914
Views
0
CrossRef citations to date
0
Altmetric
Review Article

A review of fusion framework using optical sensors and Synthetic Aperture Radar imagery to detect and map land degradation and sustainable land management in the semi-arid regions

, &
Article: 2278325 | Received 17 Jan 2023, Accepted 27 Oct 2023, Published online: 17 Nov 2023

References

  • Abdikan S, Balik Sanli F, Sunar F, Ehlers M. 2014. A comparative data-fusion analysis of multi-sensor satellite images. Int J Digital Earth. 7(8):671–687. doi: 10.1080/17538947.2012.748846.
  • Abdikan S. 2018. Exploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area. Geocarto International. 33(1):21–37. doi: 10.1080/10106049.2016.1222635.
  • Abella SR, Craig DJ, Smith SD, Newton AC, Bardgett RD, Bowman WD, Kaufmann R, Schmidt SK, Berg V, Den Africa S, et al. 2013. Catalysing natural forest restoration on degraded tropical landscapes. S Afr J Geomatics. 2(2):1–17.
  • Adeli S, Salehi B, Mahdianpari M, Quackenbush LJ, Chapman B. 2021. Moving toward L-band NASA-ISRO SAR mission (NISAR) dense time series: multipolarization object-based classification of wetlands using two machine learning algorithms. Earth Space Sci. 8(11):e2021EA001742. doi: 10.1029/2021EA001742.
  • Ahmed UI, Rabus B, Beg MF. 2020. SAR and optical image fusion for urban infrastructure detection and monitoring. Remote Sensing Technologies and Applications in Urban Environments V. doi: 10.1117/12.2579480.
  • Alemu MM. 2016. Sustainable land management. JEP. 07(04):502–506. doi: 10.4236/jep.2016.74045.
  • Al Saleh AH, Misak RF, Al Tamimi SA, Al Baker H, Malek MJ. 2019. Characterization and mapping land degradation hotspots in the terrestrial ecosystem of Kuwait. Biomed J Sci Tech Res. 14(1):1–2. doi: 10.26717/bjstr.2019.14.002487.
  • Arnal J, Mayzel I. 2020. Parallel techniques for speckle noise reduction in medical ultrasound images. Adv Eng Softw. 148:102867. doi: 10.1016/j.advengsoft.2020.102867.
  • Barrett B, Raab C, Cawkwell F, Green S. 2016. Upland vegetation mapping using Random Forests with optical and radar satellite data. Remote Sens Ecol Conserv. 2(4):212–231. doi: 10.1002/RSE2.32.
  • Baydogan E, Sarp G. 2022. Urban footprint detection from night light, optical and SAR imageries: a comparison study. Remote Sens Appl Soc Environ. 27:100775. doi: 10.1016/J.RSASE.2022.100775.
  • Bedunah DJ, Angerer JP. 2012. Rangeland degradation, poverty, and conflict: how can rangeland scientists contribute to effective responses and solutions? Rangel. Ecol. Manag. 65(6):606–612. doi: 10.2111/REM-D-11-00155.1.
  • Belenguer-Plomer MA, Tanase MA, Fernandez-Carrillo A, Chuvieco E. 2019. Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies. Remote Sens Environ. 233:111345. doi: 10.1016/j.rse.2019.111345.
  • Bindraban PS, van der Velde M, Ye L, van den Berg M, Materechera S, Kiba DI, Tamene L, Ragnarsdóttir KV, Jongschaap R, Hoogmoed M, et al. 2012. Assessing the impact of soil degradation on food production. Curr Opin Environ Sustainability. 4(5):478–488., doi: 10.1016/j.cosust.2012.09.015.
  • Bunning S, Woodfine AC, Vallée D. 2016. Informing future interventions for scaling-up sustainable land management. Lessons learned for decision-makers from a review of experiences of the terrafrica strategic investment programme on SLM in sub Saharan Africa (SIP) under the NEPAD – terrafrica partnership. Food Agric Organ Rep. I5621:1–30.
  • Cerbelaud A, Roupioz L, Blanchet G, Breil P, Briottet X. 2021. A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: evidence from three case studies in the South of France. ISPRS J Photogramm Remote Sens. 182:153–175. doi: 10.1016/j.isprsjprs.2021.10.013.
  • Chen C, He X, Guo B, Zhao X, Chu Y. 2020. A pixel-level fusion method for multi-source optical remote sensing image combining the principal component analysis and curvelet transform. Earth Sci Inform. 13(4):1005–1013. doi: 10.1007/s12145-020-00472-7.
