Amazon forest cover change mapping based on semantic segmentation by U-Nets

[1]  C. Heipke,et al.  EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON , 2020 .

[2]  K. P. Soman,et al.  Deep AlexNet with Reduced Number of Trainable Parameters for Satellite Image Classification , 2018 .

[3]  Yuliya Tarabalka,et al.  Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network , 2020, PloS one.

[4]  Maolin Xu,et al.  U-net Network for Building Information Extraction of Remote-Sensing Imagery , 2018, Int. J. Online Eng..

[5]  Hélène Gondard,et al.  Forest Management and Plant Species Diversity in Chestnut Stands of Three Mediterranean Areas , 2006, Biodiversity & Conservation.

[6]  Mounir Louhaichi,et al.  Mediterranean forest mapping using hyper-spectral satellite imagery , 2013, Arabian Journal of Geosciences.

[7]  Neil Flood,et al.  Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Florentin Wörgötter,et al.  Large Scale Palm Tree Detection in High Resolution Satellite Images Using U-Net , 2019, Remote. Sens..

[9]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Jean Pierre Henry Balbaud Ometto,et al.  Amazon deforestation in Brazil: effects, drivers and challenges , 2011 .

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Rodolfo Dirzo,et al.  Global State of Biodiversity and Loss , 2003 .

[13]  Giuseppe Scarpa,et al.  TanDEM-X Forest Mapping Using Convolutional Neural Networks , 2019, Remote. Sens..

[14]  Delphine Clara Zemp,et al.  Deforestation effects on Amazon forest resilience , 2017 .

[15]  P. Verburg,et al.  Towards better mapping of forest management patterns: A global allocation approach , 2019, Forest Ecology and Management.

[16]  M. H. Costa,et al.  Deforestation causes different subregional effects on the Amazon bioclimatic equilibrium , 2013 .

[17]  Peter M. Atkinson,et al.  Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Jürgen Kurths,et al.  A deforestation-induced tipping point for the South American monsoon system , 2017, Scientific Reports.

[19]  Marco Heurich,et al.  Understanding Forest Health with Remote Sensing -Part I - A Review of Spectral Traits, Processes and Remote-Sensing Characteristics , 2016, Remote. Sens..

[20]  Chengquan Huang,et al.  Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error , 2013, Int. J. Digit. Earth.

[21]  John R. Miller,et al.  Forest canopy closure from classification and spectral unmixing of scene components-multisensor evaluation of an open canopy , 1994, IEEE Trans. Geosci. Remote. Sens..

[22]  Renato Fontes Guimarães,et al.  Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks , 2020, Remote. Sens..

[23]  J. Terborgh,et al.  Compositional response of Amazon forests to climate change , 2018, Global change biology.

[24]  Chris Aldrich,et al.  Flotation froth image recognition with convolutional neural networks , 2019, Minerals Engineering.

[25]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[26]  Shaowen Wang,et al.  A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[27]  Shunichi Koshimura,et al.  Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami , 2018, Remote. Sens..

[28]  Vladimir V. Khryashchev,et al.  Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation , 2018, 2018 23rd Conference of Open Innovations Association (FRUCT).

[29]  Jan Verbesselt,et al.  Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series , 2017, Remote. Sens..

[30]  P. Fearnside Deforestation in Brazilian Amazonia: History, Rates, and Consequences , 2005 .

[31]  A. Alstrup,et al.  The ongoing cut-down of the Amazon rainforest threatens the climate and requires global tree planting projects: A short review. , 2019, Environmental research.

[32]  Yinghai Ke,et al.  Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery , 2018, Sensors.

[33]  Jan Verbesselt,et al.  Monitoring Deforestation at Sub-Annual Scales as Extreme Events in Landsat Data Cubes , 2016, Remote. Sens..

[34]  Silvio César Cazella,et al.  Amazon Rainforest Deforestation Daily Detection Tool Using Artificial Neural Networks and Satellite Images , 2012 .

[35]  Jacob Scharcanski,et al.  Dictionaries of deep features for land-use scene classification of very high spatial resolution images , 2019, Pattern Recognit..

[36]  G. Bonan Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests , 2008, Science.

[37]  J. D. Pilgrim,et al.  Wilderness and biodiversity conservation , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Kwon Lee,et al.  Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques , 2020, Remote. Sens..

[39]  Ricardo Dalagnol,et al.  Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images , 2020, Remote. Sens..

[40]  Lianru Gao,et al.  CNN-based Large Scale Landsat Image Classification , 2018, 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

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

[42]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[43]  H. Fritze,et al.  RELATIONSHIP BETWEEN BASAL SOIL RESPIRATION RATE, TREE STAND AND SOIL CHARACTERISTICS IN BOREAL FORESTS , 2005, Environmental monitoring and assessment.

[44]  Dirk Pflugmacher,et al.  Forest cover mapping in post-Soviet Central Asia using multi-resolution remote sensing imagery , 2017, Scientific Reports.

[45]  R. Betts,et al.  Climate Change, Deforestation, and the Fate of the Amazon , 2008, Science.

[46]  Philip M Fearnside,et al.  Deforestation in Amazonia , 2004, Science.

[47]  Rick L. Lawrence,et al.  Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest) , 2006 .

[48]  Ignácio Amigo,et al.  When will the Amazon hit a tipping point? , 2020, Nature.