Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

International Journal of Applied Earth Observation and Geoinformation 16 (2012) 5–16

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International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag

Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing Asoka Edirisinghe a,∗ , Dave Clark b , Deanne Waugh b a b

Commonwealth Scientific and Industrial Research Organization, Underwood Avenue, Floreat, WA 6014, Australia DairyNZ Ltd, Private Bag 3221, Hamilton 3240, New Zealand

a r t i c l e

i n f o

Article history: Received 18 October 2010 Accepted 17 November 2011 Keywords: Pasture biomass Dairy pasture Intensive grazing Spatio-temporal modelling SPOT-5 SPOT-4 NDVI

a b s t r a c t Pasture biomass is a vital input for management of dairy systems in New Zealand. An accurate estimate of pasture biomass information is required for the calculation of feed budget, on which decisions are made for farm practices such as conservation, nitrogen use, rotational lengths and supplementary feeding leading to profitability and sustainable use of pasture resources. The traditional field based methods of measuring pasture biomass such as using rising plate metres (RPM) are largely inefficient in providing the timely information at the spatial extent and temporal frequency demanded by commercial environments. In recent times remote sensing has emerged as an alternative tool. In this paper we have examined the Normalised Difference Vegetation Index (NDVI) derived from medium resolution imagery of SPOT-4 and SPOT-5 satellite sensors to predict pasture biomass of intensively grazed dairy pastures. In the space and time domain analysis we have found a significant dependency of time over the season and no dependency of space across the scene at a given time for the relationship between NDVI and field based pasture biomass. We have established a positive correlation (81%) between the two variables in a pixel scale analysis. The application of the model on 2 selected farms over 3 images and aggregation of the predicted biomass to paddock scale has produced paddock average pasture biomass values with a coefficient of determination of 0.71 and a standard error of 260 kg DM ha−1 in the field observed range between 1500 and 3500 kg DM ha−1 . This result indicates a high potential for operational use of remotely sensed data to predict pasture biomass of intensively grazed dairy pastures. © 2011 Elsevier B.V. All rights reserved.

1. Introduction In the temperate climatic regions of New Zealand farm paddocks can be put under grazing throughout the year. These regions experience typically high winter rainfall extending to autumn. Traditionally, pastoral farms, where animals are fed by grazing, are effectively managed ecosystems. To be profitable and sustainable, especially in the case of dairy farming, grazing practices must be geared to meet the challenges posed in maintaining the health of the pastures and animals while minimising the impact on environment. In New Zealand dairy regions, feed budgeting and utilisation of pastures have been improved by the implementation of rotational grazing systems where a defined residual pasture biomass is maintained and stock numbers are managed on the basis of both current and expected pasture biomass levels. The available pasture biomass on farms is a key variable in profitable dairy farm management. Accurate and timely information on pasture biomass is required to

∗ Corresponding author. Tel.: +61 08 92448548. E-mail address: [email protected] (A. Edirisinghe). 0303-2434/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2011.11.006

take decisions on optimising rotation lengths, supplementary feeding, nitrogen fertiliser use and conservation for facilitating better feeding practices throughout the year (Clark et al., 2006). The time between June and December is a critical period in the lactation cycle of the Waikato dairy industry. The high feed demand during this period requires effective pasture management strategies to maximise the utilisation of high quality forage. To fulfil this requirement, it is imperative that accurate and reliable mechanisms are in place to effectively measure on-farm feed resources. Traditionally, pasture biomass information in dairy paddocks was collected using pasture cuts (Cayley and Bird, 1996) visual assessments (Haydock and Shaw, 1975; Stockdale, 1984a), rising plate metre (Lile et al., 2001; Stockdale, 1984b; Thomson et al., 2001) and capacitance metre (Tucker, 1980; Vickery et al., 1980). Many farmers in New Zealand use visual assessment or RPM to make pasture biomass estimates and RPM gives reliable estimates of pasture biomass when at least 50 readings per paddock are taken in paddocks where average herbage mass is in the range between 1000 and 4000 kg DM ha−1 (Lile et al., 2001). This assessment was based on calibration of RPM heights readings to pasture biomass in kg DM ha−1 using standardised equations developed by Thomson et al. (1997, 2001). While it is accepted that both RPM

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and visual assessment based techniques when properly and regularly calibrated can account for 80–90% of the variation observed on paddock scale biomass (Clark et al., 2006), these measurements can become unreliable and error-prone when used by untrained personnel. These techniques are monotonous when used with the frequency needed to make effective management decisions and time-consuming when applied in large-scale commercial environments. Feed budgeting is used regularly on about 20% of New Zealand dairy farms (Clark et al., 2006). Lack of confidence in the accuracy (Li et al., 1998; Reeves et al., 1996; Stockdale, 1984a), high labour demand, difficulty of use and cost (Lile et al., 2001) are the main reasons for the underutilisation. The industry needs to improve these practices to achieve the efficiency needed in assessing feed resources during the critical period of a season. This can only be achieved by an accurate technique that delivers pasture biomass estimates “near-real-time” to large geographic areas with the minimum reliance on subjective human interventions. Developments in remote sensing technology over the last decade (Clark et al., 2006; Edirisinghe et al., 2011, 2000; Handcock et al., 2006) have been useful in providing solutions to satisfy the demand of the dairy industry in New Zealand overcoming these difficulties. The potential of this technology to provide information on the time and spatial scales demanded by the industry over geographically extensive areas has made remote sensing solutions attractive for the dairy industry. Plants reflect incoming solar radiation differently in the red (RED) and the near infrared (NIR) parts of the electromagnetic spectrum, which is intercepted by the sensor from the space. This difference in sensor detected plant response as a multispectral measure of reflected solar radiation can be used to create an index of plant greenness. One of the most widely used plant greenness indices is the Normalised Difference Vegetation Index (NDVI) (Tucker, 1979). The NDVI response has been investigated for rangeland vegetations (Todd et al., 1998), arid vegetation biomass (Lo Seen et al., 1995), pasture biomass (Edirisinghe et al., 2011, 2000) and growth rate (Hill et al., 2004) in extensive grazing systems. Elsewhere, Narrow band based NDVI studies and hyperspectral band depth analysis have been used to overcome saturation of broad band NDVI response from dense grass canopies to improve biomass assessment (Mutanga and Skidmore, 2004a,b) and alternative vegetation indices for vegetation biomass assessment were also considered (Huete et al., 2002). Satellite sensed spectral response of rotationally grazed dairy pasture systems has been reported by Handcock et al. (2006) and Mata et al. (2007). In a study of non-dairy extensive grazing systems in Australia NDVI has been related to the pasture biomass, through a systematic relationship (Edirisinghe et al., 2011, 2000). Some of these studies have highlighted the issue of non-photosynthetic biomass when using satellite based greenness indices such as NDVI for pasture assessment and approaches taken to remedy this problem. Weak response of greenness indices to dry or sensed pastures is the cause of this problem. The issue of saturated NDVI caused by the spectral response of high biomass pastures was another issue addressed in these studies. In some of these situations simple statistical fitting of liner curves for the relationship between NDVI and biomass seems to be not producing the desired results. The dairy pasture systems in Waikato differ from extensively managed annual pasture systems due to factors such as intensive nature of grazing, penology, climate conditions and small paddock sizes. In order to develop new remote sensing tools for assessing biomass of target dairy pastures, the knowledge of the methodologies adopted in the above studies is useful as a guide. Systematic pattern of response between the corresponding values of NDVI and biomass caused by seasonal conditions, rotational grazing and intensive management practices must be identified

