Cover photograph Use of LANDSAT-RBV data for the assessment of flooded areas for rice production forecasting in West Africa

Land degradation is difficult to assess in arid rangelands because of short-term variations in rainfall, landscape diversity and the problems of sampling very large areas. This paper shows how vegetation cover index values derived from multi-temporal remotely-sensed data can be used in association with spatial models of grazing impact on landscapes to identify grazing-induced land degradation. The method assumes that grazing effects decrease with distance from water and that temporary grazing impacts largely disappear as vegetation responds to major rainfalls. Grazing gradients (i.e., systematic changes in vegetation cover with distance from water) which remain indicate long term damage. Simple grazing gradients involve changes in average cover with distance from water and include normal, inverse and composite types, complex grazing gradients show systematic changes in cover variance with distance from water and develop where soil and runoff are being redistributed. Although complex grazing gradients may involve little change in total cover with distance from water, they are symptomatic of a reduction in the proportion of forage present. The ability to recognize grazing gradients may greatly simplify range assessment procedures and may also improve satellite image based procedures for determining erosion risk.     

An approach using animal distribution models and Landsat imagery

Abstract

Grazing animals have been responsible for changes in vegetation and for widespread erosion in Australia's arid zone. To minimize these adverse changes and to manage the intensity ofland use in relation to land capability, it is necessary to have information on the distribution and intensity of grazing and trampling, both of which are highly uneven. This paper describes a way of modelling the distribution of grazing and of generating the pattern of movement by cattle in a large paddock in central Australia for a particular configuration of watering points, fence lines and vegetation types. The method uses Landsat Multispectral Scanner (MSS) data and a cattle distribution model based on the convection—diffusion equation. The model relates the number of animals grazing to distance from water and preference for particular vegetation types. The solution to the conveetion—diffusion equation is the inverse Gaussian density function which can be used to calculate the total number of animals at a particular distance from water. The number of animals in individual grid.cells at that distance may then be generated from observed probability distributions. The cattle distribution model has to be fitted and is first derived from observed data for wet, dry and average rainfall conditions. A procedure is then described in which background changes in MSS band 5 over time and those due to grazing are separated on the basis of distance from water and vegetation type. The band 5 grazing effects provide a surrogate measure of grazing intensity and are used to estimate preferences for different vegetation types and to calibrate the animal distribution models. The results are very similar to those obtained from observed cattle distributions. The models may be used predictively to determine the effect of different watering point and fence line locations. Patterns of movement are determined by assuming that animals take the shortest paths from their grazing areas to water and cumulating the number of animals following each path. The patterns of movement derived from Landsat band 5 data are very similar to those determined from observed cattle distributions.     

Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia's rangelands

The primary objective of this study was to assess the condition of a portion of Saudi Arabia's rangelands and evaluate the effects of grazing by the animal herds of indigenous nomads over the last decade. Because of the desertic condition of these rangelands, changes in vegetation cover are more subtle than would be the case for other, less arid areas. Consequently, a new analytic methodology for the detection of desertification of arid and hyper-arid rangelands was developed specifically for this project. The conceptual framework for the analysis is the use of the coefficient of variation (COV) of the monthly Normalized Difference Vegetation Index (NDVI, maximum-value composite) as a measure of vegetative biomass change. A higher NDVI COV for a given pixel (excluding cases of changes in land use) represents a greater change in vegetation biomass in the ground area represented by that pixel. Linear regression was used to determine the trend in COV values for each pixel over the 12-year period for which data was available; pixels with a negative slope are considered to represent ground areas with decreasing amounts of vegetation. Results were validated by tests of statistical significance and by comparison of the theoretical results to vegetation change and land-cover data from the remote sensing systems and from reconnaissance flights over select areas. These desertification trend results were then combined with land-cover information to provide an assessment of desertification status.     

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.     

Sensitivity of landscape metrics to pixel size

Analysis of diversity and evenness metrics using land cover data are becoming formalized in landscape ecology. Diversity and evenness metrics are dependent on the pixel size (scale) over which the data are collected. Aerial photography was interpreted for land cover and converted into four raster data sets with 4, 12, 28, and 80m pixel sizes, representing pixel sizes up to that available on Landsat-MSS. Analysis of covariance was used to determine the effect of changing pixel size on landscape metrics. The results indicate that landscape metrics should not be dramatically affected by the change in pixel size up to 80m, provided that identical land cover classifications could be generated by sensors with different spatial resolving powers (e.g. Landsat-TM and MSS).     

