Mapping grass above-ground biomass of grazing-lands using satellite remote sensing

References

• Ayeni AO, Ogunsesan AA, Adekola OA. 2019. Provisioning ecosystem services provided by the Hadejia Nguru Wetlands, Nigeria–current status and future priorities. Sci Afr. 5:e00124.
   Google Scholar
• Askari MS, McCarthy T, Magee A, Murphy DJ. 2019. Evaluation of grass quality under different soil management scenarios using remote sensing techniques. Remote Sens. 11(15):1835.
   Google Scholar
• Ali I, Cawkwell F, Dwyer E, Green S. 2016. Modeling managed grassland biomass estimation by using multitemporal remote sensing data — A machine learning approach. IEEE J Sel Top Appl Earth Obs Remote Sens. 10(7):1–16.
   Web of Science ®Google Scholar
• Berkhout ED, Schipper RA, Van Keulen H, Coulibaly O. 2011. Heterogeneity in farmers’ production decisions and its impact on soil nutrient use: results and implications from northern Nigeria. Agric Syst. 104(1):63–74.
   Web of Science ®Google Scholar
• Bello AU. 2013. Herdsmen and farmers conflicts in North-Eastern Nigeria: causes, repercussions and resolutions. Acad J Interdiscip Stud. 2(5):129.
   Google Scholar
• Chapungu L, Nhamo L, Gatti RC. 2020. Estimating biomass of savanna grasslands as a proxy of carbon stock using multispectral remote sensing. Remote Sens Appl Soc Environ. 17:100275.
   Google Scholar
• Chirat G, Groot JC, Messad S, Bocquier F, Ickowicz A. 2014. Instantaneous intake rate of free-grazing cattle as affected by herbage characteristics in heterogeneous tropical agro-pastoral landscapes. Appl Anim Behav Sci. 157:48–60.
   Web of Science ®Google Scholar
• Chiwara P, Ogutu BO, Dash J, Milton EJ, Ardö J, Saunders M, Nicolini G. 2018. Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model. Sci Total Environ. 630:1472–1483.
   PubMed Web of Science ®Google Scholar
• Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, et al. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ. 120:25–36.
   Web of Science ®Google Scholar
• Dlamini SN, Beloconi A, Mabaso S, Vounatsou P, Impouma B, Fall IS. 2019. Review of remotely sensed data products for disease mapping and epidemiology. Remote Sens Appl Soc Environ. 14:108–118.
   Google Scholar
• Dong S, Shang Z, Gao J, Boone RB. 2020. Enhancing sustainability of grassland ecosystems through ecological restoration and grazing management in an era of climate change on Qinghai-Tibetan Plateau. Agric Ecosyst Environ. 287:106684.
   Web of Science ®Google Scholar
• Doelling DR, Haney CO, Scarino BR, Gopalan A, Bhatt R. 2016. Improvements to the geostationary visible imager ray-matching calibration algorithm for CERES Edition 4. J Atmos Ocean Technol. 33(12):2679–2698.
   Web of Science ®Google Scholar
• Graux AI, Resmond R, Casellas E, Delaby L, Faverdin P, Le Bas C, Ripoche D, Ruget F, Thérond O, Vertès F, et al. 2020. High-resolution assessment of French grassland dry matter and nitrogen yields. Eur J Agron. 112:125952.
   Web of Science ®Google Scholar
• Elvidge CD, Baugh KE, Dietz JB, Bland T, Sutton PC, Kroehl HW. 1999. Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sens Environ. 68(1):77–88.
   Web of Science ®Google Scholar
• GHG Guidelines. 2015. Green house gas emission guidelines. Volume II: above ground biomass field survey guide for baseline survey; federal democratic republic of Ethiopia ministry of agriculture. Addis Ababa, Ethiopia: GHG Guidelines.
   Google Scholar
• Hartmann L, Gabriel M, Zhou Y, Sponholz B, Thiemeyer H. 2014. Soil assessment along toposequences in rural northern Nigeria: a geomedical approach. Appl Environ Soil Sci. 2014:1–9.
