Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa

References

• Adam EM, Mutanga O, Rugege D, Ismail R. 2012. Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP. Int J Remote Sens. 33(2):552–569.
   Web of Science ®Google Scholar
• Adelabu S, Mutanga O, Adam E. 2014. Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels. ISPRS J Photogramm Remote Sens. 95:34–41.
   Web of Science ®Google Scholar
• Alonso A, Munoz-Carpena R, Kennedy RE, Murcia C. 2016. Wetland landscape spatio-temporal degradation dynamics using the new google earth engine cloud-based platform. Opportunities for non-specialists in remote sensing. Am Soc Agric Biol Eng. 59(5):1333–1344.
   Web of Science ®Google Scholar
• Amani M, Salehi B, Mahdavi S, Granger J. 2017. Spectral analysis of wetlands in Newfoundland using sentinel 2a and landsat 8 imagery. In Imaging and Geospatial Technology Forum, IGTF 2017-ASPRS Annual Conference 2017, Baltimore, MD, March 12–16.
   Google Scholar
• Ballanti L, Byrd KB, Woo I, Ellings C. 2017. Remote sensing for wetland mapping and historical change detection at the Nisqually River Delta. Sustainability. 9(11):1919.
   Web of Science ®Google Scholar
• Barajas-Gea CI. 2005. Evaluación de la diversidad de la flora en el campus Juriquilla de la UNAM. Bol-e: Órgano Comun Electrón Centro Geociencias UNAM. 1(2).
   Google Scholar
• Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F. 2012. Impacts of climate change on the future of biodiversity. Ecol Lett. 15(4):365–377.
   PubMed Web of Science ®Google Scholar
• Berhane TM, Costa H, Lane CR, Anenkhonov OA, Chepinoga VV, Autrey BC. 2019. The Influence of Region of Interest Heterogeneity on Classification Accuracy in Wetland Systems. Remote Sen. 11:551.
   PubMed Web of Science ®Google Scholar
• Berlanga-Robles CA, Ruiz-Luna A, Bocco G, Vekerdy Z. 2011. Spatial analysis of the impact of shrimp culture on the coastal wetlands on the Northern coast of Sinaloa. Mexico Ocean Coast Manage. 54(7):535–543.
   Web of Science ®Google Scholar
• Betbeder J, Hubert-Moy L, Burel F, Corgne S, Baudry J. 2015. Assessing ecological habitat structure from local to landscape scales using SAR. Ecol Indic. 52:545–557.
   Web of Science ®Google Scholar
• Bhaga TD, Dube T, Shekede MD, Shoko C. 2020. Impacts of climate variability and drought on surface water resources in Sub-Saharan Africa using remote sensing: a review. Remote Sens. 12(24):4184.
   Google Scholar
• Bhatnagar S, Gill L, Regan S, Naughton O, Johnston P, Waldren S, Ghosh B. 2020. Mapping vegetation communities inside wetlands using Sentinel-2 imagery in Ireland. Int J Appl Earth Obs Geoinformation. 88(102083):102083.
   Google Scholar
• Boon M, Greenfield R, Tesfamichael S. 2016. Wetland assessment using unmanned aerial vehicle (UAV) photogrammetry. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress, Prague, Czech Republic, July 12–19.
   Google Scholar
• Brisco B, Ahern F, Murnaghan K, White L, Canisus F, Lancaster P. 2017. Seasonal change in wetland coherence as an aid to wetland monitoring. Remote Sens. 9(2):158.
   Google Scholar
• Brisco B, Murnaghan K, Wdowinski S, Hong SH. 2015. Evaluation of RADARSAT-2 acquisition modes for wetland monitoring applications. Can J Remote Sens. 41:1–30.
   Web of Science ®Google Scholar
• Byrd K, O’Connell JL, Tommaso SD, Kelly M. 2014. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sens Environ. 149:166–180.
   Web of Science ®Google Scholar
• Caranqui J, Lozano P, Reyes J. 2016. Composición y diversidad florística de los páramos en la Reserva de Producción de Fauna Chimborazo, Ecuador. Enfoque UTE. 7(1):33–45.
   Google Scholar
• Chen L, Jin Z, Michishita R, Cai J, Yue T, Chen B, Xu B. 2014. Dynamic monitoring of wetland cover changes using time-series remote sensing imagery. Ecol Inf. 24:17–26.
   Web of Science ®Google Scholar
• Chen G, Weng Q, Hay GJ, He Y. 2018. Geographic Object-based Image Analysis (GEOBIA): emerging trends and future opportunities. GIsci Remote Sens. 58.