Table 1 Farm location details. Zone no.

Farm no.

Farm type

Longitude

Latitude

Zone 1

1 2 3 4 9 5 6 7 8 10

R C C C R C C C C C

175.361◦ E 175.447◦ E 175.368◦ E 175.364◦ E 175.339◦ E 175.697◦ E 175.775◦ E 175.448◦ E 175.403◦ E 175.368◦ E

37.842◦ S 37.826◦ S 37.769◦ S 37.751◦ S 37.776◦ S 37.655◦ S 37.738◦ S 37.991◦ S 38.001◦ S 37.568◦ S

Zone 2

Zone 3 Zone 4 Zone 5

C, commercial farm; R, research farm.

to establish robust correlations and predictive models for dairy pastures in Waikato. In intensively grazed dairy pasture systems care must be taken to minimise the effect of management practice, agronomic factors and terrain on the remotely sensed spectral information. The paddocks affected with management practices (strip-grazing, pugging and spread of fertiliser and affluent), agronomic factors (botanical composition of pastures and spread of weeds) and terrain induced effects (slope, waterlogging) should not be selected for sampling activities to avoid undesirable spectral signatures used in the analysis. The practices such as strip-grazing, in which the movement of animals in paddocks are controlled by temporarily moving electric fences, cause rapid changes to the target pasture cover. The timing of these changes should be taken into account when a remotely sensed image is analysed against the corresponding field data from such paddocks usually collected within a window of a few days from the time of satellite overpass. In this study we investigate space and time dependencies of the pixel scale spatial relationship between the satellite derived NDVI and field measured biomass of intensively grazed dairy pastures for predictive purposes. Then we assess the accuracy of the predictions against the field observations at the scale required by the dairy industry by aggregating the pixel-scale prediction to the paddock scale pasture biomass. We assume the paddock average biomass estimates derived from pixel scale predictions provide improved accuracy for feed budgeting of intensively grazed dairy pastures. Our objective is to develop a robust and highly efficient pasture biomass model applicable to a large geographic area and comparable in accuracy to current field based industry standard practices for pasture biomass assessment in the Waikato region. 2. Materials and methods 2.1. The study area The study site shown in Fig. 1 is located in the Waikato region in New Zealand. The site is centred at (latitude 37◦ 50 S, longitude 175◦ 28Y E) and spread over an approximately 3600 km2 square area. The terrain is relatively flat. The mean farm size in the region is 100 ha. The mean paddock size of commercial farms in the region is around 2 ha. The region has a temperate climate with an average annual precipitation of 1250 mm. The area is exposed to west and southwest winds from the Tasman Sea bringing in mild winter conditions and warm humid summer conditions. Pastures in the region are usually dominated by perennial ryegrass (Lolium perenne cv.) and white clover (Trifolium repens L.). Tall tree wind-breaks at the periphery of some farms and a few isolated trees in the middle of some paddocks are the only source of remnant vegetation. Ten farms from the target area were chosen for the field sampling carried out in 2006 (Table 1). A majority of these farms were designated for commercial grazing activities and two were exclusive research farms. Farms were grouped into Zones spread across

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Fig. 1. Study area in Waikato region and target farms.

the target area on the basis of geographic distribution to explore space domain analysis. The planned field sampling of pasture biomass on selected farms provided the opportunity to investigate data from the mix of environments found across the target region. 2.2. Field data The field collection of pasture biomass data was organised at paddock and sub-paddock scale under varying degree of grazing pressures throughout the growing season. A total of 10 field data collections were carried out between May and December 2006 as detailed in Table 2. All collections were completed within 4 days of the acquisition of target imagery. Field data was collected using a rising plate metre (RPM) according to the field sampling protocol that was designed to capture the spatial variability of biomass within and between paddocks on each farm (Clark et al., 2006; Thomson et al., 1997, 2001). Conversion of RPM data to biomass (kg DM ha−1 ) was carried out using a set of calibration equations specifying different intercept and slope coefficients on daily basis developed for Waikato by DairyNZ (www.dairynz.co.nz). These coefficients were estimated to closely follow the trends observed for standard seasonally based calibration coefficients established for the region as per L’Huillier and Thompson (1988) and Thomson et al. (1997, 2001). 2.2.1. Field protocol design issues The development of field data collection protocol for this study took into account a number of factors to facilitate the effective assessment of the pasture biomass above ground level that is available for grazing. The RPM is the manual method most frequently used by the dairy farmers in New Zealand and captures only the top of the canopy readily accessed by the feeding dairy cows. RPM together with visual assessment of pasture biomass are proven methodologies currently available for the Waikato dairy industry

