Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series
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Damien Arvor | Samuel Corgne | Thomas Corpetti | Julie Betbeder | Felipe R.G. Daher | Tim Blossier | Renan Le Roux | Vinicius de Freitas Silgueiro | Carlos Antonio da Silva Junior | T. Corpetti | D. Arvor | J. Betbeder | S. Corgne | Felipe R. G. Daher | V. Silgueiro | Tim Blossier
[1] Thomas Houet,et al. Can UAVs fill the gap between in situ surveys and satellites for habitat mapping? , 2020, Remote Sensing of Environment.
[2] Chandra P. Giri,et al. Land Cover Characterization and Mapping of South America for the Year 2010 Using Landsat 30 m Satellite Data , 2014, Remote. Sens..
[3] Júlia G. Ribeiro,et al. Long-Term Annual Surface Water Change in the Brazilian Amazon Biome: Potential Links with Deforestation, Infrastructure Development and Climate Change , 2019, Water.
[4] C. Woodcock,et al. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .
[5] Mariana Belgiu,et al. Random forest in remote sensing: A review of applications and future directions , 2016 .
[6] Marcel Worring,et al. Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[7] Amit P. Sheth,et al. The SSN ontology of the W3C semantic sensor network incubator group , 2012, J. Web Semant..
[8] Krzysztof Janowicz,et al. Collaborative Ontology Development for the Geosciences , 2014, Trans. GIS.
[9] Damien Arvor,et al. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil , 2011 .
[10] M. Batistella,et al. Linear mixture model applied to Amazonian vegetation classification , 2003 .
[11] S. Dufour,et al. Monitoring thirty years of small water reservoirs proliferation in the southern Brazilian Amazon with Landsat time series , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[12] Roselyne Lacaze,et al. Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service , 2020, Remote. Sens..
[13] Hankui K. Zhang,et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .
[14] Damien Arvor,et al. Ontology-based classification of remote sensing images using spectral rules , 2017, Comput. Geosci..
[15] D. Nepstad,et al. Defending public interests in private lands: compliance, costs and potential environmental consequences of the Brazilian Forest Code in Mato Grosso , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.
[16] S. Morse,et al. Challenges in Using Earth Observation (EO) Data to Support Environmental Management in Brazil , 2020, Sustainability.
[17] Damien Arvor,et al. Big earth observation time series analysis for monitoring Brazilian agriculture , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[18] Madhur Anand,et al. Uncertainty in ecosystem mapping by remote sensing , 2013, Comput. Geosci..
[19] Shawn Bowers,et al. An ontology for describing and synthesizing ecological observation data , 2007, Ecol. Informatics.
[20] Damien Arvor,et al. Earth Observation Data for Habitat Monitoring ( EODHaM ) system , 2014 .
[21] G. Asner,et al. Cloud cover in Landsat observations of the Brazilian Amazon , 2001 .
[22] L. Durieux,et al. Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective , 2013 .
[23] Helen Couclelis,et al. Ontologies of geographic information , 2010, Int. J. Geogr. Inf. Sci..
[24] J. R. Jensen. Biophysical Remote Sensing , 1983 .
[25] Ronald Kemker,et al. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.
[26] G. Asner,et al. Spatial and temporal probabilities of obtaining cloud‐free Landsat images over the Brazilian tropical savanna , 2007 .
[27] D. Sprugel,et al. Disturbance, equilibrium, and environmental variability: What is ‘Natural’ vegetation in a changing environment? , 1991 .
[28] D. Lu. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon , 2005 .
[29] Isabelle Mougenot,et al. ThesauForm - Traits: A web based collaborative tool to develop a thesaurus for plant functional diversity research , 2012, Ecol. Informatics.
[30] P. Strobl,et al. Benefits of the free and open Landsat data policy , 2019, Remote Sensing of Environment.
[31] Dirk Tiede,et al. Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass , 2019, Int. J. Digit. Earth.
[32] Jin Chen,et al. Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .
[33] Cesar Guerreiro Diniz,et al. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data , 2016 .
[34] Gilberto Câmara,et al. Comparison of Cloud Cover Detection Algorithms on Sentinel-2 Images of the Amazon Tropical Forest , 2020, Remote. Sens..
[35] Vlasios Voudouris. Towards a unifying formalisation of geographic representation: the object–field model with uncertainty and semantics , 2010, Int. J. Geogr. Inf. Sci..
[36] Shawn Bowers,et al. Advancing ecological research with ontologies. , 2008, Trends in ecology & evolution.
[37] David Morin,et al. Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series , 2017, Remote. Sens..
[38] Stefan Lang,et al. Object-based image analysis for remote sensing applications: modeling reality – dealing with complexity , 2008 .