   Google Scholar
• Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA., Brovkin V, Cox PM, Fisher V, Foley JA, Friend, AD, et al. 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models. Glob Change Biol. 7(4):357–373.
   Web of Science ®Google Scholar
• Corcoran JM, Knight JF, Gallant AL. 2013. Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in Northern Minnesota. Remote Sens. 5(7):3212–3238.
   Google Scholar
• Dadson SJ, Hall JW, Murgatroyd A, Acreman M, Bates P, Beven K, Heathwaite L, Holden J, Holman IP, Lane SN, et al. 2017. A restatement of the natural science evidence concerning catchment-based 'natural' flood management in the UK . Proc Math Phys Eng Sci. 473(2199):20160706.
   PubMed Web of Science ®Google Scholar
• Davidson NC, Fluet-Chouinard E, Finlayson CM. 2018. Global extent and distribution of wetlands: trends and issues. Mar Freshwater Res. 69(4):620–627.
   Web of Science ®Google Scholar
• Davranche A, Poulin B, Lefebvre G. 2010. Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sens Environ. 114 (3):552–562.
   Web of Science ®Google Scholar
• Davranche A, Poulin B, Lefebvre G. 2013. Mapping flooding regimes in Camargue wetlands using seasonal multispectral data. Remote Sens Environ. 138:165–171.
   Web of Science ®Google Scholar
• Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM. 2011. Beyond predictions: biodiversity conservation in a changing climate. Science. 332(6025):53–58.
   PubMed Web of Science ®Google Scholar
• de Klein JJM, van der Werf AK. 2014. Balancing carbon sequestration and GHG emissions in a constructed wetland. Ecol Eng. 66:36–42.
   Web of Science ®Google Scholar
• De Meester L, Declerck S, Stoks R, Louette G, Van De Meutter F, Bie TD, Michels E, Brendonck L. 2005. Ponds and pools as model systems in conservation biology, ecology and evolutionary biology. Aquatic Conserv: Mar. Freshw. Ecosyst. 15:715–725.
   Web of Science ®Google Scholar
• Dennison WC, Orth RJ, Moore KA, Stevenson JC, Carter V, Kollar S, Bergstrom PW, Batiuk RA. 1993. Assessing water quality with submerged aquatic vegetation. Bioscience. 43:86–94
   Web of Science ®Google Scholar
• Díaz‐Delgado R, Cazacu C, Adamescu M. 2019. Rapid Assessment of Ecological Integrity for LTER Wetland Sites by Using UAV Multispectral Mapping. Drones, 3:3.
   Google Scholar
• Dronova I. 2015. Object-based image analysis in wetland research: a review. Remote Sens. 7(5):6380–6413.
   Google Scholar
• Dronova I, Gong P, Wang L, Zhong L. 2015. Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sens Environ. 158:193– 206.
   Web of Science ®Google Scholar
• Dronova I, Gong P, Clinton NE, Wang L, Fu W, Qi S, Liu Y. 2012. Landscape analysis of wetland plant functional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sens Environ. 127:357–369.
   Web of Science ®Google Scholar
• Ellery WN, Dahlberg AC, Strydom R, Neal MJ, Jackson J. 2003. Diversion of water flow from a floodplain wetland stream: an analysis of geomorphological setting and hydrological and ecological consequences. J Environ Manage. 68:51–71.
   PubMed Web of Science ®Google Scholar
• Finlayson CM., Davidson N, Pritchard D, Milton GR, MacKay H. 2011. The Ramsar Convention and ecosystem-based approaches to the wise use and sustainable development of wetlands. J Int Wildl Law Policy. 14:176–198.
   Google Scholar
• Frazier P, Page K, Louis J, Briggs S, Robertson AI. 2003. Relating wetland inundation to river flow using Landsat TM data. Int J Remote Sens. 24:3755–3770.
   Web of Science ®Google Scholar
• Gardner AS, Moholdt G, Scambos T, Fahnstock M, Ligtenberg S, van den Broeke, M, Nilsson J. 2018. Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. The Cryosphere. 12:521–547.
   Web of Science ®Google Scholar
• Gallant AL. 2015. The challenges of remote monitoring of wetlands. Remote Sens. 7(8):10938–10950.
   Google Scholar
• Gonzalez EE, Trilla GG, Martin L, Grimson R, Kandu P. 2019. Vegetation patterns in a South American costal wetland using high high-resolution imergery. J Maps. 15(2):642–650.
