The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity

Agricultural crops and rangeland resources over above 30 million km² in some 55 Third World countries arc subject to ravages by the desert locust. Successful breeding, triggered by suitable ecological conditions when widespread rainfall results in development of vegetation in the desert-locust recession area, can produce rapid increases in desert-locust populations, resulting, if uncontrolled, in large numbers of highly mobile and devastating swarms containing billions of locusts. Satellite remote sensing offers the only possibility of monitoring the 16 million km² desert-locust recession area situated largely in remote and inaccessible deserts of northern and eastern Africa, the Near East, and south-west Asia. Use of LANDSAT and National Oceanic and Atmospheric Administration (NOAA) satellite imagery for analyses of green vegetation blooms in desert-locust breeding areas suggests an operational programme based upon the NOAA Advanced Very-High-Resolution Radiometer sensor     

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.     

Rainfall and vegetation monitoring in the Savanna Zone of the Democratic Republic of Sudan using the NOAA Advanced Very High Resolution Radiometer

NOAA-6 and NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (4?km ground resolution) data were obtained at three-day intervals throughout each of the four-month periods covering the 1980, 1983 and 1984 growing seasons, between latitudes 10° and 22° North in the Democratic Republic of Sudan. Daily rainfall data for twelve meteorological stations spanning the Savanna Zone were analysed. Rainfall in Sudan during 1980 was below normal, but in 1983 and 1984 there were moderate and severe droughts. The satellite data were used to calculate normalized difference vegetation index (NDVI) values from the visible and near-infrared bands of the satellite data. These were processed into ten-day composite data sets using the AVHRR thermal-infrared channel as a cloud screen and a temporal compositing procedure that reduces cloud contamination and selects viewing angles closest to nadir.

The ten-day composite NDVI values and the integrals of NDVI for each growing season were found to be closely correlated with rainfall. The constants of regressions between NDVI and rainfall were lower in 1983 and 1984 than in 1980, which suggests there was reduced water-use efficiency by the rangeland vegetation in drought years. It was found that July and August NDVI values were closely related to the integrated NDVI values; hence early- and mid-season NDVI data could be used to predict annual primary production. Images showing the geographical distribution of values of NDVI prepared for the three years clearly illustrate the effects of the 1983 and 1984 droughts, compared with the higher rainfall of 1980. The precision of the relationship between rainfall and the vegetation indices for the meteorological stations encourages the view that NOAA AVHRR GAC composite NDVI values can be used to monitor effective rainfall in the Savanna Zone of the Democratic Republic of Sudan     

Suivi du développement végétal au cours de l'été 1984 dans le Sahel Malien

A ground data-collection programme was initiated to establish a calibration between the normalized difference vegetation index (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and grassland biomass. Thirty sites were selected representing a range of Sahclian vegetation communities in the Gourma region of Mali and monitored during the 1984 growing season. The sites were 1?km square and located within larger areas of homogeneous terrain. The herbaceous and woody strata were sampled every fourteen days, and above-ground green biomass and rainfall data were collected. Ground and airborne radiometer data were recorded to facilitate interpretation of the satellite data, and aerial photographs were taken to provide estimates of tree and shrub density. AVHRR LAC and GAC data were acquired and a thermal cloud mask was applied to the data. NDVI values were extracted for the ground sites and correlation analysis performed. Low correlation coefficients were calculated for the ground measured green biomass and satellite NDVI (0,67). The correlation between the maximum NDVI and the total biomass produced during the season was 0,73. A value of 0,05 was determined as the NDVI associated with the minimum vegetation cover identifiable by the satellite (100 kg/ha). Explanation is given for the possible causes for such low correlations, including the very low biomass production associated with the 1984 drought conditions, atmospheric haze and dust and poor locational accuracy of the satellite data     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

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.     

基于GIS的水稻遥感估产模型研究

以NOAA/AVHRR资料为主,利用GIS技术提取水稻可能种植区域,在此基础上计算各区和各县的比值植被指数和规一化植被指数,提出的水稻遥感估产比值模型和回归模型,预报浙江省的水稻总产,1998年的拟合精度和1999年的预报精度都达到95%以上.     

