Remote Sensing of Grassland Production and Management—A Review

Abstract

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Accounting for land cover changes and degradation in the Katse and Mohale Dam catchments of the Lesotho highlands

“…Satellite remote sensing data allow for the assessment of long-term changes over large geographic regions, at sufficient spatial resolution to be useful for catchment-scale changes in land use and land cover (Weng 2002;Sohl and Sleeter 2012). Although such methods do require field data in order to train and calibrate the classification of land cover and interpret trends in indicators of vegetative productivity (Bai et al 2008;Munyati et al 2011;Venter et al 2020), they are also reproducible and sustainable in terms of future data requirements, and are therefore increasingly being used to measure land degradation (Reinermann et al 2020;Giuliani et al 2020).…”

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

“… Greenwood et al (2016) reviewed the use and application of various sensors, imaging, and other emerging technologies concerning extensive beef production, and González et al (2018) and Halachmi et al (2019) further discussed the attributes of these technologies for livestock production in general. The range of remote, near real-time monitoring technologies being developed or applied or with potential applications for free-ranging livestock and extensive grazing and foraging environments is increasing rapidly and include 1) in-field fixed and ground-, aerial-, and satellite-based measurement of pastures, invasive weeds, and soil, water, and greenhouse gas monitoring using sensors, photogrammetry ( Bloch et al, 2019 ), or other technologies including LiDAR ( Fernández-Quintanilla et al, 2018 ; Reinermann et al, 2020 ; Segarra et al, 2020 ; Weiss et al, 2020 ); 2) multi-channel, satellite-based spectrometry ( Segarra et al, 2020 ), such as WorldView-2 Satellite Sensor ( https://www.satimagingcorp.com/satellite-sensors/worldview-2/ ), which may be coupled with weather and soil grids to model and predict pasture biomass components and to guide grazing management decisions for sheep and cattle ( http://grazingapp.com.au/ ; Badgery et al, 2017 ); 3) body composition ( McPhee et al, 2017 ; Miller et al, 2019 ; Zhao et al, 2020 ) and physiological assessments ( Beiderman et al, 2014 ), including thermal imaging ( Halachmi et al, 2008 , 2013 ) to assess body temperature ( González et al, 2013 ) using devices at, or fixed to, handling facilities; 4) automated in-field liveweight measurement ( Nir et al, 2018 ) and drafting of livestock coupled with radio frequency identification ( RFID ) to determine individual or herd liveweight and growth of cattle ( Charmley et al, 2006 ; González et al, 2014 , 2018 ) and sheep ( Brown et al, 2015 ; González-García et al, 2018a , 2018b ); 5) virtual fencing using global positioning system ( GPS )-enabled collars and a mobile phone app ( https://www.agersens.com/ ) to remotely fence, move and monitor animals, and control herd or flock access to pastures and environmentally sensitive areas without the need for conventional fencing ( Campbell et al, 2019 , 2020 ); 6) on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments ( Dobos et al, 2...…”

Grassland Mowing Detection Using Sentinel-1 Time Series: Potential and Limitations

“…Although providing encouraging results, studies on grassland mowing detection using SAR time series have mainly been carried out on rather limited study areas, covering 2 to 40 grassland parcels [24,31]. In their recent review on remote sensing of grassland production and management, the authors of [32] raise a critical point in grassland mowing detection: the lack of consistent validation data. In many cases, a robust and independent validation data set is missing or lacks sufficient temporal resolution or spatial coverage.…”