Remote Sensing of Grassland Production and Management—A Review
Abstract
:1. Introduction
1.1. The Role and Importance of Grasslands Worldwide
1.2. The Definition of Grasslands
1.3. The Distribution of Grasslands
1.4. Grassland Management and its Effects
1.5. The Role of Remote Sensing in Grassland Monitoring
1.6. The Objectives and Structure of this Review
2. Materials and Methods of the Review
- The research articles had a clear focus in grasslands.
- The research articles analyzed quantitative traits of grassland production (such as biomass or productivity) and/or investigated grassland management strategies or use intensities.
- The research articles used spaceborne earth observation data.
3. Results of the Review
3.1. Overview of Remote Sensing for Biomass, Productivity and Management
3.1.1. Temporal and Spatial Patterns of the Reviewed Studies
3.1.2. The Used Sensors of the Review Studies
3.2. Methods and Results of Remote Sensing of Grassland Production
3.2.1. Investigating Grassland Production Using a Vegetation Index as Proxy
3.2.2. Mapping Grassland Production Using a Vegetation Index and Ground-Truth Data
3.2.3. Using a Modelling Approach to Estimate Grassland Production
3.2.4. Analyses of the Influencing Factors on Grassland Production
3.3. Detailed Review of Studies on Remote Sensing for Grassland Management and Use Intensity
3.3.1. Management Type, Study Areas and Parameters of Remote Sensing of Grassland Management and Use Intensity
3.3.2. Methods Used in Remote Sensing of Grassland Management and Use Intensity
3.3.3. Results and Validation of Studies on Remote Sensing of Grassland Management and Use Intensity
Management Type Detection
Analysis of Grazing Intensity
Mowing Event Detection
General Use Intensity
4. Discussion
4.1. Global Patterns, Scales and Products of Remote Sensing of Grassland Production and Management
4.2. Assessment of Remote Sensing of Grassland Production
4.2.1. Assessment of the Used Remote Sensing Sensors and Indices for Grassland Production Estimation
4.2.2. Analyses of Grassland Production Based on Empirical Relationships between Ground-Truth Data and Satellite Data
4.2.3. Estimating Grassland Production Using a Modelling Approach
4.2.4. Analyzing the Influencing Factors on Grassland Production and Productivity with Remote Sensing Data
4.3. Assessment of Remote Sensing of Grassland Management
4.3.1. Assessment of the Used Remote Sensing Sensors and Indices of Grassland Management Analysis
4.3.2. Detection of Grazing and Grazing Patterns with Remote Sensing Data
4.3.3. Grassland Mowing Detection with Remote Sensing Data
4.3.4. Remote Sensing-Based Detection and Investigation of Grassland Irrigation and Fertilization
5. Conclusions
- In total, 253 research articles were reviewed, which resulted in a current and comprehensive overview of remote sensing of grassland production traits and management studies.
- Studies on grassland production and management with remote sensing data have increased irregularly, but strongly for the last 20 years.
- The frequency of studies of grassland production and management is globally unequally distributed, where South American (5% of all studies) and African (4% of all studies) grasslands seem to be underrepresented. Therefore, there are still large grassland areas which should be further investigated, especially as many people in these countries probably strongly depend on livestock production on grasslands.
- There is a relatively small amount of studies (30%) on remote sensing of grassland production in Europe, probably due to the large management activities and consequential high variability within grassland production. Research towards detection of management strategies and events and the grassland production on these small and heterogeneously used grassland parcels are needed for successful yield estimations.
- In total, there were only six studies covering the entire globe for their analysis, and apart from LUE-model-based grassland productivity analyses, most studies took place locally. Extending the study area for investigating grassland production and management and including heterogenous grasslands while—at the same time—accounting for the variability among these is an interesting future research focus.
- Time series have always played a central role in grassland production and management analyses, whereby the Landsat and MODIS satellite fleets were in the focus. In the future, the Sentinel fleets and a combination of optical and SAR satellite data will be of high importance.
- Optical satellite data is used in 92% of research, in particular in research articles focusing on grassland production. For both grassland production and management related studies, only a few combined optical and SAR systems (4%).
- Quantitative grassland production estimations, such as biomass products, based on remote sensing data would improve from adding temporal information to the results. Especially in highly managed areas, this would facilitate yield estimations. It could also be better to improve process-based models to retrieve biomass information or to apply more advanced machine learning algorithms for an empirical relationship-based biomass analysis.
- While at the moment, within grassland production analyses, the focus lies on the influence of climate, research would improve from including spatially explicit management information into the analyses.
- Grassland management and use intensity studies based on satellite data are often conducted on a relatively small scale (90% of studies under 10,000 km2) or focus on only one intensity level or homogeneous grassland. Enlarging the study areas and incorporating diverse grasslands to better account for real conditions would be a valuable direction for future studies in this context.
- More automatized and large-scale grassland products are needed and will enable a continuous monitoring of grasslands worldwide. Thus, knowledge of the state, production and management of grasslands and the influence of climate (change) would be generated and allow for adapted management and conservation plans.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Reynolds, S.; Frame, J. Grasslands: Developments, Opportunities, Perspectives; Food & Agriculture Org.: Rome, Italy, 2005. [Google Scholar]
- White, R.; Murray, S.; Rohweder, M. Pilot Analysis of Global Ecosystems—Grassland Ecosystems; World Resources Institute: Washington, DC, USA, 2000. [Google Scholar]
- Gibson, D.J. Grasses and Grassland Ecology; Oxford University Press: Oxford, UK, 2009. [Google Scholar]
- FAO. The State of Food and Agriculture—Livestock in the Balance; Food & Agriculture Org.: Rome, Italy, 2009. [Google Scholar]
- Hatfield, R.; Davies, J. Global Review of the Economics of Pastoralism; Internatonal Union for Conservation of Nature—IUCN: Gland, Switzerland, 2006. [Google Scholar]
- Angelsen, A.; Jagger, P.; Babigumira, R.; Belcher, B.; Hogarth, N.J.; Bauch, S.; Börner, J.; Smith-Hall, C.; Wunder, S. Environmental income and rural livelihoods: A global-comparative analysis. World Dev. 2014, 64, 12–28. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gibon, A. Managing grassland for production, the environment and the landscape. Challenges at the farm and the landscape level. Livest. Prod. Sci. 2005, 96, 11–31. [Google Scholar] [CrossRef]
- Conant, R.T. Challenges and Opportunities for Carbon Sequestration in Grassland Systems—A Technical Report on Grassland Management and Climate Change Mitigation; Food & Agriculture Org.: Rome, Italy, 2010. [Google Scholar]
