Evaluation of polarimetry and interferometry of sentinel-1A SAR data for land use and land cover of the Brazilian Amazon Region

References

• Agresti A. 1996. An introduction to categorical data analysis. New York: Wiley.
   Google Scholar
• Almeida CD, Coutinho AC, Esquerdo J, Adami M, Venturieri A, Diniz CG, Dessay N, Durieux L, Gomes AR. 2016. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amaz. 46(3):291–302.
   Web of Science ®Google Scholar
• Asner GP. 2001. Cloud cover in Landsat observations of the Brazilian Amazon. Int J Remote Sens. 22(18):3855–3862.
   Web of Science ®Google Scholar
• Attarchi S, Gloaguen R. 2014. Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the hyrcanian Forest. Remote Sens. 6(5):3624–3647.
   Google Scholar
• Balzter H, Cole B, Thiel C, Schmullius C. 2015. Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sens. 7(11):14876–14898.
   Web of Science ®Google Scholar
• Barona E, Ramankutty N, Hyman G, Coomes OT. 2010. The role of pasture and soybean in deforestation of the Brazilian Amazon. Environ Res Lett. 5:1–9.
   Web of Science ®Google Scholar
• Becker BK. 2005. Geopolítica da Amazônia *. Estud Av. 19(53):71–86.
   Google Scholar
• Breiman L. 2001. Random Forests. Mach Learn. 45(1):5–32.
   Web of Science ®Google Scholar
• Borgeaud M, Wegmueller U. 1996. On the use of ERS SAR interferometry for retrieval of geo and biophysical information. Paper presented at the ESA Workshop on Applications of ERS SAR Interferometry; September 30-October 2; Zurich.
   Google Scholar
• Brisco B, Brown RJ, Snider B, Sofko GJ, Koehler JA, Wacker AG. 1991. Tillage effects on the radar backscattering coefficient of grain stubble fields. Int J Remote Sens. 12 (11):2283–2298.
   Web of Science ®Google Scholar
• Carreiras JMB, Jones J, Lucas RM, Shimabukuro YE. 2017. Mapping major land cover types and retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data. Remote Sens Environ. 194:16–32.
   Web of Science ®Google Scholar
• Carreiras JMB, Pereira JMC, Campagnolo ML, Shimabukuro YE. 2006. Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data. Remote Sens Environ. 101(3):283–298.
   Web of Science ®Google Scholar
• Carvalho R d S, Nascimento B d, Querino CAS, Silva M d, Delgado A. 2016. Comportamento das séries temporais de temperatura do ar, umidade e precipitação pluviométrica no município de Ariquemes (Rondônia-Brasil). [Air temperature, humidity and rainy precipitation time series behavior in Ariquemes town (Rondônia, Brazil)]. Rev Bras Climatol. 18:123–142.
   Google Scholar
• Carvalho TS, Magalhães A, Domingues EP. 2016. Desmatamento e a contribuição econômica da floresta na Amazônia. Estud Econ. 46(2):499–531.
   Google Scholar
• Convention on Biological Diversity (CDB). 2020. Definitions: Indicative definitions taken from the Report of the ad hoc technical expert group on forest biological diversity. [accessed 2020 February 15]. https://www.cbd.int/forest/definitions.shtml.
   Google Scholar
• Cloude SR. 2007. The dual polarisation entropy/alpha decomposition. Proc 3rd Int Work Sci Appl SAR Polarim Polarim Interferom; 1–6. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:THE+DUAL+POLARISATION+ENTROPY+/+ALPHA+DECOMPOSITION+:#0.
   Google Scholar
• Congalton RG, Green K. 2009. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton: CRC Press.
   Google Scholar
• Cortes C, Vapnik V. 1995. Support-vector networks. Mach Learn. 20(3):273–297.
   Web of Science ®Google Scholar
• Delgado-Aguilar MJ, Fassnacht FE, Peralvo M, Gross CP, Schmitt CB. 2017. Potential of TerraSAR-X and sentinel 1 imagery to map deforested areas and derive degradation status in complex rain forests of ecuador. Int Forest Rev. 19(1):102–118.
   Web of Science ®Google Scholar
• Du P, Samat A, Waske B, Liu S, Li Z. 2015. Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS J Photogramm Remote Sens. 105:38–53.
   Web of Science ®Google Scholar
• Duro DC, Franklin SE, Dubé MG. 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens Environ. 118:259–272.
   Web of Science ®Google Scholar
• Engelbrecht J, Theron A, Vhengani L, Kemp J. 2017. A simple normalized difference approach to burnt area mapping using multi-polarisation C-Band SAR. Remote Sens. 9:1–13.
