Correction of atmospheric effects of satellite remote sensing data (Landsat MSS-NOAA AVHRR) for surface canopy investigations

Abstract

A new correction method for atmospheric effects in Landsat-MSS and NOAA AVHRR data is presented which uses only the remotely-sensed multispectral data. The method is based on a new quasi-single-variable radiative transfer model, and as a first step we assumed that the surface is covered by vegetation. For Landsat-MSS data the method was developed for the tasseled cap indices using known empirical relationships among them. For NOAA AVHRR data ‘ cap-like’ indices and the average reflectance of the average canopy in the visible band known from Landsat-MSS data were used. The method was used in yield forecasting project in north-eastern part of Hungary and provided a significant enhancement in the quality of remotely sensed data.     

Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina

We used multiple regression analysis to relate evapotranspiration (ET), computed from a water balance technique, to both thermal infrared and normalized difference vegetation index data obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board on the National Oceanic and Atmospheric Administration (NOAA) satellite. This approach, based on only remotely sensed data, provided a reliable estimate of ET over the Pampas, the main agricultural region of Argentina. The relationship between spectral data and ET was more sensitive to the dates than to the sites used to generate the models.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

On the correlation of indices of vegetation and surface temperature over south-eastern Australia

Abstract

Relationships between radiant surface temperature (T _R) and vegetation indices for scenes with equal areas of forest and agricultural land use were studied using a Landsat Thematic Mapper (TM) scene during spring and a NOAA-Advanced Very High Resolution Radiometer (AVHRR) scene during summer. The relationships between TR and the Normalized Difference (ND) index of vegetation for agricultural land use were different from those for forests. At the same T _R, the forests had lower near infrared reflectance. This caused the ND of forests to fall below the T _R/ND relationships formed by agricultural land use. This difference between forest and agricultural land use did not exist when visible reflectance (VIS) was used as the index of vegetation. When the two land use systems were combined VIS accounted for about 86 per cent of the variance in T _R. The slope of the relationships between VIS and T _R differed for TM and AVHRR scenes. This was explained by differences in the T _R and VIS reflectance of surfaces with near-zero evaporation. These surfaces were predominantly bare soil in the TM scene and senesced vegetation in the AVHRR scene.     

RS、GIS、GPS在西北农业大开发中的应用前景

遥感(RS)、地理信息系统(GIS)和全球定位系统(GPS)作为三大高新技术(“3S”技术),可以 独立地,也可以相互补充地为农业生产和开发提供强大的技术支撑。它们能快速准确地获取农业生 产系统的多维信息,尤其是时间维的信息,能综合性地管理和处理属性数据和空间数据,并能为农 业生产的决策提供相应的技术服务,进而精确地指导农业生产,促进生态环境的良性发展。论述了 “3S”技术在西北地区农业开发中的应用前景,着重于土壤水分的遥感反演以及干旱和荒漠化的动 态监测。     

Sharpened Mapping of Tropical Forest Biophysical Properties from Coarse Spatial Resolution Satellite Sensor Data

Forest biophysical properties are typically estimated and mapped from remotely sensed data through the application of a vegetation index. This generally does not make full use of the information content of the remotely sensed data, using only the data acquired in a limited number of spectral channels, and may provide a relatively crude spatial representation of the biophysical variable of interest. Using imagery acquired by the NOAA AVHRR, it is shown that a standard neural network may use all the spectral channels available in a remotely sensed data set to derive more accurate estimates of the biophysical properties of tropical forests in Ghana than a series of vegetation indices. Additionally, the spatial representation derived can be refined by fusion with finer spatial resolution imagery, achieved with the application of a further neural network.     

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C₃ and C₄ species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C₃- and C₄-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C₄ grasslands), whereas the C₃-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.     

Effet de la reésolution spatiale sur des proprieeacute;teés statistiques des images satellites: une eétude de cas

Abstract

The instruments on such satellites as Landsat or SPOT present the advantage provided by high spatial resolution. This advantage is tempered by their low time resolution. Therefore, it is not always possible to monitor seasonal variations of parameters such as the normalized vegetation index. The AVHRR instrument on board the NOAA satellites has a very high repetitivity but a very low spatial resolution. In our research we proposed to monitor the normalized vegetation index with this low-resolution instrument. It is therefore of interest to examine the relations between high- and low-resolution images for using the AVHRR data as a means of interpolation between two MSS images. This problem is addressed here using satellite images of an important agricultural region in France. In terms of the transformation of the mean radiometric values, it is shown that a linear transformation exists to calculate the AVHRR data from those of the MSS. but the relation presents a strong dependency on the observed scene. The effect on the higher-order statistical properties is studied through the transformation of the images textures by progressively degrading the MSS images. It is shown that a threshold, which depends on the scene, exists on the resolution below which all statistical information disappears.     

