Utility of AVHRR data for land degradation assessment: a case study

AVHRR data are widely used to monitor vegetation greenness and to provide a gross measure of primary production throughout the world. This paper examines whether AVHRR data can be used for determining the extent of land degradation in arid rangelands under commercial grazing using models of vegetation dynamics and animal grazing behaviour developed for Landsat-MSS data. These models are applied after large rainfall events and either search for systematic change in average vegetation cover across relatively uniform landscapes with increasing distance from stock watering points or examine the magnitude of vegetation response to rainfall for each pixel.

We applied the models where previous work with Landsat-MSS had demonstrated the extent of grazing impact. An index of vegetation cover using adjusted AVHRR channel 1 values produced trends in wet period average vegetation cover with increasing distance from water similar to, but less pronounced than, those obtained with MSS data. NDVI produced inconsistent and often ambiguous results when compared with the MSS data. AVHRR-derived vegetation indices were unusable in degradation assessment procedures which require pixel-scale vegetation response models. The large AVHRR pixel, even in LAC mode, creates difficulties in detecting grazing impact. Landscape changes as a result of grazing occur at a finer scale and are therefore subsumed within the pixel. Misregistration of multi-temporal images further reduces the ability to detect grazing impact on a pixel basis when such change is occurring within the pixel.

We conclude that despite their cost attractiveness, AVHRR data are inappropriate for the reliable detection of grazing impact using grazing gradient methods in the large paddocks of arid rangelands.     

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.     

Assessing rangeland degradation using multi temporal satellite images and grazing pressure surface model in Upper Mustang, Trans Himalaya, Nepal

This study aims to map and discriminate rangeland degradation from the effects of precipitation variability and thereby identify the driving forces of degradation in the grazing areas of Ghiling in Upper Mustang, Nepal. Landsat MSS, TM, ETM, and SPOT images covering the years 1976-2008 were analyzed. 8 km resolution NOAA NDVI from 1981to 2006 were used to identify the long term interrelationship between vegetation greenness and precipitation variability. The use of time series residual of the NDVI/precipitation linear regression to normalize the precipitation effect on vegetation productivity and identify the long term degradation was extended at the local scale. A weighted grazing pressure surface model was developed combining information from satellite images, topography, forage availability and detailed field work data on points of livestock concentration, herders' ranking of forage quality and grazing pattern in each pasture unit. The grazing pressure of a given site was defined as the product of annual net stocking density and the inverse of the total friction of livestock movement. While annual precipitation was found as the dominant factor for the interannual vegetation variability, degradation in Upper Mustang was the result of grazing induced change and some localized natural processes.     

A study of moisture conditions in the Pantanal of Brazil using satellite imagery

The combined use of additive viewing and digital processing of LANDSAT-2 imagery of part of the Pantanal of Brazil has allowed detailed maps of the drainage network to be constructed. The distributions have been made of wet and dry areas, including differentiations of clear water, water containing suspended sediments, and categories of land with differing moisture conditions. Some unconventional use of color filters and MSS band combinations are suggested in order to extract maximum information from the imagery. Density slicing has allowed gray-scale values to be placed on the three categories of land identified. The distribution of the identified categories are verified by comparing the information from the visual classification with the classes isolated by density slicing.     

What determines the spectral reflectance of the Negev-Sinai sand dunes

The difference in spectra reflectance between the north-western Negev desert and the adjacent Sinai sand dunes has attracted the attention of many scientists. Remote sensing analysis of three Landsat Multi-Spectral Scanner (MSS) images acquired in the summer of 1984, 1987, 1989, followed by intensive field work, indicate that the area is not homogenous and is undergoing a quick recovery from the intensive grazing that it suffered between 1968 and 1982. The outcome is a gradual decrease in the brightness of the Negev between 1984 and 1989 in all MSS bands except band 7 which shows very little or no change with time. This is due to the increase in biogenic crust and vegetation cover. The Sinai bare sand shows the opposite trend of little to no change in all bands but band 7, which shows an increase in reflectance with time. This is probably due to the effect of further destruction of vegetation in the Sinai after 1982.

In the northern part of the area, dunes are low, have a high percentage of fines (silt and clay) in the interdune areas, and stabilize quickly. This area is also covered by dense carpets of annuals which have a reflectance that is lower than that of the crusted area, but higher than the reflectance of Artemisia monosperma, the most widespread shrub. The contrast ratios, measured in the field between the Sinai bare sand and the Negev biogenic crust (on the same sand) is similar to the contrast ratios measured by Landsat MSS between the Sinai and the Negev.     