   Google Scholar
• Hashim H, Abd Latif Z, Adnan NA. 2019. Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) PLEIADES Imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLII-4/W16:237–240.
   Google Scholar
• Hara M, Okada S, Ichizuka M, Shigehara K, Moriyama T, Sugimori Y. 2004. Monitoring of fishing lights intensity for squid fishing vessels by means DMSP/OLS nightlight imagery. J Adv Marine Sci Technol Soc. 9(0):99–108.
   Google Scholar
• Horion S, Fensholt R, Tagesson T, Ehammer A. 2014. Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel. Int J Remote Sens. 35(7):2493–2515.
   Web of Science ®Google Scholar
• Jaji AZ, Elelu N, Mahre MB, Jaji K, Mohammed LIG, Audu Likita M, Kigir ES, Onwuama KT, Saidu ASS. 2017. Herd growth parameters and constraints of camel rearing in Northeastern Nigeria. Pastoralism. 7(1):16.
   Google Scholar
• Jin X, Liu S, Dang P, Yu R, Dang H, Tan X. 2019. Correction for calibration error in Hy-2b scanning microwave radiometer. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Piscataway (NJ): IEEE; p. 8984–8951.
   Google Scholar
• Li F, Zeng Y, Luo J, Ma R, Wu B. 2016. Modeling grassland aboveground biomass using a pure vegetation index. Ecol Indic. 62:279–288.
   Web of Science ®Google Scholar
• Letu H, Hara M, Yagi H, Naoki K, Tana G, Nishio F, Shuhei O. 2010. Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects. Int J Remote Sens. 31(16):4443–4458.
   Web of Science ®Google Scholar
• Munyati C. 2018. Spatial variations in plant nutrient concentrations in tissue of a grass species as influenced by grazing intensity in a confined savannah rangeland. J Arid Environ. 155:46–58.
   Web of Science ®Google Scholar
• Mutanga O, Skidmore AK. 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int J Remote Sens. 25(19):3999–4014.
   Web of Science ®Google Scholar
• Modernel P, Picasso V, Do Carmo M, Rossing WAH, Corbeels M, Soca P, Dogliotti S, Tittonell P. 2019. Grazing management for more resilient mixed livestock farming systems on native grasslands of southern South America. Grass Forage Sci. 74(4):636–649.
   Web of Science ®Google Scholar
• McNairn H, Protz R. 1993. Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using Thematic Mapper. Can J Remote Sens. 19 (2):152–159.
   Google Scholar
• Miura T, Huete AR, Yoshioka H, Holben BN. 2001. An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sens Environ. 78(3):284–298.
   Web of Science ®Google Scholar
• Main-Knorn M, Pflug B, Louis J, Debaecker V. 2015. Calibration and validation plan for the L2A processor and products of the Sentinel-2 mission. Proceedings of International Symposium on Remote Sensing of Environment (ISRSE). Vol. 40. Göttingen, Germany: Copernicus Publications; p. 1249–1255.
   Google Scholar
• Mohammed M, Khan MB, Bashier EBM. 2016. Machine learning: algorithms and applications. Boca Raton (FL): CRC Press.
   Google Scholar
• Nakano T, Bavuudorj G, Iijima Y, Ito TY. 2020. Quantitative evaluation of grazing effect on nomadically grazed grassland ecosystems by using time-lapse cameras. Agric Ecosyst Environ. 287:106685.
   Web of Science ®Google Scholar
• Pritchard R, Ryan CM, Grundy I, van der Horst D. 2018. Human appropriation of net primary productivity and rural livelihoods: findings from six villages in Zimbabwe. Ecol Econ. 146:115–124.
   Web of Science ®Google Scholar
• Porqueddu C, Ates S, Louhaichi M, Kyriazopoulos AP, Moreno G, del Pozo A, Ovalle C, Ewing MA, Nichols PGH. 2016. Grasslands in ‘Old World’and ‘New World’Mediterranean‐climate zones: past trends, current status and future research priorities. Grass Forage Sci. 71(1):1–35.