Determination of land surface spectral reflectances using Meteosat and NOAAA/AVHRR Shortwave Channel Data

Abstract

A method to derive surface spectral reflectances from currently available Meteosat geostationary and NOAA/AVHRR polar orbiting satellite data is described. Broadband reflectance was derived from Meteosat measurements while NOAA/AVHRR vegetation index provided a spectral weighting which enabled the spectral reflectances on either side of 0-7 μm to be estimated. The method takes into account satellite calibrations, viewing geometry, and correction of some atmospheric effects. Conversion from narrow-band to broadband reflectances is discussed. The method was applied to a month of data to obtain the surface spectral reflectances of Africa which are compared with some data sets used by climate modellers, in order to assess them and to monitor their seasonal and interannual changes on a global scale.     

A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US

We present a dryland irrigation mapping methodology that relies on remotely sensed inputs from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument, globally extensive ancillary sources of gridded climate and agricultural data and on an advanced image classification algorithm. The methodology involves four steps. First, we use climate-based indices of surface moisture status and a map of cultivated areas to generate a potential irrigation index. Next, we identify remotely-sensed temporal and spectral signatures that are associated with presence of irrigation defined as full or partial artificial application of water to agricultural areas under dryland conditions excluding irrigated pastures, paddy rice fields, and other semiaquatic crops. Here, the temporal indices are based on the difference in annual evolution of greenness between irrigated and non-irrigated crops, while spectral indices are based on the reflectance in the green and are sensitive to vegetation chlorophyll content associated with moisture stress. Third, we combine the climate-based potential irrigation index, remotely sensed indices, and learning samples within a decision tree supervised classification tool to make a binary irrigated/non-irrigated map. Finally, we apply a tree-based regression algorithm to derive the fraction of irrigated area within each pixel that has been identified as irrigated. Application of the proposed procedure over the continental US in the year 2001 produces an objective and comprehensive map that exhibits expected patterns: there is a strong east-west divide where the majority of irrigated areas is concentrated in the arid west along dry lowland valleys. Qualitative assessment of the map across different climatic and agricultural zones reveals a high quality product with sufficient detail when compared to existing large area irrigation databases. Accuracy assessment indicates that the map is highly accurate in the western US but less accurate in the east. Comparison of area estimates made with the new procedure to those reported at the state and county levels shows a strong correlation with a small bias and an estimated RMSE of 2500 km², or little over 2% of the total irrigated area in the US. As a result, the future application of the new procedure at a global scale is promising but may require a better potential irrigation index, as well as the use of remotely sensed skin temperature measurements.     

Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data

The first year of Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for derivation of biophysical variables in Senegal, West Africa. The dynamic range of the two MODIS vegetation indices (VIs)—the continuity vegetation index (CVI) and the enhanced vegetation index (EVI)—is generally much larger than for the NOAA AVHRR normalized difference vegetation index (NDVI) data, indicating the importance of the change in near-infrared wavelength configuration from the NOAA AVHRR sensor to the MODIS sensor. Senegal is characterized by a pronounced gradient in the vegetation density covering a range of agro-climatic zones from arid to humid and it is found that the MODIS CVI values saturate for high VI values while the EVI demonstrates improved sensitivity for high biomass. Compared to NOAA AVHRR the MODIS VIs generally correlate better to the MODIS fraction of absorbed photosynthetically active radiation (fAPAR) absorbed by vegetation canopies and the leaf area index (LAI; the one-sided green leaf area per unit ground area). CVI is found to correlate better to both fAPAR and LAI than is the case for EVI because of the larger dynamic range of the CVI data. This suggests that the problem of background contamination on VIs from soil is not as severe in Senegal as has been found in other semi-arid African areas.     

Analysis of the temporal and spatial vegetation patterns in a semi-arid environment observed by NOAA AVHRR imagery and spectral ground measurements

The borderline between Israel and Sinai is characterized by a sharp contrast that is caused by the low spectral reflectance on the Israeli side (Negev desert) and the high spectral reflectance on the bare Egyptian side (Sinai desert). This contrast across the political border has been discussed in many publications over the last two decades. In this study, satellite images acquired by NOAA Advanced Very High Resolution Radiometer (AVHRR) over a time period of 3 years (June 1995 to June 1998) were analysed. In addition, extensive field studies were carried out on the Israel side of the border. The current research shows that the reflectance values in Sinai seem to be quite stable over the entire year, however reflectance values in the Negev show a significant difference between the dry and the rainy seasons. Comparison between the AVHRR-derived Normalized Difference Vegetation Index (NDVI ) values and rainfall data from the Negev shows that the highest AVHRR-derived NDVI values occur a few weeks after the main rainfall. Field observations, based on spectrometer measurements of different surface components (bare sands, biological soil crusts, annuals, and perennials) and estimation of vegetation cover on the Israeli side of the border, show that the peak NDVI of the perennials occurs at the same time as the satellite observed peak. The spectral difference between both sides of the border during the dry season is caused by the dense cover of the higher vegetation and the biological soil crusts and by the photosynthetic activity of perennials during the dry season. The highest difference between both sides during the rainy season is caused by the photosynthetic activity and vegetation cover of the annuals and perennials.     

Satellite and ground-based pasture production assessment in Niger: 1986-1988

Abstract

The standing crop of herbaceous biomass produced during the 2-4?month summer rainy season by the annual grasses in the Sahel zone provides an indication of resource availability for livestock for the following 9-month dry season. Combined use of NOAA advanced very high resolution radiometer (AVHRR) local area coverage (LAC) satellite data and biomass data, obtained through vegetation sampling of 25-100 km₂ areas, allowed the development of a method for biomass assessment in Niger. Vegetation sampling involved both visual estimates and clipped plots (double sampling). The relationship between time-integrated normalized difference vegetation index (NDVI) statistics derived from NOAA AVHRR LAC data (dependent variable) and total herbaceous biomass (independent variable) was obtained through regression analysis. An inverse prediction was used to estimate biomass from the satellite data. Biomass maps and statistics of the grasslands were produced for the end of each rainy season: 1986, 1987 and 1988. This information is being used for planning purposes by the pastoral resource managers of the Government of Niger.     

Interpretation of Nimbus-7 37 GHz microwave brightness temperature data in semi-arid southern Africa

Abstract

Monthly 37 GHz microwave polarization difference temperatures (MPDT) derived from the Nimbus-7 scanning multichannel microwave radiometer (SMMR) for southern Africa from 1979 to 1985 are compared with rainfall and Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. MPDT rose sharply during a drought episode which occurred within the period included in the data. The rise was seen not only in the growing season, but also in the dry season MPDT when no actively photosynthetic, water-containing leaves are present. The results suggest that scattering of the emitted microwave radiation by dead and living vegetation is a more important factor than has previously been recognized. The sensitivity of MPDT to small quantities of dry vegetation encourages the hope that standing dead vegetation and plant litter may be remotely sensed. In the absence of vegetation, rough terrain reduced the MPDT whereas a damp surface increased it.     

Exploring the utility of NOAA AVHRR middle infrared reflectance to monitor the impacts of ENSO-induced drought stress on Sabah rainforests

The ever-wet tropics are under threat from ENSO events and there is a need for a monitoring system to analyse and describe their responses to such events. This letter explores the relative value of using NOAA AVHRR middle infrared (MIR) reflectance data and NDVI data for the monitoring of ENSO-related drought stress of a tropical forest ecosystem in Sabah, Malaysia. Relationships between rainfall and MIR reflectance were examined. Correlation coefficients are generally large and significant (at 0.1 level) while those between rainfall and NDVI were small and insignificant. This letter concludes that there is potential in using MIR reflectance for monitoring the effects of ENSO-induced drought stress on these forests and this has a bearing on how NOAA AVHRR data may be used to further our knowledge on the impacts of ENSO events on tropical forest environments.     

A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery

Abstract

The imaging frequency and synoptic coverage of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) make possible for the first time a phenological approach to vegetation cover classification in which classes are defined in terms of the timing, the duration and the intensity of photosynthetic activity. This approach, which exploits the strong, approximately linear relationship between the amount of solar irradiance absorbed by plant pigments and shortwave vegetation indices calculated from red and near-infrared reflectances, involves a supervised binary decision tree classification of phytophenological variables derived from multidate normalized difference vegetation index (NDVI) imagery. A global phytophenological classification derived from NOAA global vegetation index imagery is presented and discussed. Although interpretation of the various classes is limited considerably by the quality of global vegetation index imagery, the data show clearly the marked temporal asymmetry of terrestrial photosynthetic activity.     

Synergism between NOAA-AVHRR and Meteosat data for studying vegetation development in semi-arid West Africa

Abstract

Rainfall estimates, based on cold cloud duration estimated from Meteosat data, are compared with vegetation development depicted by data of the normalized difference vegetation index (NDVI) from the National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) for part of the Sahel. Decadal data from the 1985 and 1986 growing seasons are examined to determine the synergism of the datasets for rangeland monitoring. There is a general correspondence between the two datasets with a marked lag between rainfall and NDVI of between 10 and 20 days. This time lag is particularly noticeable at the beginning of the rainy season and in the more northern areas where rainfall is the limiting factor for growth. Principal component analysis was used to examine deviations from the general relationship between rainfall and the NDVI. Areas of low NDVI values for a given input of rainfall were identified: at a regional scale, they give an indication of areas of low production potential and possible degradation of ecosystems. This study demonstrates in a preliminary way the synergism of such datasets derived from satellite--borne sensors with coarse spatial resolution, which may provide new information for the improved management of the Sahelian grasslands.     

Modelling forest stand volume and tree density using Landsat ETM+ data

Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources. In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETM+) reflectance values. We used multivariate regression technique to predict stand volume and tree density. The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R² = 43%; root mean square error (RMSE) = 97.4 m³ ha^?1) than other ETM+ bands and vegetation indices. In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R² = 73.4%; RMSE = 170.13 ha^?1) than other combinations of ETM⁺ bands and vegetation indices. Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETM+ reflectance values and the utility of transformed bands in modelling stand volume and tree density. Based on the results of this study, we conclude that ETM+ data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area. Forest managers could use ETM+ data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions.