CERES :: Browsing by Author "Taylor, John C."

Browsing by Author "Taylor, John C."

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Open Access
Application of GIS and remote sensing for land use planning in the arid areas of Jordan
(2000-01) Ziadat, Feras M.; Taylor, John C.
Land suitability analysis formed part of a land use planning exercise in a development project aimed at improving agricultural productivity in the transitional Badia region of Jordan. Soil observations and soil maps were available at three levels of detail with differing coverage: level one (1:250,000 scale -complete cover), level two (1:50,000 scale - part cover) and level three (1:10,000 scale very limited cover). The development project selected the FAO Framework for Land Evaluation as the basis for land suitability analysis. This research investigated seven different calculation approaches for the processing of soil observations within soil map polygons using a GIS to derive land suitability ratings. These methods either use the soil observations to calculate the suitability of each soil mapping polygon or an interpolation technique (Voronoi diagram or Triangulated Irregular Network) between observation points. The overall map purity and homogeneity with respect to land characteristics were used to evaluate these methods. The quality of suitability maps varied according to the level of soil mapping and the method of processing the soil observations. The relative performance of the processing methods is discussed and recommendations for each level of mapping are proposed. The results showed that the purity of suitability maps was between 60 and 70% at the highest level of detail. Thus they should be used with caution for site specific analyses. Statements of map quality should be appended to suitability maps. The soil maps and observation points were derived and collected in a previous soil survey programme and georeferenced by map reading before the widespread availability of the Global Positioning System (GPS). When the data were integrated and overlaid on a satellite image within a GIS, a number of inconsistencies in georeferencing the data and in the attributes attached to them were revealed. Investigation and correction of these evolved into a major component of this work. Systematic errors caused by the use of different datums to georeference soil maps and observation points in the Jordan Soil and Climate Information System (JOSCIS) were detected. The map reading procedure also caused unsystematic errors in the locations of soil observations, which were re-measured at a sample of original observation sites using GPS. The correction of the unsystematic errors was not feasible due to the difficulty and cost of relocating all observation points. Errors in the attributes attached to the observation points were caused by survey recording procedures, highlighting the need for an examination of the data before analysis. The systematic and attribute errors were corrected and the implication for suitability analysis examined. The areas and spatial distribution of different suitability classes were affected increasingly as the level of mapping became more detailed. The presence of all these errors was sufficient to create errors in the derived land suitability maps, which could lead to incorrect land use planning decisions. The integration of satellite imagery, soil observations and soil mapping polygons within a GIS was indispensable for quality control of the data. The highest purities of suitability maps using existing soil mapping polygons were between 60% to 70% at level three but they only covered veiy limited areas. This indicated the need to extend mapping at this detail for site-specific planning and if possible, to increase the purity of soil mapping units. This was investigated by integrating satellite imagery and topographic data in a GIS. A 3-D perspective view of a Landsat TM image using an air photo-derived DEM was the most promising way of using the available data. Further research is needed to investigate the interactive use of air photo-derived DEMs and Landsat images, with more focus applied to site specific planning and field verification of the technique. Although this work was necessarily focussed on the issues and problems particular to one data set used in a Jordanian context, a number of general lessons have been learned. Firstly, careful examination of all input data is necessary to eliminate georeferencing and attribute errors. Secondly, overlay of input data onto a geocoded satellite image is extremely useful for detecting potential sources of input data errors and is recommended. And thirdly, GIS is indispensable for investigating existing data for errors and exploring new methods of analysis.
Open Access
Application of multi-spectral remote sensing for crop discrimination in Afghanistan
(Cranfield University, 2008-03) Bennington, Allison L.; Taylor, John C.
The spectral properties of poppy and other annual crops vary considerably throughout their growth and development. Until the publication of this research the spectral signature of poppy and its contrast with neighbouring crops in Afghanistan was undefined. The aim of this work was to investigate the application of remote sensing to discriminate poppy from other cover types using spectral signatures obtained from the analysis of multi-spectral imagery. The consistency of discrimination through time for different geographical regions was of particular interest. A review of previous poppy studies identified weaknesses with existing methods used to monitor poppy and provide reference data to validate resulting maps. Weaknesses were in the main due to the limited availability of quantifiable knowledge on the spectral-temporal properties of cover types and the lack of accuracy measures necessary to validate poppy identification. To overcome the lack of quantitative knowledge this research characterises the spatial and temporal variability of poppy spectral response patterns. A methodology was developed to acquire multi-temporal IKONOS images, aerial photographs and ground data covering two growth cycles across a range of sites in Afghanistan. Optimum techniques were developed to facilitate the collection of training pixels for each cover type to satisfy the assumptions of the supervised Maximum Likelihood classification (MLC). Spectral signatures of cover types were examined using the Jeffries Matusita distance measure to identify signature separability and predict classification accuracy. The accuracy of each MLC was assessed using error matrices, Kappa statistics and regression. Results confirm that sufficient spectral contrast exists between poppy and other crops during poppy flowering which enables accurate discrimination. A relationship was found between overall spectral separability and classification accuracy, showing separability can be used to predict classification accuracy at flowering. At other times insufficient differences exist between the spectral reflectance of other crops and poppy. Multi-temporal image classifications achieved greater accuracy than their corresponding single date classifications in the majority of cases studied.