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.     

Satellite remote sensing of rangelands in Botswana I. Landsat MSS and herbaceous vegetation

Field measurements of the cover and biomass of live and dead herbaceous vegetation, the cover of trees and shrubs and the area of bare ground were made for rangelands in three study sites in eastern Botswana between September 1983 and April 1984. The sites were selected to be representative of Terminalia sericea, Cotophospermum mopane and Combretum apiculatum-Acacia nigrescens woodland savannas, which, taken together, occupy a large part of eastern and northern Botswana. Mean herbaceous biomass varied from 0 to 563?kg ha^?1, cover from 0 to 21 per cent and bare ground from 57 to 85 per cent. The mean tree canopy cover in each site was approximately 30 per cent, with a range of 0-50. Landsat miiltispectral scanner (MSS) data were obtained for May, August, November 1983, January and March-April 1984. Nine MSS pixels were registered with 20 sample plots in each site and the ratios of mean band-7 to band-5 digital numbers were calculated. The variation in these ratios between the three sites and four dates on which the data were acquired was analyzed with respect to the field measurements.

The results indicate that the biomass and cover of live herbaceous vegetation and the bare ground individually account for quite small, but significant proportions of the variation in band ratio for all four observation dates taken together. However, when factors that specified site and date were included in the multiple regression models, 75·7, 77·9 and 64·1 per cent of the variation in herb biomass, cover and bare ground respectively were accounted for. Multitemporal integration of the band ratios accounted for 70·3 per cent of the variation in the end-of-season biomass of herbaceous vegetation, without the need to use a site factor. These highly significant relationships were achieved without including measurements of the canopy cover of trees in the models. Analysis of the individual site data revealed some negative relationships between band ratios and both biomass and Cover of herbaceous vegetation in one site, which seem to be a result of a strong negative relationship between the cover of herbaceous vegetation and trees in this vegetation type.

It was concluded that predictive equations could be constructed which enable cover and biomass of herbaceous vegetation and the area of bare ground to be estimated from Landsat MSS band-7/band-5 ratios, but only if the relationship is applied to sites having the same type of vegetation as that for which the equations were derived. Stratification of the scene using vegetation maps is therefore an important requirement for the application of remote sensing by Landsat MSS to the monitoring of the rangelands in Botswana     

Subpixel forest cover in central Africa from multisensor, multitemporal data

Seven Landsat Multispectral Scanner (MSS) scenes in central Africa were coregistered with 8 km resolution data from the 1987 AVHRR Pathfinder Land data set. Percent forest cover in each 8 km grid cell was derived from the classified MSS scenes. Linear relationships between percent forest cover and 30 multitemporal metrics derived from all AVHRR optical and thermal channels were determined. Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for those metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest cover in various locations in the study area using multiple linear regression and regression trees. Overall, the multiple linear regression provided more accurate results. Predicted percent forest cover estimates were within 20% of the “actual” percent forest cover (derived from the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest cover were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions.     

Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.     

Statistical models of fragmented land cover and the effect of coarse spatial resolution on the estimation of area with satellite sensor imagery

The feasibility of correcting for errors in apparent extent of land cover types on coarse spatial resolution satellite imagery was analysed using a modelling approach. The size distributions for small burn scars mapped with two Landsat Multi-spectral Scanner (MSS) images and ponds mapped with an ERS-1 synthetic aperture radar (SAR) image were measured using geographical information system (GIS) software. Regression analysis showed that these size distributions could be modelled with two types of statistical distributions a power distribution and an exponential distribution. A comparison of the size distributions of small burn scars as observed with the Landsat MSS imagery to the distribution observed with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery indicated that distortions due to the coarse spatial resolution of AVHRR caused overestimation of the burn area. This bias was primarily caused by detection in two or three AVHRR pixels of burns whose actual size was on the order of a single AVHRR pixel. Knowledge of the type of the actual size distribution of small fragments in a scene and the causes of distortion may lead to methods for correcting area estimates involving models of the size distribution observed with coarse imagery and requiring little or no recourse to fine scale 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.     

Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series

There is a pressing need for an objective, repeatable, systematic and spatially explicit measure of land degradation. In northeastern South Africa (SA), there are large areas of the former homelands that are widely regarded as degraded. A time-series of seasonally integrated 1 km, Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data was used to compare degraded rangelands [mapped by the National Land Cover (NLC) using Landsat Thematic Mapper (TM) imagery] to nondegraded rangelands within the same land capability units (LCUs). Nondegraded and degraded areas in the same LCU (paired areas) were compared by: (i) testing for differences in spatial mean ∑NDVI values, (ii) calculating the relative degradation impact (RDI) as the difference between the spatial mean ∑NDVI values of paired areas expressed as a percentage of nondegraded mean value, (iii) investigating the relationship between RDI and rainfall and (iv) comparing the resilience and stability of paired areas in response to natural variations in rainfall. The ∑NDVI of degraded areas was significantly lower for most of the LCUs. Relative degradation impacts (RDI) across all LCUs ranged from 1% to 20% with an average of 9%. Although ∑NDVI was related to rainfall, RDI was not. Degraded areas were no less stable or resilient than nondegraded. However, the productivity of degraded areas, i.e., the forage production per unit rainfall, was consistently lower than nondegraded areas, even within years of above normal rainfall. The results indicate that there has not been a catastrophic reduction in ecosystem function within degraded areas. Instead, degradation impacts were reflected as reductions in productivity that varied along a continuum from slight to severe, depending on the specific LCU.     

Remotely sensed surface water extent as an indicator of short-term changes in ecohydrological processes in sub-Saharan Western Africa

The highly variable rainfall in the arid and semi-arid regions of sub-Saharan Western Africa drives both surface water availability and vegetation cover. Recent studies have established linkages between rainfall and vegetation cover at local to regional scales, but no study related yet remote sensing derived rainfall and vegetation cover to the available surface water. A new dataset based on SPOT VEGETATION (VGT) represents surface water bodies (SWB) in the arid and semi-arid regions of sub-Saharan Western Africa. Water bodies represent the integrated hydrological response of a catchment, and changes in their spatial extent involve complex interactions at the catchment scale. We analyzed time series of remotely sensed vegetation cover, rainfall and surface water extent for the period 1999–2008, and could detect and statistically demonstrate the links between these biophysical variables. Our findings for two regions in Mali and Burkina Faso suggest that vegetation cover is positively related to the amount of available surface water for those catchments that are mainly covered by annual plants. The observed relationships between remotely sensed variables allow developing ecological indicators that can indicate short-term changes in arid and semi-arid ecosystems at local to regional scales.     

Remote sensing of savanna vegetation changes in Eastern Zambia 1972-1989

The use of the historical Landsat Multi-Spectral Scanner (MSS) archive to monitor changes in savanna vegetation between 1972 and 1989 in the South Luangwa National Park region, Eastern Zambia, was investigated. Land-cover types in the region were mapped and major changes in land cover from 1972 to 1989 were detected from MSS data. Woody canopy cover, which provides a quantitative measure of woodland structure, was estimated for woodland vegetation from MSS data using a linear relationship between woody canopy cover and red reflectance. The canopy cover changes estimated from MSS data agreed with those measured from multitemporal aerial photographs (r=0.94). Woody canopy cover changed significantly in the region from 1972 to 1989 and revealed strong spatial patterns of deforestation in Colophospermum mopane woodland on alluvial soils and vegetation regrowth of valley miombo vegetation and riverine woodland. This information on the spatial patterns of canopy cover change from 1972 to 1989 suggests certain criteria that any causative process must satisfy, and it provides a baseline for the National Park and Wildlife Services to manage the natural resources in the region. The canopy cover estimated from MSS data also provides an important input to biophysical and climatic process models for estimating the impact of vegetation structure on vegetation and climate processes.     

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.     