Pixel-mixing effects and their significance to identifying snow condition from LANDSAT MSS data

Abstract

Radiometric measurements on snow in LANDSAT MSS wavebands have shown a correlation between snow condition and the ratio of green/infrared (band 4/band 7) wavebands. Systematic changes from wet dense snow at tow altitudes to dry less dense snow at higher altitudes should be revealed by a decrease in the intensity ratio band 4/band 7 with altitude. However, analysis of spring LANDSAT MSS images for the Cairngorm Mountains shows that the intensity ratio band 4/band 7 actually increases with altitude. A mixed-pixel hypothesis is invoked to account for this pattern. The results suggest that only in areas where the snow cover is continuous can information on snow condition be reliably obtained from LANDSAT MSS data.     

Statistical models for polarimetric data: consequences,testing and validity

Low resolution polarimetric data gathered by the AirSAR system over the Feltwell U.K. agriculturaltestsite reveals Gaussian behaviour at C band for all vegetation types, but clear evidence of texture at longer wavelengths. The measurements are compared with the predictions of a polarimetric texture model based on a multivariate K distribution (which includes the Gaussian distribution as a special case), from which distributions of the inphase component, amplitude, amplitude ratio, phase difference and real hermitian product between channels are derived. Kolmogorov-Smirnov fits to these marginal distributions verify that C, Land P band observations over a range of vegetation types are consistent with the model, but there is evidence of the model breaking down in cereal fields at P band. The departure from Gaussian behaviour with increasing wavelength is strongest for cereals; less marked trends are observed for root vegetables, while forest appears Gaussian at all wavelengths. These results are unexpected, since texture is normally associated with high resolution, particularly in woodland. Moment based measures are investigated as indicators of distribution and as discriminators of vegetation type. The order parameter of the K distribution appears useful in both respects. These results help to clarify the information content of polarimetric data, but raise questions about how best to use the data (e.g., for classification), and are hard to reconcile with simple physical models.     

Model of pathogen transmission between livestock and white-tailed deer in fragmented agricultural and forest landscapes

The study summarizes the current knowledge on infection and recovery of white-tailed deer and cattle, and integrates this knowledge into the Soil and Water Assessment Tool (SWAT) via a new add-on module SIR (Susceptible - Infected - Recovered) for predicting pathogen transmission between livestock and deer. New processes modeled by the SWAT-SIR model include: (a) seasonal changes in deer population and habitat; (b) resource selection and seasonal changes in foliage consumption by deer; (c) ingestion of pathogens with water, foliage and via grooming soiled hide by deer and grazing cattle; (d) infection and recovery of deer and co-grazing cattle; (e) pathogen shedding by infected animals; (f) survival of pathogens in manure; (g) kinetic release of pathogens from applied manure and fecal material. The model output is linked with ARC-GIS to allow spatial and temporal analysis of pathogen distribution across the watershed for specific land use, weather and management scenarios.     

The relations between spectral data and water in a crop production environment

The results of a correlation study between high spatial resolution (1m) Multi-Spectral Scanner (MSS) data and reference data with particular emphasis to soil-plant water parameters are presented. Digital images of the Management Systems Evaluation Area (MSEA) research site in south central Ohio were acquired using MSS mounted on an aircraft. Gravimetric soil water, plant water, and plant residue information relating to MSS data were collected in April, July, August, and September, 1994. It was found that the correlation between MSS data and soil water was changed by the presence of crop residue versus bare soil. The combined water estimates from the soil and plant enhanced the correlation structure between MSS parameters and soil-plant water variables. It was also shown that when using soil water content data below the 0.5-2.0 cm soil depth, the correlation with MSS parameters decreased.     

Suspended sediment dynamics from repetitive Landsat data

Abstract

Repetitive Landsat CCT data were used to monitor the motion of non-uniformly distributed suspended sediments in a New Zealand tidal basin. The movement of such suspended sediment was followed using all four MSS bands with non-uniformly distributed suspended sediment being differentiated from bottom features by the data from two satellite overpasses. As pan of this study penetration depths of 45-50±5 cm and 10-15 ±5 cm were found for channels MSS 5 and MSS 6 over the sediment-laden tidal inlet. The difference in penetration depths within each MSS band was ascribed to differing concentrations of uniformly distributed suspended sediments between the two overpasses.     

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     

Quantifying livestock effects on bunchgrass vegetation with Landsat ETM+ data across a single growing season