   Web of Science ®Google Scholar
• Gopal B. 2016. Should ‘wetlands’ cover all aquatic ecosystems and do macrophytes make a difference to their ecosystem services. Folia Geobot. 51(3):209–226.
   Web of Science ®Google Scholar
• Grenfell S, Grenfell M, Ellery W, Job N, Walters D. 2019. A genetic geomorphic classification system for Southern African Palustrine wetlands: global implications for the management of wetlands in drylands. Front Environ Sci. 7:174.
   Web of Science ®Google Scholar
• Guo C, Guo X. 2016. Estimating leaf chlorophyll and nitrogen content of wetland emergent plants using hyperspectral data in the visible domain. Spectrosc Lett. 49(3):180–187.
   Web of Science ®Google Scholar
• Guo M, Li J, Sheng C, Xu J, Wu L. 2017. A review of wetland remote sensing. Sensors. 17(4):777.
   PubMed Web of Science ®Google Scholar
• Gxokwe S, Dube T, Mazvimavi D. 2020. Multispectral remote sensing of wetlands in semi-arid and arid areas: a review on applications, challenges and possible future research directions. Remote Sens. 12(24):4190.
   Web of Science ®Google Scholar
• Hagan JM, Pealer S, Whitman AA. 2006. Do small headwater streams have a riparian zone defined by plant communities? Can J For Res. 36:2131–2140.
   Web of Science ®Google Scholar
• Han XX, Chen XL, Feng L. 2015. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sens Environ. 156:426–437.
   Web of Science ®Google Scholar
• Henderson FM, Lewis AJ. 2008. Radar detection of wetland ecosystems: a review. Int J Remote Sens. 29(20): 5809–5835.
   Web of Science ®Google Scholar
• Hladik C, Alber M. 2012. Accuracy assessment and correction of a LIDAR-derived salt marsh digital elevation model. Remote Sens Environ. 121:224–235.
   Web of Science ®Google Scholar
• Hu S, Niu Z, Chen Y, Li L, Zhang H. 2017. Global wetlands: potential distribution, wetland loss, and status. Sci Total Environ. 586:319–327.
   PubMed Web of Science ®Google Scholar
• Intergovernmental Panel on Climate Change (IPCC). 2013. Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, editors. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge (UK): Cambridge University Press. p. 1535.
   Google Scholar
• Janišová M, Michalcová D, Bacaro G, Ghisla A. 2014. Landscape effects on diversity of semi-natural grasslands. Agric Ecosyst Environ. 182:47–58.
   Web of Science ®Google Scholar
• Jogo W, Hassan R. 2010. Balancing the use of wetlands for economic well-being and Ecological Security. The case of Limpopo Wetland in southern Africa. Research Repository of the University of Pretoria. Elsevier.
   Google Scholar
• Kabiri S, Allen M, Okuonzia JT, Akello B, Ssabaganzi R, Mubiru D. 2020. Detecting level of wetland encroachment for urban agriculture in Uganda using hyper-temporal remote sensing. AAS Open Res. 3:18.
   Google Scholar
• Kakuba SJ, Kanyamurwa JM. 2021. Management of wetlands and livelihoods opportunities in Kinawataka wetland, Kampala, Uganda. Environ Challenges. 2:100021.
   Google Scholar
• Kent M, Coker P. 1992. Vegetation description and analysis: a practical approach. Boca Raton: CRC Press.
   Google Scholar
• Khosravi I, Safari A, Homayouni S, McNairn H. 2017. Enhenced decision tree ensembles for land-cover mapping from fully polarimetric SAR data. Int J Remote Sens. 38(23):7138–7160.
   Web of Science ®Google Scholar
• Klemas V. 2013. Remote sensing of coastal wetland biomass: an overview. J Coastal Res. 290(5):1016–1028.
   Google Scholar
• Klein I, Dietz U, Dech S, Kuenzer C. 2015. Results of the global waterpack: a novel product to assess inland water body dynamics on a daily basis. Remote Sens Lett. 6(1):78–87.
   Web of Science ®Google Scholar
• Kumar MD, Panda R, Niranjan V, Bassi N. 2013. Technology choices and institutions for improving economic and livelihood benefits from multiple uses tanks in western Orissa. In: Kumar MD, Sivamohan MVK, Bassi N, editors. Water management, food security and sustainable agriculture in developing economies, Chapter 8. Oxford (UK): Routledge.
   Google Scholar
• Kokaly RF, Despain DG, Clark RN, Livo, KE. 2003. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sens Environ. 84:437–456.