Open Access
The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan
(Taylor and Francis, 2014-08-26) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Juniper, Graham R.
We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems.
Open Access
Assessment of crop performance potential using remote sensing
(1992) Zmuda, A. D.; Taylor, John C.
This report demonstrates the potential use of optical remote sensing for monitoring the growth and development of winter sown wheat at the field scale. Spectral vegetation indices have been shown to be correlated to important agronomic variables i.e. Leaf Area Index, per cent cover, intercepted solar radiation and grain yield. For winter sown cereals, relationships to grain yield at harvest have been proposed at various times during the growth of the crop. However the precise timing of the remotely sensed input to such models has not received investigation in relation to sowing date, variety and development stage. Further the effect of fungal diseases on such models is not well understood. To address these problems, commercial agronomy trials were used to monitor the reflectance patterns of winter wheat over two growing seasons. During growth and development of the crop, the wheat was destructively sampled and the apex development stage was recorded. Key stages of apex development were found in which spectral data was correlated to grain yield. The relationships were found to be complex in relation to sowing date, variety and fungicide treatment and may therefore not be applicable on a year to year basis.
Open Access
Calibration methodology for mapping within-field crop variability using remote sensing
(Elsevier, 2003-04) Wood, G. A.; Taylor, John C.; Godwin, R. J.
A successful method of mapping within-field crop variability of shoot populations in wheat (Triticum aestivum) and barley (Hordeum vulgare L.) is demonstrated. The approach is extended to include a measure of green area index (GAI). These crop parameters and airborne remote sensing measures of the normalised difference vegetation index (NDVI) are shown to be linearly correlated. Measurements were made at key agronomic growth stages up to the period of anthesis and correlated using statistical linear regression based on a series of field calibration sites. Spatial averaging improves the estimation of the regression parameters and is best achieved by sub-sampling at each calibration site using three 0·25 m2 quadrats. Using the NDVI image to target the location of calibration sites, eight sites are shown to be sufficient, but they must be representative of the range in NDVI present in the field, and have a representative spatial distribution. Sampling the NDVI range is achieved by stratifying the NDVI image and then randomly selecting within each of the strata; ensuring a good spatial distribution is determined by visual interpretation of the image. Similarly, a block of adjacent fields can be successfully calibrated to provide multiple maps of within-field variability in each field using only eight points per block representative of the NDVI range and constraining the sampling to one calibration site per field. Compared to using 30 or more calibration sites, restricting samples to eight does not affect the estimation of the regression parameters as long as the criteria for selection outlined in this paper is adhered to. In repeated tests, the technique provided regression results with a value for the coefficient of determination of 0·7 in over 85% of cases. At farm scale, the results indicate an 80–90% probability of producing a map of within crop field variability with an accuracy of 75–99%. This approach provides a rapid tool for providing accurate and valuable management information in near real-time to the grower for better management and for immediate adoption in precision farming practices, and for determining variable rates of nitrogen, fungicide or plant growth regulators.
Open Access
Developing strategies for spatially variable nitrogen application in cereals II: wheat
(Elsevier , 2003-04) Welsh, J. P.; Wood, G. A.; Godwin, R. J.; Taylor, John C.; Earl, R.; Blackmore, S.; Knight, S. M.
For precision agriculture to provide both economic and environmental benefits over conventional farm practice, management strategies must be developed to accommodate the spatial variability in crop performance that occurs within fields. Experiments were established in crops of winter barley (Hordeum vulgare L.) over three seasons. The aim of which was to evaluate a set of variable rate nitrogen strategies and examining the spatial variation in crop response to applied N. The optimum N application rate varied from 90 to in excess of 160 kg [N] ha−1 in different parts of the field, which supports the case for applying spatially variable rates of N. This, however, is highly dependent on seasonal variations, e.g. the quantity and distribution of rainfall and the effect that this has on soil moisture deficits and crop growth. Estimates of yield potential, produced from either historic yield data or shoot density maps derived from airborne digital photographic images, were used to divide experimental strips into management zones. These zones were then managed according to two N application strategies. The results from the historic yield approach, based on 3 yr of yield data, were inconsistent, and it was concluded that that this approach, which is currently the most practical commercial system, does not provide a suitable basis for varying N rates. The shoot density approach, however, offered considerably greater potential as it takes account of variation in the current crop. Using this approach, it was found that applying additional N to areas with a low shoot population and reducing N to areas with a high shoot population resulted in an average strategy benefit of up to 0·36 t ha−1 compared with standard farm practice.