   Web of Science ®Google Scholar
• Pfeiffer M, Langan L, Linstädter A, Martens C, Gaillard C, Ruppert JC, Higgins SI, Mudongo EI, Scheiter, S. 2019. Grazing and aridity reduce perennial grass abundance in semi-arid rangelands–Insightsfrom a trait-based dynamic vegetation model. Ecological Modelling. 395:11–22.
   Google Scholar
• Pipatsitee P, Eiumnoh A, Praseartkul P, Ponganan N, Taota K, Kongpugdee S, Cha-Um, S. 2019. Non-destructive leaf area estimation model for overall growth performances in relation to yield attributes of cassava (Manihot esculenta Cranz) under water deficit conditions. Notulae Botanicae. 47(3):580–591.
   Google Scholar
• Rouse JW, Haas RW, Schell JA, Deering DW, Harlan JC. 1974. Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation NASA/GSFCT type III final report. Greenbelt, MD.
   Google Scholar
• Sola I, García-Martín A, Sandonís-Pozo L, Álvarez-Mozos J, Pérez-Cabello F, González-Audícana M, Llovería RM. 2018. Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes. Int J Appl Earth Obs Geoinf. 73:63–76.
   Web of Science ®Google Scholar
• Strohbach BJ. 2018. Making more of vegetation classification results: a livestock farming suitability index as tool for land-use planning in Namibia. Phytocoenologia. 48(1):7–22.
   Web of Science ®Google Scholar
• Symeonakis E, Higginbottom T, Petroulaki K, Rabe A. 2018. Optimisation of savannah land cover characterisation with optical and SAR data. Remote Sens. 10(4):499.
   Google Scholar
• Shoko C, Mutanga O, Dube T. 2019. Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography. Afr J Ecol. 57(4):477–489.
   Web of Science ®Google Scholar
• Tsiobani ET, Yiakoulaki MD, Menexes G. 2019. Seasonal variation in water buffaloes’ diet grazing in wet grasslands in Northern Greece. Hacquetia. 18(2):201–212.
   Google Scholar
• Tesfaye AA, Awoke BG. 2020. Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem. Spatial Inform Res. 29:109–121.
   Web of Science ®Google Scholar
• Wang J, Xiao X, Bajgain R, Starks P, Steiner J, Doughty RB, Chang Q. 2019. Estimating leaf area index and aboveground biomass of grazing pastures using sentinel-1, sentinel-2 and Landsat images. ISPRS J Photogramm Remote Sens. 154:189–201.
   Web of Science ®Google Scholar
• Wang G, Liu S, Liu T, Fu Z, Yu J, Xue B. 2019. Modelling above-ground biomass based on vegetation indexes: a modified approach for biomass estimation in semi-arid grasslands. Int J Remote Sens. 40(10):3835–3854.
   Web of Science ®Google Scholar
• Wang Z, Yu Q, Guo L. 2020. Quantifying the impact of the grain-for-green program on ecosystem health in the typical agro-pastoral ecotone: a case study in the Xilin Gol League, Inner Mongolia. Int J Environ Res Public Health. 17(16):5631.
   Web of Science ®Google Scholar
• Wang M, Yang KM, Wang GP. 2020. Relative radiometric calibration of yaw data without calibration field of hyperspectral images based on harmonic analysis. Int J Remote Sens. 41(14):5429–5442.
   Web of Science ®Google Scholar
• Xu K, Su Y, Liu J, Hu T, Jin S, Ma Q, Zhai Q, Wang R, Zhang J, Li Y, et al. 2020. Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data. Ecol Indic. 108:105747.
   Web of Science ®Google Scholar
• Zhang L, Juenger TE, Lowry DB, Behrman KD. 2019. Climatic impact, future biomass production, and local adaptation of four switchgrass cultivars. GCB Bioenergy. 11(8):956–970.
   Google Scholar
• Zhou W, Li J, Yue T. 2020. Grassland degradation restoration and constructing green ecological protective screen. Remote sensing monitoring and evaluation of degraded grassland in China. Singapore: Springer; p. 125–138.
   Google Scholar