Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series

[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.