Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series

Hill, Michael J.; Donald, G. E.

doi:10.1016/s0034-4257(02)00128-1

Cited by 151 publications

(70 citation statements)

References 16 publications

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“…This is consistent with findings of Hill and Donald (2003) who noted the value of NDVI time series data to assessments of production responses, either through vegetation or by stocking rate, even when using retrospective analyses, such as those made here with the Cicerone farmlet trial. Similarly, the value of NDVI time series data has been recognised in southern Spain, where studies examined vegetation patterns under different managements as influenced by variations in precipitation and temperature (Durante et al 2009).…”

Section: Discussionsupporting

confidence: 91%

Satellite derived evidence of whole farmlet and paddock responses to management and climate

Donald

Scott

Vickery³

2013

Anim. Prod. Sci.

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Abstract. Satellite imagery was used to assess differences between three treatments in a grazing enterprise systems study of three 53-ha farmlets on the Northern Tablelands of New South Wales, Australia. The study involved a comparison between a typical control farmlet (B) with one with higher levels of sown pasture and soil fertility (A) and one employing intensive rotational grazing (C). Landsat thematic mapper data were used to derive normalised difference vegetation index (NDVI) and spectral class images for eight dates from before the commencement of the farmlet trial (June 2000) to annual spring measurements in September-October of each year from 2000 to 2006 across all paddocks of each farmlet. The Landsat imagery taken before the commencement of the farmlet treatments (June 2000) showed only small differences between the three farmlets, confirming that the allocation of land to the farmlets had been without bias. The assessments using Landsat NDVI in spring over 7 years showed differences in green herbage resulting from the variation in rainfall received over different years as well as differences between the farmlets. The Landsat NDVI images showed increasing and significant differences in pasture greenness over time, especially between Farmlet A and Farmlets B and C. In addition, there were significant differences in pasture spectral classes between Farmlet A and Farmlets B and C, with a significant correlation with higher levels of sown perennial and annual grasses and legumes on Farmlet A. Using different statistical tools, several relationships were found between NDVI and spectral class data and explanatory variables of farmlet, paddock, sowing phase, modelled soil moisture and recent grazing activity. The moderate resolution Landsat data across the entire area of each farmlet proved to be especially useful for assessing pastures within every paddock used in this farmlet study. In addition, moderate resolution imaging spectro radiometer NDVI satellite data were collated for weekly intervals from September 2003 to December 2006 in order to assess seasonal pasture growth patterns on each of the farmlets. These patterns were significantly correlated with a growth index calculated from temperature and available soil moisture, and showed that the growth on the three farmlets was closer to a highly productive reference paddock than a low input, unsown pasture in another reference paddock. The satellite data facilitated the detection of significant differences in pasture botanical composition, soil fertility, grazing management, climate and season. The ready availability of quality remote sensed imagery, combined with the significance of the relationships established, confirms that the technology is a valuable objective tool for both farming systems research and for managing entire farms.

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Section: Discussionsupporting

confidence: 91%

Satellite derived evidence of whole farmlet and paddock responses to management and climate

Donald

Scott

Vickery³

2013

Anim. Prod. Sci.

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“…However, the sensitivity of TINDVI for crop production depends on rainfall seasonality; it can be a good indicator for crop production when water is the limiting factor for crop growth [55]. Considering the profound correlation between TINDVI and yield in cropping fields, the high temporal variability in TINDVI in good zone than in poor zone coincide well with the yield data observation in the study area.…”

Section: Relationship Between Management Zone and Temporal Variabilitsupporting

confidence: 73%

“…Previous research has reported that TINDVI can be a good indicator for crop production [20] [44] [55]. However, the sensitivity of TINDVI for crop production depends on rainfall seasonality; it can be a good indicator for crop production when water is the limiting factor for crop growth [55].…”

Section: Relationship Between Management Zone and Temporal Variabilitmentioning

confidence: 99%

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

Araya¹,

Ostendorf²,

Lyle³

et al. 2017

ARS

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Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop growth variability across a cropping field. The study was conducted at a wheat cropping field in Minnipa, South Australia. The field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroradiometer (MODIS) for 15 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015); producing spatial trend and temporal variability maps of phenological metrics; and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the "good" zone compared with the "poor" zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal growth variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.

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“…In this respect, vegetation indices that combine spectral measurements in the visible and near infrared spectral wavebands have been widely used to discriminate vegetation species. The normalized fifference vegetation index (NDVI), defined as the difference between the near infrared and red reflectance divided by the sum of the two, is undoubtedly the most widely used of the many indices available as it responds clearly to change in the amount of green biomass (Tucker, 1979;Hill and Donald, 2003), chlorophyll content (Dawson et al, 2003), fire (Telesca and Lasaponara, 2006) and climate variability (Gong and Shi, 2003). A pioneering global study using satellite-based information has identified vegetation species with the objective of creating a coherent worldwide 8 km land cover map (Defries et al, 1995).…”

Section: S Faroux Et Al: Ecoclimap-ii/europementioning

confidence: 99%

ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

et al. 2013

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Abstract. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types -principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land -as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

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Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series

Cited by 151 publications

References 16 publications

Satellite derived evidence of whole farmlet and paddock responses to management and climate

Satellite derived evidence of whole farmlet and paddock responses to management and climate

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

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