Satellite-derived evaporation from Lake Eyre,South Australia

Abstract

Lake Eyre is a large salt lake situated on the Australian continent, draining an area of over 1 million km². The lake is usually dry, but it floods occasionally; the last few significant events occurring in 1984, 1974 and 1950. During the 1984 filling, satellite data from NOAA Advanced Very High Resolution Radiometer (AVHRR) were received and archived. These data have now been analysed to derive the areal extent of standing water as a function of time and hence the evaporation rate, assuming exchanges due to groundwater flow and gains due to rainfall and runoff are negligibly small. The highest levels were reached on 3 February 1984 and the lake was dry by the end of March 1985. Maximum evaporation of 280 mm occurred in February 1984 and a minimum of 60 mm occurred in July 1984. The total evaporation for the period February 1984 to February 1985 is estimated to be 2190 mm ± 100 mm for Lake Eyre North and 1980 mm ± 110 mm for Lake Eyre South. These estimates compare favourably with field data collected over the same period. It is suggested that the satellite data could provide a reasonable estimate of evaporation from shallow lakes in other semi-arid, inaccessible regions.     

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.     

Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing

Fire is an important natural disturbance process in many ecosystems, but humans can irrevocably change natural fire regimes. Quantifying long-term change in fire regimes is important to understand the driving forces of changes in fire dynamics, and the implications of fire regime changes for ecosystem ecology. However, assessing fire regime changes is challenging, especially in grasslands because of high intra- and inter-annual variation of the vegetation and temporally sparse satellite data in many regions of the world. The breakdown of the Soviet Union in 1991 caused substantial socioeconomic changes and a decrease in grazing pressure in Russia's arid grasslands, but how this affected grassland fires is unknown. Our research goal was to assess annual burned area in the grasslands of southern Russia before and after the breakdown. Our study area covers 19,000 km² in the Republic of Kalmykia in southern Russia in the arid grasslands of the Caspian plains. We estimated annual burned area from 1985 to 2007 by classifying AVHRR data using decision tree algorithm, and validated the results with RESURS, Landsat and MODIS data. Our results showed a substantial increase in burned area, from almost none in the 1980s to more than 20% of the total study area burned in both 2006 and 2007. Burned area started to increase around 1998 and has continued to increase, albeit with high fluctuations among years. We suggest that it took several years after livestock numbers decreased in the beginning of the 1990s for vegetation to recover, to build up enough fuel, and to reach a threshold of connectivity that could sustain large fires. Our burned area detection algorithm was effective, and captured burned areas even with incomplete annual AVHRR data. Validation results showed 68% producer's and 56% user's accuracy. Lack of frequent AVHRR data is a common problem and our burned area detection approach may also be suitable in other parts of the world with comparable ecosystems and similar AVHRR data limitations. In our case, AVHRR data were the only satellite imagery available far enough back in time to reveal marked increases in fire regimes in southern Russia before and after the breakdown of the Soviet Union.     

Crop yield estimation by satellite remote sensing

Two methods for estimating the yield of different crops in Hungary from satellite remote sensing data are presented. The steps of preprocessing the remote sensing data (for geometric, radiometric, atmospheric and cloud scattering correction) are described. In the first method developed for field level estimation, reference crop fields were selected by using Landsat Thematic Mapper (TM) data for classification. A new vegetation index (General Yield Unified Reference Index (GYURI)) was deduced using a fitted double-Gaussian curve to the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data during the vegetation period. The correlation between GYURI and the field level yield data for corn for three years was R ²=0.75. The county-average yield data showed higher correlation (R ²=0.93). A significant distortion from the model gave information of the possible stress of the field. The second method presented uses only NOAA AVHRR and officially reported county-level yield data. The county-level yield data and the deduced vegetation index, GYURRI, were investigated for eight different crops for eight years. The obtained correlation was high (R ²=84.6–87.2). The developed robust method proved to be stable and accurate for operational use for county-, region- and country-level yield estimation. The method is simple and inexpensive for application in developing countries, too.     

The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models

Fraction of green vegetation, fg, and green leaf area index, Lg, are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction models regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (fg) and vertical (Lg) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate fg=(NDVI-NDVI0)/(NDVI8-NDVI0), where NDVIo (bare soil) and NDVI (dense vegetation) are specified as global constants independent of vegetation/soil type. Global (0.15o)2 spatial resolution monthly maps of fg were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface fluxes were improved.     

A Review of: “Environmental Information Management and Analysis: Ecosystem 10 global scales”. Edited by W. K. MICHENER,J. W. BRUNT and S. G. STAFFORD (London: Taylor &: Francis Ltd, 1994). [Pp. 535.] Price £55,00.