   Web of Science ®Google Scholar
• Kuenzer C, Bluemel A, Gebhardt S, Quoc TV, Dech S. 2011. Remote sensing of Mangrove ecosystems: a review. Remote Sen. 3:878–928.
   Web of Science ®Google Scholar
• Laba M, Blair B, Down R, Monger B, Philpot W, Smith S, Sullivan P, Baveye PC. 2010. Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery. Remote Sens Environ. 114(4):876–886.
   Web of Science ®Google Scholar
• Li M, Im J, Beier C. 2013. Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GIsci Remote Sens. 50:361–384.
   Web of Science ®Google Scholar
• Landmann T, Schramm M, Colditz RR, Dietz A, Dech S. 2010. Wide area wetland mapping semi-arid Africa using 250-MODIS metrics and topographic variables. Remote Sens. 2(7):1751–1766.
   Google Scholar
• Li Y, Shi Y, Qureshi S, Bruns A, Zhu X. 2014. Applying the concept of spatial resilience to socio-ecological systems in the urban wetland interface. Ecol Indic. 42:135–146.
   Web of Science ®Google Scholar
• Liu T, Abd-Elrahman. 2018. Deep convolutional neural network training enrichment using multi-view object-based analysis of unmanned aerial systems imagery for wetlands classification. ISPRS J Photogramm Remote Sens. 139:154–170.
   Web of Science ®Google Scholar
• Lou Y, Pan Y, Gao C, Jiang M, Lu X, Xu YJ. 2016. Response of plant height, species richness and aboveground biomass to flooding gradient along vegetation zones in floodplain wetlands, Northeast China. PLoS One. 11(4):e0153972.
   PubMed Web of Science ®Google Scholar
• Luo S, Wang C, Xi X, Pan F, Qian M, Peng D, Nie S, Qin H, Lin Y. 2017. Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data. Int J Appl Earth Obs Geoinf. 58:107–117.
   Web of Science ®Google Scholar
• Luyssaert S, Inglima I, Jung M, Richardson, AD, Reichstein M, Papale D, Piao SL, Schulze ED, Wingate L, Matteucci G, et al. 2007. CO2 balance of boreal, temperate, and tropical forests derived from a global batabase. Glob Change Biol. 13:250–2537.
   Web of Science ®Google Scholar
• Macfarlane DM, Bredin IP, Adams JB, Zungu MM, Bate GC, Dickens CWS. 2014. Preliminary guideline for the determination of buffer zones for rivers wetlands and estuaries. Final Consolidated Report. WRC Report No. TT 610/14. Pretoria (South Africa): Water Research Commission.
   Google Scholar
• Mahdavi S, Salehi B, Amani M, Granger JE, Brisco B, Huang W, Hanson A. 2017. Object-based classification of wetlands in Newfoundland and Labrador using multi-temporal PolSAR data. Can J Remote Sens. 43(5):432–450.
   Web of Science ®Google Scholar
• Mahdianpari M, Salehi B, Rezaee M, Mohammadimanesh F, Zhang Y. 2018. Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens. 10(7):1119.
   Google Scholar
• Mahdianpari M, Salehi B, Rezaee M, Mohammadimanesh F, Zhang Y. 2018. Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens. 10:1119.
   Google Scholar
• Mansour K, Mutanga O, Everson T. 2013. Spectral discrimination of increaser species as an indicator of rangeland degradation using field spectrometry. J Spatial Sci. 58(1):101–117.
   Web of Science ®Google Scholar
• Marambanyika T, Beckedahl H. 2016. The missing link between awareness and the implementation of wetland policy and legislation in communal areas of Zimbabwe. Wetlands Ecol Manage. 24(5):545–563.
   Web of Science ®Google Scholar
• Medeiros CD, Scoffoni C, John GP, Bartlett MK, Inman-Narahari F, Ostertag R, Cordell S, Giardian C, Sack L. 2019. An extensive suite of functional traits distinguishes Hawaiian wet and dry forests and enables prediction of species vital rates. Functional Ecology. 33:712–734.
   Web of Science ®Google Scholar
• McCartney MP, Rebelo LM, Senaratna SS, de Silva S. 2010. Wetlands, agriculture and poverty reduction. Colombo (Sri Lanka): International Water Management Institute (IWMI). 39 p. (IWMI Research Report 137).
   Google Scholar
• Melendez-Pastor I, Navarro-Pedreno J, Gomez I, Koch M. 2010. Detecting drought induced environmental changes in a Mediterranean wetland by remote sensing. Appl Geogr. 30(2):254–262.
