Spatial monitoring of grassland management using multi-temporal satellite imagery

Stumpf, Felix; Schneider, Manuel K.; Keller, Armin; Mayr, Andreas; Rentschler, Tobias; Meuli, R. G.; Schaepman, Michael E.; Liebisch, Frank

doi:10.1016/j.ecolind.2020.106201

Cited by 53 publications

(37 citation statements)

References 93 publications

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“…About one-third of the earth's terrestrial surface and 70% of the global agricultural area is covered by grassland ecosystems [1], making them one of the most common land use types worldwide [2,3]. Apart from providing forage for livestock production [2,4], grasslands fulfil several functions and ecosystem services related to soil quality (carbon sequestration and controlling erosion and wildfires), hydrological balance, and climate change [4,5], which make them essential [1]. In addition to preserving biodiversity and supporting ecological processes (such as carbon storage, water purification, or erosion control) grasslands are vital for global food security [2], since they provide an important setting for agricultural activities such as livestock production [3].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

et al. 2021

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The evolution of dryland pasture quality is closely related to the seasonal and inter-annual variability characteristic of the Mediterranean climate. This variability introduces great unpredictability in the dynamic management of animal grazing. The aim of this study is to evaluate the potential of two complementary tools (satellite images, Sentinel-2 and proximal optical sensor, OptRx) for the calculation of the normalized difference vegetation index (NDVI), to monitor in a timely manner indicators of pasture quality (moisture content, crude protein, and neutral detergent fiber). In two consecutive years (2018/2019 and 2019/2020) these tools were evaluated in six fields representative of dryland pastures in the Alentejo region, in Portugal. The results show a significant correlation between pasture quality degradation index (PQDI) and NDVI measured by remote sensing (R2 = 0.82) and measured by proximal optical sensor (R2 = 0.83). These technological tools can potentially make an important contribution to decision making and to the management of livestock production. The complementarity of these two approaches makes it possible to overcome the limitations of satellite images that result (i) from the interference of clouds (which occurs frequently throughout the pasture vegetative cycle) and (ii) from the interference of tree canopy, an important layer of the Montado ecosystem. This work opens perspectives to explore new solutions in the field of Precision Agriculture technologies based on spectral reflectance to respond to the challenges of economic and environmental sustainability of extensive livestock production systems.

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Section: Introductionmentioning

confidence: 99%

“…According to Lugassi et al [8], the quality of the plants consumed by livestock in pastures is an important factor for their productivity. The timely knowledge of pasture quality is, therefore, vital for correct evaluation of the animal feed demand [9] and, consequently, for the establishment of sustainable agriculture [5].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

et al. 2021

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“…Gomez-Gimenez et al [170] integrated mowing frequency, grazing intensity and livestock density estimations to analyze grazing intensity in Canton Zurich, Switzerland. In a recent study, use intensity classes of grasslands were generated based on management events detected with NDVI time series [173].…”

Section: General Use Intensitymentioning

confidence: 99%

Remote Sensing of Grassland Production and Management—A Review

2020

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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.

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“…These methods are designed to analyze the changes of ground objects through different mechanisms. LandTrendr detects abrupt change by the segmentation method and, between the abrupt changes, a slope is fitted for each segment to capture the gradual changes, and this approach has been used for insect infestation detection and forest change detection [44,47,51,52]. The VCT normalizes each Landsat image into a forest probability index and uses a thresholding method to detect forest disturbance.…”

Section: Introductionmentioning

confidence: 99%

Co-Evolution of Emerging Multi-Cities: Rates, Patterns and Driving Policies Revealed by Continuous Change Detection and Classification of Landsat Data

et al. 2020

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The co-evolution of multi-cities has emerged as the primary form of urbanization in China in recent years. However, the processes, patterns, and coordination are not well characterized and understood, which hinders the understanding of the driving forces, consequences, and management of polycentric urban development. We used the Continuous Change Detection and Classification (CCDC) algorithm to integrate all available Landsat 5, 7, and 8 images and map annual land use and land cover (LULC) from 2001 to 2017 in the Chang–Zhu–Tan urban agglomeration (CZTUA), a typical urban agglomeration in China. Results showed that the impervious surface in the study area expanded by 371 km2 with an annual growth rate of 2.25%, primarily at the cost of cropland (169 km2) and forest (206 km2) during the study period. Urban growth has evolved from infilling being the dominant type in the earlier period to mainly edge-expansion and leapfrogging in the core cities, and from no dominant type to mainly leapfrogging in the satellite cities. The unfolding of the “cool center and hot edge” urban growth pattern in CZTUA, characterized by higher expansion rates in the peripheral than in the core cities, may signify a new form of the co-evolution of multi-cities in the process of urbanization. Detailed urban management and planning policies in CZTUA were analyzed. The co-evolution of multi-cities principles need to be studied in more extensive regions, which could help policymakers to promote sustainable and livable development in the future.

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Spatial monitoring of grassland management using multi-temporal satellite imagery

Cited by 53 publications

References 93 publications

Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

Remote Sensing of Grassland Production and Management—A Review

Co-Evolution of Emerging Multi-Cities: Rates, Patterns and Driving Policies Revealed by Continuous Change Detection and Classification of Landsat Data

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