- Dass, P.; Houlton, B.Z.; Wang, Y.; Warlind, D. Grasslands may be more reliable carbon sinks than forests in California. Environ. Res. Lett. 2018, 13, 074027. [Google Scholar] [CrossRef]
- Baer, S.; Kitchen, D.; Blair, J.; Rice, C. Changes in ecosystem structure and function along a chronosequence of restored grasslands. Ecol. Appl. 2002, 12, 1688–1701. [Google Scholar] [CrossRef]
- Xiaojun, N.; Xiaodan, W.; Suzhen, L.; Shixian, G.; Haijun, L. 137Cs tracing dynamics of soil erosion, organic carbon and nitrogen in sloping farmland converted from original grassland in Tibetan plateau. Appl. Radiat. Isot. 2010, 68, 1650–1655. [Google Scholar] [CrossRef]
- Reichstein, M.; Ciais, P.; Papale, D.; Valentini, R.; Running, S.; Viovy, N.; Cramer, W.; Granier, A.; Ogee, J.; Allard, V.; et al. Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: A joint flux tower, remote sensing and modelling analysis. Glob. Chang. Biol. 2007, 13, 634–651. [Google Scholar] [CrossRef]
- Hilpold, A.; Seeber, J.; Fontana, V.; Niedrist, G.; Rief, A.; Steinwandter, M.; Tasser, E.; Tappeiner, U. Decline of rare and specialist species across multiple taxonomic groups after grassland intensification and abandonment. Biodivers. Conserv. 2018, 27, 3729–3744. [Google Scholar] [CrossRef]
- Di Giulio, M.; Edwards, P.J.; Meister, E. Enhancing insect diversity in agricultural grasslands: The roles of management and landscape structure. J. Appl. Ecol. 2001, 38, 310–319. [Google Scholar] [CrossRef]
- Lengyel, S.; Déri, E.; Magura, T. Species richness responses to structural or compositional habitat diversity between and within grassland patches: A multi-taxon approach. PLoS ONE 2016, 11, e0149662. [Google Scholar] [CrossRef] [Green Version]
- Dixon, A.; Faber-Langendoen, D.; Josse, C.; Morrison, J.; Loucks, C. Distribution mapping of world grassland types. J. Biogeogr. 2014, 41, 2003–2019. [Google Scholar] [CrossRef]
- Faber-Langendoen, D.; Josse, C. World Grasslands and Biodiversity Patterns: A Report to IUCN Ecosystem Management Programme; NatureServe: Arlington County, VA, USA, 2010. [Google Scholar]
- Allaby, M. A dictionary of Plant Sciences; Oxford University Press: Oxford, UK, 2012. [Google Scholar]
- Suttie, J.M.; Reynolds, S.G.; Batello, C. Grasslands of the World; Food & Agriculture Org.: Rome, Italy, 2005. [Google Scholar]
- Allen, V.G.; Batello, C.; Berretta, E.; Hodgson, J.; Kothmann, M.; Li, X.; McIvor, J.; Milne, J.; Morris, C.; Peeters, A.; et al. An international terminology for grazing lands and grazing animals. Grass Forage Sci. 2011, 66, 2–28. [Google Scholar] [CrossRef]
- Ramankutty, N.; Evan, A.T.; Monfreda, C.; Foley, J.A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 2008, 22, GB1003. [Google Scholar] [CrossRef]
- Ramankutty, N.A.T.; Evan, C.; Monfreda, C.; Foley, J.A. Global Agricultural Lands: Pastures, 2000, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Available online: https://doi.org/10.7927/H47H1GGR (accessed on 7 May 2020).
- NOAA. National Centers for Environmental Information—Global Historical Climatology Network Monthly; NOAA National Centers for Environmental Information: Asheville, NC, USA, 2011.
- Robinson, T.P.; Wint, G.W.; Conchedda, G.; Van Boeckel, T.P.; Ercoli, V.; Palamara, E.; Cinardi, G.; D’Aietti, L.; Hay, S.I.; Gilbert, M. Mapping the global distribution of livestock. PLoS ONE 2014, 9, e96084. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chi, D.; Wang, H.; Li, X.; Liu, H.; Li, X. Assessing the effects of grazing on variations of vegetation NPP in the Xilingol Grassland, China, using a grazing pressure index. Ecol. Indic. 2018, 88, 372–383. [Google Scholar] [CrossRef]
- Thenkabail, P.S.; Biradar, C.M.; Noojipady, P.; Dheeravath, V.; Li, Y.; Velpuri, M.; Gumma, M.; Gangalakunta, O.R.P.; Turral, H.; Cai, X.; et al. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int. J. Remote Sens. 2009, 30, 3679–3733. [Google Scholar] [CrossRef]
- Dlamini, P.; Chivenge, P.; Chaplot, V. Overgrazing decreases soil organic carbon stocks the most under dry climates and low soil pH: A meta-analysis shows. Agric. Ecosyst. Environ. 2016, 221, 258–269. [Google Scholar] [CrossRef]
- Han, G.; Hao, X.; Zhao, M.; Wang, M.; Ellert, B.H.; Willms, W.; Wang, M. Effect of grazing intensity on carbon and nitrogen in soil and vegetation in a meadow steppe in Inner Mongolia. Agric. Ecosyst. Environ. 2008, 125, 21–32. [Google Scholar] [CrossRef]
- Butterbach-Bahl, K.; Gundersen, P.; Ambus, P.; Augustin, J.; Beier, C.; Boeckx, P.; Dannenmann, M.; Sanchez Gimeno, B.; Ibrom, A.; Kiese, R.; et al. Nitrogen processes in terrestrial ecosystems. In The European Nitrogen Assessment: Sources, Effects and Policy Perspectives; Cambridge University Press: Cambridge, UK, 2011; pp. 99–125. [Google Scholar]
- Jarvis, S.; Hutchings, N.J.; Brentrup, F.; Olesen, J.E.; van der Hoek, K.W. Nitrogen flows in farming systems across Europe. In European Nitrogen Assessment; Cambridge University Press: Cambridge, UK, 2011; pp. 211–228. [Google Scholar]
- Vries, F.T.; de Bloem, J.; Quirk, H.; Stevens, C.J.; Bol, R.; Bardgett, R.D. Extensive management promotes plant and microbial nitrogen retention in temperate grassland. PLoS ONE 2012, 7, e51201. [Google Scholar] [CrossRef] [Green Version]
- Müller, I.B.; Buhk, C.; Lange, D.; Entling, M.H.; Schirmel, J. Contrasting effects of irrigation and fertilization on plant diversity in hay meadows. Basic Appl. Ecol. 2016, 17, 576–585. [Google Scholar] [CrossRef]
- Bernhardt-Römermann, M.; Römermann, C.; Sperlich, S.; Schmidt, W. Explaining grassland biomass–the contribution of climate, species and functional diversity depends on fertilization and mowing frequency. J. Appl. Ecol. 2011, 48, 1088–1097. [Google Scholar] [CrossRef] [Green Version]
- Moinet, G.Y.K.; Cieraad, E.; Turnbull, M.H.; Whitehead, D. Effects of irrigation and addition of nitrogen fertiliser on net ecosystem carbon balance for a grassland. Sci. Total Environ. 2017, 579, 1715–1725. [Google Scholar] [CrossRef] [PubMed]
- Kuemmerle, T.; Erb, K.; Meyfroidt, P.; Müller, D.; Verburg, P.H.; Estel, S.; Haberl, H.; Hostert, P.; Jepsen, M.R.; Kastner, T.; et al. Challenges and opportunities in mapping land use intensity globally. Curr. Opin. Environ. Sustain. 2013, 5, 484–493. [Google Scholar] [CrossRef]
- Chen, H.; Shao, L.; Zhao, M.; Zhang, X.; Zhang, D. Grassland conservation programs, vegetation rehabilitation and spatial dependency in Inner Mongolia, China. Land Use Policy 2017, 64, 429–439. [Google Scholar] [CrossRef]
- Cojocariu, L.; Horablaga, M.; Marian, F.; Bostan, C.; Mazăre, V.; Stroia, M.S. Implementation of the ecological European network Natura 2000 in the area of grasslands and hayfields. Res. J. Agric. Sci. 2010, 42, 398–404. [Google Scholar]
- Leisher, C.; Hess, S.; Boucher, T.M.; Beukering, P.; van Sanjayan, M. Measuring the impacts of community-based grasslands management in Mongolia’s Gobi. PLoS ONE 2012, 7, e30991. [Google Scholar] [CrossRef]
- Wu, W.; De Pauw, E.; Zucca, C. Using remote sensing to assess impacts of land management policies in the Ordos rangelands in China. Int. J. Digit. Earth 2013, 6, 81–102. [Google Scholar] [CrossRef]
- European Commission (EU) Regulation No 1305/2013 of the European Parliament and of the Council of 17 December 2013 on support for rural development by the European Agricultural Fund for Rural Development (EAFRD) and repealing Council Regulation (EC) No 1698/2005. OJ L (Off. J. Eur.n Union L 347/487) 2013, 347, 487–548.