Open Access
Developing Strategies for spatially variable nitrogen application in cereals, I: Winter barley
(Elsevier , 2003-04) Welsh, J. P.; Wood, G. A.; Godwin, R. J.; Taylor, John C.; Earl, R.; Blackmore, S.; Knight, S. M.
For precision agriculture to provide both economic and environmental benefits over conventional farm practice, management strategies must be developed to accommodate the spatial variability in crop performance that occurs within fields. Experiments were established in crops of winter barley (Hordeum vulgare L.) over three seasons. The aim of which was to evaluate a set of variable rate nitrogen strategies and examining the spatial variation in crop response to applied N. The optimum N application rate varied from 90 to in excess of 160 kg [N] ha−1 in different parts of the field, which supports the case for applying spatially variable rates of N. This, however, is highly dependent on seasonal variations, e.g. the quantity and distribution of rainfall and the effect that this has on soil moisture deficits and crop growth. Estimates of yield potential, produced from either historic yield data or shoot density maps derived from airborne digital photographic images, were used to divide experimental strips into management zones. These zones were then managed according to two N application strategies. The results from the historic yield approach, based on 3 yr of yield data, were inconsistent, and it was concluded that that this approach, which is currently the most practical commercial system, does not provide a suitable basis for varying N rates. The shoot density approach, however, offered considerably greater potential as it takes account of variation in the current crop. Using this approach, it was found that applying additional N to areas with a low shoot population and reducing N to areas with a high shoot population resulted in an average strategy benefit of up to 0·36 t ha−1 compared with standard farm practice.
Open Access
Image segmentation for improved consistency in image-interpretation of opium poppy
(Taylor and Francis, 2016-02-18) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Brewer, Timothy R.
The image-interpretation of opium poppy crops from very high resolution satellite imagery forms part of the annual Afghanistan opium surveys conducted by the United Nations Office on Drugs and Crime and the United States Government. We tested the effect of generalization of field delineations on the final estimates of poppy cultivation using survey data from Helmand province in 2009 and an area frame sampling approach. The sample data was reinterpreted from pan-sharpened IKONOS scenes using two increasing levels of generalization consistent with observed practice. Samples were also generated from manual labelling of image segmentation and from a digital object classification. Generalization was found to bias the cultivation estimate between 6.6% and 13.9%, which is greater than the sample error for the highest level. Object classification of image-segmented samples increased the cultivation estimate by 30.2% because of systematic labelling error. Manual labelling of image-segmented samples gave a similar estimate to the original interpretation. The research demonstrates that small changes in poppy interpretation can result in systematic differences in final estimates that are not included within confidence intervals. Segmented parcels were similar to manually digitized fields and could provide increased consistency in field delineation at a reduced cost. The results are significant for Afghanistan’s opium monitoring programmes and other surveys where sample data are collected by remote sensing.
Open Access
Improved estimates of opium cultivation in Afghanistan using imagery-based stratification
(Taylor and Francis, 2017-03-30) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.
The United Nations O ce on Drugs and Crime and the US Government make extensive use of remote sensing to quantify and monitor trends in Afghanistans illicit opium production. Cultivation gures from their independent annual surveys can vary because of systematic di erences in survey methodologies relating to spectral strati cation and the addition of a pixel bu er to the agricultural area. We investigated the e ect of strati cation and bu ering on area estimates of opium poppy using SPOT5 imagery covering the main opium cultivation area of Helmand province and sample data of poppy elds interpreted from very high resolution satellite imagery. The e ect of resolution was investigated by resampling the original 10 m pixels to 20, 30 and 60 m, representing the range of available imagery. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Strati cation reduced the con dence interval by improving the precision of estimates. Cultivation estimates of poppy using 40 spectral strata and a sample fraction of 1.1% had a similar precision to direct expansion estimates using a 2% sample fraction. Strati ed estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area had a signi cant e ect on poppy cultivation estimates in Afghanistan, where the area of total agricultural production can vary signi cantly between years. The ndings of this research explain di erences in cultivation gures of the opium monitoring programmes in Afghanistan and recommendations can be applied to improve resource monitoring in other geographic areas.
