Satellite-based assessment of grassland yields

Grant, K.; Siegmund, Robert; Wagner, M.; Hartmann, Stephan

doi:10.5194/isprsarchives-xl-7-w3-15-2015

Cited by 11 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SAR-based backscattering information showed differences in the amplitude during mowing events [172], but there was no significant relationship found between the backscatter and biophysical properties of grasslands [158]. The coherence of an X-band sensor was shown to correlate both with wet above-ground biomass and vegetation height of grasslands.…”

Section: Mowing Event Detectionmentioning

confidence: 91%

“…A classification of optical sensor-based vegetation indices resulted in an overall accuracy of 85% for only extensively used grasslands [130]. Rule-based change detection approaches applied on SAR backscatter data lead to successful detections of cutting events, for example, in Grant et al [172], 72% of the events were detected. Even though datasets of the entire growing season were included in the classifications, the temporal information was usually not considered within these methods.…”

Section: Mowing Event Detectionmentioning

confidence: 99%

“…In contrast to that, discrete time series can be obtained using parameters derived from SAR data. As described in Section 3.3.3, several studies already investigated the performance of radar-based parameters, mainly backscatter amplitude, interferometric coherence and polarimetry-based decomposition parameters mostly for mowing event detection [155,156,158,172]. Especially the temporal coherence seems to be able to detect mowing events successfully [154,158].…”

Section: Assessment Of the Used Remote Sensing Sensors And Indices Ofmentioning

confidence: 99%

See 2 more Smart Citations

Remote Sensing of Grassland Production and Management—A Review

2020

View full text Add to dashboard Cite

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.

show abstract

Section: Mowing Event Detectionmentioning

confidence: 91%

Section: Mowing Event Detectionmentioning

confidence: 99%

Section: Assessment Of the Used Remote Sensing Sensors And Indices Ofmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing of Grassland Production and Management—A Review

2020

View full text Add to dashboard Cite

show abstract

“…For example, recent experience has evidenced the possibility of using coherence between two S1 SAR scenes for detecting mowing events in Estonia [12], as well as for grassland cutting dates in Germany [13]. High coherence values due to backscattering from the ground were linked to ploughed bare fields and low vegetation height [14,15].…”

Section: High-resolution Remote Sensing Grasslands Monitoringmentioning

confidence: 99%

Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations

2018

View full text Add to dashboard Cite

The introduction of high-resolution Sentinels combined with the use of high-quality digital agricultural parcel registration systems is driving the move towards at-parcel agricultural monitoring. The European Union’s Common Agricultural Policy (CAP) has introduced the concept of CAP monitoring to help simplify the management and control of farmers’ parcel declarations for area support measures. This study proposes a proof of concept of this monitoring approach introducing and applying the concept of ‘markers’. Using Sentinel-1- and -2-derived (S1 and S2) markers, we evaluate parcels declared as grassland in the Gelderse Vallei in the Netherlands covering more than 15,000 parcels. The satellite markers—respectively based on crop-type deep learning classification using S1 backscattering and coherence data and on detecting bare soil with S2 during the growing season—aim to identify grassland-declared parcels for which (1) the marker suggests another crop type or (2) which appear to have been ploughed during the year. Subsequently, a field-survey was carried out in October 2017 to target the parcels identified and to build a relevant ground-truth sample of the area. For the latter purpose, we used a high-definition camera mounted on the roof of a car to continuously sample geo-tagged digital imagery, as well as an app-based approach to identify the targeted fields. Depending on which satellite-based marker or combination of markers is used, the number of parcels identified ranged from 2.57% (marked by both the S1 and S2 markers) to 17.12% of the total of 11,773 parcels declared as grassland. After confirming with the ground-truth, parcels flagged by the combined S1 and S2 marker were robustly detected as non-grassland parcels (F-score = 0.9). In addition, the study demonstrated that street-level imagery collection could improve collection efficiency by a factor seven compared to field visits (1411 parcels/day vs. 217 parcels/day) while keeping an overall accuracy of about 90% compared to the ground-truth. This proposed way of collecting in situ data is suitable for the training and validating of high resolution remote sensing approaches for agricultural monitoring. Timely country-wide wall-to-wall parcel-level monitoring and targeted in-season parcel surveying will increase the efficiency and effectiveness of monitoring and implementing agricultural policies.

show abstract

“…Its radar images are now available routinely every 12 days and are systematically used for land monitoring. Sentinel-1A data have been used in studies to determine waterlines, to estimate ocean wave heights and to assess grassland yields [ 21 , 22 , 23 ]. SAR data from Sentinel-1A before and during a flood can be used to extract the flood-inundated areas fast and effectively; however, these data have rarely been used in previous Oncomelania hupensis research.…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential High-Risk Habitats within the Transmission Reach of Oncomelania hupensis after Floods Based on SAR Techniques in a Plane Region in China

Shi

Qiu

et al. 2017

IJERPH

View full text Add to dashboard Cite

Schistosomiasis japonica is an infectious disease caused by Schistosoma japonicum, and it remains endemic in China. Flooding is the main hazard factor, as it causes the spread of Oncomelania hupensis, the only intermediate host of Schistosoma japonicum, thereby triggering schistosomiasis outbreaks. Based on multi-source real-time remote sensing data, we used remote sensing (RS) technology, especially synthetic aperture radar (SAR), and geographic information system (GIS) techniques to carry out warning research on potential snail habitats within the snail dispersal range following flooding. Our research result demonstrated: (1) SAR data from Sentinel-1A before and during a flood were used to identify submerged areas rapidly and effectively; (2) the likelihood of snail survival was positively correlated with the clay proportion, core area standard deviation, and ditch length but negatively correlated with the wetness index, NDVI (normalized difference vegetation index), elevation, woodland area, and construction land area; (3) the snail habitats were most abundant near rivers and ditches in paddy fields; (4) the rivers and paddy irrigation ditches in the submerged areas must be the focused of mitigation efforts following future floods.

show abstract

Satellite-based assessment of grassland yields

Cited by 11 publications

References 4 publications

Remote Sensing of Grassland Production and Management—A Review

Remote Sensing of Grassland Production and Management—A Review

Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations

Identification of Potential High-Risk Habitats within the Transmission Reach of Oncomelania hupensis after Floods Based on SAR Techniques in a Plane Region in China

Contact Info

Product

Resources

About