- Nestola, E.; Calfapietra, C.; Emmerton, C.A.; Wong, C.; Thayer, D.R.; Gamon, J.A. Monitoring grassland seasonal carbon dynamics, by integrating MODIS NDVI, proximal optical sampling, and eddy covariance measurements. Remote Sens. 2016, 8, 260. [Google Scholar] [CrossRef] [Green Version]
- Atzberger, C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens. 2013, 5, 949–981. [Google Scholar] [CrossRef] [Green Version]
- Rast, M.; Painter, T.H. Earth Observation Imaging Spectroscopy for Terrestrial Systems: An Overview of Its History, Techniques, and Applications of Its Missions. Surv. Geophys. 2019, 40, 303–331. [Google Scholar] [CrossRef]
- Transon, J.; d’Andrimont, R.; Maugnard, A.; Defourny, P. Survey of hyperspectral earth observation applications from space in the sentinel-2 context. Remote Sens. 2018, 10, 157. [Google Scholar] [CrossRef] [Green Version]
- Verrelst, J.; Malenovsk, Z.; Van der Tol, C.; Camps-Valls, G.; Gastellu-Etchegorry, J.-P.; Lewis, P.; North, P.; Moreno, J. Quantifying vegetation biophysical variables from imaging spectroscopy data: A review on retrieval methods. Surv. Geophys. 2019, 40, 589–629. [Google Scholar] [CrossRef] [Green Version]
- Hill, M.J.; Donald, G.E.; Vickery, P.J. Relating radar backscatter to biophysical properties of temperate perennial grassland. Remote Sens. Environ. 1999, 67, 15–31. [Google Scholar] [CrossRef]
- Wegmuller, U.; Werner, C. Retrieval of vegetation parameters with SAR interferometry. IEEE Trans. Geosci. Remote Sens. 1997, 35, 18–24. [Google Scholar] [CrossRef]
- McNairn, H.; Brisco, B. The application of C-band polarimetric SAR for agriculture: A review. Can. J. Remote Sens. 2004, 30, 525–542. [Google Scholar] [CrossRef]
- McNairn, H.; Shang, J. A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. In Multitemporal Remote Sensing; Springer: Cham, Switzerland, 2016; pp. 317–340. [Google Scholar]
- Ali, I.; Cawkwell, F.; Dwyer, E.; Barrett, B.; Green, S. Satellite remote sensing of grasslands: From observation to management. J. Plant Ecol. 2016, 9, 649–671. [Google Scholar] [CrossRef] [Green Version]
- Hill, M.J. Grazing agriculture—managed pasture, grassland and rangeland. In Manual of Remote Sensing, Remote Sensing for Natural Resource Management and Environmental Monitoring; Ustin, S.L., Ed.; Wiley International: New York, NY, USA, 2004; Volume 4, p. 768. [Google Scholar]
- Lu, D. The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 2006, 27, 1297–1328. [Google Scholar] [CrossRef]
- Schellberg, J.; Hill, M.J.; Gerhards, R.; Rothmund, M.; Braun, M. Precision agriculture on grassland: Applications, perspectives and constraints. Eur. J. Agron. 2008, 29, 59–71. [Google Scholar] [CrossRef]
- Shoko, C.; Mutanga, O.; Dube, T. Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space. ISPRS J. Photogramm. Remote Sens. 2016, 120, 13–24. [Google Scholar] [CrossRef]
- Tucker, C.J. A critical review of remote sensing and other methods for non-destructive estimation of standing crop biomass. Grass Forage Sci. 1980, 35, 177–182. [Google Scholar] [CrossRef]
- Tueller, P.T. Remote sensing technology for rangeland management applications. Rangel. Ecol. Manag./J. Range Manag. Arch. 1989, 42, 442–453. [Google Scholar] [CrossRef]
- Tueller, P. Remote sensing in the management of rangelands. Ann. Arid Zone 1995, 34, 191–207. [Google Scholar]
- Wachendorf, M.; Fricke, T.; Möckel, T. Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands. Grass and Forage Sci. 2018, 73, 1–14. [Google Scholar] [CrossRef]
- Franklin, K.; Molina-Freaner, F. Consequences of Buffelgrass Pasture Development for Primary Productivity, Perennial Plant Richness, and Vegetation Structure in the Drylands of Sonora, Mexico. Conserv. Biol. 2010, 24, 1664–1673. [Google Scholar] [CrossRef]
- Gao, Q.; Schwartz, M.W.; Zhu, W.; Wan, Y.; Qin, X.; Ma, X.; Liu, S.; Williamson, M.A.; Peters, C.B.; Li, Y. Changes in Global Grassland Productivity during 1982 to 2011 Attributable to Climatic Factors. Remote Sens. 2016, 8, 384. [Google Scholar] [CrossRef] [Green Version]
- Gu, Y.; Wylie, B.K. Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations. Remote Sens. Environ. 2015, 171, 291–298. [Google Scholar] [CrossRef] [Green Version]
- Kath, J.; Brocque, A.F.L.; Reardon-Smith, K.; Apan, A. Remotely sensed agricultural grassland productivity responses to land use and hydro-climatic drivers under extreme drought and rainfall. Agric. For. Meteorol. 2019, 268, 11–22. [Google Scholar] [CrossRef]
- Qamer, F.M.; Xi, C.; Abbas, S.; Murthy, M.S.; Ning, W.; Anming, B. An assessment of productivity patterns of grass-dominated Rangelands in the Hindu Kush Karakoram Region, Pakistan. Sustainability 2016, 8, 961. [Google Scholar] [CrossRef] [Green Version]
- Reeves, M.C.; Baggett, L.S. A remote sensing protocol for identifying rangelands with degraded productive capacity. Ecol. Indic. 2014, 43, 172–182. [Google Scholar] [CrossRef]
- Ricotta, C.; Reed, B.C.; Tieszen, L.T. The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains. Int. J. Remote Sens. 2003, 24, 4421–4431. [Google Scholar] [CrossRef]
- Tucker, C.J.; Justice, C.; Prince, S. Monitoring the grasslands of the Sahel 1984-1985. Int. J. Remote Sens. 1986, 7, 1571–1581. [Google Scholar] [CrossRef]
- Yang, L.; Wylie, B.K.; Tieszen, L.L.; Reed, B.C. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the US northern and central Great Plains. Remote Sens. Environ. 1998, 65, 25–37. [Google Scholar] [CrossRef]
- Yin, F.; Deng, X.; Jin, Q.; Yuan, Y.; Zhao, C. The impacts of climate change and human activities on grassland productivity in Qinghai Province, China. Front. Earth Sci. 2014, 8, 93–103. [Google Scholar] [CrossRef]
- Zhou, Y.; Zhang, L.; Xiao, J.; Chen, S.; Kato, T.; Zhou, G. A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands. Rangel. Ecol. Manag. 2014, 67, 9–18. [Google Scholar] [CrossRef]
- Yan, D.; Scott, R.; Moore, D.; Biederman, J.; Smith, W. Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data. Remote Sens. Environ. 2019, 223, 50–62. [Google Scholar] [CrossRef]
