Monitoring East African vegetation using AVHRR data

NOAA Advanced Very High Resolution Radiometer satellite data are applied to regional vegetation monitoring in East Africa. Normalized Difference Vegetation Index (NDVI) data for a one-year period from May 1983 are used to examine the phenology of a range of vegetation types. The integrated NDVI data for the same period are compared with an ecoclimatic zone map of the region and show marked similarities. Particular emphasis is placed on quantifying the phenology of the Acacia Commiphora bushlands. Considerable variation was found in the phenology of the bushlands as determined by the satellite NDVI, and is explained through the high spatial variability in the distribution of rainfall and the resulting green-up of the vegetation. The relationship between rainfall and NDVI is further examined for selected meteorological stations existing within the bushland. A preliminary estimate is made of the length of growing season using an NDVI thresholding technique     

An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska,U.S.A.

Research designed to better define relations between 1-km multitemporal AVHRR-derived NDVI data and selected climatological parameters, soil hydrological properties and land cover characteristics is summarized. Bi-weekly maximum value composite NDVI data and concurrently measured meteorological data acquired in 1990 and 1991 for Nebraska were utilized to study relations between NDVI and accumulated growing degree days,soil temperature, precipitation and potential evapotranspiration. Temporal change in NDVI was found to be closely linked with the temperature regime. NDVI-precipitation and NDVI-potential evapotranspiration relations exhibited time lags, although the length of lag varied with land cover type, precipitation, and soil hydrologic properties. NDVI response to precipitation was stronger in natural grasslands and grassland/wet meadows than in areas of irrigated cropland and mixed crop/ grass. NDVI-climate relations were strongest where vegetation was developed on soils with low root zone available water capacity and high permeability. Relations derived by using NDVI values over 3 pixel by 3 pixel windows showed little difference from those using single 1 km pixel. This may reflect both the relatively homogeneous land cover characteristics of the study area and the effect of off-nadir viewing geometry on AVHRR data acquisition.     

Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983

Normalized difference vegetation index data derived from the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite for the 1983 growing season for the Sahelian Zone of Niger are compared with biomass estimates derived from an empirical grassland productivity model. The model used daily rainfall data to estimate the potential biomass production for fourteen meteorological stations through the growing season. A good general correspondence (r = 0·75) was seen between the productivity model and the satellite-derived integrated NDV1, although specific differences were apparent between actual and potential biomass. The study shows the utility of high-temporal-resolution satellite data for monitoring grassland conditions at a local and regional scale and emphasizes the importance of a maximum value compositing approach to the analysis. The study also shows the potential of the satellite data for quantifying phenological characteristics of vegetation     

Mapping of jasperoid in the Cedar Mountains,Utah, U.S.A., using imaging spectrometer data

Jasperoid, an important indicator of disseminated gold deposits, is mapped in the Cedar Mountains, Utah U.S.A. using data gathered by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Ratio analysis is employed to detect the sharp rise in reflectance between 1000 nm and 1700 nm in the jasperoid spectrum due to iron absorption at about 900 nm in association with quartz and bound water. A pre-requisite is to remove the effects of dead and senescent vegetation from the ratio image. This is achieved using a mask of dead vegetation created by absorption band-depth analysis of ligno-cellulose absorptions in the Short-Wave Infra-red (SWIR). The absorption feature at about 2080 nm was used, but this choice is partly dictated by the limestone host rocks which exhibit a strong absorption feature which overlaps with the longer wavelength lingo-cellulose absorptions. Validation of the ratio map is accomplished by overlaying a geologic boundary which defined an area of jasperoid in the field.     

Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery

During 1997 Papua New Guinea (PNG) experienced an intense drought. Emergency famine relief operations provided many subsistence agricultural communities with food, water and health provisions during the height of the drought. The locations of relief operations were based on a rapid and spatially explicit extensive field survey conducted at the height of the drought for all PNG. We have tested the utility of composite Advanced Very High Resolution Radiometer (AVHRR) data to assist in a rapid assessment of drought conditions in PNG. Composited data were used to provide a means to overcome the frequent cloudy conditions that exist in PNG. To assess the drought we divided land surface temperatures (T _s) by the Normalized Difference Vegetation Index (NDVI). The ratio (T _s/NDVI) increases during times of drought. This is due to the increase in T _s associated with more net radiation being partitioned into the sensible heat flux and the decrease in NDVI associated with decreasing amounts of plant cover. A time series of T _s/NDVI is a rapid indicator of the drought at the country and province level. We calculated the integral under the T _s     

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.     

Volcano detection and monitoring using AVHRR data: the Krafla eruption, 1984

Abstract. Many volcanic eruptions go essentially unmonitored. Potentially the Advanced Very High Resolution Radiometer (AVHRR), with its global coverage, frequent return period, and sensitivity in the thermal infrared, represents a data source capable of monitoring surface volcanic activity unrecorded by ground observations or other satellite sensors. In this study an attempt is made to demonstrate this potential by extracting information for the 1984 eruption at Krafla, Iceland. Seven cloud-free AVHRR images were available for the 14 day period of eruptive activity. The surface activity was detectable as a major thermal anomaly in all three of the longer wavelength channels and was vigorous enough during one night-time pass to be detectable in the near-infrared channel (0.725-1.1μm). Channel 2 and 4 radiance data were used to calculate the size and temperature of sub-pixel heat sources within the lava flow field, and a heat source at 1050° C was estimated as occupying an area of approximately 240000 m², which was distributed across 20 pixels. Detection and measurement of volcanic heat sources at such short wavelengths using low spatial resolution data has rarely been reported before. Field reports and maps were used to guide and confirm the analysis. Digital number variations within the anomaly could be related to various known features of the eruption. To monitor the eruption a weighted average method was derived and used to sharpen up the images, and the density sliced sharpened images enabled the development of the eruption to be mapped. Results compared well with field reports, suggesting that AVHRR and similar systems could be a useful source of data for monitoring eruptions where contemporaneous field observations are unavailable or incomplete.     

Cloud cover and the monitoring of strawburning using AVHRR data

Abstract

We have conducted a cloud-cover analysis of AVHRR scenes of Britain for April-September for the years 1982-1985 and interpreted the results in terms of the usefulness of the data for the studying of agricultural strawburning.     

A simple method for Are growth mapping using AVHRR channel 3 data

AHVRR Channel 3 data have been used widely for forest fire detection and mapping. However, little attention has been paid to the use of these data for daily fire growth monitoring. A simple method for fire growth mapping using channel 3 data is presented. An 18000 hectare forest fire affecting the Mediterranean coast of Spain is used as a case study. Discrimination of burned area was performed on every image after multitemporal registration. A thermal threshold was established to mask out fire pixels in both diurnal and nocturnal images. GIS overlay techniques were used lo obtain a synthesis map of the daily evolution of the fire. This product can generate valuable input for fire behaviour programmes to improve our understanding of the factors affecting fire spread and fire severity.     

Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

While existing remote sensing-based drought indices have characterized drought conditions in arid regions successfully, their use in humid regions is limited. We propose a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite. Each variable was scaled from 0 to 1 to discriminate the effect of drought from normal conditions, and then combined with the selected weights. When tested against in-situ Palmer Drought Severity Index (PDSI), Palmer's Z-Index (Z-Index), 3-month Standardized Precipitation Index (SPI), and 6-month SPI data during a ten-year (2000-2009) period, SDCI performed better than existing indices such as NDVI and Vegetation Health Index (VHI) in the arid region of Arizona and New Mexico as well as in the humid region of North Carolina and South Carolina. The year-to-year changes and spatial distributions of SDCI over both arid and humid regions generally agreed to the changes documented by the United States Drought Monitor (USDM) maps.     

Characterizing tropical forest regeneration in Cameroon using NOAA AVHRR data

To facilitate estimation of the carbon sink associated with tropical forests in Cameroon, regenerating and mature forests were mapped using an unsupervised classification of AVHRR channels 1, 2 and 3. Stages of regeneration were defined using nonlinear relationships between AVHRR channel 3 radiance and basal area, estimated using data collected from 183 plots (1 ha in size) in an area south-east of the capital, Yaounde. The overall extent and patterns of regenerating forest were comparable to those generated in previous studies. Older stages of regeneration could not, however, be discriminated adequately from mature forest, suggesting that areas of tropical forest disturbance may be underestimated when mapped using AVHRR data. closed tropical forests were regenerating and that their rate of expansion million ha y 1. These regenerating forests accumulate biomass more rapidly     

Monitoring regional drought using the Vegetation Condition Index

NDVI (Normalized Difference Vegetation Index) images generated from NOAA AVHRR GVI data were recently used to monitor large scale drought patterns and their climatic impact on vegetation. The purpose of this study is to use the Vegetation Condition Index (VCI) to further separate regional NDVI variation from geographical contributions in order to assess regional drought impacts. Weekly NDVI data for the period of July 1985 to June 1992 were used to produce NDVI and VCI images for the South American continent. NDVI data were smoothed with a median filtering technique for each year. Drought areas were delineated with certain threshold values of the NDVI and VCI. Drought patterns delineated by the NDVI and VCI agreed quite well with rainfall anomalies observed from rainfall maps of Brazil. NDVI values reflected the different geographical conditions quite well. Seasonal and interannual comparisons of drought areas delineated by the VCI provided a useful tool to analyse temporal and spatial evolution of regional drought as well as to estimate crop production qualitatively. It is suggested that VCI data besides NDVI may be used to construct a large scale crop yield prediction model.     

A simple cloud masking approach using NOAA AVHRR daytime data for tropical areas

A simple method for cloud detection for AVHRR daytime data is presented and checked for equatorial/tropical areas based on a study area in northeastern Brazil. Five different cloud masking techniques based on visible and infrared spectral information for cloud detection are calibrated. The significant differences between the equatorial threshold obtained in this work and the midlatitude thresholds given by Saunders and Kriebel and by Thiermann and Ruprecht are compared and discussed. Results from the cloud masking algorithm are presented and comments are made about the problems related to the automatic cloud detection algorithm presented in this study.     

Location of tuna resources in Indian waters using NOAA AVHRR data

Abstract

The world demand for tuna resources is ever increasing and there is scope for better economic returns in terms of foreign exchange earnings. It is one of the least exploited resources of the Indian seas. Remote sensing based studies on the tuna environment began in the seventies in the Gulf of Guinea. This study helped to establish a fishing strategy during the eighties. But so far this has not been attempted in Indian waters. With the basic understanding that most of the species of tuna respond directly to temperature, a study using NOAA AVHRR data was carried out to locate tuna resources. Thermal data of NOAA AVHRR for eight dates in the 1989-90 season were analysed to generate sea surface temperature (SST) images. Catch per unit effort (CPUE) of tuna longline data acquired from the Fishery Survey of India pertaining to fishing conducted by chartered vessels, was plotted on the SST images. Yellowfin tuna (YFT) comprises the maximum catch plus small quantities of marlins. It was observed that almost all the data points were located near the edge of warm water (27°-29°C). A relation between average CPUE of YFT and multi-channel sea surface temperature (MCSST) charts generated by OPC (the Ocean Products Centre) of NOAA was established. It shows on an average an increasing trend in the CPUE of YFT from 26° C (hooking rate ～ 1 per cent) to 29°C (hooking rate ～3 65 per cent) and then shows a drop with further rise in temperature. Since YFT is known to be present in a wide range of temperatures, it can be concluded that the location of warm water edges having a gradient of about 1°C and the above mentioned range of temperature will be desirable in locating tuna potential areas.     

Remote sensing of aerosols over the oceans using AVHRR data Theory,practice and applications

Abstract

The basic features of single- and dual-channel aerosol retrieval algorithms based on matching radiances measured in AVHRR channels -1 (～0.58-0.68 μm) and -2( ～0.73-1.10μm) with model computations will be described. The use of the NOAA/NESDIS single-channel algorithm will be illustrated with examples of detection and mapping of enhanced atmospheric turbidity over the oceans. The effects of variations in the physical and radiative properties of atmospheric aerosols, and in atmospheric ozone and water vapour, will be briefly discussed in the light of model sensitivity analyses.     

Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information

A spatially explicit dataset of aboveground live forest biomass was made from ground measured inventory plots for the conterminous U.S., Alaska and Puerto Rico. The plot data are from the USDA Forest Service Forest Inventory and Analysis (FIA) program. To scale these plot data to maps, we developed models relating field-measured response variables to plot attributes serving as the predictor variables. The plot attributes came from intersecting plot coordinates with geospatial datasets. Consequently, these models serve as mapping models. The geospatial predictor variables included Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; land cover proportions and other data from the National Land Cover Dataset (NLCD); topographic variables; monthly and annual climate parameters; and other ancillary variables. We segmented the mapping models for the U.S. into 65 ecologically similar mapping zones, plus Alaska and Puerto Rico. First, we developed a forest mask by modeling the forest vs. nonforest assignment of field plots as functions of the predictor layers using classification trees in See5©. Secondly, forest biomass models were built within the predicted forest areas using tree-based algorithms in Cubist©. To validate the models, we compared field-measured with model-predicted forest/nonforest classification and biomass from an independent test set, randomly selected from available plot data for each mapping zone. The estimated proportion of correctly classified pixels for the forest mask ranged from 0.79 in Puerto Rico to 0.94 in Alaska. For biomass, model correlation coefficients ranged from a high of 0.73 in the Pacific Northwest, to a low of 0.31 in the Southern region. There was a tendency in all regions for these models to over-predict areas of small biomass and under-predict areas of large biomass, not capturing the full range in variability. Map-based estimates of forest area and forest biomass compared well with traditional plot-based estimates for individual states and for four scales of spatial aggregation. Variable importance analyses revealed that MODIS-derived information could contribute more predictive power than other classes of information when used in isolation. However, the true contribution of each variable is confounded by high correlations. Consequently, excluding any one class of variables resulted in only small effects on overall map accuracy. An estimate of total C pools in live forest biomass of U.S. forests, derived from the nationwide biomass map, also compared well with previously published estimates.     

Determining the rate of forest conversion in Mato Grosso,Brazil, using Landsat MSS and AVHRR data

Abstract

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and non-forest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. Slate-wide estimates of forest conversion indicate that between 1981 and 1984, 353966 ha ±77 000 ha (0·4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis)and fire activity (estimated using AVHRR data)was noisy (R²= 0·41). The results suggest that AVHRR data may be put to better use as a stratification tool rather than as a subsidiary variable in list sampling.     

VLSI research in the U.S.A.

Co-operative R&D in Advanced Information Technologies is not a Japanese privilege.     

A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data

In this paper, a new methodology to estimate the biomass (organic matter) of conifer-dominated boreal forests is developed. It aims to estimate biomass of extensive areas where ground data are limited. First, the principal models are computed using ground measurements and high resolution satellite images. Spectral models are then applied directly to a calibrated AVHRR image mosaic covering the entire area of interest. This methodology was tested quantitatively in Finland, where detailed forest measurement data are available, on an area reaching from the west coast of Norway to the Ural mountains. The methodology appeared to perform beyond pre-test expectation.     

Exploring relationships between ENSO and vegetation vigour in the south-east USA using AVHRR data

This research explores the relationship between El Nino/Southern Oscillation (ENSO), captured by equatorial Pacific Ocean Sea Surface Temperature (SST), and interannual variation in vegetation vigour in the southeast USA, captured by Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI), for the period 1982-1992. The moving average and 'baseline' methods (anomaly from the long term mean) were used to extract interannual patterns in the NDVI signature for croplands, deciduous forests and evergreen forests. The ENSO cycle was measured using mean SST anomalies and the percentage of SST cells above certain threshold values (e.g. 1.0° C above the long term mean). The baseline method indicated a weak, yet persistent, negative correlation between ENSO warm phase events and vegetation vigour in the south-east USA. The moving average method yielded similar results but produced higher correlation values (-0.45 to-0.76, significant at the 0.01 level). Use of the 2.0° C threshold SST anomaly was found to yield the highest correlation values as it captures not only the presence but also the intensity of ENSO warm phase events. These results indicate that there is a clear and recognizable, though inconsistent, relationship between ENSO and vegetation vigour in the south-east USA.