Open Access
Influence of digital elevation models derived from remote sensing on spatio-temporal modelling of hydrologic and erosion processes
(Cranfield University, 2004-11) Bundela, Devendra Singh; Taylor, John C.
LISEM, a physically-based distributed and dynamic erosion model within the PCRaster GIS, is used to investigate the influence of different spatial representations of input parameters on surface hydrologic and erosion processes at three antecedent soil moisture levels for a 6-hour heavy storm at catchment scale. Two derived DEMs viz. Cartometric and PulSAR DEMs and three public domain DEMs viz. Landmap, ASTER and SRTM were used in this study. These five DEMs of various original resolutions along with a land use and land cover map and a soil map of the Saltdean catchment were resampled into five spatial representations at 20, 40, 60, 80 and 100 m grid-cell sizes to create input parameters at each resolution. Spaceborne radar interferometry was investigated for generating a suitable DEM for modelling in the context of developing countries having poor availability of quality DEMs. The land use and land cover map was derived from SPOT-1 data and the infiltration parameters were estimated from the 1:250 000 soil map using pedotransfer functions. Crop, soil and soil surface parameters were estimated for possible field conditions in the catchment. Subsequently, twenty-five LISEM databases of 30 input parameters each were created in PCRaster and tested in the model. The results show that at increasing the grid-cell size of a DEM, the slope gradient flattens and the drainage length shortens. Both of these have competing effects on runoff and sediment flow routing. The catchment area also increases at larger grid-cell sizes and influences these processes, which are then normalised for the comparison of various resolution results. In the absence of observed runoff and average soil loss data, a relative evaluation across resolutions and DEMs was carried out in the context of developing countries. The results indicate that the PulSAR and Landmap DEMs have higher variations in runoff and average soil loss than the ASTER DEM, Cartometric DEM, and SRTM DEM at coarser resolutions at all three moisture levels with respect to their result at 20 m. The SRTM DEM has lower variability than other DEMs at finer resolutions. It is demonstrated that resampling a medium resolution SRTM DEM at smaller grid-cell sizes does not improve the prediction of runoff and soil erosion. At 100 m resolution, the runoff is over predicted as compared to an 80 m resolution. Hence, high resolution DEMs should be resampled to 80 m grid-cell size, but the resampling reduces the spatial variability drastically. The results also indicate that the prediction of runoff is improved for the PulSAR DEM and Landmap DEM, and is slightly improved for the ASTER DEM as compared to the Cartometric DEM, but it is not improved for the SRTM DEM. It is related with their slope gradients. The results support that the average soil loss is improved for the PulSAR DEM and Landmap DEM and is slightly degraded for the SRTM DEM as compared to the Cartometric DEM. It also suggests that both are suitable for erosion prediction due to higher slope gradient mapped by remote sensing. The ASTER DEM did not produce reliable soil losses at all the moisture levels. Therefore, it should not be used for the prediction of soil erosion. The results also indicate that small grid-cell size produces detailed soil erosion and deposition outputs, which help in identifying the exact location of sediment source and sink areas necessary for planning the effective conservation strategy in the catchment.
Open Access
Monitoring post-fire vegetation cover regeneration in the european Mediterranean basin by means of remote sensing
(Cranfield University, 2007-11) Solans Vila, José Pablo; Barbosa, Paulo; Taylor, John C.
Obtaining quantitative information about the recovery of fire affected ecosystems is of utmost importance from the management and decision-making point of view. Nowadays the concern about natural environment protection and recovery is much greater than in the past. However, the resources and tools available for its management are still not sufficient. Thus, attention and precision are needed when decisions must be taken. Quantitative estimates on how the vegetation is recovering after a fire can be of help for evaluating the necessity of human intervention on the fire-affected ecosystem, and their importance will grow as the problem of forest fires, climate change and desertification increase. This thesis presents a comparison of methods to extract quantitative estimates of vegetation cover regrowth in burned areas with remote sensing data. In order to eliminate possible sources of error, a thorough pre-processing was carried out, including a careful geometric correction (reaching RMSE lower than 0.3 pixels), a topographic correction by means of a constrained Minneart model and a combination of absolute and relative atmospheric correction methods. Pseudo Invariant Features (PIF) were identified either by visual inspection methods or by a new automated selection method based in temporal Principal Component Analysis (PCA), which has been called multi-Temporal n-Dimensional Principal Component Analysis (mT-nD-PCA). This automated method demonstrated its capability in selecting accurate and objective PIFs within the satellite images. Spectral Mixture Analysis (SMA) was compared against quantitative vegetation indices which are based on well known traditional vegetation indices like Normalised Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI). Accuracy assessment was performed by regressing vegetation cover results obtained with each method, against field data gathered during the field work campaigns. Results obtained showed how vegetation cover fractions obtained with the NDVI based quantitative index were the most accurate, being superior to the rest of the techniques applied, including SMA.