- Wylie, B.; Howard, D.; Dahal, D.; Gilmanov, T.; Ji, L.; Zhang, L.; Smith, K. Grassland and Cropland Net Ecosystem Production of the US Great Plains: Regression Tree Model Development and Comparative Analysis. Remote Sens. 2016, 8, 944. [Google Scholar] [CrossRef] [Green Version]
- Edirisinghe, A.; Hill, M.J.; Donald, G.E.; Hyder, M. Quantitative mapping of pasture biomass using satellite imagery. Int. J. Remote Sens. 2011, 32, 2699–2724. [Google Scholar] [CrossRef]
- Edirisinghe, A.; Clark, D.; Waugh, D. Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing. Int. J. Appl. Earth Obs. Geoinf. 2012, 16, 5–16. [Google Scholar] [CrossRef]
- Smith, R.C.; Adams, M.; Gittins, S.; Gherardi, S.; Wood, D.; Maier, S.; Stovold, R.; Donald, G.; Khohkar, S.; Allen, A. Near real-time Feed On Offer (FOO) from MODIS for early season grazing management of Mediterranean annual pastures. Int. J. Remote Sens. 2011, 32, 4445–4460. [Google Scholar] [CrossRef]
- Magiera, A.; Feilhauer, H.; Waldhardt, R.; Wiesmair, M.; Otte, A. Modelling biomass of mountainous grasslands by including a species composition map. Ecol. Indic. 2017, 78, 8–18. [Google Scholar] [CrossRef]
- Ramoelo, A.; Cho, M.A.; Mathieu, R.; Madonsela, S.; van de Kerchove, R.; Kaszta, Z.; Wolff, E. Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. Int. J. Appl. Earth Obs. Geoinf. 2015, 43, 43–54. [Google Scholar] [CrossRef]
- Wang, Y.; Wu, G.; Deng, L.; Tang, Z.; Wang, K.; Sun, W.; Shangguan, Z. Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm. Sci. Rep. 2017, 7, 6940. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zeng, N.; Ren, X.; He, H.; Zhang, L.; Zhao, D.; Ge, R.; Li, P.; Niu, Z. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm. Ecol. Indic. 2019, 102, 479–487. [Google Scholar] [CrossRef]
- Zhang, B.; Zhang, L.; Xie, D.; Yin, X.; Liu, C.; Liu, G. Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. Remote Sens. 2016, 8, 10. [Google Scholar] [CrossRef] [Green Version]
- Baghi, N.G.; Oldeland, J. Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid, saline rangelands in North-East Iran? Int. J. Remote Sens. 2019, 40, 8223–8235. [Google Scholar] [CrossRef]
- Yin, G.; Li, A.; Wu, C.; Wang, J.; Xie, Q.; Zhang, Z.; Nan, X.; Jin, H.; Bian, J.; Lei, G. Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance. ISPRS Int. J. Geo-Inf. 2018, 7, 242. [Google Scholar] [CrossRef] [Green Version]
- Ali, I.; Cawkwell, F.; Green, S.; Dwyer, N. Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 5060–5063. [Google Scholar]
- Ali, I.; Cawkwell, F.; Dwyer, E.; Green, S. Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 3254–3264. [Google Scholar] [CrossRef]
- Li, F.; Jiang, L.; Wang, X.; Zhang, X.; Zheng, J.; Zhao, Q. Estimating grassland aboveground biomass using multitemporal MODIS data in the West Songnen Plain, China. J. Appl. Remote Sens. 2013, 7, 073546. [Google Scholar] [CrossRef]
- Li, F.; Zheng, J.; Wang, H.; Luo, J.; Zhao, Y.; Zhao, R. Mapping grazing intensity using remote sensing in the Xilingol steppe region, Inner Mongolia, China. Remote Sens. Lett. 2016, 7, 328–337. [Google Scholar] [CrossRef]
- Quan, X.; He, B.; Yebra, M.; Yin, C.; Liao, Z.; Zhang, X.; Li, X. A radiative transfer model-based method for the estimation of grassland aboveground biomass. Int. J. Appl. Earth Obs. Geoinf. 2017, 54, 159–168. [Google Scholar] [CrossRef]
- Xie, Y.; Sha, Z.; Yu, M.; Bai, Y.; Zhang, L. A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecol. Model. 2009, 220, 1810–1818. [Google Scholar] [CrossRef]
- Yang, S.; Feng, Q.; Liang, T.; Liu, B.; Zhang, W.; Xie, H. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region. Remote Sens. Environ. 2018, 204, 448–455. [Google Scholar] [CrossRef]
- Marsett, R.C.; Qi, J.; Heilman, P.; Biedenbender, S.H.; Watson, M.C.; Amer, S.; Weltz, M.; Goodrich, D.; Marsett, R. Remote sensing for grassland management in the arid southwest. Rangel. Ecol. Manag. 2006, 59, 530–540. [Google Scholar] [CrossRef]
- Wehlage, D.C.; Gamon, J.A.; Thayer, D.; Hildebrand, D.V. Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements. Remote Sens. 2016, 8, 872. [Google Scholar] [CrossRef] [Green Version]
- Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; François, C.; Ustin, S.L. PROSPECT+ SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 2009, 113, S56–S66. [Google Scholar] [CrossRef]
- Bella, D.; Faivre, R.; Ruget, F.; Seguin, B.; Guerif, M.; Combal, B.; Weiss, M.; Rebella, C. Remote sensing capabilities to estimate pasture production in France. Int. J. Remote Sens. 2004, 25, 5359–5372. [Google Scholar] [CrossRef]
- Donald, G.; Gherardi, S.; Edirisinghe, A.; Gittins, S.; Henry, D.; Mata, G. Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia. Anim. Prod. Sci. 2010, 50, 611–615. [Google Scholar] [CrossRef]
- Hill, M.J.; Donald, G.E.; Hyder, M.W.; Smith, R.C. Estimation of pasture growth rate in the south west of Western Australia from AVHRR NDVI and climate data. Remote Sens. Environ. 2004, 93, 528–545. [Google Scholar] [CrossRef]
- Monteith, J. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 1972, 9, 747–766. [Google Scholar] [CrossRef] [Green Version]
- Monteith, J.L. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. Lond. B Biol. Sci. 1977, 281, 277–294. [Google Scholar]
- Sun, B.; Li, Z.; Gao, Z.; Guo, Z.; Wang, B.; Hu, X.; Bai, L. Grassland degradation and restoration monitoring and driving forces analysis based on long time-series remote sensing data in Xilin Gol League. Acta Ecol. Sin. 2017, 37, 219–228. [Google Scholar] [CrossRef]
- Zhang, Y.; Qi, W.; Zhou, C.; Ding, M.; Liu, L.; Gao, J.; Bai, W.; Wang, Z.; Zheng, D. Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982. J. Geogr. Sci. 2014, 24, 269–287. [Google Scholar] [CrossRef]
- Potter, C.S.; Randerson, J.T.; Field, C.B.; Matson, P.A.; Vitousek, P.M.; Mooney, H.A.; Klooster, S.A. Terrestrial ecosystem production: A process model based on global satellite and surface data. Glob. Biogeochem. Cycles 1993, 7, 811–841. [Google Scholar] [CrossRef]
- Yu, R.; Evans, A.J.; Malleson, N. Quantifying grazing patterns using a new growth function based on MODIS Leaf Area Index. Remote Sens. Environ. 2018, 209, 181–194. [Google Scholar] [CrossRef] [Green Version]