   Web of Science ®Google Scholar
• Meli P, Benayas JMR, Balvanera P, Ramos MM. 2014. Restoration enhances wetland biodiversity and ecosystem service supply, but results are context-dependent: a meta-analysis. PLoS One. 9(4):e93507.
   PubMed Web of Science ®Google Scholar
• Mitchell MS, Rutzmoser SH, Wigley TB, Loehle C, Gerwin JA, Keyser PD, Lancia RA, Perry RW, Reynolds CJ, Thill RE, et al. 2006. Relationships between avian richness and landscape structure at multiple scales using multiple landscapes. For Ecol Manage. 221(1-3):155–‒169.
   Web of Science ®Google Scholar
• Mizuochi H, Hiyama T, Ohta T, Fujioka Y, Kambatuku JR, Iijima M, Nasahara KN. 2017. Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: an integrated use of AMSR series, MODIS, and Landsat. Remote Sens Environ. 199:370–388.
   Web of Science ®Google Scholar
• Muro J, Canty M, Conradsen K, Hüttich C, Nielsen A, Skriver H, Remy F, Strauch A, Thonfeld F, Menz G. 2016. Short-term change detection in wetlands using Sentinel-1 time series. Remote Sens. 8(10):795.
   Google Scholar
• Mutanga O, Adam E, Cho MA. 2012. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf. 18:399–406.
   Web of Science ®Google Scholar
• Mutanga O, Skidmore A. 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
• Nguyen HH, Dargus P, Moss P, Aziz AA. 2017. Land-use and socio-ecological drivers of wetland conversion in Ha Tien Plain, Mekong Delta, Vietnam. Land Use Policy. 64(1):101–113.
   Google Scholar
• Nhamo L, Magidi J, Dickens C. 2017. Determining wetland spatial extent and seasonal variation of inundated area using multispectral remote sensing. Water SA. 43(4).
   Web of Science ®Google Scholar
• Nicolet P. 2003. The classification and conservation value of wetland plant and macroinvertebrate assemblages in temporary ponds in England and Wales. Unpublished PhD Thesis, Oxford Brookes University
   Google Scholar
• Niu KC, Schmid B, Choler P, Du GZ. 2012. Relationship between reproductive allocation and relative abundance among 32 species of a Tibetan Alpine Meadow: effects of fertilization and grazing. PLoS ONE. 7:e35448.
   PubMed Web of Science ®Google Scholar
• Novoa V, Rojas O, Ahumada-Rudolph R, Sáez K, Fierro P, Rojas C. 2020. Coastal wetlands: ecosystems affected by urbanization? Water. 12(3):698.
   Web of Science ®Google Scholar
• O’Grady D, Leblanc M. 2014. Radar mapping of broad-scale inundation: challenges and opportunities in Australia. Stoch Environ Res Risk Assess. 28(1):29–38.
   Web of Science ®Google Scholar
• Oliver-Cabrera T, Wdowinski S. 2016. InSAR-based mapping of tidal inundation extent and amplitude in Louisiana coastal wetlands. Remote Sens. 8(5):393.
   Google Scholar
• Ordoyne C, Friedl MA. 2008. Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades. Remote Sens Environ. 112(11):4107–4119.
   Web of Science ®Google Scholar
• Orimoloye IR, Kalumba AM, Mazinyo SN, Nel W. 2018. Geospatial analysis of wetland dynamics: wetland depletion andbiodiversity conservation of Isimangaliso Wetland, South Africa. J King Saud Univ Sci. 32(1):90–96.
   Web of Science ®Google Scholar
• Osorio RJ, Linhoss A, Dash P. 2020. Evaluation of mash terrace for wetland restoration: a remote sensing approach. Water. 12(2):336.
   Web of Science ®Google Scholar
• Ozesmi SL, Bauer ME. 2002. Satellite remote sensing of wetlands. Wetl Ecol Manag. 10(5):381– 402.
   Google Scholar
• Pande-Chhetri R, Abd-Elrahman A, Liu T, Morton J, Wilhelm VL. 2017. Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery. Eur J Remote Sens. 50:564–576.
   Web of Science ®Google Scholar
• Pau S, Edwards EJ, Still CJ. 2013. Improving our understanding of environmental controls on the distribution of C3 and C4 grasses. Glob Change Biol. 19(1):184–196.
   PubMed Web of Science ®Google Scholar
• Peimer AW, Krzywicka AE, Cohen DB, Van den Bosch K, Buxton VL, Stevenson NA, Matthews JW. 2017. National-level wetland policy specificity and goals vary according to political and economic indicators. Environ Manage. 59(1):141–153.