Open Access
Monitoring rangeland vegetation in the Sahel by Landsat MSS and NOAA AVHRR
(Cranfield University, 1991-07) Hiederer, Roland; Taylor, John C.; D'Souza, G.
Quantities of herbaceous vegetation of Sahelian rangelands in Niger and Mali were compared to vegetation indices (VI) derived from Landsat MSS and NOAA AVHRR LAC images. Field data was collected in 1985,1988 and 1989 in Niger and an appropriate sampling scheme for the study area was developed. Herbaceous vegetation could be estimated to within t 150 kgha 1 at an 80% confidence level up to 1300 kgha -1. Establishing site positions was found to be a primary obstacle when selecting suitable sampling areas. Suggested is the use of Landsat MSS image hard-copies in combination with a global positioning system. Landsat MSS and NOAA AVHRR LAC data were available for dates corresponding to field surveys of 1985 and 1988. While Landsat MSS scenes were geometrically corrected to maps, NOAA AVHRR images were registered to Landsat MSS with a simulated resolution of 1.1 km. Data from both satellites were radiometrically corrected and standardized to atmospheric conditions to the image with the highest relative scene contrast for each study area. These reference images were identified on the basis of bare soil spectral reflectance values and a binary decision tree. Five methods of resampling image data to represent field sites were applied. - The image data sampling methods were found to have a significant influence on spectral reflectance values attributed to a site and, consequently, on the relationship between ground and satellite VIs. Ratio, normalized difference and perpendicular VIs (RVI, NDVI and PVI) were computed for each step of pre-processing procedures. For Landsat MSS VIs were also derived from average spectral reflectance values of bands 3 and 4 to simulate NOAA AVHRR channel 2. VIs were compared for the same sensor, between sensors and related to field data by using linear and logarithmic regression analyses. RVIs and NDVIs achieved very similar results, while PVIs showed a more variable relationship to ground data. Overall, VIs from simulated NOAA AVHRR channel 2 values were found to be not superior to those derived from just band 4. NOAA AVHRR VIs could be related to Landsat MSS ratio VIs by a single regression line for 1985 and 1988 growing seasons for Niger and Mali survey sites. For the inter-calibration a simulation of the NOAA AVHRR pixel size was found to be better suited than the high resolution Landsat MSS data.
Open Access
Real-time measures of canopy size as a basis for spatially varying nitrogen applications to winter wheat sown at different seed rates
(Elsevier, 2003-04) Wood, G. A.; Welsh, J. P.; Godwin, R. J.; Taylor, John C.; Earl, R.; Knight, S. M.
Experiments at two sites growing winter wheat show that in order to manage a wheat canopy more effectively, the use of specific remote sensing techniques both to monitor crop canopy expansion, and to determine variable nitrogen applications at key timings is required. Variations in seed rate were used to achieve a range of initial crop structures, and treatments were compared to standard farm practice. In the first year, the effect of varying seed rate (250, 350 and 450 seeds m−2) on crop structure, yield components and grain yield, was compared to the effects of underlying spatial variation. Plant populations increased up to the highest rate, but shoot and ear populations peaked at 350 seeds m−2. Compensation through an increased number of grains per ear and thousand grain weight resulted in the highest yield and gross margin at the lowest seed rate. In later experiments, the range of seed rates was extended to include 150 seeds m−2, each sown in 24 m wide strips split into 12 m wide halves. One half received a standard nitrogen dose of 200 kg [N] ha−1, the other a variable treatment based on near ‘real-time’ maps of crop growth. Both were split into three applications, targeted at mid-late tillering (early March), growth stages GS30-31 (mid April) and GS33 (mid May). At each timing, calibrated aerial digital photography was used to assess crop growth in terms of shoot population at tillering, and canopy green area index at GS30-31 and GS33. These were compared to current agronomic guidelines. Application rates were then varied below or above the planned amount where growth was above- or below-target, respectively. In the first field, total nitrogen doses in the variable treatments ranged from 188 to 243 kg [N] ha−1, which gave higher yields than the standards at all seed rates in the range 0·36–0·78 t ha−1 and gross margins of £17 to £60 ha−1. In the second field, variable treatments ranged from 135 to 197 kg [N] ha−1 that resulted in lower yields of −0·32 to +0·30 t ha−1. However, in three out of the four seed rates, variable treatments produced higher gross margins than the standard, which ranged from £2 to £20 ha−1. In both fields, the greatest benefits were obtained where the total amount of applied nitrogen was similar to the standard, but was applied variably rather than uniformly along the strips. Simple nitrogen balance calculations have shown that variable application of nitrogen can have an overall effect on reducing the nitrogen surplus by one-third.