- Xiao, X.; Zhang, Q.; Braswell, B.; Urbanski, S.; Boles, S.; Wofsy, S.; Moore III, B.; Ojima, D. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens. Environ. 2004, 91, 256–270. [Google Scholar] [CrossRef]
- You, Y.; Wang, S.; Ma, Y.; Wang, X.; Liu, W. Improved Modeling of Gross Primary Productivity of Alpine Grasslands on the Tibetan Plateau Using the Biome-BGC Model. Remote Sens. 2019, 11, 1287. [Google Scholar] [CrossRef] [Green Version]
- Maselli, F.; Argenti, G.; Chiesi, M.; Angeli, L.; Papale, D. Simulation of grassland productivity by the combination of ground and satellite data. Agric. Ecosyst. Environ. 2013, 165, 163–172. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Li, A.; Bian, J. Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China. Remote Sens. 2016, 8, 168. [Google Scholar] [CrossRef] [Green Version]
- Fan, J.-W.; Shao, Q.-Q.; Liu, J.-Y.; Wang, J.-B.; Harris, W.; Chen, Z.-Q.; Zhong, H.-P.; Xu, X.-L.; Liu, R.-G. Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai–Tibet Plateau, China. Environ. Monit. Assess. 2010, 170, 571–584. [Google Scholar] [CrossRef]
- Jia, W.; Liu, M.; Wang, D.; He, H.; Shi, P.; Li, Y.; Wang, Y. Uncertainty in simulating regional gross primary productivity from satellite based models over northern China grassland. Ecol. Indic. 2018, 88, 134–143. [Google Scholar] [CrossRef]
- Tan, K.; Ciais, P.; Piao, S.; Wu, X.; Tang, Y.; Vuichard, N.; Liang, S.; Fang, J. Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands. Glob. Biogeochem. Cycles 2010, 24. [Google Scholar] [CrossRef]
- He, H.; Liu, M.; Xiao, X.; Ren, X.; Zhang, L.; Sun, X.; Yang, Y.; Li, Y.; Zhao, L.; Shi, P.; et al. Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands. J. Geophys. Res. Biogeosci. 2014, 119, 466–486. [Google Scholar] [CrossRef] [Green Version]
- Rossini, M.; Cogliati, S.; Meroni, M.; Migliavacca, M.; Galvagno, M.; Busetto, L.; Cremonese, E.; Julitta, T.; Siniscalco, C.; Morra di Cella, U.; et al. Remote sensing-based estimation of gross primary production in a subalpine grassland. Biogeosciences 2012, 9, 2565–2584. [Google Scholar] [CrossRef] [Green Version]
- Wang, Z.; Xiao, X.; Yan, X. Modeling gross primary production of maize cropland and degraded grassland in northeastern China. Agric. For. Meteorol. 2010, 150, 1160–1167. [Google Scholar] [CrossRef]
- Wu, W.X.; Wang, S.Q.; Xio, X.M.; Yu, G.R.; Fu, Y.L.; Hao, Y.B. Modeling gross primary production of a temperate grassland ecosystem in Inner Mongolia, China, using MODIS imagery and climate data. Sci. China Ser. D Earth Sci. 2008, 51, 1501–1512. [Google Scholar] [CrossRef]
- Zhou, Y.; Xiao, X.; Wagle, P.; Bajgain, R.; Mahan, H.; Basara, J.B.; Dong, J.; Qin, Y.; Zhang, G.; Luo, Y.; et al. Examining the short-term impacts of diverse management practices on plant phenology and carbon fluxes of Old World bluestems pasture. Agric. For. Meteorol. 2017, 237, 60–70. [Google Scholar] [CrossRef] [Green Version]
- Gao, T.; Xu, B.; Yang, X.; Deng, S.; Liu, Y.; Jin, Y.; Ma, H.; Li, J.; Yu, H.; Zheng, X.; et al. Aboveground net primary productivity of vegetation along a climate-related gradient in a Eurasian temperate grassland: Spatiotemporal patterns and their relationships with climate factors. Environ. Earth Sci. 2017, 76, 56. [Google Scholar] [CrossRef]
- Jia, X.; Xie, B.; Shao, M.; Zhao, C. Primary Productivity and Precipitation-Use Efficiency in Temperate Grassland in the Loess Plateau of China. PLoS ONE 2015, 10, e0135490. [Google Scholar] [CrossRef] [Green Version]
- Li, Z.; Huffman, T.; McConkey, B.; Townley-Smith, L. Monitoring and modeling spatial and temporal patterns of grassland dynamics using time-series MODIS NDVI with climate and stocking data. Remote Sens. Environ. 2013, 138, 232–244. [Google Scholar] [CrossRef]
- Zhao, F.; Xu, B.; Yang, X.; Xia, L.; Jin, Y.; Li, J.; Zhang, W.; Guo, J.; Shen, G. Modelling and analysis of net primary productivity and its response mechanism to climate factors in temperate grassland, northern China. Int. J. Remote Sens. 2019, 40, 2259–2277. [Google Scholar] [CrossRef]
- Petrie, M.D.; Brunsell, N.A.; Vargas, R.; Collins, S.; Flanagan, L.B.; Hanan, N.; Litvak, M.E.; Suyker, A.E. The sensitivity of carbon exchanges in Great Plains grasslands to precipitation variability. J. Geophys. Res. Biogeosci. 2016, 121, 280–294. [Google Scholar] [CrossRef] [Green Version]
- Mao, D.; Wang, Z.; Li, L.; Ma, W. Spatiotemporal dynamics of grassland aboveground net primary productivity and its association with climatic pattern and changes in Northern China. Ecol. Indic. 2014, 41, 40–48. [Google Scholar] [CrossRef]
- Xiong, Q.; Xiao, Y.; Halmy, M.W.A.; Dakhil, M.A.; Liang, P.; Liu, C.; Zhang, L.; Pandey, B.; Pan, K.; El Kafraway, S.B.; et al. Monitoring the impact of climate change and human activities on grassland vegetation dynamics in the northeastern Qinghai-Tibet Plateau of China during 2000–2015. J. Arid Land 2019, 11, 637–651. [Google Scholar] [CrossRef] [Green Version]
- Wang, R.; Gamon, J.A.; Emmerton, C.A.; Springer, K.R.; Yu, R.; Hmimina, G. Detecting intra-and inter-annual variability in gross primary productivity of a North American grassland using MODIS MAIAC data. Agric. For. Meteorol. 2020, 281, 107859. [Google Scholar] [CrossRef]
- Flanagan, L.B.; Adkinson, A.C. Interacting controls on productivity in a northern Great Plains grassland and implications for response to ENSO events. Glob. Chang. Biol. 2011, 17, 3293–3311. [Google Scholar] [CrossRef]
- Li, F.; Chen, J.Q.; Zeng, Y.; Wu, B.F.; Zhang, X.Q. Renewed Estimates of Grassland Aboveground Biomass Showing Drought Impacts. J. Geophys. Res. Biogeosci. 2018, 123, 138–148. [Google Scholar] [CrossRef] [Green Version]
- Wu, X.; Li, P.; Jiang, C.; Liu, P.; He, J.; Hou, X. Climate changes during the past 31 years and their contribution to the changes in the productivity of rangeland vegetation in the Inner Mongolian typical steppe. Rangel. J. 2014, 36, 519–526. [Google Scholar] [CrossRef]
- Dusseux, P.; Gong, X.; Corpetti, T.; Hubert-Moy, L.; Corgne, S. Contribution of radar images for grassland management identification. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, Edinburgh, UK, 23 October 2012; Volume 8531, pp. 24–30. [Google Scholar]