as the reference methods for evaluating new technologies such as satellite based biomass predictions (Clark et al., 2006). While quadrat cuts to ground level gives a more accurate assessment of biomass it can include non-green and dry stems from parts of the pasture often not always accessible to feeding cows. Waikato dairy industry’s preference for RPM over quadrat pasture cuts to ground level also influenced by the significant cost saving involved and efficiency of data collection. These reasons are deemed more important than the implications of marginally reduced accuracy with the RPM. The design of the sampling protocol also took into account the differences in resolution between the two satellite sensors (SPOT-4 and SPOT-5) used for imagery acquisition. In developing the prediction model, sampling had to be conducted at the pixel scale of the target imagery to avoid large uncertainties that could be introduced if other scales (patch or paddock) were used. Given the ±1 pixel geo-registration error, a minimum of 3 by 3 pixel area on the ground consisting of 9 pixels is required to ensure the inclusion of the target NDVI pixel within the spatial extent of the sample area. The pastures in this grid sampling area should be uniform enough to expect the same NDVI value for all 9 pixels. To account for the resolution differences of SPOT-4 and 5 images the grid space of 9 pixels was defined as 60 m × 60 m and 30 m × 30 m, respectively. To enable a single protocol to be used in the field sampling a 60 m × 60 m grid space was adopted as the standard. The concurrent field samplings were conducted for every successfully acquired image. However, due to practical limitations the maximum of 3 days from the time of satellite overpass was allowed for completion of the field sampling in all the test farms. Given the rate at which pastures are growing, it was assumed that this delay in field sampling is not affecting the spectral response of the target pastures significantly from the spectral response observed in the satellite imagery.

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Table 2 Details of field sampling and image acquisition in 2006. Sampling event number

Field sampling dates

Farms sampled

Imaging date

SE0 30/Apr–1/May06 7 06/May06 5 27/Jun/06 SE1 26–28/Jun/06 SE2 14–20/Jul/06 6 16/Jul/06 SE3 27–31/Jul/06 7 28/Jul/06 10–11/Aug/06 6 09/Aug/06 SE4 28–30/Aug/06 6 27/Aug/06 SE5 15–18/Sep/06 8 14/Sep/06 SE6 8 05/Oct/06 SE7 5–8/Oct/06 SE8 9–11/Nov/06 5 09/Nov/06 SE9 11–14/Dec/06 8 11/Dec/06 Additional field data and imagery collected for paddock average biomass assessment 10–11/Aug/06 4, 9 09/Aug/06 SE4 5–8/Oct/06 4, 9 05/Oct/06 SE7 4, 9 11/Dec/06 SE9 11–14/Dec/06

2.2.2. Collecting RPM readings on grids For each target paddock the 60 m × 60 m sampling grid was set up with permanently located markers in a representative area of ‘uniform pasture’ at least 10 m away from farm fence lines and by avoiding areas affected with topping and pugging and areas of bare ground in the paddocks. For each grid, from an identified start point, four parallel transects of 60 m length of each were plated across the marked grid with a RPM (Fig. 2). The average plate height at 10 m intervals along the tracks was recorded for conversion to pasture biomass. The transects were separated from the grid edge with spacing of 7.5 m, 15 m, 15 m, 15 m and 7.5 m from left to right, in all cases (Fig. 2). Global positioning system (GPS) readings with an accuracy of ±5 m were taken at 10 m intervals during the plating process on grids. 2.2.3. Additional field data The dairy industry standard practice requires new pasture biomass monitoring tools to be assessed against paddock average biomass data (Clark et al., 2006; L’Huillier and Thompson, 1988; Lile et al., 2001; Thomson et al., 2001). The accuracy of the satellite predicted biomass data is therefore necessary to be evaluated in the paddock scale rather than in pixel scale. Paddock average pasture biomass data for validation of satellite predictions in our study were collected using RPM on Farms 4 and 9 (Table 2), by following

Spot satellite

View angle (incidence)

Sun angle (elevation)

5 5 5 5 4 5 4 4 5 4

−29.1 −29.1 +25.4 −23.8 −29.3 +13.5 −17.5 −23.4 −23.7 −05.6

27.4 24.2 25.0 23.6 26.2 34.5 39.2 46.6 56.2 61.6

4 4 4

−29.3 −23.4 −05.6

26.2 46.6 61.6

the industry wide practice of plating 50–80 points across a paddock using the RPM and calculating the average reading per paddock as recommended by Thomson et al. (1997). Paddock average data collected on 35 paddocks from Farms 4 and 13 paddocks from Farm 9 during sampling SE4 on 09 August, SE7 on 05 October and SE9 on 11 December 2006 were used for the validation analysis. The RPM calibrated paddock mean biomass for Waikato dairy pastures with uncertainty of Residual Standard Error (RSE) ranging from 350 to 450 kg DM ha−1 (Thomson et al., 1997), accounts for 80–90% of the variation observed in the Winter-early spring period (Clark et al., 2006). 2.2.4. Selection of farms and paddocks for sampling The farm locations (Table 1 and Fig. 1) were selected to provide as much geographical diversity as possible within the target area. Property selection was based on factors such as paddock shape, size, topography and location of the farm and paddocks for ease of sampling. Paddocks closer to fences with windbreaks and trees obstructing the view of the satellite sensor and having a potential to create large shadows were not selected for sampling. Narrow paddocks were avoided and large, regularly shaped, paddocks were selected to avoid the effect of edge (mix) pixels on the analysis. The paddocks selected for conservation as silage were also excluded. Paddocks were chosen to cover a biomass potentially from 1500 to 3500 kg DM ha−1 with steps of approximately 500 kg DM ha−1 . This was necessary to capture the critical range of biomass needed to be optimally managed under normal commercial practices in Waikato region. The terrain condition is important for field sampling. Paddocks with relatively flat terrains were selected to avoid the effects of significant bidirectional reflectance variation (BRDF) impacts on the analysis. For each farm in Table 1, 10 suitable paddocks were chosen and the boundaries geo-located and marked with posts for placing the sampling grids. The field sampling procedure was designed also to test any Zone specific bias in the field data. The grouping of farms within individual Zones was designed such a way that enabled sampling to be conducted by the same field crew at every sampling event for greater efficiency. This test would indicate whether the use of same crews would introduce some bias in the sampling data. Field data was collected to fully facilitate the analysis of biomass variability within-farm, between-farms and between-zones. 2.3. Satellite imagery

Fig. 2. The layout of the sampling gird and the direction of RPM recording track.

