Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

[1]  N. Ramankutty,et al.  Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000 , 2008 .

[2]  Anne H. Schistad Solberg.  Contextual data fusion applied to forest map revision , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  Patrick Oswald,et al.  Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes , 2018, Remote. Sens..

[4]  Thomas J. Fuchs,et al.  A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes , 2016 .

[5]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[6]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[7]  Jessica L. McCarty,et al.  Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery , 2017 .

[8]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[9]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[10]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[11]  R. Dickinson,et al.  Seasonal changes in leaf area of Amazon forests from leaf flushing and abscission , 2011 .

[12]  Xavier Blaes,et al.  Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt , 2018, Remote Sensing of Environment.

[13]  Giorgos Mountrakis,et al.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research , 2016 .

[14]  David P. Roy,et al.  The global Landsat archive: Status, consolidation, and direction , 2016 .

[15]  C. Milesi,et al.  Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? , 2012 .

[16]  Stéphane Dupuy,et al.  A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM) , 2017, Remote. Sens..

[17]  P. Gong,et al.  Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa , 2018, Remote Sensing of Environment.

[18]  Philip B. Duffy,et al.  Biogeophysical impacts of cropland management changes on climate , 2006 .

[19]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[20]  Emma Izquierdo-Verdiguier,et al.  A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems , 2018, Remote. Sens..

[21]  Oguz Gungor,et al.  Evaluation of random forest method for agricultural crop classification , 2012 .

[22]  M. Turner,et al.  LANDSCAPE ECOLOGY : The Effect of Pattern on Process 1 , 2002 .

[23]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[24]  R. Dwivedi,et al.  Textural analysis of IRS-1D panchromatic data for land cover classification , 2002 .

[25]  Marjolein F. A. Vogels,et al.  Spatio-Temporal Patterns of Smallholder Irrigated Agriculture in the Horn of Africa Using GEOBIA and Sentinel-2 Imagery , 2019, Remote. Sens..

[26]  Craig A. Coburn,et al.  A multiscale texture analysis procedure for improved forest stand classification , 2004 .

[27]  H. Eva,et al.  Monitoring 25 years of land cover change dynamics in Africa: A sample based remote sensing approach , 2009 .

[28]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[29]  Tom Evans,et al.  Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling , 2015, Remote. Sens..

[30]  Pinki Mondal,et al.  Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India , 2020, Remote. Sens..

[31]  Joanne C. White,et al.  Optical remotely sensed time series data for land cover classification: A review , 2016 .

[32]  M. Castillo-Santiago,et al.  Estimation of tropical forest structure from SPOT-5 satellite images , 2010 .

[33]  E. LeDrew,et al.  Remote sensing of aquatic coastal ecosystem processes , 2006 .

[34]  David B. Lobell,et al.  Smallholder maize area and yield mapping at national scales with Google Earth Engine , 2019, Remote Sensing of Environment.

[35]  Jindi Wang,et al.  Impacts of Leaf Age on Canopy Spectral Signature Variation in Evergreen Chinese Fir Forests , 2018, Remote Sensing.

[36]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[37]  Forrest R. Stevens,et al.  Multitemporal settlement and population mapping from Landsat using Google Earth Engine , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[38]  H. Groote,et al.  The cost of accuracy in crop area estimation , 2005 .

[39]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[40]  Pinki Mondal,et al.  Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors , 2013 .

[41]  Marijke F. Augusteijn,et al.  Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier , 1995, IEEE Trans. Geosci. Remote. Sens..

[42]  Niti B. Mishra,et al.  Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with Random Forest , 2014 .

[43]  Steffen Fritz,et al.  Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps , 2015, Remote. Sens..

[44]  Lizhe Wang,et al.  Assessing Different Feature Sets' Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery , 2017, Remote. Sens..

[45]  Tarmo Virtanen,et al.  Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes , 2019, Remote Sensing of Environment.

[46]  C. Wright,et al.  Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data , 2007 .

[47]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[48]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[49]  D. Lobell,et al.  Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .

[50]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[51]  Arnon Karnieli,et al.  redicting forest structural parameters using the image texture derived from orldView-2 multispectral imagery in a dryland forest , Israel , 2011 .

[52]  Claudio Zucca,et al.  Enhancing the performance of regional land cover mapping , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[53]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[54]  Saeid Homayouni,et al.  The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform , 2018, Remote. Sens..

[55]  F. Parmiggiani,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[56]  Hannah Jacobson,et al.  A novel approach to mapping land conversion using Google Earth with an application to East Africa , 2015, Environ. Model. Softw..

[57]  Budiman Minasny,et al.  Using Google's cloud-based platform for digital soil mapping , 2015, Comput. Geosci..

[58]  Keith C. Clarke,et al.  Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[59]  Kenichi Tatsumi,et al.  Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data , 2015, Comput. Electron. Agric..

[60]  P. Defourny,et al.  Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery , 2006 .

[61]  J. Morison,et al.  Resource-conserving agriculture increases yields in developing countries. , 2006, Environmental science & technology.

[62]  Prasad S. Thenkabail,et al.  Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003 , 2010 .

[63]  Anitha Dasari,et al.  Methodology to map the spread of an invasive plant (Lantana camara L.) in forest ecosystems using Indian remote sensing satellite data , 2010 .

[64]  Isaac Luginaah,et al.  Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings , 2018, GeoJournal.

[65]  P. Atkinson,et al.  Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture , 2012 .

[66]  Fernando Pellon de Miranda,et al.  The semivariogram in comparison to the co-occurrence matrix for classification of image texture , 1998, IEEE Trans. Geosci. Remote. Sens..

[67]  André R. S. Marçal,et al.  An evaluation of changes in a mountainous rural landscape of Northeast Portugal using remotely sensed data , 2011 .

[68]  Jinwei Dong,et al.  Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.

[69]  Herman Eerens,et al.  Crop mapping in countries with small-scale farming: a case study for West Shewa, Ethiopia , 2013 .

[70]  J. L. Gittleman,et al.  The biodiversity of species and their rates of extinction, distribution, and protection , 2014, Science.

[71]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[72]  Hugh Eva,et al.  First Results From the Phenology-Based Synthesis Classifier Using Landsat 8 Imagery , 2015, IEEE Geoscience and Remote Sensing Letters.

[73]  Ö. Akar,et al.  Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey , 2015 .

[74]  Steffen Fritz,et al.  Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale , 2012, Remote. Sens..

[75]  A. Lotsch,et al.  Assessment of remotely sensed and statistical inventories of African agricultural fields , 2008 .

[76]  Herman H. Shugart,et al.  Remote sensing of structural complexity indices for habitat and species distribution modeling. , 2010 .

[77]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[78]  P. Atkinson,et al.  Relating SAR image texture to the biomass of regenerating tropical forests , 2005 .

[79]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[80]  Steffen Fritz,et al.  Highlighting continued uncertainty in global land cover maps for the user community , 2011 .

[81]  Rick Mueller,et al.  Mapping global cropland and field size , 2015, Global change biology.

[82]  Liangzhi You,et al.  An analysis of methodological and spatial differences in global cropping systems models and maps , 2015 .