- Dusseux, P.; Corpetti, T.; Hubert-Moy, L. Temporal kernels for the identification of grassland management using time series of high spatial resolution satellite images. In Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium—IGARSS, Melbourne, Australia, 21–26 July 2013; pp. 3258–3260. [Google Scholar]
- Price, K.P.; Guo, X.; Stiles, J.M. Comparison of Landsat TM and ERS-2 SAR data for discriminating among grassland types and treatments in eastern Kansas. Comput. Electron. Agric. 2002, 37, 157–171. [Google Scholar] [CrossRef]
- Asam, S.; Klein, D.; Dech, S. Estimation of grassland use intensities based on high spatial resolution LAI time series. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, XL-7/W3, 285–291. [Google Scholar] [CrossRef] [Green Version]
- Franke, J.; Keuck, V.; Siegert, F. Assessment of grassland use intensity by remote sensing to support conservation schemes. J. Nat. Conserv. 2012, 20, 125–134. [Google Scholar] [CrossRef]
- Kurtz, D.B.; Schellberg, J.; Braun, M. Ground and satellite based assessment of rangeland management in sub-tropical Argentina. Appl. Geogr. 2010, 30, 210–220. [Google Scholar] [CrossRef]
- Halabuk, A.; Mojses, M.; Halabuk, M.; David, S. Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series. Remote Sens. 2015, 7, 6107–6132. [Google Scholar] [CrossRef] [Green Version]
- Siegmund, R.; Grant, K.; Wagner, M.; Hartmann, S. Satellite-based monitoring of grassland: Assessment of harvest dates and frequency using SAR. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, Proceedings of SPIE, Edinburgh, UK, 26 October 2016; Volume 9998. [Google Scholar]
- Taravat, A.; Wagner, M.P.; Oppelt, N. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sens. 2019, 11, 711. [Google Scholar] [CrossRef] [Green Version]
- Barrett, B.; Nitze, I.; Green, S.; Cawkwell, F. Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote Sens. Environ. 2014, 152, 109–124. [Google Scholar] [CrossRef] [Green Version]
- Dusseux, P.; Hubert-Moy, L.; Lecerf, R.; Gong, X.; Corpetti, T. Identification of grazed and mown grasslands using a time series of high-spatial-resolution remote sensing images. In Proceedings of the 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), Trento, Italy, 12–14 July 2011; pp. 145–148. [Google Scholar]
- Siegmund, R.; Redl, S.; Wagner, M.; Hartmann, S. Grassland monitoring based on Sentinel-1. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, Strasbourg, France, 21 October 2019; Volume 11149, pp. 1–19. [Google Scholar]
- Dusseux, P.; Corpetti, T.; Hubert-Moy, L.; Corgne, S. Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring. Remote Sens. 2014, 6, 6163–6182. [Google Scholar] [CrossRef] [Green Version]
- Lopes, M.; Fauvel, M.; Girard, S.; Sheeren, D. Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels. Remote Sens. 2017, 9, 688. [Google Scholar] [CrossRef] [Green Version]
- Schuster, C.; Schmidt, T.; Conrad, C.; Kleinschmit, B.; Foerster, M. Grassland habitat mapping by intra-annual time series analysis—Comparison of RapidEye and TerraSAR-X satellite data. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 25–34. [Google Scholar] [CrossRef]
- Bekkema, M.E.; Eleveld, M. Mapping Grassland Management Intensity Using Sentinel-2 Satellite Data. GI_Forum 2018 2018, 1, 194–213. [Google Scholar] [CrossRef]
- Schuster, C.; Ali, I.; Lohmann, P.; Frick, A.; Foerster, M.; Kleinschmit, B. Towards Detecting Swath Events in TerraSAR-X Time Series to Establish NATURA 2000 Grassland Habitat Swath Management as Monitoring Parameter. Remote Sens. 2011, 3, 1308–1322. [Google Scholar] [CrossRef] [Green Version]
- Guo, X.; Price, K.P.; Stiles, J. Grasslands Discriminant Analysis Using Landsat TM Single and Multitemporal Data. Photogramm. Eng. Remote Sens. 2003, 69, 1255–1262. [Google Scholar] [CrossRef]
- Guo, X.; Wilmshurst, J.; McCanny, S.; Fargey, P.; Richard, P. Measuring spatial and vertical heterogeneity of grasslands using remote sensing techniques. J. Environ. Inform. 2004, 3, 24–32. [Google Scholar] [CrossRef] [Green Version]
- Hajj, M.E.; Baghdadi, N.; Belaud, G.; Zribi, M.; Cheviron, B.; Courault, D.; Hagolle, O.; Charron, F. Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data. Remote Sens. 2014, 6, 10002–10032. [Google Scholar] [CrossRef] [Green Version]
- John, R.; Chen, J.; Giannico, V.; Park, H.; Xiao, J.; Shirkey, G.; Ouyang, Z.; Shao, C.; Lafortezza, R.; Qi, J. Grassland canopy cover and aboveground biomass in Mongolia and Inner Mongolia: Spatiotemporal estimates and controlling factors. Remote Sens. Environ. 2018, 213, 34–48. [Google Scholar] [CrossRef]
- Liu, Y.; Zha, Y.; Gao, J.; Ni, S. Assessment of grassland degradation near Lake Qinghai, West China, using Landsat TM and in situ reflectance spectra data. Int. J. Remote Sens. 2004, 25, 4177–4189. [Google Scholar] [CrossRef]
- Numata, I.; Roberts, D.A.; Chadwick, O.A.; Schimel, J.; Sampaio, F.R.; Leonidas, F.C.; Soares, J.V. Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sens. Environ. 2007, 109, 314–327. [Google Scholar] [CrossRef]
- Roeder, A.; Udelhoven, T.; Hill, J.; del Barrio, G.; Tsiourlis, G. Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece. Remote Sens. Environ. 2008, 112, 2863–2875. [Google Scholar] [CrossRef]
- Wylie, B.; Meyer, D.; Tieszen, L.; Mannel, S. Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study. Remote Sens. Environ. 2002, 79, 266–278. [Google Scholar] [CrossRef]
- Todd, S.W.; Hoffer, R.M.; Milchunas, D.G. Biomass estimation on grazed and ungrazed rangelands using spectral indices. Int. J. Remote Sens. 1998, 19, 427–438. [Google Scholar] [CrossRef]
- Blanco, L.J.; Ferrando, C.A.; Biurrun, F.N. Remote sensing of spatial and temporal vegetation patterns in two grazing systems. Rangel. Ecol. Manag. 2009, 62, 445–451. [Google Scholar] [CrossRef]
- Grant, K.; Wagner, M.; Siegmund, R.; Hartmann, S. The use of radar images for detecting when grass is harvested and thereby improve grassland yield estimates. In Proceedings of the Grassland Science in Europe, Grassland Science Federation, Wageningen, The Netherlands, 14–17 June 2015; Volume 20, pp. 419–421. [Google Scholar]