Cloud free imagery over the target farms was sourced from SPOT-4 and SPOT-5 multispectral satellite sensors. These sensors provide spectral information in the green, red, near infrared (NIR) and short wave infrared (SWIR) bands. The pixel resolutions of the imagery from the two sensors are 20 m and 10 m, respectively. Ten

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images in total out of which four SPOT-4 images and the rest SPOT5 images (Table 2) were selected. These images were acquired from May to December 2006, to coincide with the field sampling events detailed in Table 2. The spatial resolution of the imagery was chosen to provide a statistically adequate number (>36) of pixels in a typical dairy farm paddock in the Waikato region, which is important for acquiring a reliable paddock scale spectral response. The difference in image pixel size for the two sensors is significant in comparison to the median paddock size (1.7 ha) of the target farms. This difference could have an effect on the calculation of paddock average spectral responses given the number of pixels in the paddock involved in the calculation are different for the two sensors. All images were radiometrically calibrated to spectral reflectance, geometrically corrected and ortho-rectified to the NZTM map grid projection using standard image processing routines. Radiometrically calibrated reflectance values of red (RED) and near infrared (NIR) spectral bands were used to calculate the Normalised Difference Vegetation Index (NDVI) (Tucker, 1979) according to Eq. (1). NDVI =

NIR − RED NIR + RED

(1)

The NDVI calculates the relative contrast between RED and NIR reflectance influenced by the proportion of photosynthetically active (growing) vegetation over a landscape and it is a measure of the greenness of the target vegetation at the sensor altitude. The SPOT images were acquired with a range of view angles (−30◦ to 30◦ ) and Sun elevation angles as described in Table 2. 2.4. Ancillary data Paddock boundary maps, digital maps of roads, contemporary aerial photography and 20 m digital elevation maps formed the bulk of ancillary data for the study. All spatial ancillary data were standardised to NZTM map projection. 2.5. Data preparation and analysis The GPS measurements of RPM reading locations within the sampling grids were converted from WGS 84 to NZGD2000 datum and NZTM map projection for standardisation with the projection of the satellite imagery. Data extraction for spatial analyses in pixel or grid and paddock scales, relationship establishment and model building between the NDVI and pasture biomass data were conducted on a GIS platform after the satellite imagery was processed using well established methods in an image processing package. The raster layers of target NDVI and gridded biomass data layers were overlaid on the vector layer of the paddock boundary maps and the relevant NDVI data for specific paddocks were extracted within the GIS environment. Potential spatial shift between the pasture girds and the corresponding NDVI pixels was minimised by maintaining a high precision in the geo-registration process. The data analysis was aimed at exploring the variability of the pixel scale relationship between NDVI and field biomass data over the season subject to intensive grazing pressure and its dependency on spatial distribution of farms and Zones. 2.6. Spatial statistics and model synthesis GIS based zonal statistic operations were used on relevant image and grid scale field (RPM) data layers for each sampling event to generate statistics such as average NDVI and biomass. Biomass data points across each sample grid area were averaged to produce a single representative biomass value for the grid. All NDVI values across the 9 pixels in the corresponding 3 × 3

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Table 3 Variance and log of the variance in biomass for different NDVI ranges for SE1 to SE9 (sampling events). NDVI range

No. observations

≤0.60 >0.60 and ≤0.66 >0.66 and ≤0.70 >0.70 and ≤0.74 >0.74 and ≤0.75 >0.75 and ≤0.76 >0.76 and ≤0.77 >0.77 and ≤0.78 >0.78 and ≤0.79 >0.79 and ≤0.80 >0.80

24 21 19 19 17 15 21 21 21 23 26

Biomass variance 24,916 16,021 20,373 29,737 195,658 92,958 159,096 189,978 102,141 151,088 202,107

Log(biomass) variance 0.011 0.006 0.008 0.008 0.040 0.021 0.022 0.029 0.015 0.020 0.022

pixel area were averaged to produce a representative NDVI value. As the grids were designed to guarantee the exact positioning of individual target pixels on the ground the scale of the grid data could assume to be equal to the pixel scale. The grid averages of biomass and NDVI data were matched and paired as the basic data unit that was used in the subsequent analysis. Additionally, withingrid statistics such as median, minimum, maximum and standard deviation were calculated for the RPM and biomass data. These statistics were necessary to detect and remove non-uniform pasture grids from the analysis. The mean grid values of biomass and NDVI were used for the regression analysis to establish pixel scale relationships for fitting curves of biomass prediction. Paddock scale NDVI statistics were calculated by overlying the target NDVI data layers on paddock boundary maps in the GIS environment. 3. Results 3.1. Visualisation of data and assessment of variance The grid based mean biomass and NDVI data relevant to different paddocks and each sampling event in 2006 (Table 2) are plotted in Fig. 3. That includes collections from May (SE0) to December (SE9). Fig. 3 shows some consistent trends between biomass and NDVI data over the collection period. The initial analysis however, focuses on data belonging to the lactation period defined between the sampling events SE1 (June 2006) and SE9 (December 2006). The month of May is outside this period. The time of year has a large effect with higher biomass (for a given NDVI) as the year progresses. Time of year and the farming practices associated with each time period impact on the NDVI versus biomass relationship. For example, through June to July there is a reduction in maximum biomass observed in paddocks as fodder consumption by the cattle exceeds the daily pasture growth, i.e. net loss and the minimum biomass or residue after grazing is stable. In August and September, the trend reverses as higher daily temperatures lead to increases in pasture growth, allowing net accumulation, but at the same time the minimum biomass, i.e. residual biomass after grazing increases through selective grazing practice. From October to December the range of both biomass and NDVI decreases. In practice paddocks are often removed from the grazing rotation to conserve fodder and when this happens there is an added pressure on the available feed resource as the rotation speed increases. Decrease in high end NDVI is likely to be associated with the maturity of pastures as rainfall decreases. There is also a tight, approximately linear relationship between biomass and NDVI for values below the mid-0.70s except for November and December months. For NDVI above 0.70s the variation in biomass values is appreciably larger than that for NDVI in the mid-0.70s (see Table 3). This is probably due to increased error in the RPM