  • Chen R. 2015. The analysis of image fusion based on improved Brovery transform. Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference, 12. doi: 10.2991/iiicec-15.2015.251.
  • Cherubin MR, Karlen DL, Cerri C, EP, Franco ALC, Tormena CA, Davies CA, Cerri CC. 2016. Soil quality indexing strategies for evaluating sugarcane expansion in Brazil. PLoS One. 11(3):e0150860. doi: 10.1371/journal.pone.0150860.
  • Chetia S, Borkotoky K, Medhi S., Dutta P., Basumatary M. 2020. Land use land cover monitoring and change detection of Tinsukia, India. Int J Innov Technol Exploring Eng. 9(6):502–506. doi: 10.35940/ijitee.f3814.049620.
  • Chirakkal S, Bovolo F, Misra AR, Bruzzone L, Bhattacharya A. 2021. A general framework for change detection using multimodal remote sensing data. IEEE J Sel Top Appl Earth Observ Remote Sens. 14:10665–10680. doi: 10.1109/JSTARS.2021.3119358.
  • Cui S, Ma A, Zhang L, Xu M, Zhong Y. 2022. MAP-Net: SAR and optical image matching via image-based convolutional network with attention mechanism and spatial pyramid aggregated pooling. IEEE Trans Geosci Remote Sens. 60:1–13. doi: 10.1109/TGRS.2021.3066432.
  • Das B, Pal SC, Malik S, Chakrabortty R. 2019. Modeling groundwater potential zones of Puruliya district, West Bengal, India using remote sensing and GIS techniques. Geol Ecol Landscapes. 3(3):223–237. doi: 10.1080/24749508.2018.1555740.
  • DEA-NAP. 2018. Second national action programme for South Africa to combat desertification land degradation and the effects of drought (2018-2030). Available from https://www.dffe.gov.za/sites/default/files/docs/nap_desertification_land_degradation_droughteffects.pdf
  • Deepthy Mary A, Hepzibah Christinal A, Abraham Chandy D, Singh A, Pushkaran M. 2020. Speckle noise suppression in 2D ultrasound kidney images using local pattern based topological derivative. Pattern Recognit Lett. 131:49–55. doi: 10.1016/j.patrec.2019.12.005.
  • Dimov D, Kuhn J, Conrad C. 2016. Assessment of cropping system diversity in the Fergana Valley through image fusion of Landsat 8 and Sentinel-1. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. III-7:173–180. doi: 10.5194/isprsannals-III-7-173-2016.
  • Dourado GF, Motta JS, Filho ACP, Scott DF, Gabas SG. 2019. The use of remote sensing indices for land cover change detection. Anuário IGEO UFRJ. 42(2):72–85. doi: 10.11137/2019_2_72_85.
  • Du Y, Zhang Y, Ling F, Wang Q, Li W, Li X. 2016. Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens. 8(4):354. doi: 10.3390/rs8040354.
  • Dubovyk O. 2017. The role of remote sensing in land degradation assessments: opportunities and challenges. Eur J Remote Sens. 50(1):601–613. doi: 10.1080/22797254.2017.1378926.
  • Ehlers M, Klonus S, Johan Åstrand P, Rosso P. 2010. Multi-sensor image fusion for pansharpening in remote sensing. Int J Image Data Fusion. 1(1):25–45. doi: 10.1080/19479830903561985.
  • Euillades P, Euillades L, Pepe A, Mastro P, Falabella F, Imperatore P, Tang Y, Rosell P. 2021. Recent advancements in multi-temporal methods applied to new generation SAR systems and applications in South America. J South Am Earth Sci. 111:103410. doi: 10.1016/j.jsames.2021.103410.
  • Faour G. 2014. Detection and mapping of long-term land degradation and desertification in Arab region using MODESERT. Lebanese Sci J. 15(2):119–131.
  • Gambardella C, Parente R, Ciambrone A, Casbarra M. 2021. A principal components analysis-based method for the detection of cannabis plants using representation data by remote sensing. Data. 6(10):108. doi: 10.3390/data6100108.
  • Garcia RA, Hedley JD, Tin HC, Fearns PRCS. 2015. A method to analyze the potential of optical remote sensing for benthic habitat mapping. Remote Sens. 7(10):13157–13189. doi: 10.3390/rs71013157.
