Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data

Crabbe, Richard Azu; Lamb, David; Edwards, Clare

doi:10.3390/rs11030253

Cited by 11 publications

(7 citation statements)

References 57 publications

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“…Many previous studies have focused on annual crops, grasses and forests [21][22][23][24], while relatively few have investigated discriminating between perennial fruit tree crops [16,25]. This task is challenging because many of these crops have similar temporal reflectance profiles [17] and perennial tree species classification accuracies are often lower than annual crops [26].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Brinkhoff

Vardanega²,

Robson

2019

Remote Sensing

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Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Brinkhoff

Vardanega²,

Robson

2019

Remote Sensing

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“…Recently, Shoko et al (2020) stated that the grass classification ability varies over time due to the vegetation's phenological, physiological, and morphological seasonal changes. Additionally, Crabbe et al (2019) explored the potential of Sentinel-1 (SAR) to classify C3 and C4 grasses in the grazed landscape using the random forest algorithm and found overall accuracy of 86% and kappa coefficients of 0.77. Ara et al (2021) used satellite images from Sentinel-2 and supervised classification to monitor differences in pasture botanical composition among paddocks and seasonal changes to support grazing management decisions.…”

Section: Botanical Composition Classificationmentioning

confidence: 99%

“…Additionally, Crabbe et al. (2019) explored the potential of Sentinel‐1 (SAR) to classify C3 and C4 grasses in the grazed landscape using the random forest algorithm and found overall accuracy of 86% and kappa coefficients of 0.77. Ara et al.…”

Section: Applications In Grazingland Managementmentioning

confidence: 99%

Precision livestock farming applied to grazingland monitoring and management—A review

et al. 2023

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To meet the expected demand for food while protecting animal welfare, environmental sustainability, and profitability, animal production efficiency must improve. Improvements in grazinglands management techniques can impact livestock production efficiency. The current stage of artificial intelligence development, mainly machine learning techniques, remote sensing (RS), and precision agriculture technologies, automatizes data collection and raises the monitoring capacity to support on‐farm decision‐making. This literature review presents current developments in precision livestock farming (PLF) applied to grazinglands monitoring and management, demonstrates some knowledge gaps, and discusses potential solutions of grazinglands management issues. Although the implementation of precision technologies in grazing systems is advancing rapidly, challenges, such as lack of reliable reference data and low variability of datasets used to calibrate models, are examples of constraints to be addressed in future studies. More effort in terms of relationship strengthening between farmers and researchers, benefits elucidation, cooperation among professionals with different expertise, and software or app development must be directed to make the knowledge accessible and largely implemented in field conditions.

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“…A greater number of studies used satellites than UAS sensors (Figure 6), and fewer studies used SAR (Synthetic Aperture Radar). The main objective for combining sensors [45,118,132] is to address cloud contamination, especially in places where cloud poses significant challenges (i.e., tropical rainforest, mountain regions, polar and monsoon areas) [32,74,99,[133][134][135], with multi-temporal sensors approach [99,136,137] or by using SAR imagery [7,132,138,139]. Other objectives include comparing model performances between sensors [7,34,45,132,140,141] and when greater detail is needed for field measurements and species discrimination [114,142,143].…”

Section: Description Of Remote Sensing Technologies Usedmentioning

confidence: 99%

“…est, mountain regions, polar and monsoon areas) [32,74,99,[133][134][135], with multi-temporal sensors approach [99,136,137] or by using SAR imagery [7,132,138,139]. Other objectives include comparing model performances between sensors [7,34,45,132,140,141] and when greater detail is needed for field measurements and species discrimination [114,142,143].…”

Section: Description Of Remote Sensing Technologies Usedmentioning

confidence: 99%

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

Ogungbuyi,

Mohammed,

Ara

et al. 2023

Remote Sensing

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The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.

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Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data

Cited by 11 publications

References 57 publications

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Precision livestock farming applied to grazingland monitoring and management—A review

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

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