   PubMed Web of Science ®Google Scholar
• Pettorelli N, Nagendra H, Rocchini D, Rowcliffe M, Williams R, Ahumada J, Agelo CD, Atzberger C, Boyd D, Buchanan G, et al. 2017. Remote sensing in ecology and conservation: three years on. Remote sens Ecol Con. 3(2):53–56.
   Web of Science ®Google Scholar
• Prospere K, Mclaren K, Wilson B. 2014. Plant species discrimination in a tropical wetland using in situ hyperspectral data. Remote Sens. 6(9):8494–8523.
   Google Scholar
• Prigent C, Papa F, Aires F, Jiménez C, Rossow WB, Matthews E. 2012. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys Res Lett. 39:L08403.
   Web of Science ®Google Scholar
• Psomas A, Kneubühler M, Huber S, Itten K, Zimmermann N. 2011. Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats. Int J Remote Sens. 32(24):9007–9031.
   Web of Science ®Google Scholar
• Ramoelo A, Cho MA, Mathieu R, Madonsela S, van de Kerchove R, Kaszta Z, Wolff E. 2015. 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. 43:43–54.
   Web of Science ®Google Scholar
• Ramsar Convention Secretariat. 2010. Handbook 17. Designating Ramsar sites: Appendix A, Annex I. Ramsar handbooks for the wise use of wetlands. 4th ed. Gland: Ramsar Convention Secretariat. http://www.ramsar.org/pdf/lib/hbk4-17.pdf.
   Google Scholar
• Ramsar Convention Secretariat. 2013. The Ramsar Convention Manual: a guide to the Convention on Wetlands (Ramsar, Iran, 1971), 6th ed. Gand (Switzerland): Ramsar Convention Secretariat.
   Google Scholar
• Ramsar Convention Secretariat (RCS). 2010. Handbook 17. Designating Ramsar sites: Appendix A, Annex I. Ramsar handbooks for the wise use of wetlands. 4th ed. Gland: Ramsar Convention Secretariat. http://www.ramsar.org/pdf/lib/hbk4-17.pdf.
   Google Scholar
• Rapinel S, Hubert-Moy L, Clement B. 2015. Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping. Int J Appl Earth Obs Geoinf. 37:56–64.
   Web of Science ®Google Scholar
• Rebelo AJ, Scheunders P, Esler KJ, Meire P. 2017. Detecting, mapping and classifying wetland fragments at a landscape scale. Remote Sens Appl. 8:212–223.
   Google Scholar
• Rebelo LM, McCartney MP, Finlayson CM. 2010. Wetlands of Sub-Saharan Africa: distribution and contribution of agriculture to livelihoods. Wetlands Ecol Manage. 18(5):557–572.
   Web of Science ®Google Scholar
• Reis V, Hermoso V, Hamilto KS, Ward D, Fluet-Chouinard E, Lehner B, Linke S. 2017. A global assessment of inland wetland conservation status. BioScience. 67(6):523–533.
   Web of Science ®Google Scholar
• Rivera-Monroy VH, Ellioton C, Narra S, Meselhe E, Zhao X, White E, Sasser CE, Visser JM, Meng X, Wang H, et al. 2019. Wetland biomass and productivity in coastal Louisiana: base line data (1976–2015) and knowledge gaps for the development of spatially explicit models for ecosystem restoration and rehabilitation initiatives. Water. 11(2054).
   Google Scholar
• Roberts DA, Dennison PE, Gardner M, Hetzel YL, Ustin SL, Lee C. 2003. Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible Infrared Imaging Spectrometer. IEEE Trans Geosci Remote Sens. 41:1297–1310.
   Web of Science ®Google Scholar
• Scott DB, Frail-Gauthier J, Mudie PJ. 2014. Coastal wetlands of the world: geology, ecology, distribution and applications. Cambridge: Cambridge University Press.
   Google Scholar
• Sibanda M, Mutanga O, Rouget M. 2015. Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments. ISPRS J Photogramm Remote Sens. 110:55–65.
   Web of Science ®Google Scholar
• Sieben EJJ, Nyambeni T, Mtshali H, Corry FTJ, Venter CE, MacKenzie DR, Matela TE, Pretorius L, Kotze DC. 2016. The herbaceous vegetation of subtropical freshwater wetlands in South Africa: classification, description and explanatory environmental factors. S Afr J Bot. 104:158–166.