- Rossi, M.; Niedrist, G.; Asam, S.; Tonon, G.; Tomelleri, E.; Zebisch, M. A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics. Remote Sens. 2019, 11, 296. [Google Scholar] [CrossRef] [Green Version]
- Stendardi, L.; Karlsen, S.R.; Niedrist, G.; Gerdol, R.; Zebisch, M.; Rossi, M.; Notarnicola, C. Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions. Remote Sens. 2019, 11, 542. [Google Scholar] [CrossRef] [Green Version]
- Tamm, T.; Zalite, K.; Voormansik, K.; Talgre, L. Relating Sentinel-1 interferometric coherence to mowing events on grasslands. Remote Sens. 2016, 8, 802. [Google Scholar] [CrossRef] [Green Version]
- Voormansik, K.; Jagdhuber, T.; Olesk, A.; Hajnsek, I.; Papathanassiou, K.P. Towards a detection of grassland cutting practices with dual polarimetric TerraSAR-X data. Int. J. Remote Sens. 2013, 34, 8081–8103. [Google Scholar] [CrossRef]
- Voormansik, K.; Jagdhuber, T.; Zalite, K.; Noorma, M.; Hajnsek, I. Observations of cutting practices in agricultural grasslands using polarimetric SAR. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 9, 1382–1396. [Google Scholar] [CrossRef]
- Zalite, K.; Voormansik, K.; Praks, J.; Antropov, O.; Noorma, M. Towards detecting mowing of agricultural grasslands from multi-temporal COSMO-SkyMed data. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 5076–5079. [Google Scholar]
- Zalite, K.; Antropov, O.; Praks, J.; Voormansik, K.; Noorma, M. Monitoring of agricultural grasslands with time series of X-band repeat-pass interferometric SAR. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 9, 3687–3697. [Google Scholar] [CrossRef]
- Sankey, T.T.; Sankey, J.B.; Weber, K.T.; Montagne, C. Geospatial Assessment of Grazing Regime Shifts and Sociopolitical Changes in a Mongolian Rangeland. Rangel. Ecol. Manag. 2009, 62, 522–530. [Google Scholar] [CrossRef]
- Courault, D.; Hadria, R.; Ruget, F.; Olioso, A.; Duchemin, B.; Hagolle, O.; Dedieu, G. Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region. Hydrol. Earth Syst. Sci. Discuss. 2010, 14, 1731–1744. [Google Scholar] [CrossRef] [Green Version]
- Estel, S.; Mader, S.; Levers, C.; Verburg, P.H.; Baumann, M.; Kuemmerle, T. Combining satellite data and agricultural statistics to map grassland management intensity in Europe. Environ. Res. Lett. 2018, 13, 074020. [Google Scholar] [CrossRef]
- Griffiths, P.; Nendel, C.; Pickert, J.; Hostert, P. Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series. Remote Sens. Environ. 2020, 238, 111124. [Google Scholar] [CrossRef]
- Kolecka, N.; Ginzler, C.; Pazur, R.; Price, B.; Verburg, P.H. Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series. Remote Sens. 2018, 10, 1221. [Google Scholar] [CrossRef] [Green Version]
- Dusseux, P.; Vertès, F.; Corpetti, T.; Corgne, S.; Hubert-Moy, L. Agricultural practices in grasslands detected by spatial remote sensing. Environ. Monit. Assess. 2014, 186, 8249–8265. [Google Scholar] [CrossRef] [PubMed]
- Price, K.P.; Guo, X.; Stiles, J.M. Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas. Int. J. Remote Sens. 2002, 23, 5031–5042. [Google Scholar] [CrossRef]
- Xu, H.; Wang, X.; Zhang, X. Alpine grasslands response to climatic factors and anthropogenic activities on the Tibetan Plateau from 2000 to 2012. Ecol. Eng. 2016, 92, 251–259. [Google Scholar] [CrossRef]
- Xu, D.; Chen, B.; Yan, R.; Yan, Y.; Sun, X.; Xu, L.; Xin, X. Quantitative monitoring of grazing intensity in the temperate meadow steppe based on remote sensing data. Int. J. Remote Sens. 2019, 40, 2227–2242. [Google Scholar] [CrossRef]
- Long, Y.; Li, Z.; Wei, L.; Hua-Kun, Z. Using remote sensing and GIS technologies to estimate grass yield and livestock carrying capacity of alpine grasslands in Golog Prefecture, China. Pedosphere 2010, 20, 342–351. [Google Scholar]
- Ma, Q.; Chai, L.; Hou, F.; Chang, S.; Ma, Y.; Tsunekawa, A.; Cheng, Y. Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau. Sustainability 2019, 11, 417. [Google Scholar] [CrossRef] [Green Version]
- Gomez-Gimenez, M.; de Jong, R.; Peruta, R.D.; Keller, A.; Schaepman, M.E. Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators. Remote Sens. Environ. 2017, 198, 126–139. [Google Scholar] [CrossRef]
- Paudel, K.P.; Andersen, P. Assessing rangeland degradation using multi temporal satellite images and grazing pressure surface model in Upper Mustang, Trans Himalaya, Nepal. Remote Sens. Environ. 2010, 114, 1845–1855. [Google Scholar] [CrossRef]
- Grant, K.; Siegmund, R.; Wagner, M.; Hartmann, S. Satellite-based assessment of grassland yields. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, 40, 15. [Google Scholar] [CrossRef] [Green Version]
- Stumpf, F.; Schneider, M.K.; Keller, A.; Mayr, A.; Rentschler, T.; Meuli, R.G.; Schaepman, M.; Liebisch, F. Spatial monitoring of grassland management using multi-temporal satellite imagery. Ecol. Indic. 2020, 113, 106201. [Google Scholar] [CrossRef]
- Blair, J.; Nippert, J.; Briggs, J. Grassland ecology. In Ecology and the Environment; Springer: New York, NY, USA, 2014; pp. 389–423. [Google Scholar]
- Sinha, S.; Jeganathan, C.; Sharma, L.; Nathawat, M. A review of radar remote sensing for biomass estimation. Int. J. Environ. Sci. Technol. 2015, 12, 1779–1792. [Google Scholar] [CrossRef] [Green Version]
- Wiseman, G.; McNairn, H.; Homayouni, S.; Shang, J. RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 4461–4471. [Google Scholar] [CrossRef]
- Berger, K.; Atzberger, C.; Danner, M.; D’Urso, G.; Mauser, W.; Vuolo, F.; Hank, T. Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: A review study. Remote Sens. 2018, 10, 85. [Google Scholar] [CrossRef] [Green Version]
- Psomas, A.; Kneubühler, M.; Huber, S.; Itten, K.; Zimmermann, N. Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats. Int. J. Remote Sens. 2011, 32, 9007–9031. [Google Scholar] [CrossRef]
- Pacheco-Labrador, J.; El-Madany, T.S.; Martin, M.P.; Gonzalez-Cascon, R.; Carrara, A.; Moreno, G.; Perez-Priego, O.; Hammer, T.; Moossen, H.; Henkel, K.; et al. Combining hyperspectral remote sensing and eddy covariance data streams for estimation of vegetation functional traits. Biogeosci. Discuss. 2020. preprint. [Google Scholar] [CrossRef] [Green Version]
- Atzberger, C.; Darvishzadeh, R.; Schlerf, M.; Le Maire, G. Suitability and adaptation of PROSAIL radiative transfer model for hyperspectral grassland studies. Remote Sens. Lett. 2013, 4, 55–64. [Google Scholar] [CrossRef]
- Atzberger, C.; Darvishzadeh, R.; Immitzer, M.; Schlerf, M.; Skidmore, A.; Le Maire, G. Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy. Int. J. Appl. Earth Obs. Geoinf. 2015, 43, 19–31. [Google Scholar] [CrossRef] [Green Version]
- Darvishzadeh, R.; Atzberger, C.; Skidmore, A.; Schlerf, M. Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. ISPRS J. Photogramm. Remote Sens. 2011, 66, 894–906. [Google Scholar] [CrossRef]
- Running, S.W.; Nemani, R.R.; Heinsch, F.A.; Zhao, M.; Reeves, M.; Hashimoto, H. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 2004, 54, 547–560. [Google Scholar] [CrossRef]
- Gaffney, R.; Porensky, L.M.; Gao, F.; Irisarri, J.G.; Durante, M.; Derner, J.D.; Augustine, D.J. Using APAR to Predict Aboveground Plant Productivity in Semi-Arid Rangelands: Spatial and Temporal Relationships Differ. Remote Sens. 2018, 10, 1474. [Google Scholar] [CrossRef] [Green Version]
- Niu, B.; He, Y.; Zhang, X.; Fu, G.; Shi, P.; Du, M.; Zhang, Y.; Zong, N. Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau. Remote Sens. 2016, 8, 592. [Google Scholar] [CrossRef] [Green Version]
- Zhu, X.; Pei, Y.; Zheng, Z.; Dong, J.; Zhang, Y.; Wang, J.; Chen, L.; Doughty, R.B.; Zhang, G.; Xiao, X. Underestimates of grassland gross primary production in MODIS standard products. Remote Sens. 2018, 10, 1771. [Google Scholar] [CrossRef] [Green Version]
- Zhu, X.; Li, C.; Tang, L. Assessing the spatiotemporal dynamic of NPP in desert steppe and its response to climate change from 2003 to 2017: A case study in Siziwang banner. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, Strasbourg, France, 21 October 2019; Volume 11149, pp. 449–454. [Google Scholar]
- Lei, T.; Wu, J.; Li, X.; Geng, G.; Shao, C.; Zhou, H.; Wang, Q.; Liu, L. A new framework for evaluating the impacts of drought on net primary productivity of grassland. Sci. Total Environ. 2015, 536, 161–172. [Google Scholar] [CrossRef] [PubMed]
- Reinermann, S.; Gessner, U.; Asam, S.; Kuenzer, C.; Dech, S. The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics. Remote Sens. 2019, 11, 1783. [Google Scholar] [CrossRef] [Green Version]
- Demombynes, G.; Kiringai, J. The Drought and Food Crisis in the Horn of Africa: Impacts and Proposed Policy Responses for Kenya; World Bank: Washington, DC, USA, 2011. [Google Scholar]
- Vogel, A.; Scherer-Lorenzen, M.; Weigelt, A. Grassland resistance and resilience after drought depends on management intensity and species richness. PLoS ONE 2012, 7, e36992. [Google Scholar] [CrossRef]
- Na, Y.; Li, J.; Hoshino, B.; Bao, S.; Qin, F.; Myagmartseren, P. Effects of Different Grazing Systems on Aboveground Biomass and Plant Species Dominance in Typical Chinese and Mongolian Steppes. Sustainability 2018, 10, 4753. [Google Scholar] [CrossRef] [Green Version]
- Schmidt, M.; Carter, J.; Stone, G.; O Reagain, P. Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland. Remote Sens. 2016, 8, 989. [Google Scholar] [CrossRef] [Green Version]
- Bégué, A.; Arvor, D.; Bellon, B.; Betbeder, J.; De Abelleyra, D.; PD Ferraz, R.; Lebourgeois, V.; Lelong, C.; Simes, M.; R Verón, S. Remote sensing and cropping practices: A review. Remote Sens. 2018, 10, 99. [Google Scholar] [CrossRef] [Green Version]
- Ozdogan, M.; Yang, Y.; Allez, G.; Cervantes, C. Remote sensing of irrigated agriculture: Opportunities and challenges. Remote Sens. 2010, 2, 2274–2304. [Google Scholar] [CrossRef] [Green Version]
- Kawamura, K.; Akiyama, T.; Yokota, H.; Tsutsumi, M.; Yasuda, T.; Watanabe, O.; Wang, S. Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China. Agricu. Ecosyst. Environ. 2005, 107, 83–93. [Google Scholar] [CrossRef]
- Yang, X.; Guo, X.; Fitzsimmons, M. Assessing light to moderate grazing effects on grassland production using satellite imagery. Int. J. Remote Sens. 2012, 33, 5087–5104. [Google Scholar] [CrossRef]
- Thornton, P.K.; van de Steeg, J.; Notenbaert, A.; Herrero, M. The impacts of climate change on livestock and livestock systems in developing countries: A review of what we know and what we need to know. Agric. Syst. 2009, 101, 113–127. [Google Scholar] [CrossRef]
- Reinfelds, I. Monitoring and Assessment of Surface Water Abstractions for Pasture Irrigation from Landsat Imagery: Bega–Bemboka River, NSW, Australia. Water Resour. Manag. 2011, 25, 2319–2334. [Google Scholar] [CrossRef]
- Cilia, C.; Panigada, C.; Rossini, M.; Meroni, M.; Busetto, L.; Amaducci, S.; Boschetti, M.; Picchi, V.; Colombo, R. Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sens. 2014, 6, 6549–6565. [Google Scholar] [CrossRef] [Green Version]
- Xia, T.; Miao, Y.; Wu, D.; Shao, H.; Khosla, R.; Mi, G. Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sens. 2016, 8, 605. [Google Scholar] [CrossRef] [Green Version]
Search Aspect | Synonyms/Search Terms |
---|---|
Management and Use Intensity | harvest*, cut*, mow*, irrigat*, fertiliz*, graz*, management, monitoring, “use intensity,” intensity |
Production Traits | biomass, production, productivity, quantity, yields |
Grasslands | grassland*, pasture*, meadow*, steppe*, rangeland* |
Remote Sensing | “remote sensing,” “earth observation,” satellite* |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Reinermann, S.; Asam, S.; Kuenzer, C. Remote Sensing of Grassland Production and Management—A Review. Remote Sens. 2020, 12, 1949. https://doi.org/10.3390/rs12121949
Reinermann S, Asam S, Kuenzer C. Remote Sensing of Grassland Production and Management—A Review. Remote Sensing. 2020; 12(12):1949. https://doi.org/10.3390/rs12121949
Chicago/Turabian StyleReinermann, Sophie, Sarah Asam, and Claudia Kuenzer. 2020. "Remote Sensing of Grassland Production and Management—A Review" Remote Sensing 12, no. 12: 1949. https://doi.org/10.3390/rs12121949
APA StyleReinermann, S., Asam, S., & Kuenzer, C. (2020). Remote Sensing of Grassland Production and Management—A Review. Remote Sensing, 12(12), 1949. https://doi.org/10.3390/rs12121949