Preventing rangeland degradation: a shared problem for Australia and China

Hodgkinson, Ken C.; Wang, Deli

doi:10.1071/rj20068

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…in providing end users (i.e., government, range managers, commercial farm enterprise, herders) with customer service that meets specified objectives. Such objectives include (a) providing information that supports the stocking rate and carrying capacity and (b) providing a monitoring system that can reduce degradation and conserve extensive grasslands [27]. End users (e.g., government) view this approach as a knowledge-based conservation strategy.…”

Section: Analysing End Users' Perception and Adoption Of The Remote S...mentioning

confidence: 99%

“…The mechanism by which plant functional types (e.g., C3, C4) adapt to environmental stresses or ecological disturbances has been classified into two main features they possess, i.e., structural and functional characteristics [9]. Pasture composition, functioning, and structure can be vulnerable to the harmful effects of climate change and anthropogenic activities (i.e., overgrazing), and when a critical threshold [27][28][29] is reached, may hit degradation tipping points.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Analysing End Users' Perception and Adoption Of The Remote S...mentioning

confidence: 99%

Section: Introductionmentioning

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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“…Currently, there are approximately 20 million yak and 50 million Tibetan sheep grazing in the alpine grassland, making it one of the regions with high grazing pressure worldwide ( Wang et al., 2020 ). The interactions of soil, plant, animal, and grazing management affect the production and stability of grassland ecosystems, and also provide the driving force for the functions and services of the alpine grassland on the TP ( Hodgkinson and Wang, 2020 ; Sun et al, 2022 ). For example, grazing exclusion has been widely adopted by the local and central government, predominantly through the use of fences, to restore the degraded alpine grasslands since 2003 ( Sun et al., 2021 ).…”

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confidence: 99%

Editorial: Patterns, functions, and processes of alpine grassland ecosystems under global change

et al. 2022

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Editorial on the Research Topic:Patterns, functions, and processes of alpine grassland ecosystems under global change Alpine grassland on the Tibetan Plateau (TP) accounts for about 62% of the total area of the TP (Wang et al., 2022). These grassland ecosystems provide important ecological services and functions in pastoral production, biodiversity conservation, carbon storage, water resource regulation, cultural inheritance, tourism, and recreation etc. (Miehe et al., 2019; Sun et al., 2020; Wang et al., 2022). In the past serval decades, the alpine grassland on the TP has experienced rapid climate change and intensified human activities (He et al., 2021), the regulatory mechanisms and drivers are remained controversial.Climate changes have been evident across the TP since the beginning of the 1970s (Yang et al., 2014). For instance, the mean annual air temperature has increased by about 0.4°C, which is twice that of the global average, however, a decrease in precipitation has been found in many other regions (Mountain Research Initiative EDW Working Group, 2015). Also, the degradation of permafrost has been accelerated as manifested by ground surface subsidence and thaw settlement of permafrost soils (Mu et al., 2020), and the total number of lakes increased from 868 in 1990 to 1207 in 2015, leading to the increase of total water surface area of the lakes at a rate of 383.5 km2 yr-1 (Sun et al., 2018). With the steady increase of human population and domestic livestock number (Wei et al., 2020), overgrazing has become a serious challenge with overstocking rates of 27-89% in the Frontiers in Plant Science frontiersin.org 01

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Preventing rangeland degradation: a shared problem for Australia and China

Cited by 2 publications

References 48 publications

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

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

Editorial: Patterns, functions, and processes of alpine grassland ecosystems under global change

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