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A. Edirisinghe et al. / International Journal of Applied Earth Observation and Geoinformation 16 (2012) 5–16 F4 F6

F7

F8 F5

F2 F1

0.50 0.60 0.70 0.80

SE5-Aug 06

F9 F10

F3

0.50 0.60 0.70 0.80

SE6-Sep 06

SE7-Oct 06

SE8-Nov 06

SE9-Dec 06 5000 4000 3000

Biomass Kg DM ha-1

2000

SE0-May 06

SE1-June 06

SE2-Jul 06

SE3-Jul 06

SE4-Aug 06

1000

5000 4000 3000 2000 1000 0.50 0.60 0.70 0.80

0.50 0.60 0.70 0.80

0.50 0.60 0.70 0.80

NDVI Fig. 3. Grid-average biomass versus NDVI for SE0 (May 2006 sampling) to SE9 (December 2006 sampling) for Farms 1–10 (F1 to F10).

prediction of biomass (up to and above 3500 kg DM ha−1 ) and the impact of the loss of sensitivity of NDVI or ‘saturation’ towards the high end of the NDVI scale, i.e. above 0.80. The NDVI ranges were tested to identify significant shifts. Fig. 3 and the statistics in Table 3 indicate there are two distinct trends between biomass and NDVI shown for the range of NDVI ≤ 0.74 and for the range of NDVI > 0.74. The NDVI range from 0.74 to 0.75 (inclusive) shows a significant increase in variance of biomass in comparison to lower ranges in the table. 3.2. Modelling of Waikato pasture biomass To estimate biomass of Waikato pastures regularly, we need a robust model which provides the accuracy demanded by the industry. Such a model should be simple enough to be implemented in practice and effective enough to be operationally deployed. By maintaining a minimum number of input parameters efficiency of the model performance could be improved, if biomass outputs are to be delivered regularly on near-real time basis. Past remote sensing studies (Edirisinghe et al., 2011, 2000; Mutanga and Skidmore, 2004a,b) show that characteristics of the NDVI versus pasture biomass relationship such as temporal and spatial dependencies, non-linearity, regional or zonal effects across geographic span within the target area and their interactions likely to have greater impact on the final form of the Waikato model. In the analysis of Western Australian (WA) annual pastures Edirisinghe et al. (2011) have identified a significant linear relationship between NDVI and biomass. The slope of this relationship has found to be systematically changing over the time with the progression of the season as pasture matures. The development of the Waikato model could benefit from the WA biomass modelling experience if we can find ways to account for changes in NDVI response caused by major differences in penology of pastures (annual versus perennial), grazing management strategies (extensive versus intensive) and pasture composition between WA and Waikato. This NDVI response also needs to be investigated for its dependent on inter

and intra seasonal conditions and different geographic locations of farms within the scene. A statistical investigation is required to ascertain the exact pattern (linear or non-linear) of the relationship between NDVI and pasture biomass to model Waikato pasture biomass effectively.

3.2.1. Assessment of spatial and temporal dependency of observed biomass on NDVI The statement log(biomass) ∼ (NDVI + Time + Zone + NDVI.Time + NDVI.Zone) statistically models the general response characteristics between the spatially dependent variables biomass and NDVI for both space and time changes. The space and time are defined as the sampling Zone within each satellite scene and the period within each active season (Time), respectively. The term ‘Time’ allows the data to be grouped across time by integrating both the Sample Time (Sampling event) and the month of data collection (Table 2). The concept of space within the scene is defined by a set of polygons representing farm boundaries delineating spatially separated areas spread across the Waikato region (see Fig. 1). The term ‘Zone’ groups the neighbouring farms geographically within the region as per Table 1. This definition allows any significant response change between NDVI and biomass caused by changing geographic location of zones (group of farms) within the target area to be investigated. The interaction terms ‘NDVI.Time’ and ‘NDVI.Zone’ in the above statistical statement explore the interaction between NDVI and specific sampling event or to target farms in a zone in relation to pasture biomass sampled on the grids. Having discussed this general scheme for analysis of NDVI response to biomass, in the next section, we are going to explore applicability of different terms of the above statement and their interactions to identify consistent patterns, deviations and significance of behaviour of slopes and offsets of potential curves to be fitted. This would be achieved through step by step analysis of variance and probabilities in our experimental data, to statistically identify the optimum curve for explaining this relationship.

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sampling time. It is possible that these changes are due to not only zone effects but may be caused by other factors, such as changes in measurement bias, RPM calibration to biomass, paddock selections for sampling grid placement or climate conditions between sampling events. In the next stage of the modelling process we would like to fully investigate possible refinements and simplifications in both temporal (Sampling) and spatial (Zone) terms that will make the final model more practical and efficient for operational estimation of pasture biomass in Waikato.

Fig. 4. Fitted regression lines and scatter plot of log(biomass) versus NDVI (≤0.74) for SE1 to SE9 (sampling events).

3.2.2. Development of relationships between NDVI and biomass A number of statistically fitted curves were evaluated through the general statement presented in the previous section to establish more specific relationships between the NDVI and biomass variables. A model with effects of NDVI, sampling time (Sampling), zone (Zone) and all possible interactions was fitted to the data with NDVI ≤ 0.74 and non-significant terms omitted. The increasing biomass variation with increasing level of biomass, log(biomass) was examined as the response variable (Fig. 4). NDVI data with values above 0.74 were excluded as this would have exerted an excessive influence on the curve to reduce the goodness of fit for NDVI ≤ 0.74 and consequently had an adverse effect on the predictive ability of the fitted curve. The data used were sufficient to produce a good explanatory linear fit. The resultant variance ratios of an accumulated analysis of variance indicated very large effects (p < 0.001) of NDVI and sampling time. The effects of other two variables Zone and Sampling.Zone interaction also found to be relatively significant (p < 0.01) at F value. The percent variance explained by the fitted curve is 86.1. Out of which NDVI and Sampling accounted for 18.3 and 63.4%, respectively combining to 81.7%. Zone and Sampling.Zone accounted for additional 1.1 and 3.3%, respectively. Total variance accounted for by all four variables was 86.1%. The effect of time of sampling demonstrates a systematic increase of the intercept with time from June (SE1) to December (SE9) as illustrated in Fig. 4. The figure shows a distribution of regression lines with an identically fixed slope that could be combined and grouped with similar offset values without significantly changing their distribution. While the zone effect was significant (p < 0.01), including it in the model only explained the variance by a further 1.1% to that explained by NDVI and sampling time. Therefore, it could be argued that the best model to use for prediction is one excluding zone, which would allow a single model to be applied to all zones or biomass to be predicted for any farm without referring to the Zone term. This has obvious practical benefits. The sampling time and zone interaction term (Sampling.Zone) explained a further 3.3% of the variation, but involved 22 extra parameters. Additionally, the significance of the zone by sampling time interaction indicates that the zone effect changes with

3.2.3. Simplification of the relationship To simplify the NDVI versus biomass relationship and further explore significant differences in zone effects over time, sampling dates were grouped into four different time intervals (“Period”). These groups of dates for different periods are defined as ‘JunJul’ – SE1, SE2 and SE3, ‘Aug’ – SE4 and SE5, ‘SepOct’ – SE6 and Se7, and ‘NovDec’ – SE8 and SE9. “Period” in this analysis is a factor for further statistical analysis. A generalised linear model with the main effects of NDVI, Period, Zone and all possible interactions was fitted and non-significant terms omitted. The accumulated Analysis of Variance for the resulting model was calculated. Examining each of these periods separately for zone effects though the accumulated analysis of variance data reveals different orders of significance for the different periods. The ‘JunJul’ Period had 3 levels of order of significance in zone effects. In that order, Zones 1 and 5 placed above Zone 2, and Zones 3 and 4 were placed below Zone 2. In the ‘Aug’ and ‘SepOct’ Periods there were no significant differences amongst the zone effects were found. In the ‘NovDec’ Period the effects of Zones 1, 4 and 5 were placed above the significance of effect of Zones 1 and 2. The zone effects in ‘JunJul’ Period were found to be quite different to those in the ‘NovDec’ Period. The variance ratios and the percentage variance calculated explained the various terms in the fitted model again indicated that the contribution of zone was relatively small (0.5%) irrespective of what caused it. This evidence suggests that the zone effects were insignificant and could be omitted from the fitted model without significantly losing the accuracy. The fitness of a simple model with terms of NDVI and ‘Period’ was then investigated by excluding ‘Zone’. The new interaction between NDVI and Period was found to be no longer significant (p = 0.259). The common form of the fitted curve in this occasion for different periods is explained by Eq. (2): log(biomass) = A × NDVI + B

(2)

where A and B are slope and offset coefficients of the fitted equation, respectively. The regression of the curve fit was evaluated for the maintenance of the same slope for different periods, while the change of offset alone was set to explain the variation of model fit for those periods. This was achieved by allowing only insignificant change to the standard error (stde) of fit from the stde with the best fit scenario, in which different slopes and offsets were fitted for different periods. When the slope was statistically fixed at 0.01122, different offset values, 6.64, 6.72, 6.83 and 7.13 were estimated for the ‘JunJul’, ‘Aug’, ‘SepOct’ and ‘NovDec’ Periods, respectively. Additionally, the offset 6.80 was estimated for Period of ‘May’ using the same fixed slope. The fixed slope and systematically increasing offset from ‘JunJul’ to ‘NovDec’ periods explained the accumulation in residual biomass observed in the field as a result of repetitive grazing in the time of lactation cycle. Fitted curves for data covering the duration of the lactation cycle (SE1 to SE9) extrapolated up to maximum NDVI are shown in Fig. 5. The curves appear to fit well for NDVI ≤ 0.74, while highlighting the total lack of fit for the rest of NDVI data except for the ‘NovDec’ (SE9) period. The challenge is to overcome this lack of fitness. The next stage of the modelling

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0.50 0.60 0.70 0.80

SE6

0.50 0.60 0.70 0.80

SE7

SE8

SE9

4000

Biomass Kg DM ha-1

3000 2000

SE1

SE2

SE3

SE4

1000

SE5

4000 3000 2000 1000 0.50 0.60 0.70 0.80

0.50 0.60 0.70 0.80

0.50 0.60 0.70 0.80

NDVI Fig. 5. Fitted relationships for estimated biomass versus NDVI data (≤0.74) extrapolated to the full range of NDVI plotted on the back ground of the corresponding grid average biomass data () for SE1 (June) through to SE9 (December) 2006.

process will extend the curve fitting process to explain the relationship between NDVI and biomass for NDVI values beyond 0.74. 3.2.4. Extending the relationship for NDVI > 0.74 A significant increase in variance was observed for NDVI above 0.74 for the important lactation cycle of the season represented in our data samples from SE1 (June 2006) to SE7 (October 2006) (see Fig. 5). This increase is needed to be quantified for the periods ‘JunJul’, ‘Aug’ and ‘SepOct’ for NDVI > 0.74. However, a significant increase in variance was not seen for SE8 (November 2006) and SE9 (December 2006) in Fig. 5. The NDVI values between 0.60 and 0.80 were grouped again into ranges as defined in Table 3 for samplings SE1 to SE7 and they were examined individually this time for different periods. A simple linear regression was fitted to the data for NDVI > 0.74 for each period ‘JunJul’, ‘Aug’ and ‘SepOct’. Variance levels for each range and different periods were examined for all the NDVI ranges. A significant shift of the amount of biomass variance, similar to the trend shown in Table 3, was observed for those 3 periods when moving from the range of NDVI ≤ 0.74 to the higher ranges (NDVI > 0.74). The result for individual periods highlighted a significant point of inflexion at NDVI = 0.74. To simplify the application of Eq. (2) for a given season in Waikato, with first three defined “Periods” of ‘JunJul’, ‘Aug’ and ‘SepOct’, it is possible to extend Eq. (2) for NDVI > 0.74 in a statistically continuous way as per Eq. (3) and as shown in Fig. 6. Fifth period ‘May’ was also included in this figure to show the behaviour of the NDVI versus biomass relationship for comparison with the ‘JunJul’ lactation period. log(biomass) = A × NDVI + B + 0.04692 × [NDVI − 0.74]

(3)

where coefficient A = 0.01122 is the slope and the value of offset B is either 6.64, 6.72, 6.83, 7.13 or 6.80 depending on the “Period” as defined for Eq. (2), the square brackets are defined as: [x] = x if x > 0 and 0 if x < 0. In the case of the ‘NovDec’ Period Eq. (2) remains effective for NDVI values >0.74 and Eq. (3) type extension was not required. The two curves fitted through Eq. (3) for NDVI values in the range up

to 0.74 and above 0.74 together form a new pixel scale model for predicting pasture biomass of the target farms in Waikato.

3.3. Assessment of uncertainty of the model predictions The data from 2006 was used to conduct an accuracy assessment of the model developed in the study. In order to assess the relevance of the model, the curves generated from the model equations were plotted in Fig. 6 on the background 2006 grid average data points. The increasing intercept from June to December shown in Fig. 6 by fitted regression lines is the main characteristic of the spatio-temporal model developed for the lactation period. The higher intercept of May than that of June indicates that month of May is outside the key lactation cycle of that year. Even though, the model was characterised by looking at the data distribution relevant to sampling SE1 to SE9, it is clear that the relationship between NDVI and biomass for data from SE0 also follows a similar trend. This knowledge is useful for potential adjustment of the predictive model if pasture biomass in Waikato is required to be assessed during autumn before the start of the critical lactation cycle. The higher ‘May’ Period intercept (6.8) relative to intercept of ‘JunJul’ Period (6.64) was caused by accumulated residue from the previous season. When NDVI ≤ 0.74 and the data sets from within the lactation period were used for the error analysis, the standard error of predictions at the satellite pixel level is found to be 0.11 in the log(biomass) scale. This error translates to a 12% standard error of prediction using the equations on the original scale for SE1 to SE9, or 95% confidence limit within about 25%. This produces a Residual Standard Error (RSE) of 240 kg DM ha−1 in the range between 1000 (min. assumed value for example SE3) and 3000 (max. assumed value for SE9) kg DM ha−1 pasture biomass. This standard error applies to pixels scale predictions derived on the basis of grid based averages with an NDVI ≤ 0.74. However, the standard error of prediction for satellite-derived paddock average biomass will be significantly lower than that for individual pixels above. This is due to the fact that calculation of average using all the pixels in the paddock lowers the error. In the case of NDVI > 0.74, the

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0.50 0.60 0.70 0.80

SE5

13

0.50 0.60 0.70 0.80

SE6

SE7

SE8

SE9 5000 4000

Biomass Kg DM ha-1

3000 2000

SE0

SE1

SE2

SE3

1000

SE4

5000 4000 3000 2000 1000 0.50 0.60 0.70 0.80

0.50 0.60 0.70 0.80

0.50 0.60 0.70 0.80

NDVI Fig. 6. Fitted curves to the estimated biomass versus NDVI data (for both ≤0.74 and >0.74) plotted on the background of corresponding grid average biomass data () for SE0 (May) through to SE9 (December) 2006.

standard error of pixel scale predictions is 0.17 in the log(biomass) scale, which translates to an 18% standard error of prediction using the equations on the original scale for SE1 to SE9 which cover the lactation period, or a 95% confidence limit within about 36%. This produces a Residual Standard Error (RSE) of 495 kg DM ha−1 in the range between 1500 (min. assumed for SE1) and 4000 kg DM ha−1 pasture biomass (max. assumed for SE6). 3.4. Assessment of paddock scale model predictions The new model was used to estimate paddock average pasture biomass for a select set of 12 and 35 paddocks on Farms 4 and 9 (see Table 4), respectively. The SPOT-4 satellite imagery (20 m pixels) corresponding to three sampling events SE4, SE7 and SE9 was used for the analysis (Table 2). The pixel by pixel calculated pasture biomass data (Fig. 7) was aggregated to estimate paddock mean pasture biomass for the selected paddocks for the 3 images. The RPM based paddock average data collected from the target paddocks (Table 2) were used for the assessment of satellite predictions. The remotely sensed paddock average pasture biomass of Farms 4 and 9 were compared with the corresponding RPM based paddock average pasture biomass data. The comparison statistics between the predicted and observed biomass is presented in Table 4. The values of coefficient of determination (r2 ) varied between 0.44

and 0.75, and the values of Residual Standard Error (RSE) varied between 91 and 415 kg DM ha−1 for the individual farms and three sampling events. The regression relationships between the remotely sensed and field observed biomass for Farm 4 for the three combined samplings events produced an r2 value of 0.71 and an RSE of 232.5 kg DM ha−1 (Fig. 8). For Farm 9 the r2 and RSE were 0.71 and 309.3 kg DM ha−1 , respectively (Fig. 8). When combined data from the three sampling events and all target paddocks of two farms were used the r2 and RSE were 0.71 and 260.1 kg DM ha−1 , respectively (Fig. 9). In summary, SPOT-4 imagery based paddock-average pasture biomass RSE values lie between 91 and 415 kg DM ha−1 for the individual farms analysed. The RSE for the combined data was 260.1 kg DM ha−1 . When the RSE is expressed as a percentage of the mid-point in the biomass range from 1513 to 3560 kg DM ha−1 , the error of prediction on average is approximately 10% (ranges 4–16%). The range of the RSE for the error analyses is lower than those reported for the RPM based paddock mean biomass data (L’Huillier and Thompson, 1988; Thomson et al., 1997). 4. Discussion The standard error for the prediction of paddock average pasture biomass (SEpad ) would be markedly lower than it is for a pixel within a grid. That is due to the fact that the prediction for a paddock

Table 4 Predicted versus observed comparison statistics for the test farms. Farm

Image date

Sampling no./date

No. paddocks

r2

RSE (kg DM ha−1 )

Biomass range (kg DM ha−1 )

Farm 4

09-08-06 05-10-06 11-12-06 09-08-06 05-10-06 11-12-06 –

SE4/09-08-06 SE7/05-10-06 SE9/11-12-06 SE4/09-08-06 SE7/05-10-06 SE9/11-12-06 –

35 35 34 12 13 13 142

0.44 0.61 0.72 0.53 0.75 0.61 0.71

200.7 250.0 91.4 228.1 415.3 193.0 260.1

1555–2952 1765–3443 2327–3457 1684–2774 1513–3457 2498–3560 1513–3560

Farm 9

Combined

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Fig. 7. Predicted biomass maps for the pair of Farms 4 and 9 for 3 days in 2006–2009 August (top left), 05 October (top right) and 11 December (bottom left).

Fig. 8. Relationship between the satellite – predicted and field-observed biomass for Farms 4 (left) and 9 (right) in 2006 – SE4, SE7 and SE9 (sampling events).

is calculated by using the total number of satellite pixels located in it. The Residual Standard Error of prediction for paddock average biomass (RSEpad ) is statistically expressed as

Observed paddock mean biomass (kg DM ha -1)

RSEpad = 3600 y = 0.9656x + 240.36 R2 = 0.71 RSE=260.1 kg DM ha-1

3000

2400

1800

1200 1200

1800

2400

3000

3600 -1

Estimated paddock mean biomass (kg DM ha ) Fig. 9. Relationship between the satellite-predicted and field-observed biomass for combined Farms 4 and 9 data in 2006 for SE4, SE7 and SE9.

RSEpixel √ N

(4)

where RSEpixel is the standard error of prediction of new observations at the pixel level and N is the number of spatially un-correlated (i.e. independent) pixels in the paddock. It is clear from Eq. (4) that as the number of pixels (N) for a given paddock increases the RSEpad decreases. Therefore, the selection of sensor and the resulting pixel size will affect the RSEpad . The use of SPOT-5 (10 m pixels) imagery will provide an increase in the accuracy of prediction compared with using SPOT-4 imagery (20 m pixels). It is likely that a typical dairy paddock generally would have a mosaic of pixels above and below NDVI of 0.74, hence the calculation of the paddock mean would require the execution of both parts of the model. In studies involving multi-temporal analysis of NDVI the variation of atmospheric radiative transfer effects (Holben, 1986; Vermote and Vermeulen, 1999) are usually assessed and corrected if shown to have significant influence. A sensitivity analysis conducted by Gray and Fearns (2008) assessed the effect of atmosphere on the Waikato NDVI of our study derived from SPOT-4 and 5 sensors. This study revealed less than 3% variability in NDVI was caused by the maximum possible aerosol loading in the atmosphere. This could only happen when the visibility level is reduced to an extremely low 20 km, which is

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well below the visibility of 30 km or above expected in the typically clear atmospheric condition under which the imagery was acquired for our study. This level of visibility points to a much lower variation of NDVI than 3% due to atmospheric effects and is well within the natural variation of NDVI data used in the study, irrespective of the prevailing atmosphere. The overall error of paddock average biomass estimation in our study including the perceived atmospheric effects was 260.1 kg DM ha−1 RSE or ±10% uncertainty of the measured value. This level of error in prediction is lower than the RPM based uncertainty (ranging from 350 to 450 kg DM ha−1 ) for standard techniques used by the dairy industry in Waikato (Clark et al., 2006; L’Huillier and Thompson, 1988; Thomson et al., 1997). In other studies in New Zealand (Sanderson et al., 2001) errors up to 440 kg DM ha−1 have been reported for perennial pastures based on mainly ryegrass and white clover. The error demonstrated in our study suggests that no additional atmospheric corrections are warranted. The time and space domain analysis conducted in this study highlighted that in the Waikato region we can confidently apply one form of the predictive model across entire target area (scene) at a given time and different forms of the predictive model is required pertinent to varying times (“Period’s”) in the lactation cycle from June to December. Given the limited data availability for this study we could not investigate intra seasonal dependency of the model as we would have liked. An independent validation study covering a whole Waikato season together with current study would provide an opportunity to further evaluate the model for both inter and intra seasonal consistency. Such a study would also provide a platform to identify strength and weakness of the model for operational applications and an opportunity for greater improvement. 5. Conclusion The sampling protocol developed in this study has facilitated the provision of the high quality field data on the sampling grids to establish a robust and positive (81%) relationship between biomass and NDVI. In the analysis of this relationship a significant influence of time of year was noted and no effect due to zone or change of space within the target area was identified. The results of the analysis also revealed that the offset of the biomass prediction equations increases over time during the lactation period, when a constant slope is maintained, thereby explaining the accumulation of residual pasture biomass following repetitive grazing events within paddocks. The standard error of fit for pixel scale data was 12% (240 kg DM ha−1 ) and 18% (495 kg DM ha−1 ) for the first and second parts of the predictive model, respectively. The model estimated paddock average biomass explains up to 71% of the variation of the field observations with a Residual Standard Error (RSE) of 260.1 kg DM ha−1 or 10% on average for the range between 1500 and 3500 kg DM ha−1 . These results demonstrate the capability of the developed model to predict paddock mean pasture biomass across a season using satellite imagery with an accuracy level comparable to current field based techniques used by the New Zealand dairy industry. Results revealed that NDVI is a reasonable tool to estimate Waikato pasture biomass. The model presented here is a significant step towards the development of an operational remote sensing capability for delivering regular pasture biomass updates to the Waikato dairy industry for achievement of sustainable farming practices. Acknowledgements We appreciate the coordination effort of Sue Petch of DairyNZ and the assistance of DairyNZ field technical services team. We

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thank CSIRO’s Gonzalo Mata, Graham Donald, Steve Gherardi and Elizabeth Hulm in their support of this research. We appreciate the statistical advice and support given by Andrew van Burgle. We acknowledge the funders Fonterra, DairyNZ and Foundation for Research, Science and Technology (FRST). References Cayley, J.W.D., Bird, P.R., 1996. Techniques for Measuring Pastures. Pastoral and Veterinary Institute, Hamilton, Victoria, Australia. Clark, D.A., Litherland, A., Mata, G., Burling-Claridge, R., 2006. Pasture monitoring from space. Proceedings of the South Island Dairy Event Conference 7, 108–112. Edirisinghe, A., Hill, M.J., Donald, G.E., Hyder, M., 2011. Quantitative mapping of biomass using satellite imagery. International Journal of Remote Sensing 32, 2699–2724. Edirisinghe, A., Hill, M.J., Donald, G.E., Wheaton, G.A., Hyder, M., Smith, R.C.G., 2000. Estimating feed-on-offer and pasture growth rate using remote sensing. 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