Open Access
Remote sensing of opium poppy cultivation in Afghanistan
(Cranfield University, 2016-01-04) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.
This work investigates differences in the survey methodologies of the monitoring programmes of the United Nations Office on Drugs and Crime (UNODC) and the US Government that lead to discrepancies in quantitative information about poppy cultivation. The aim of the research is to improve annual estimates of opium production. Scientific trials conducted for the UK Government (2006–2009) revealed differences between the two surveys that could account for the inconsistency in results. These related to the image interpretation of poppy from very high resolution satellite imagery, the mapping of the total area of agriculture and stratification using full coverage medium resolution imagery. MODIS time-series profiles of Normalised Difference Vegetation Index (NDVI), used to monitor Afghanistan’s agricultural system, revealed significant variation in the agriculture area between years caused by land management practices and expansion into new areas. Image interpretation of crops was investigated as a source of bias within the sample using increasing levels of generalisation in sample interpretations. Automatic segmentation and object-based classification were tested as methods to improve consistency. Generalisation was found to bias final estimates of poppy up to 14%. Segments were consistent with manual field delineations but object-based classification caused a systematic labelling error. The findings show differences in survey estimates based on interpretation keys and the resolution of imagery, which is compounded in areas of marginal agriculture or years with poor crop establishment. Stratified and unstratified poppy cultivation estimates were made using buffered and unbuffered agricultural masks at resolutions of 20, 30 and 60 m, resampled from SPOT-5 10 m data. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Decreasing the resolution of the imagery and buffering increased unstratified estimates. Stratified estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area explained differences in cultivation figures of the opium monitoring programmes in Afghanistan. Supporting methods for yield estimation for opium poppy were investigated at field sites in the UK in 2004, 2005 and 2010. Good empirical relationships were found between NDVI and the yield indicators of mature capsule volume and dry capsule yield. The results suggested a generalised relationship across all sampled fields and years (R2 >0.70) during the 3–4 week period including poppy flowering. The application of this approach in Afghanistan was investigated using VHR satellite imagery and yield data from the UNODC’s annual survey. Initial results indicated the potential of improved yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling. The recommendations for poppy cultivation surveys are: the use of image-based stratification for improved precision and reducing differences in the agricultural mask, and use of automatic segmentation for improved consistency in field delineation of poppy crops. The findings have wider implications for improved confidence in statistical estimates from remote sensing methodologies.
Open Access
Soil factors and their influence on within-field crop variability II: spatial analysis and determination of management zones
(Elsevier, 2003-04) Taylor, John C.; Wood, G. A.; Earl, R.; Godwin, R. J.
Spatial variation of crop yields was examined in three trial cereal fields in England from 1994 through 1997. The fields were managed with uniform inputs but there were considerable differences in the spatial patterns and magnitudes of variation between fields and seasons. Up to 50% of the yield variation was within the tramline spacing distance (20â 24 m) and this appeared to relate to crop management practices rather than underlying soil factors. Longer-range variation generally increased up to field scale but was not constant between seasons. Longer-range variation was more apparent in dry years and was attributable to soil variation. Soil series differences coincided with yield differences in dry years when the soil series differences could be expected to create large differences in soilâ water relationships. Soil electrical conductivity, measured by electromagnetic induction (EMI), was investigated as a surrogate for detailed soil coring. Field zones created by EMI also coincided with yield differences and zones were similar to those delineated by soil series with expected differences in soilâ water relationships. The EMI observations were found to be a useful and cost-effective surrogate for representing soil variability in fields likely to create yield variations. Subdivision of fields into management zones using multi-variate K-means cluster analysis of historical yield and EMI observations formed an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies. The appropriateness of site-specific management has to be assessed annually because magnitude and pattern of variation changes from season to season.
Open Access
Soil Factors and their Influence on Within-Field Crop Variability, I: Field Observation of Soil Variation
(Elsevier Science B.V., Amsterdam, 2003-04-01T00:00:00Z) Earl, R.; Taylor, John C.; Wood, G. A.; Bradley, I.; James, Iain T.; Waine, Toby W.; Welsh, J. P.; Godwin, R. J.; Knight, S. M.