[39] Steffen Fritz,et al. Geo-Wiki: An online platform for improving global land cover , 2012, Environ. Model. Softw..
[40] R. Lucas,et al. Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping , 2007 .
[41] Thomas Blaschke,et al. Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[42] Svatava Janoušková,et al. Sustainable Development Goals: A need for relevant indicators , 2016 .
[43] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[44] Krzysztof Janowicz,et al. Observation‐Driven Geo‐Ontology Engineering , 2012, Trans. GIS.
[45] Russell G. Congalton,et al. Global Land Cover Mapping: A Review and Uncertainty Analysis , 2014, Remote. Sens..
[46] David M. Mark,et al. Toward a Theoretical Framework for Geographic Entity Types , 1993, COSIT.
[47] R. Sack. A Concept of Physical Space in Geography , 2010 .
[48] J. Sachs. From Millennium Development Goals to Sustainable Development Goals , 2012, The Lancet.
[49] Jungho Im,et al. Support vector machines in remote sensing: A review , 2011 .
[50] Luis Guanter,et al. Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images , 2016, Remote. Sens..
[51] Maria Adamo,et al. Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy , 2020, Remote. Sens..
[52] Chandra P. Giri,et al. Next generation of global land cover characterization, mapping, and monitoring , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[53] D. Arvor,et al. The 2008 map of consolidated rural areas in the Brazilian Legal Amazon state of Mato Grosso: Accuracy assessment and implications for the environmental regularization of rural properties , 2021 .
[54] Pedro Walfir M. Souza Filho,et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine , 2020, Remote. Sens..
[55] Alexander Maedche,et al. Exploring principles of user-centered agile software development: A literature review , 2015, Inf. Softw. Technol..
[56] C. Woodcock,et al. Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis , 2020, Remote Sensing of Environment.
[57] C. Justice,et al. High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.
[58] J. Pekel,et al. High-resolution mapping of global surface water and its long-term changes , 2016, Nature.
[59] P. Couteron,et al. Predicting tropical forest stand structure parameters from Fourier transform of very high‐resolution remotely sensed canopy images , 2005 .
[60] Andreas Uhl,et al. Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects , 2018, Remote Sensing of Environment.
[61] Bin Jiang,et al. Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information , 2016, ISPRS Int. J. Geo Inf..
[62] E. Lambin,et al. The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.
[63] Damien Arvor,et al. Remote Sensing and Cropping Practices: A Review , 2018, Remote. Sens..
[64] Michael Dixon,et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .
[65] Lorenzo Bruzzone,et al. Automatic Spectral Rule-Based Preliminary Mapping of Calibrated Landsat TM and ETM+ Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[66] Maria Petrou,et al. Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system , 2014 .
[67] Thomas R. Gruber,et al. A translation approach to portable ontology specifications , 1993, Knowl. Acquis..
[68] S. Morse,et al. Information from Earth Observation for the Management of Sustainable Land Use and Land Cover in Brazil: An Analysis of User Needs , 2020, Sustainability.
[69] A. Comber,et al. You know what land cover is but does anyone else?…an investigation into semantic and ontological confusion , 2005 .
[70] S. Lang,et al. Geons – domain-specific regionalization of space , 2014 .
[71] Alexei Lyapustin,et al. Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[72] B. He,et al. Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery , 2019, Remote Sensing of Environment.
[73] Geoffrey J. Hay,et al. Image objects and geographic objects , 2008 .
[74] Qinhuo Liu,et al. Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity , 2019, Remote. Sens..
[75] Z. Kalensky. AFRICOVER Land Cover Database and Map of Africa , 1998 .
[76] Palma Blonda,et al. Automatic Spectral-Rule-Based Preliminary Classification of Radiometrically Calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery—Part I: System Design and Implementation , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[77] Max J. Egenhofer,et al. Human conceptions of spaces: Implications for GIS , 1997 .
[78] M. Bustamante,et al. Rural Environmental Registry: An innovative model for land-use and environmental policies , 2018, Land Use Policy.
[79] Damien Arvor,et al. Ontologies to interpret remote sensing images: why do we need them? , 2019, GIScience & Remote Sensing.
[80] Scott L. Powell,et al. Bringing an ecological view of change to Landsat‐based remote sensing , 2014 .
[81] Luis González Abril,et al. A model for qualitative colour comparison using interval distances , 2013, Displays.
[82] François Waldner,et al. Automated annual cropland mapping using knowledge-based temporal features , 2015 .
[83] David E. Knapp,et al. Automated mapping of tropical deforestation and forest degradation: CLASlite , 2009 .
[84] Tiziana Simoniello,et al. A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses , 2018, Remote Sensing of Environment.