  • Ghoniemy TM, Hammad MM, Amein AS, Mahmoud TA. 2023. Multi-stage guided-filter for SAR and optical satellites images fusion using Curvelet and Gram Schmidt transforms for maritime surveillance. Int J Image Data Fusion. 14(1):38–57. doi: 10.1080/19479832.2021.2003446.
  • Gonzalez-Roglich M, Zvoleff A, Noon M, Liniger H, Fleiner R, Harari N, Garcia C. 2019. Synergizing global tools to monitor progress towards land degradation neutrality: trends. Earth and the World Overview of Conservation Approaches and Technologies sustainable land management database. Environ Sci Policy. 93:34–42. doi: 10.1016/j.envsci.2018.12.019.
  • Helman D, Mussery A, Lensky IM, Leu S. 2014. Detecting changes in biomass productivity in a different land management regimes in drylands using satellite-derived vegetation index. Soil Use Manage. 30(1):32–39. doi: 10.1111/sum.12099.
  • Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW. 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sens Environ. 170:121–132. doi: 10.1016/j.rse.2015.09.004.
  • Hermosilla T, Wulder MA, White JC, Coops NC. 2019. Prevalence of multiple forest disturbances and impact on vegetation regrowth from interannual Landsat time series (1985–2015). Remote Sens Environ. 233:111403. doi: 10.1016/j.rse.2019.111403.
  • Heydari SS, Mountrakis G. 2018. Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sens Environ. 204:648–658. doi: 10.1016/j.rse.2017.09.035.
  • Hillmer H, Woidt C, Istock A, Kobylinskiy A, Nguyen DT, Ahmed N, Brunner R, Kusserow T. 2021. Role of nanoimprint lithography for strongly miniaturized optical spectrometers. Nanomaterials. 11(1):164. doi: 10.3390/nano11010164.
  • Hua M, Chen Y, Zhang T, Zhou M, Zou W, Wu J. 2022. A speckle noise suppression method in phase-only holographic display based on an improved Gerchberg–Saxton algorithm. Optik. 251:168407. doi: 10.1016/j.ijleo.2021.168407.
  • Hughes LH, Marcos D, Lobry S, Tuia D, Schmitt M. 2020. A deep learning framework for matching of SAR and optical imagery. ISPRS J Photogramm Remote Sens. 169:166–179. doi: 10.1016/j.isprsjprs.2020.09.012.
  • IPBES. 2018. The IPBES assessment report on land degradation and restoration. In: Montanarella L, Scholes R, Brainich A, editors. Secretariat of the intergovernmental science-policy platform on biodiversity and ecosystem services. Bonn, Germany: Zenodo; p. 1–744. doi: 10.5281/zenodo.3237392.
  • Jabbar M, Chen X. 2008. Land degradation due to salinization in arid and semi-arid regions with the aid of geo-information techniques. Geo-Spatial Inform Sci. 11(2):112–120. doi: 10.1007/s11806-008-0013-z.
  • Jeong W, Son K, Cho J, Yang H, Park NC. 2019. Suppression algorithm of speckle noise for parallel phase-shift digital holography. Opt Laser Technol. 112:93–100. doi: 10.1016/j.optlastec.2018.10.053.
  • Jin Y, Dollevoet R, Li Z. 2022. Numerical simulation and characterization of speckle noise for laser Doppler vibrometer on moving platforms (LDVom). Opt Lasers Eng. 158:107135. doi: 10.1016/j.optlaseng.2022.107135.
  • Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ETA, et al. 2016. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8(1):70. doi: 10.3390/rs8010070.
  • Joshi N, Mitchard ETA, Woo N, Torres J, Moll-Rocek J, Ehammer A, Collins M, Jepsen MR, Fensholt R. 2015. Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data. Environ Res Lett. 10(3):034014. doi: 10.1088/1748-9326/10/3/034014.
  • Karaoğlu O, Bilge HŞ, Uluer İ. 2022. Removal of speckle noises from ultrasound images using five different deep learning networks. Eng Sci Technol Int J. 29:101030. doi: 10.1016/j.jestch.2021.06.010.
  • Karimi N, Taban MR. 2021. A convex variational method for super resolution of SAR image with speckle noise. Signal Process Image Commun. 90:116061. doi: 10.1016/j.image.2020.116061.
  • Khare S, Kaushik P. 2021. Speckle filtering of ultrasonic images using weighted nuclear norm minimization in wavelet domain. Biomed Signal Process Control. 70:102997. doi: 10.1016/j.bspc.2021.102997.
  • Koyama CN, Watanabe M, Hayashi M, Ogawa T, Shimada M. 2019. Mapping the spatial-temporal variability of tropical forests by ALOS-2 L-band SAR big data analysis. Remote Sens Environ. 233:111372. doi: 10.1016/j.rse.2019.111372.
  • Kulkarni SC, Rege PP. 2020. Pixel level fusion techniques for SAR and optical images: a review. Information Fusion. 59:13–29. doi: 10.1016/j.inffus.2020.01.003.
  • Kumar V, Kumar Dubey A, Gupta M, Singh V, Butola A, Singh Mehta D. 2021. Speckle noise reduction strategies in laser-based projection imaging, fluorescence microscopy, and digital holography with uniform illumination, improved image sharpness, and resolution. Opt Laser Technol. 141:107079. doi: 10.1016/j.optlastec.2021.107079.
  • Lal R. 2015. Restoring soil quality to mitigate soil degradation. Sustain. Times. 7(5):5875–5895. doi: 10.3390/su7055875.
  • Leal AS, Paiva HM. 2019. A new wavelet family for speckle noise reduction in medical ultrasound images. Measurement. 140:572–581. doi: 10.1016/j.measurement.2019.03.050.
  • Lehmann EA, Caccetta PA, Zhou ZS, McNeill SJ, Wu X, Mitchell AL. 2012. Joint processing of landsat and ALOS-PALSAR data for forest mapping and monitoring. IEEE Trans Geosci Remote Sens. 50(1):55–67. doi: 10.1109/TGRS.2011.2171495.
  • Li D, Yu W, Wang K, Jiang D, Jin Q. 2021. Speckle noise removal based on structural convolutional neural networks with feature fusion for medical image. Signal Process Image Commun. 99:116500. doi: 10.1016/j.image.2021.116500.
  • Li D. 2017. Remote sensing image fusion: a practical guide. Geo-Spatial Inform Sci. 20(1):56–56. doi: 10.1080/10095020.2017.1288843.
  • Liedtke CE, Growe S. 2001. Knowledge-based concepts for the fusion of multisensor and multitemporal aerial images. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) Vol. 2032. doi: 10.1007/3-540-45134-x_14.
  • Lindquist EJ, D’Annunzio R. 2016. Assessing global forest land-use change by object-based image analysis. Remote Sens. 8(8):678. doi: 10.3390/rs8080678.
  • Liniger H, Harari N, van Lynden G, Fleiner R, de Leeuw J, Bai Z, Critchley W. 2019. Achieving land degradation neutrality: the role of SLM knowledge in evidence-based decision-making. Environ Sci Policy. 94:123–134. doi: 10.1016/j.envsci.2019.01.001.
  • Masemola C, Cho MA, Ramoelo A. 2019. Assessing the effect of seasonality on leaf and canopy spectra for the discrimination of an alien tree species, Acacia mearnsii, from co-occurring native species using parametric and nonparametric classifiers. IEEE Trans Geosci Remote Sens. 57(8):5853–5867. doi: 10.1109/TGRS.2019.2902774.
  • Meadows ME, Hoffman MT. 2002. The nature, extent and causes of land degradation in South Africa: legacy of the past, lessons for the future? Area. 34(4):428–437. doi: 10.1111/1475-4762.00100.
  • Meraner A, Ebel P, Zhu XX, Schmitt M. 2020. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion. ISPRS J Photogramm Remote Sens. 166:333–346. doi: 10.1016/J.ISPRSJPRS.2020.05.013.
  • Mercier A, Betbeder J, Rumiano F, Baudry J, Gond V, Blanc L, Bourgoin C, Cornu G, Ciudad C, Marchamalo M, et al. 2019. Evaluation of Sentinel-1 and 2 time series for land cover classification of forest-agriculture mosaics in temperate and tropical landscapes. Remote Sens. 11(8):979. doi: 10.3390/rs11080904.
  • Mirzabaev A, Nkonya E, von Braun J. 2015. Economics of sustainable land management. Curr Opin Environ Sustainability. 15:9–19. doi: 10.1016/j.cosust.2015.07.004.
  • Mishra B, Susaki J. 2014. Optical and SAR data integration for automatic change pattern detection. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. II-7:39–46. doi: 10.5194/isprsannals-II-7-39-2014.
  • Mitri G, Nader M, Van der Molen I, Lovett J. 2014. Evaluating exposure to land degradation in association with repetitive armed conflicts in North Lebanon using multi-temporal satellite data. Environ Monit Assess. 186(11):7655–7672. doi: 10.1007/s10661-014-3957-5.
  • Mussa M, Teka H, Mesfin Y. 2017. Land use/cover change analysis and local community perception towards land cover change in the lowland of Bale rangelands, Southeast Ethiopia. Int J Biodiver Conserv. 9(12):363–372.
  • Nakano N, Hangai M, Nakanishi H, Mori S, Nukada M, Kotera Y, Ikeda HO, Nakamura H, Nonaka A, Yoshimura N. 2011. Macular ganglion cell layer imaging in preperimetric glaucoma with speckle noise-reduced spectral domain optical coherence tomography. Ophthalmology. 118(12):2414–2426. doi: 10.1016/J.OPHTHA.2011.06.015.
  • Nazarova T, Martin P, Giuliani G. 2020. Monitoring vegetation change in the presence of high cloud cover with sentinel-2 in a lowland tropical forest region in Brazil. Remote Sens. 12(11):1829. doi: 10.3390/rs12111829.
  • Nigussie Z, Tsunekawa A, Haregeweyn N, Adgo E, Nohmi M, Tsubo M, Aklog D, Meshesha DT, Abele S. 2017. Factors influencing small-scale farmers’ adoption of sustainable land management technologies in north-western Ethiopia. Land Use Policy. 67:57–64. doi: 10.1016/j.landusepol.2017.05.024.
  • Nkonya E, Mirzabaev A, von Braun J. 2015. Economics of land degradation and improvement - a global assessment for sustainable development. Cham: Springer. doi: 10.1007/978-3-319-19168-3.
  • Nkonya E, von Braun J, Mirzabaev A, Le QB, Kwon HY, Kirui O. 2013. Economics of land degradation initiative: methods and approach for global and national assessments. SSRN J. ZEF - Discussion Papers on Development Policy No. 183. doi: 10.2139/ssrn.2343636.
  • Nzuza P, Ramoelo A, Odindi J, Kahinda JM, Madonsela S. 2021. Predicting land degradation using Sentinel-2 and environmental variables in the Lepellane catchment of the Greater Sekhukhune District, South Africa. Phys Chem Earth. 124:102931. doi: 10.1016/j.pce.2020.102931.
  • Ochoa PA, Fries A, Mejía D, Burneo JI, Ruíz-Sinoga JD, Cerdà A. 2016. Effects of climate, land cover and topography on soil erosion risk in a semiarid basin of the Andes. Catena. 140:31–42. doi: 10.1016/j.catena.2016.01.011.
  • Osio A, Pham MT, Lefèvre S. 2020. Spatial processing of sentinel imagery for monitoring of acacia forest degradation in Lake Nakuru Riparian Reserve. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. V-3-2020(3):525–532. doi: 10.5194/isprs-annals-V-3-2020-525-2020.
  • Park SE, Jung YT, Kim HC. 2022. Monitoring permafrost changes in central Yakutia using optical and polarimetric SAR data. Remote Sens Environ. 274:112989. doi: 10.1016/j.rse.2022.112989.
  • Rajah P, Odindi J, Mutanga O. 2018. Feature level image fusion of optical imagery and Synthetic Aperture Radar (SAR) for invasive alien plant species detection and mapping. Remote Sens Appl: Soc Environ. 10:198–208. doi: 10.1016/j.rsase.2018.04.007.
  • Rajah P. 2018. The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping. Available from https://researchspace.ukzn.ac.za/handle/10413/18104
  • 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. doi: 10.1016/j.jag.2014.12.010.
  • Ramoelo A, Stolter C, Joubert D, Cho MA, Groengroeft A, Madibela OR, Zimmermann I, Pringle H. 2018. Rangeland monitoring and assessment: a review. Biodivers Ecol. 6:170–176. doi: 10.7809/b-e.00320.
  • Rani K, Sharma R, Stroppiana D, Boschetti M, Azar R, Barbieri M, Collivignarelli F, Gatti L, Fontanelli G, Busetto L, et al. 2017. A short note image fusion techniques. J Indian Soc Remote Sens. 45(1):215–227.
  • Reed MS, Buenemann M, Atlhopheng J, Akhtar-Schuster M, Bachmann F, Bastin G, Bigas H, Chanda R, Dougill AJ, Essahli W, et al. 2011. Cross-scale monitoring and assessment of land degradation and sustainable land management: a methodological framework for knowledge management. Land Degrad Dev. 22(2):261–271. doi: 10.1002/ldr.1087.
  • Reed MS, Stringer LC, Dougill AJ, Perkins JS, Atlhopheng JR, Mulale K, Favretto N. 2015. Reorienting land degradation towards sustainable land management: linking sustainable livelihoods with ecosystem services in rangeland systems. J Environ Manage. 151:472–485. doi: 10.1016/j.jenvman.2014.11.010.
  • Reiche J, Hamunyela E, Verbesselt J, Hoekman D, Herold M. 2018. Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sens Environ. 204:147–161. doi: 10.1016/j.rse.2017.10.034.
  • Reiche J, Souzax CM, Hoekman DH, Verbesselt J, Persaud H, Herold M. 2013. Feature level fusion of multi-temporal ALOS PALSAR and Landsat data for mapping and monitoring of tropical deforestation and forest degradation. IEEE J Sel Top Appl Earth Observ Remote Sens. 6(5):2159–2173. doi: 10.1109/JSTARS.2013.2245101.
  • Ritse V, Basumatary H, Kulnu AS, Dutta G, Phukan MM, Hazarika N. 2020. Monitoring land use land cover changes in the Eastern Himalayan landscape of Nagaland, Northeast India. Environ Monit Assess. 192(11):711. doi: 10.1007/s10661-020-08674-8.
  • Robertson LD, Davidson A, McNairn H, Hosseini M, Mitchell S, Abelleyra D d, Veron S, Defourny P, Le Maire G, Planells M, et al. 2019. Using dense time-series of C-band SAR imagery for classification of diverse, worldwide agricultural systems. International Geoscience and Remote Sensing Symposium (IGARSS); p. 6231–6234. doi: 10.1109/IGARSS.2019.8898364.
  • Rotich B, Ojwang D. 2021. Trends and drivers of forest cover change in the Cherangany hills forest ecosystem, Western Kenya. Global Ecol Conserv. 30:e01755. doi: 10.1016/j.gecco.2021.e01755.
  • Salentinig A, Gamba P. 2016. Multiscale multisensor decision level data fusion for urban mapping. 4th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2016 - Proceedings. doi: 10.1109/EORSA.2016.7552768.
  • Sanz MJ, Vente J d, Chotte J-L, Bernoux M, Kust G, Ruiz I, Almagro M, Alloza J-A, Vallejo R, Castillo V, et al. 2017. Sustainable Land Management contribution to successful land-based climate change adaptation and mitigation. A report of the science-policy interface. https://www.unccd.int/sites/default/files/documents/2017-09/UNCCD_Report_SLM_web_v2.pdf.
  • Schmidt J, Fassnacht FE, Förster M, Schmidtlein S. 2018. Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status. Remote Sens Ecol Conserv. 4(3):225–239. doi: 10.1002/rse2.68.
  • Schmidtlein S, Tichý L, Feilhauer H, Faude U. 2010. A brute‐force approach to vegetation classification. J Veg Sci. 21(6):1162–1171. doi: 10.1111/j.1654-1103.2010.01221.x.
  • Schmitt M, Hughes LH, Zhu XX. 2018. THE SEN1-2 dataset for deep learning in SAR-optical data fusion. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. IV-1(1):141–146. doi: 10.5194/isprs-annals-IV-1-141-2018.
  • Selvaraj R, Nagarajan SK. 2021. Land cover change detection from remotely sensed IoT data for assessment of land degradation: a survey. J Info Know Mgmt. 20(supp01):2140011. doi: 10.1142/S0219649221400116.
  • Sharma LK, Gupta R, Pandey PC. 2021. Future aspects and potential of the remote sensing technology to meet the natural resource needs. In: Pandey PC, Sharma LK, editors. Advances in remote sensing for natural resource monitoring. Hoboken, NJ: Wiley; p. 445–464. doi: 10.1002/9781119616016.ch22.
  • Smith WK, Dannenberg MP, Yan D, Herrmann S, Barnes ML, Barron-Gafford GA, Biederman JA, Ferrenberg S, Fox AM, Hudson A, et al. 2019. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sens Environ. 233:111401., doi: 10.1016/j.rse.2019.111401.