A fundamental component of adopting the concept of precision farming in practice is the ability to measure spatial variation in soil factors and assess the influence of this on crop variability in order to apply appropriate management strategies. The aim of this study was to appraise potential methods for measuring spatial variability in soil type, nutrient status and physical properties in practical farming situations. Five fields that are representative of more than 30% of soils used for arable production in England and Wales were selected for use as case studies. Maps of soil type were generated from a conventional hand auger survey on a 100 m grid and the excavation of targeted soil profile pits. These were compared with those refined using a mechanised soil coring device and scans of electromagnetic inductance (EMI) carried out while the soil could reasonably be considered to be at, or near, field capacity moisture content. In addition, soil sampling for nutrient analyses was conducted on a 50 m grid to examine the spatial variation in nutrient status. Conventional methods for sampling soil were found to be appropriate for identifying soil types at specific locations within the field sites, however, they were time- consuming to perform which placed an economic and therefore a practical limitation on the sampling density possible. The resulting data were considered to be too sparse for demarcating soil type boundaries for use in the context of precision farming. The location of soil boundaries were refined by using the mechanised soil corer, however, the limitation of this was found to be the time required to analyse the soil cores produced. Maps of soil variation generated from EMI scans conducted at field capacity appear to reflect the underlying variation in soil type observed in maps generated using the mechanised soil corer. and, therefore, this approach has potential as a cost-effective, data- rich, surrogate for measures of soil variability. Results from analyses of soil samples for measurement of nutrient status indicated that whilst there was considerable variation in macro- and micro-nutrient levels in each field, with the exception of pH, these levels were above commonly accepted agronomic limits. Results did however demonstrate the potential for addressing variation in critical factors such as pH at specific locations, however, there is a need to develop protocols for targeting sampling in order to reduce costs.
Open Access
Spatiotemporal Hydrological Modelling with GIS for the Upper Mahaweli Catchment, Sri Lanka
(Cranfield University, 1997-07) Premalal de Silva, Ranjith; Taylor, John C.
Sustainability of water resources is imperative for the continued prosperity of Sri Lanka where the economy is dependent upon agriculture. The Mahaweli river is the longest in Sri Lanka, with the upper catchment covering an area of 3124 sq .km .. The Mahaweli Development programme, a major undertaking in the upper catchment has been implemented with the aims of providing Mahaweli water to the dry zone of the country through a massive diversion scheme and also for generating hydropower. Under this programme, seven large reservoirs have been constructed across the river and large scale land use changes in the catchment have occurred during the last two decades. Critics now say that the hydrological regime has been adversely affected due to indiscriminate land use changes and, as a result, river flows have diminished during the last two decades, thus jeopardising the expectations of this massive development programme. Reforestation programmes have been recommended because of the benefits of forest in resource conservation and also the water derived from fog interception. Selection of the best sites for these forest plantations for maximum benefits, especially in terms of water yield from fog interception has the utmost importance. This created the need for a comprehensive model to represent the hydrology and to simulate the hydrological dynamics of the catchment In conceptual terms, GIS is well suited for modelling with large and complex databases associated with hydrological parameters. However, hydrological modelling efforts in GIS are constrained by the limitations in the representation of time in its spatial data ,structures. The SPANS GIS software used in this study provided the capability of linking spatially distributed numerical parameters with corresponding tabulated data through mathematical and statistical expressions while implicitly representing temporality through iterative procedures.The spatial distribution of land use was identified through the supervised classification of IRS-IA LISS II imagery. Daily rainfall data for a 30 year period and corresponding gauging locations derived from GPS were managed and retrieved through a Lotus 1-2- 3 database. The fog interception component was estimated based on elevation and the monsoon season. Hydrological processes such as interception and evapotranspiration were derived from individual sub models and finally combined within the overall hydrological model structure. The model was run with daily time steps on numerical 'values of each quad cell of the thematic coverage. The information on flow derived from the model was depicted as a series of thematic maps in addition to the time series of numerical values at subcatchment and catchment outlets. The results confirmed that the model is capable of simulating catchment response of the UMCA successfully. The time dimension was accommodated through a senes of non-interactive REXX programmes in developing the customised version of the model. It is concluded that the software architecture of SPANS GIS is capable of accommodating spatiotemporal modelling implicitly in its spatial data structures although changes in the model structure may necessitate considerable reprogramming. Sensitivity of the model for different spatial interpolation techniques was evaluated. Further, sensitivity of the model for the defined hydrological parameters, spatial 'resolution and land use was also assessed. The model is sensitive to land use changes in the catchment and it shows 15-35% annual increase of runoff when forests are converted to grassland. Further studies are required to develop a more detailed set of hydrological parameters for the model.