Assessment of rangeland degradation and development of a strategy for rehabilitation

We use satellite data from different sensor systems to analyze and explain the causes, processes, and impacts of desertification in a Steppe grazing area in Syria, with the aim of supporting the formulation of a strategy for rehabilitating desertified areas. Through the mapping of parameters such as barley fields, eolian sand distribution, and drainage patterns from Thematic Mapper (TM) data, we identified barley cultivation as one major reason for increased sand erosion or its downhill deposition. With regard to the degradation of natural vegetation covers, we discriminate between climate-triggered and human-induced vegetation degradation by analyzing the natural response pattern of vegetation to rainfall. For the monitoring of vegetation covers, we used composited 10-day interval 8-km Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data from 1981 to 1996. A consistently changing response of vegetation to rainfall over this time period, expressed in the residuals of the NDVI_max/Rainfall linear regression calculations, is interpreted as nonclimate or human driven, where correlations between residuals and the time of their occurrence produce correlation coefficients >|0.6|. Pixels showing a negative temporal trend in residuals coincide with areas that are most heavily used by humans. Heavily used areas were located through detecting nomadic campsites from Indian Remote Sensing Satellite (IRS)-1C data. By combining campsite distribution with census data, such as flock size, average annual offtake, and grazing habits, we assess grazing pressures and put them in relation to the natural resources. This information provided the basis for the definition of protected areas or rehabilitation plots, and for elaborating measures to support the Steppe dwellers.     

Response of NDVI dynamics to precipitation in the Beijing–Tianjin sandstorm source region

This article analysed the spatio-temporal changes in vegetation cover in the Beijing–Tianjin sandstorm source region in China and related these changes to vegetation types based on the Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data set from 1982 to 2006. The annual maximum NDVI and peak time were identified. The different periods (1–12 months) of accumulated precipitation before the peak time were then calculated at the grid scale for each year. On this basis, the NDVI–rainfall relationship and the temporal responses in this area were studied by calculating the correlation coefficient between the annual maximum NDVI and different periods (1–12 months) of accumulated precipitation before the occurrence of the annual maximum NDVI for each pixel. The results show an upward trend in regional vegetation, a significant recovery efficiency for grassland, and the evident degradation of cropland. Peak plant growth is significantly related to precipitation and is strongly positively correlated with precipitation in the previous period (1 month) regardless of vegetation type. The regions showing the strongest correlations between peak plant growth and 1 month cumulative rainfall are the western desert grassland, grassland to forest in the transitional hill regions, the mountains of Yanshan, and the Greater Hinggan Mountains.     

Mapping the density of woody vegetative cover using Landsat MSS digital data

Abstract

Woody vegetation is an important environmental component in most landscapes. The occurrence of some forms of woody cover is indicative of resource degradation whereas degradation is indicated by lack of cover in other situations. A capacity to measure cover, and hence change, to an acceptable levelof accuracy is thus an important indicator of resource degradation under most conditions. A means of estimating the percentage cover within individual resolution elements of scanner data has been developed and evaluated, with the results achieved being reported in this article. These results are sufficient for the technique to be included as an operational component in a rangelands monitor in New South Wales.     

Temporal and spatial vegetation cover changes in Israeli transition zone: AVHRR-based assessment of rainfall impact

The ability of NOAA/AVHRR data to monitor vegetation response to rainfall in three different vegetation zones was assessed along a north-south transect in Israel. The NDVI database was developed from atmospherically- and radiometrically-corrected NDVI composites from observations spanning three years. Three vegetation zones, Mediterranean region, transition zone, and semiarid region, were geographically separated by means of NDVI values. Based on three years of AVHRR observations during a relatively dry year and two years with near average rainfall, the phenological characteristics for all three vegetation zones were very similar and stable. The results showed that only a few AVHRR observations are necessary to monitor the seasonal and spatial variability of vegetation cover in different climatic zones located in Israel. The NDVI of the Israeli transition zone was found to be very sensitive to rainfall. The difference between maximum and minimum NDVI values in rainy season in the transition zone was at least two times higher than that in the Mediterranean and the semiarid regions. This phenomenon can be used as an indicator of any environmental changes in this region.     

Estimating sub-pixel components of a semi-arid woodland

Mixture models were applied to Landsat-MSS and -TM data in a semi-arid woodland in central western New South Wales, Australia to extract information on soil, herbage and tree cover. There was a significant correlation (r² = 0-71) between estimated and ground data for tree cover using the TM data, with a mean error of ± 143 per cent, and a mean error of ±11-2 per cent for the bare soil cover estimates. Only general trends were observed using the MSS data. The main areas of confusion were between senesced herbage and soil, and between green grass and the green leaves of trees.