  • Stringer LC, Harris A. 2014. Land degradation in Dolj County, Southern Romania: environmental changes, impacts and responses. Land Degrad Dev. 25(1):17–28. doi: 10.1002/ldr.2260.
  • Stringer LC, Reed MS. 2007. Land degradation assessment in southern Africa: integrating local and scientific knowledge bases. Land Degrad Dev. 18(1):99–116. doi: 10.1002/ldr.760.
  • Symeonakis E, Higginbottom TP, Petroulaki K, Rabe A. 2018. Optimisation of savannah land cover characterisation with optical and SAR data. Remote Sens. 10(4):499. doi: 10.3390/rs10040499.
  • Taddese G. 2001. Land degradation: a challenge to Ethiopia. Environ Manage. 27(6):815–824. doi: 10.1007/s002670010190.
  • Taxak N, Singhal S. 2019. High PSNR based image fusion by weighted average brovery transform method. Proceedings of 3rd International Conference on 2019 Devices for Integrated Circuit, DevIC 2019. doi: 10.1109/DEVIC.2019.8783400.
  • Thomas R, Reed M, Clifton K, Appadurai N, Mills A, Zucca C, Kodsi E, Sircely J, Haddad F, Hagen C, et al. 2018. A framework for scaling sustainable land management options. Land Degrad Dev. 29(10):3272–3284. doi: 10.1002/ldr.3080.
  • Thomas R, Reed MS, Clifton K, Services CR, Appadurai N. 2017. Modalities for scaling up sustainable land management and restoration of degraded land. https://repo.mel.cgiar.org/handle/20.500.11766/6590.
  • Togliatti K, Hartman T, Walker VA, Arkebauer TJ, Suyker AE, VanLoocke A, Hornbuckle BK. 2019. Satellite L–band vegetation optical depth is directly proportional to crop water in the US Corn Belt. Remote Sens Environ. 233:111378. doi: 10.1016/j.rse.2019.111378.
  • Torres L, Becceneri JC, Freitas CC, Sant’Anna SJS, Sandri S. 2016. Learning OWA filters parameters for SAR imagery with multiple polarizations. In: Yang X-S, Papa JP, editors. Bio-inspired computation and applications in image processing. New York: Academic Press; p. 269–284. doi: 10.1016/B978-0-12-804536-7.00012-0.
  • Traoré S, Ouattara K, Ilstedt U, Schmidt M, Thiombiano A, Malmer A, Nyberg G. 2015. Effect of land degradation on carbon and nitrogen pools in two soil types of a semi-arid landscape in West Africa. Geoderma. 241-242:330–338. doi: 10.1016/j.geoderma.2014.11.027.
  • Tufail R, Ahmad A, Javed MA, Ahmad SR. 2022. A machine learning approach for accurate crop type mapping using combined SAR and optical time series data. Adv Space Res. 69(1):331–346. doi: 10.1016/j.asr.2021.09.019.
  • UNCCD. 2017a. Scaling up sustainable land management and restoration of degraded land. Global Land Outlook, March, 25. Available from https://www.unccd.int/resources/publications/scaling-sustainable-land-management-and-restoration-degraded-land
  • UNCCD. 2017b. The UNCCD  securing life on land. Available from https://catalogue.unccd.int/819_Securing_Life_on_Land_ENG.pdf
  • Urban M, Schellenberg K, Morgenthal T, Hirner A, Gessner U, Mogonong B, Zhang Z, Baade J, Collett A, Schmullius C. 2021. Using Sentinel-1 and Sentinel-2 time series for Slangbos mapping in the Free State Province, South Africa. Remote Sens. 13(17):3342.
  • van Beijma S, Comber A, Lamb A. 2014. Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens Environ. 149:118–129. doi: 10.1016/j.rse.2014.04.010.
  • Varadarajan D, Magnain C, Fogarty M, Boas DA, Fischl B, Wang H. 2022. A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images. NeuroImage. 257:119304. doi: 10.1016/j.neuroimage.2022.119304.
  • Von Maltitz GP, Gambiza J, Kellner K, Rambau T, Lindeque L, Kgope B. 2019. Experiences from the South African land degradation neutrality target setting process. Environ Sci Policy. 101:54–62. doi: 10.1016/j.envsci.2019.07.003.
  • Wairiu M. 2017. Land degradation and sustainable land management practices in Pacific Island Countries. Reg Environ Change. 17(4):1053–1064. doi: 10.1007/s10113-016-1041-0.
  • Wang F, Krause S, Rembe C. 2022. Signal diversity for the reduction of signal dropouts and speckle noise in a laser-Doppler extensometer. Measurement: Sensors. 22:100377. doi: 10.1016/j.measen.2022.100377.
  • Wang L, Diao C, Xian G, Yin D, Lu Y, Zou S, Erickson TA. 2020. A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sens Environ. 248:112002. doi: 10.1016/j.rse.2020.112002.
  • Webb NP, Van Zee JW, Karl JW, Herrick JE, Courtright EM, Billings BJ, Boyd R, Chappell A, Duniway MC, Derner JD, et al. 2017. Enhancing wind erosion monitoring and assessment for US rangelands. Rangelands. 39(3-4):85–96. doi: 10.1016/j.rala.2017.04.001.
  • World Bank. 2008. Management land, energy. doi: 10.1596/978-0-8213-7432-0.
  • Yin D, Gu Z, Zhang Y, Gu F, Nie S, Feng S, Ma J, Yuan C. 2020. Speckle noise reduction in coherent imaging based on deep learning without clean data. Opt Lasers Eng. 133:106151. doi: 10.1016/j.optlaseng.2020.106151.
  • Zeng Y, Núñez A, Li Z. 2022. Speckle noise reduction for structural vibration measurement with laser Doppler vibrometer on moving platform. Mech Syst Sig Process. 178:109196. doi: 10.1016/j.ymssp.2022.109196.
  • Zhang C, Feng Y, Hu L, Tapete D, Pan L, Liang Z, Cigna F, Yue P. 2022. A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images. Int J Appl Earth Obs Geoinf. 109:102769. doi: 10.1016/j.jag.2022.102769.
  • Zhang G, Song R, Ding B, Zhu Y, Xue H, Tu J, Hang J, Ye X, Xu D. 2021. Laplacian pyramid based non-linear coherence diffusion for real-time ultrasound image speckle reduction. Appl Acoust. 183:108298. doi: 10.1016/j.apacoust.2021.108298.
  • Zhang J, Yang J, Zhao Z, Li H, Zhang Y. 2010. Block-regression based fusion of optical and SAR imagery for feature enhancement. Int J Remote Sens. 31(9):2325–2345. doi: 10.1080/01431160902980324.
  • Zhang P, Ban Y, Nascetti A. 2021. Learning U-Net without forgetting for near real-time wildfire monitoring by the fusion of SAR and optical time series. Remote Sens Environ. 261:112467. doi: 10.1016/j.rse.2021.112467.
  • Zhang R, Tang X, You S, Duan K, Xiang H, Luo H. 2020. A novel feature-level fusion framework using optical and SAR remote sensing images for land use/land cover (LULC) classification in cloudy mountainous area. Appl Sci. 10(8):2928. doi: 10.3390/app10082928.
  • Zhang S, Yao L, Sun A, Tay Y. 2019. Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv. 52(1):1–38.
  • Zhang W, Xu M. 2019. Translate SAR data into optical image using IHS and wavelet transform integrated fusion. J Indian Soc Remote Sens. 47(1):125–137. doi: 10.1007/s12524-018-0879-7.
  • Zhao W, Qu Y, Chen J, Yuan Z. 2020. Deeply synergistic optical and SAR time series for crop dynamic monitoring. Remote Sens Environ. 247:111952. doi: 10.1016/j.rse.2020.111952.
  • Zhao W, Qu Y, Zhang L, Li K. 2022. Spatial-aware SAR-optical time-series deep integration for crop phenology tracking. Remote Sens Environ. 276:113046. doi: 10.1016/j.rse.2022.113046.
  • Zheng Z, Ma A, Zhang L, Zhong Y. 2021. Deep multisensor learning for missing-modality all-weather mapping. ISPRS J Photogramm Remote Sens. 174:254–264. doi: 10.1016/j.isprsjprs.2020.12.009.
  • Zhou X, Zhuo J, Krahenbuhl P. 2019. Bottom-up object detection by grouping extreme and center points. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; p. 850–859.
  • Zimbres B, Rodríguez-Veiga P, Shimbo JZ, da Conceição Bispo P, Balzter H, Bustamante M, Roitman I, Haidar R, Miranda S, Gomes L, et al. 2021. Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome. For Ecol Manage. 499:119615. doi: 10.1016/j.foreco.2021.119615.