   Web of Science ®Google Scholar
• Slagter B, Tsendbazar N-E, Vollrath A, Reiche J. 2020. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: a case study in St. Lucia wetlands, South Africa. Int J Appl Obs Geoinf. 86:102009.
   Web of Science ®Google Scholar
• Son N-T, Chen C-F, Chang N-B, Chen C-R, Chang L-Y, Thanh B-X. 2015. Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE J Sel Top Appl Earth Observations Remote Sens. 8(2):503–510.
   Web of Science ®Google Scholar
• Space Applications Centre (SAC). 2011. National wetland atlas. SAC, Indian Space Research, Ahmedabad.
   Google Scholar
• Stephenson PJ, Ntiamoa-Baidu Y, Simaika JP. 2020. The use of traditional and modern tools for monitoring wetlands biodiversity in Africa: challenges and opportunities. Front Environ Sci. 8:61.
   Web of Science ®Google Scholar
• Sutton PC, Anderson SJ, Costanza R, Kubiszewski I. 2016. The ecological economics of land degradation: impacts on ecosystem service values. Ecol Econ. 129:182–192.
   Web of Science ®Google Scholar
• Szantoi Z, Escobedo F, Abd-Elrahman A, Smith S, Pearlstine L. 2013. Analyzing fine-scale wetland composition using high resolution imagery and texture features. Int J Appl Earth Obs Geoinf. 23:204–212.
   Web of Science ®Google Scholar
• TEEB. 2013., The economics of ecosystems and biodiversity for water and wetlands. London and Brussels, Institute for European Environmental Policy (IEEP) & Ramsar Secretariat. 78.
   Google Scholar
• Thamaga KH, Dube T. 2019. Understanding seasonal dynamics of invasive water hyacinth (Eichhornia crassipes) in the Greater Letaba river system using Sentinel-2 satellite data. GIScience Remote Sens. 56(8):1355–1377.
   Web of Science ®Google Scholar
• Tian S, Zhang X, Tian J, Sun Q. 2016. Random forest classification of wetland land covers from multi-sensor data in the arid region of Xinjiang, China. Remote Sens. 8(11):954.
   Google Scholar
• Tiner RW, Lang MW, Klemas VV. 2015. Remote sensing of wetlands: applications and advances. Boca Raton (FL): CRC Press.
   Google Scholar
• Tiner RW. 2003. Estimated extent of geographically isolated wetlands in selected areas of the United States. Wetlands. 23:636–652.2.0.CO;2]
   Web of Science ®Google Scholar
• Titus JG, Anderson KE, Cahoon DR, Gesch DB, Gill SK, Gutierrez BT, Thieler ER, Williams SJ. 2009. Coastal sensitivity to sea-level rise: a focus on the mid-atlantic region. a report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Washington (DC): U.S. Climate Change Science Program and the Subcommittee on Global Change Research.
   Google Scholar
• Truus L. 2011. Estimation of above ground biomass of wetland, biomass of wetland. In: I Atazadeh, editor. Biomass and remote sensing of biomass. Shanghai (China): InTech; p. 75–86.
   Google Scholar
• Turner KT, Bergh J, Barendregt A, Straaten J, Maltby E. 2000. Ecological-economic analysis of wetlands: scientific integration for management and policy. Ecol Econ. 35(1):7–23.
   Web of Science ®Google Scholar
• Ustin SL, Gamon JA. 2010. Remote sensing of plant functional types. New Phytol. 186(4):795–816.
   PubMed Web of Science ®Google Scholar
• Van Asselen S, Verburg PH, Vermaat JE, Janse JH. 2013. Drivers of wetland conversion: a global meta-analysis. PLoS One. 8 (11):e81292.
   PubMed Web of Science ®Google Scholar
• van Dam AA, Kipkemboi J, Rahman MM, Gettel GM. 2013. Linking hydrology, ecosystem function, and livelihood outcomes in African papyrus wetlands using a Bayesian network model. Wetlands. 33(3):381–397.
   Web of Science ®Google Scholar
• Vasconcelos MJ, Mussá-Biai JC, Araújo A, Diniz MA. 2002. Land cover change in two protected areas of Guinea-Bissau (1956–1998). Appl Geogr. 22:139–156.
   Web of Science ®Google Scholar
• Villa JA, Mitch WJ. 2015. Carbon sequestration in different wetland plant communities in the Big Cypress Swamp region of southwest Florida. Int J Biodivers Sci Ecosyst Serv Manag. 11(1):17–28.