   Google Scholar
• Foody GM. 2004. Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens. 70(5):627–633.
   Web of Science ®Google Scholar
• Freeman A, Durden SL. 1998. A Three-Component Scattering Model for Polarimetric SAR Data. IEEE Trans Geosci Remote Sens. 36(3):963–973.
   Web of Science ®Google Scholar
• Fu B, Wang Y, Campbell A, Li Y, Zhang B, Yin S, Xing Z, Jin X. 2017. Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data. Ecol Indic. 73:105–117.
   Web of Science ®Google Scholar
• Furtado L. d A, Silva TSF, Novo E. d M. 2016. Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon várzea wetlands. Remote Sens Environ. 174:212–222.
   Web of Science ®Google Scholar
• Gaboardi C. 2003. Utilização de imagem de coerência SAR para classificação do uso da terra: Floresta Nacional do Tapajós [master’s thesis]. São Jos Instituto Nacional de Pesquisas Espaciais.
   Google Scholar
• Gama FF, dos Santos JR, Mura JC. 2016. Continuous monitoring of biophysical Eucalyptus sp. parameters using interferometric synthetic aperture radar data in P and X bands. J Appl Remote Sens. 10(2):026002.
   Google Scholar
• Gosselin G, Touzi R, Cavayas F. 2013. Polarimetric Radarsat-2 wetland classification using the Touzi decomposition: case of the Lac Saint-Pierre Ramsar wetland. Can J Remote Sens. 39(6):491–506.
   Web of Science ®Google Scholar
• Jacob AW, Vicente-Guijalba F, Lopez-Martinez C, Lopez-Sanchez JM, Litzinger M, Kristen H, Mestre-Quereda A, Ziolkowski D, Lavalle M, Notarnicola C, et al. 2020. Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple. IEEE J Sel Top Appl Earth Observations Remote Sensing. 13:535–552.
   Web of Science ®Google Scholar
• Khalil RZ, Saad-Ul-Haque. 2018. InSAR Coherence-based land cover classification of Okara, Pakistan. Egypt J Remote Sens Sp Sci. 21:S23–S28.
   Web of Science ®Google Scholar
• Lee JS. 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell. 2 (2):165–168.
   PubMed Web of Science ®Google Scholar
• Li Z, Chen H, White JC, Wulder MA, Hermosilla T. 2020. Int J Appl Earth Obs Geoinformation Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data. Int J Appl Earth Obs Geoinf. 85:102007.
   Web of Science ®Google Scholar
• Lucas RM, Honzák M, Amaral ID, Curran PJ, Foody GM. 2002. Forest regeneration on abandoned clearances in central Amazonia. Int J Remote Sens. 23(5):965–988.
   Web of Science ®Google Scholar
• Lucas RM, Honzák M, Curran PJ, Foody GM, Milne R, Brown T, Amaral S. 2000. Mapping the regional extent of tropical forest regeneration stages in the Brazilian Legal Amazon using NOAA AVHRR data. Int J Remote Sens. 21(15):2855–2881.
   Web of Science ®Google Scholar
• Mansaray LR, Wang F, Huang J, Yang L, Kanu AS. 2019. Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel- 2A datasets. Geocarto Int. 0:1–21.
   Google Scholar
• Martins VS, Novo E, Lyapustin A, Aragão L, Freitas SR, Barbosa C. 2018. Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis. ISPRS J Photogramm Remote Sens. 145:309–327.
   Web of Science ®Google Scholar
• McNemar Q. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika. 12(2):153–157.
   PubMed Web of Science ®Google Scholar
• Mercier A, Betbeder J, Rumiano F, Baudry J, Gond V, Blanc L, Bourgoin C, Cornu G, Ciudad C, Marchamalo M. 2019. Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest – Agriculture Mosaics in Temperate and Tropical Landscapes. Remote Sens. 11:1–20.
   Google Scholar
• Mishra VN, Prasad R, Kumar P, Srivastava PK, Rai PK. 2017. Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information. J Appl Remote Sens. 11(04):046003.
   Google Scholar
• Mishra VN, Prasad R, Rai PK, Vishwakarma AK, Arora A. 2019. Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data. Earth Sci Inform. 12(1):71–86.
   Web of Science ®Google Scholar
• Neeff T, Lucas RM, Santos J. d, Brondizio ES, Freitas CC. 2006. Area and Age of Secondary Forests in Brazilian Amazonia 1978 – 2002: An Empirical Estimate. Ecosystems. 9(4):609–623.