Grassland systems provide important habitat for native biodiversity and forage for livestock, with livestock grazing playing an important role influencing sustainable ecosystem function. Traditional field techniques to monitor the effects of grazing on vegetation are costly and limited to small spatial scales. Remote sensing has the potential to provide quantitative and repeatable monitoring data across large spatial and temporal scales for more informed grazing management. To investigate the ability of vegetation metrics derived from remotely sensed imagery to detect the effect of cattle grazing on bunchgrass grassland vegetation across a growing season, we sampled 32 sites across four prescribed stocking rates on a section of Pacific Northwest bunchgrass prairie in northeastern Oregon. We collected vegetation data on vertical structure, biomass, and cover at three different time periods: June, August, and October 2012 to understand the potential to measure vegetation at different phenological stages across a growing season. We acquired remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) data closest in date to three field sampling bouts. We correlated the field vegetation metrics to Landsat spectral bands, 14 commonly used vegetation indices, and the tasselled cap wetness, brightness, and greenness transformations. To increase the explanatory value of the satellite-derived data, full, stepwise, and best-subset multiple regression models were fit to each of the vegetation metrics at the three different times of the year. Predicted vegetation metrics were then mapped across the study area. Field-based results indicated that as the stocking rate increased, the mean vegetation amounts of vertical structure, cover, and biomass decreased. The multiple regression models using common vegetation indices had the ability to discern different levels of grazing across the study area, but different spectral indices proved to be the best predictors of vegetation metrics for differing phenological windows. Field measures of vegetation cover yielded the highest correlations to remotely sensed data across all sampling periods. Our results from this analysis can be used to improve grassland monitoring by providing multiple measures of vegetation amounts across a growing season that better align with land management decision making.     

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.     

一种分区的全向传感器栅栏覆盖构建算法

栅栏覆盖是传感器网络覆盖控制的研究热点之一.提出一种全向传感器栅栏分区构建算法(FCOIS).算法中节点采取全向传感器感知模型,依照节点初始分布状态划分子区域,使每个子区域内节点个数尽量相等,并根据每个子区域内节点的分布情况确定栅栏的形成区间.在每个子区域内,依照从左至右的顺序构建栅栏,当各子区域的栅栏构建完毕后,采用贪婪算法对相邻子区域间栅栏的空隙进行填充.仿真结果证明该算法能够以较低的总能耗、平均能耗构建栅栏,显著节省了节点的使用数量与通信开销.     

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann–Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.     

Wetland change detection on the Kafue Flats,Zambia, by classification of a multitemporal remote sensing image dataset

The paper describes a remote sensing change detection approach used to assess change on a section of the Kafue Flats floodplain wetland system in southern Zambia, which is under the pressures of reduced regional rainfall and damming and water abstraction by man. Four images from September 1984 (Landsat MSS), 1988 (Landsat MSS), 1991 (Landsat TM) and 1994 (Landsat TM) were used. Being near-anniversary images, the change detection error introduced by mere seasonal differences was minimized. Following atmospheric correction of the reference (1994) image, the images were radiometrically normalized and geometrically registered to a common map projection. Each image was separately classified into categories of open water, dense green vegetation, sparse green vegetation, very sparse green vegetation, dry and burnt land. Similar, supervised maximum likelihood classification procedures were employed on all images. The classified images produced were analysed for change in each land-cover category by overlaying them in a Geographic Information System (GIS) framework. The results indicated spatial reduction in area of dense green vegetation in upstream sections of the wetland. Inter-image changes in this land-cover class could be explained by the variations in the timing of regulated flood events on the Kafue Flats. The methodology employed appears to be applicable to monitoring southern Africa's inland wetland systems.     

Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape

An investigation is conducted, for a complex vegetated land area, into the statistical relationship between remotely sensed thermal emissions and reflected spectral radiance. The Kauth—Thomas Tasseled Cap transformation is employed to infer the albedo and amount of green vegetation present from Landsat multispectral scanner (MSS) observations. Reflective data and thermal infrared data were acquired from the Heat Capacity Mapping Mission (HCMM) satellite along with reflective data from the Landsat 3 MSS for a site near Hartford, CT on a single date. Results are presented which indicate that thermal emissions had the greatest association with the amount of vegetation as indicated by a multispectral index, while albedo did not exhibit any substantial relationship with these emissions. These findings are explained in terms of the enhanced latent heat flux to the atmosphere associated with actively transpiring vegetation.     

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.     

Effects of corn stalk orientation and water content on passive microwave sensing of soil moisture

A field experiment was conducted utilizing artificial arrangements of plant components during the summer of 1982 to examine the effects of corn canopy structure and plant water content on microwave emission. Truck-mounted microwave radiometers at C (5 GHz) and L (1.4 GHz) band sensed vertically and horizontally polarized radiation concurrent with ground observations of soil moisture and vegetation parameters. Results indicate that the orientation of cut stalks and the distribution of their dielectric properties through the canopy layer can influence the microwave emission measured from a vegetation/soil scene. The magnitude of this effect varies with polarization and frequency and with the amount of water in the plant, disappearing at low levels of vegetation water content. Although many of the canopy structures and orientations studied in this experiment are somewhat artificial, they serve to improve our understanding of microwave energy interactions within a vegetation canopy and to aid in the development of appropriate physically based vegetation models.     

Coastal definition using Landsat data†

Abstract

An automated technique is described, for extracting land-water boundaries from Landsat MSS images by means of ‘density contour-threading’ of the band 7 data. The resulting coastlines, etc. can then be transformed to fit any map projection—in particular the British National Grid—with the aid of selected ground control points.