Abstract

Satellite indices of vegetation from the Australian continent were calculated from May 1986 to April 1987 from NOAA-9 AVHRR (Advanced Very High Resolution Radiometer) and Nimbus-7 SMMR (Scanning Multichannel Microwave Radiometer) satellite data. The visible (VIS) and near infrared (N1R) reflectances and their combination, the Normalized Difference (ND) Vegetation Index were calculated from the AVHRR sensor. From the SMMR, the microwave Polarization Difference (PD) was calculated as the difference between the vertically and horizontally polarized brightness temperatures at 37 GHz. The AVHRR data were gridded to match the 25 km spatial resolution of the SMMR 37 GHz data and both data sets were analysed to provide a temporal resolution of one month. Using a one month lag, the ND, PD, VIS and NIR, indices were plotted against rainfall and water balance estimates of evaporation, calculated using the monthly rainfall data and long term averages of pan evaporation from 74 locations covering a range of vegetation types. The monthly plots had wide scatter. This scatter was reduced markedly by aggregating the data over twelve months, leading to the conclusion that direct satellite monitoring of annual evaporation across the Australian continent using PD or VIS is feasible for areas with evaporation less than 600 mm y^?1. The ND relationship was limited by scatter and the PD and VIS relationships by their saturation above 600 mm y^?1, which spanned about two-thirds of the continental range studied. Scatter was reduced and ND had a predictive range above 600 mm y^?1 if evaporation was normalized by evaporative demand. But prior knowledge of potential evaporation is needed in this approach. The NIR reflectance of forests were consistently lower than neighbouring areas of agriculture, thus ND may underpredict the evaporation of forests relative to agriculture. Temporal resolution of the satellite indices over periods of one month could not be evaluated due to spatial and temporal variability of climatic and biological factors not accounted for in the water balance estimates of evaporation.     

Mesoscale grid rainfall estimation from AVHRR and GMS data

Areal rainfall averages derived from rain-gauge observations suffer from limitations not only due to sampling but also because gauges are usually distributed with a spatial bias towards populated areas and against areas with high elevation and slope. For a large river basin, however, heavy rainfall in the mountain upstream can result in severe flooding downstream. In this study, cloud-indexing and cloud model-based techniques were applied to Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Meteorological Satellite (GMS) imager data based on the cloud-top brightness temperature (T _B) and processed for estimating mesoscale grid rainfall. This study aims to improve and refine rainfall estimation in Malaysian monsoons based on cloud model techniques for operational pre-flood forecasting using readily available near-real-time satellite data such as the National Oceanic and Atmospheric Administration (NOAA)-AVHRR and GMS imager. Rain rates between 3 and 12 mm h^?1 were assigned to cloud pixels of hourly coverage AVHRR or GMS data over the Langat Basin area for the duration of the monsoon rainfall event of 27 September to 8 October 2000 in Malaysia. The observed rainfall and quantitative precipitation forecast (QPF) showed an R ² value of 0.9028, while the observed rainfall run-off (RR; recorded) and its simulated data had an R ² value of 0.9263 and the QPF run-off and its simulated data had an R ² value of 0.815. The rainfall estimate was used to simulate the flood event of the catchment. The estimated rainfall over the catchment showed similar flood area coverage to the observed flood event.     

Crop condition and yield simulations using Landsat and MODIS

Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6×1.6-km² grid scale were comparable to county yields reported by the USDA-National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.     

Water and heat regimes modelling for a vast territory using remote-sensing data

Methods have been developed to assess water and heat regime characteristics of a vast agricultural region for vegetation season based on the model of land-surface–atmosphere interaction. The model is adjusted to assimilate estimates of the land-surface and meteorological characteristics derived from Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration (AVHRR/NOAA), Moderate Resolution Imaging Spectroradiometer/Earth Observing System Terra & Aqua (MODIS/EOS Terra and Aqua), and Scanning Enhanced Visible and Infrared Imager/Meteosat-9 (SEVIRI/Meteosat-9) data. The case study has been carried out for the agricultural Central Black Earth region of European Russia of 227,300 km² for the 2009–2012 vegetation seasons. The methods of satellite data processing have been developed or refined, which provide the retrieval of vegetation characteristics, land-surface temperature, and precipitation. The techniques for the assimilation of satellite-based products in the model have been developed. Some major water regime characteristics have been generated such as soil water content, evapotranspiration, and others.     

Technical note: Using NOAA and FY imagery to track plumes caused by the 2003 bombing of Baghdad

In this study, the usability of a 1.1 km satellite receiving station in Greece as an operational tool for monitoring wide scale man-made disasters is demonstrated. The plumes caused by major fires in Baghdad during the 2003 bombing campaign were detected and monitored using the real-time acquisition of National Oceanic and Atmospheric Administration (NOAA) and Feng Yun satellite imagery at the Foundation for Research and Technology - Hellas (FORTH) satellite receiving station. A set of false colour composites of Advanced Very High Resolution Radiometer (AVHRR) and Multichannel Visible and Infrared Scan Radiometer (MVISR) images was used to investigate the dispersion of the plumes, which may contain toxic substances, over the wider Baghdad area, as well as to estimate plume spatial characteristics. In some cases, the area covered by these plumes was estimated to be more than 6000 km².     

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.