   Web of Science ®Google Scholar
• Ngo KD, Lechner AM, Vu TT. 2020. Remote Sensing Applications: Society and Environment Land cover mapping of the Mekong Delta to support natural resource management with multi-temporal Sentinel-1A synthetic aperture radar imagery. Remote Sens Appl Soc Environ. 17:1–14.
   Google Scholar
• Paneque-Gálvez J, Mas J-F, Moré G, Cristóbal J, Orta-Martínez M, Luz AC, Guèze M, Macía MJ, Reyes-García V. 2013. Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity. Int J Appl Earth Obs Geoinf. 23:372–383.
   Web of Science ®Google Scholar
• Plank S, Jüssi M, Martinis S, Twele A. 2017. Mapping of flooded vegetation by means of polarimetric sentinel-1 and ALOS-2/PALSAR-2 imagery. Int J Remote Sens. 38(13):3831–3850.
   Web of Science ®Google Scholar
• R Core Team. 2018. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
   Google Scholar
• Sica F, Pulella A, Nannini M, Pinheiro M, Rizzoli P. 2019. Repeat-pass SAR interferometry for land cover classification: a methodology using Sentinel-1 Short-Time-Series. Remote. Remote Sens Environ. 232:111277.
   Web of Science ®Google Scholar
• Shang X, Chisholm LA. 2013. Classification of australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms. IEEE J Sel Top Appl Earth Observ Remote Sens. 7(6):2481–2489.
   Web of Science ®Google Scholar
• Shuai G, Zhang J, Basso B, Pan Y, Zhu X, Zhu S, Liu H. 2019. Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image. Int J Appl Earth Obs Geoinf. 74:1–15.
   Web of Science ®Google Scholar
• Small D, Schubert A. 2008. Guide to ASAR geocoding. Zurich: Remote Sensing Laboratories, University of Zurich.
   Google Scholar
• Soares-Filho BS, Nepstad DC, Curran L, Cerqueira GC, Garcia RA, Ramos CA, Voll E, Mcdonald A, Lefebvre P, Schlesinger P, et al. 2005. Cenários de desmatamento para a Amazônia. Estud Av. 19(54):137–152.
   Google Scholar
• Souza CM, Siqueira JV, Sales MH, Fonseca AV, Ribeiro JG, Numata I, Cochrane MA, Barber CP, Roberts DA, Barlow J. 2013. Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens. 5(11):5493–5513.
   Google Scholar
• Srivastava HS, Patel P, Navalgund RR. 2006. Application potentials of synthetic aperture radar interferometry for land-cover mapping and crop-height estimation. Current. 91:783–788.
   Google Scholar
• TerraBrasilis. 2020. Desmatamento. INPE. [accessed 2020 February 15]. http://terrabrasilis.dpi.inpe.br/app/dashboard/deforestation/biomes/amazon/increments.
   Google Scholar
• Ulaby FT, Allen CT, Eger G, Kanemasu E. 1984. Relating the microwave backscattering coefficient to leaf area index. Remote Sens Environ. 14(1-3):113–133.
   Web of Science ®Google Scholar
• Vaeza RF, Oliveira P. C d, Maia AG, Disperati AA. 2011. Uso e Ocupação do Solo em Bacia Hidrográfica Urbana a Partir de Imagens Orbitais de Alta Resolução. Floram. 17(1):23–29.
   Google Scholar
• Varghese AO, Suryavanshi A, Joshi AK. 2016. Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data. Int J Remote Sens. 37(3):694–709.
   Web of Science ®Google Scholar
• Wang L, Liu H. 2006. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int J Geogr Inf Sci. 20(2):193–213.
   Web of Science ®Google Scholar
• Waske B, Braun M. 2009. Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS J Photogramm Remote Sens. 64(5):450–457.
   Web of Science ®Google Scholar
• Wegmüller U, Santoro M, Werner C, Cartus O. 2015. On the estimation and interpretation of sentinel-1 TOPS InSAR coherence. Eur Sp Agency, (Special Publ ESA SP. SP-731; 1–4.
   Google Scholar
• Wegmüller U, Werner C. 1997. Retrieval of vegetation parameters with SAR interferometry. IEEE Trans Geosci Remote Sens. 35(1):18–24.
   Web of Science ®Google Scholar
• Whyte A, Ferentinos KP, Petropoulos GP. 2018. A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms. Environ Model Softw. 104:40–54.
   Web of Science ®Google Scholar
• Yang C, Wu G, Ding K, Shi T, Li Q, Wang J. 2017. Improving land use/land cover classification by integrating pixel unmixing and decision tree methods. Remote Sens. 9: 1–16. 
   Google Scholar