Open Access
Strategic monitoring of crop yields and rangeland conditions in Southern Africa with remote sensing
(2000-01) Sannier, Christophe; Taylor, John C.
The monitoring of vegetation resources is of vital importance for Southern African countries because of the dominance of agriculture in the economy. The use of remote sensing techniques in a national or local planning context is particularly adapted to the Southern African conditions because large areas can be covered regularly with minimal requirements for field based infrastructure. Furthermore, relatively low-cost receiving stations have been installed in the meteorological departments and other local institutions, which makes satellite data available in realtime. Real-time acquisition is essential for the operational monitoring of vegetation development and remote sensing plays a significant role in three main areas: • Inventory or mapping of cover types • Monitoring of vegetation conditions relative to the norm • Estimates of biomass In this research operational techniques were developed in each of these areas with the participation and involvement of users. Remote sensing and field survey techniques for inventory and mapping of cover types were adapted and developed from existing experience in the European context to match the requirements in Southern Africa. The need for an unbiased sample of field observations, for the calibration of digital classification of satellite imagery was identified and methodology for its collection demonstrated. Methods developed for the inventory of crop types in Europe were successfully adapted to the African rangeland. The levels of classification accuracy achieved were similar to that obtained in the European context for a classification scheme of equivalent complexity. A Vegetation Productivity Indicator (VPI) was developed for monitoring vegetation conditions based on real-time acquisition of NOAA HRPT imagery from a local receiving station and a historical Normalised Difference Vegetation Index (NDVI) archive. The VPI maps show departure from normal vegetation response using methodology similar to the analysis of extreme events in hydrology, in near real-time. The method was successfully implemented in Zambia to monitor maize production and in Namibia to monitor rangeland. The VPI was significantly correlated with rainfall. The technique was successfully transferred to the Department of Meteorological Services in Botswana where VPI maps are produced routinely and presented to the inter-ministerial drought committee for assessing rangeland conditions. Methodology for rapid biomass assessment was developed using simple physiognomic plant parameters. Field Measurements were taken in four different cover types (grassland, steppe and shrub and tree savanna) and correlated with the NDVI derived from the satellite observations in Etosha National Park, Namibia in near real-time. The pooled regression relationship which was obtained was highly statistically significant. However, the regression model excluding the two savanna types exhibited a higher correlation suggesting that there might be a separate relationship between savanna biomass and NDVI. Biomass maps were produced using the pooled relationship and their potential for operational targeting of areas suitable for prescribed burning was illustrated. Although the methods and techniques in this work were developed using time series of NOAA-AVHRR and the NDVI, they can all be adapted to include data from new sensors systems and other vegetation indices as they become available. Methods demonstrated in this work can be integrated to form a suitable framework for a national vegetation resources monitoring system. This would assist Southern African governments in making decisions related to vegetation resources by providing sound and timely technical advice.
Open Access
Survey and monitoring of opium poppy and wheat in Afghanistan: 2003-2009
(2010-04-01) Taylor, John C.; Waine, Toby W.; Juniper, Graham R.; Simms, Daniel M.; Brewer, Timothy R.
An integrated application of remote-sensing technology was devised and applied in Afghanistan during 2003–2009 providing critical information on cereal and poppy cultivation and poppy eradication. The results influenced UK and international policy and counter-narcotics actions in Afghanistan.
Open Access
Towards improving the accuracy of opium yield estimates with remote sensing
(Taylor and Francis, 2014-08-28) Waine, Toby W.; Simms, Daniel M.; Taylor, John C.; Juniper, Graham R.
Yearly estimates of illicit opium production are key metrics for assessing the effectiveness of the counter-narcotics policy in Afghanistan. Poor security often prevents access to sample locations and puts pressure on field surveyors, resulting in biased sampling and errors in data recording. Supportive methods using aerial digital photography for improving yield estimates were investigated in the UK in 2004, 2005, and 2010. There were good empirical relationships between normalized difference vegetation index and poppy yield indicators (mature capsule volume and dry capsule yield) for individual fields. The results suggested a good generalized relationship across all sampled fields and years (R2 > 0.70) during the 3–4 week period including poppy flowering. Regression estimates using this relationship with the imagery counteracted bias in the sample estimate of yield, reduced sample error, and enabled the production of detailed maps showing the poppy yield distribution. The application of this approach using very-high-resolution satellite imagery was investigated in the context of the annual opium survey in Afghanistan. Initial results indicated the potential for bias correction of yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling.