolutions shows that the spatial organization of both agricultural and natural landscapes, tropical forest in this case, changes throughout a single season. Transitions across major ecological zones are detected across a range of resolutions, though the undersampling employed in the generation of the coarser resolution products is found to exert some limitations on the spatial representivity of these data; this varies both with geographical area and time. These observations highlight the importance of a consideration of scale when using AVHRR data for vegetation monitoring, and emphasize the need for dilTerent scales of observation (both in temporal and spatial terms) for different problems and at different times of the year.     

Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data

National Oceanic and Atmospheric Administration (NOAA) satellite data from the Advanced Very High Resolution Radiometer (AVHRR) sensor were analysed to document the vegetation biomass dynamics associated with the regional desert-locust upsurge in West Africa during 1980/81, which affected an area of some 600 000 km² in Mali, Niger and Algeria. Comparisons were made among locust population survey reports, rainfall records from eighteen stations in the same area, and the satellite data in vegetation index format. The satellite-recorded temporal and spatial distributions of desert vegetation biomass were closely correlated with both the locust population surveys and the available rainfall data. An attempt was made to develop a quantitative relationship between a satellite-derived potential breeding activity factor (PBAF) and the observed desert locust populations. Analysis of the multitemporal satellite data set indicates that, had the NOAA/AVHRR vegetation index data been operationally available in June 1980, effective preventive control measures would have only been necessary for an area of 600 km².     

Systematic corrections of AVHRR image composites for temporal studies

For quantitative studies of vegetation dynamics, satellite data need to be corrected for spurious effects. In this study, we have applied several changes to an earlier advanced very high resolution radiometer (AVHRR) processing methodology (ABC3; [Remote Sens. Environ. 60 (1997) 35; J. Geophys. Res.-Atmos. 102 (1997) 29625; Can. J. Remote Sens. 23 (1997) 163]), to better represent the various physical processes causing contamination of the AVHRR measurements. These included published recent estimates of the NOAA-11 and NOAA-14 AVHRR calibration trajectories for channels 1 and 2; the best available estimates for the water vapour, aerosol and ozone amounts at the time of AVHRR data acquisition; an improved bidirectional reflectance algorithm that also takes into consideration surface topography; and an improved image screening algorithm for contaminated pixels. Unlike the previous study that compared the composite images to a single-date AVHRR image, we employed coincident TM images to approximate the AVHRR pixel field of view during the data acquisition. Compared to ABC3, the modified procedure ABC3V2 was found to improve the accuracy of AVHRR pixel reflectance estimates, both in the sensitivity (slope) of the regression and in r². The improvements were especially significant in AVHRR channel 1. In comparison with reference values derived from two full TM scenes, the corrected AVHRR surface reflectance estimates had average standard errors values of ±0.009 for AVHRR C1, ±0.019 for C2, and ±0.04 for NDVI; the corresponding r² values were 0.55, 0.80, and 0.50, respectively. The changes in ABC3V2 were not able to completely remove interannual variability for land cover types with little or no vegetation cover, which would be expected to remain stable over time, and they increased the interannual variability of mixed forest and grassland. These results are attributed to a combination of increased sensitivity to interannual dynamics on one hand, and the inability to remove all sources of noise for barren or sparsely vegetated northern land cover types on the other.