   Google Scholar
• Vörösmarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, Glidden S, Bunn SE, Sullivan CA, Liermann CR, et al. 2010. Global threats to human water security and river biodiversity. Nature. 467(7315):555–561.
   PubMed Web of Science ®Google Scholar
• Wang L, Dronova I, Gong P, Yang W, Li Y, Liu Q. 2012. A new time series vegetation–water index of phenological–hydrological trait across species and functional types for Poyang Lake wetland ecosystem. Remote Sens Environ. 125:49–63.
   Web of Science ®Google Scholar
• Were D, Kansiime F, Fetahi T, Cooper A, Jjuuko C. 2019. Carbon sequenstration by wetlands: a critical review of enhancement measures for climate change mitigation. Earth Syst Environ. 3(2):327–340.
   Google Scholar
• White L, Ryerson RA, Pasher J, Duffe J. 2020. State of science assessment of remote sensing of greater lakes coastal wetlands: responding to an operational requirement. Remote Sens. 12(18):3024.
   Google Scholar
• Wilen JE, Smith MD, Lockwood D, Botsford LW. 2002. Avoiding surprises: incorporating fisherman behavior into management models. Bull Mar Sci. 70:553 e575.
   Web of Science ®Google Scholar
• Wu Q, Lane CR, Xuecao L, Zhao K, Zhou Y, Clinton N, DeVries B, Golden HE, Lang M. 2019. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sens Environ. 228:1–13.
   PubMed Web of Science ®Google Scholar
• Wood A, Dixon A, McCartney MP. 2013. People-centred wetland management. In: A Wood, A Dixon, MP McCartney, editors. Wetland management and sustainable livelihoods in Africa. New York (NY): Routledge; p. 1–42.
   Google Scholar
• Wu W, Yang Z, Tian B, Huang Y, Zhou Y, Zhang L. 2018. Impacts of coastal reclamation on wetlands: loss, resilience, and sustainable management. Estuarine Coastal Shelf Sci. 210:153–161.
   Web of Science ®Google Scholar
• Xiaolong W, Jingyi H, Ligang X, Rongrong W, Yuwei C. 2014. Soil characteristics in relation to vegetation communities in the wetlands of Poyang lake, China. Wetlands. 34(4):829–839.
   Web of Science ®Google Scholar
• Xie G, Zhang C, Zhen L, Zhang L. 2017. Dynamic changes in the value of China’s ecosystem services. Ecosyst Serv. 26:146–154.
   Web of Science ®Google Scholar
• Xie S, Liu L, Zhang X, Yang J, Chen X, Gao Y. 2019. Automatic land-cover mapping using Landsat time series data based on google earth engine. Remote Sens. 11(24):3023.
   Google Scholar
• Xing L, Tang X, Wang H, Fan W, Wang G. 2018. Monitoring monthly surface water dynamics of Dongting Lake using Sentinel-1 data at 10 m. PeerJ. 6:e4992.
   PubMed Web of Science ®Google Scholar
• Xu T, Weng B, Yan D, Wang K, Li X, Bi W, Li M, Cheng X, Liu Y. 2019. Wetlands of international Importance: status, threats, and Future protection. Int J Environ Res Public Health. 16:1818.
   PubMed Web of Science ®Google Scholar
• Yaranga R, Custodio M, Chanamé Z, Pantoja R. 2018. Diversidad florística de pastizales según formación vegetal en la subcuenca del río Shullcas. Sciagropecu. 9(4):511–517.
   Google Scholar
• Yin S, Li X, Wu W. 2017. Comparative analysis of NPP changes in global tropical forests from 2001 to 2013. IOP Conf Ser Earth Environ Sci. 57(012009):012009.
   Google Scholar
• Zhang C, Wen L, Wang Y, Liu C, Zhou Y, Lei G. 2020. Can constructed wetlands be wildlife refuges? A review of their potential biodiversity conservation value. Sustainability. 12(4):1442.
   Web of Science ®Google Scholar
• Zheng D, Mi J, Ravesteijn W, Qiu F. 2014. Responsible resource management: the predicament and reform path for Chinese wetland conservation. Wetlands Ecol Manage. 22(5):509–521.
   Web of Science ®Google Scholar
• Zoffoli JP, Latorre BA, Naranjo P. 2008. Hairline, a postharvest cracking disorder in table grapes induced by sulfur dioxide. Postharvest Biol Technol. 47:90–97.
   Web of Science ®Google Scholar
• Zomer RJ, Trabucco A, Ustin SL. 2009. Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. J Environ Manag. 90:2170–2177.
   PubMed Web of Science ®Google Scholar