Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul (UFMS), Avenida Costa e Silva, 79070-900, Campo Grande, Mato Grosso do Sul, Brazil Post-Graduate Program in Environment and Regional Development, University of Western São Paulo (UNOESTE), Rodovia Raposo Tavares, km 572-Limoeiro, 19067-175, Presidente Prudente, São Paulo, Brazil Faculty of Engineering and Architecture and Urbanism, University of Western São Paulo (UNOESTE), Rodovia Raposo Tavares, km 572-Limoeiro, 19067-175, Presidente Prudente, São Paulo, Brazil Department of Geography and Environmental Management, University of Waterloo (UW), Waterloo, ON N2L 3G1, Canada

[1]  Ilkka Pölönen,et al.  Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks , 2020, Remote. Sens..

[2]  K. Mejía,et al.  Mauritia flexuosa (Arecaceae: Calamoideae), an Amazonian palm with cultivation purposes in Peru , 2007 .

[3]  S. Franklin,et al.  Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data , 2018 .

[4]  Asli Ozdarici-Ok,et al.  Automatic detection and delineation of citrus trees from VHR satellite imagery , 2015 .

[5]  Clement Atzberger,et al.  Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data , 2018, Remote. Sens..

[6]  Megan M. Lewis,et al.  Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability , 2020, Remote. Sens..

[7]  Eija Honkavaara,et al.  Evaluation of Hyperspectral Multitemporal Information to Improve Tree Species Identification in the Highly Diverse Atlantic Forest , 2020, Remote. Sens..

[8]  N. Coops,et al.  Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada , 2010 .

[9]  Clement Atzberger,et al.  Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data , 2012, Remote. Sens..

[10]  Raul Queiroz Feitosa,et al.  Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery , 2020, Sensors.

[11]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[12]  Blake M. Allan,et al.  The application of Unmanned Aerial Vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems , 2020, Remote Sensing of Environment.

[13]  Helio Garcia Leite,et al.  Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR , 2019, Remote. Sens..

[14]  Xueliang Zhang,et al.  Deep learning in remote sensing applications: A meta-analysis and review , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[15]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  L. Bruzzone,et al.  Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data , 2012 .

[17]  Gerardo Flores,et al.  Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery , 2019, Remote. Sens..

[18]  V. Liesenberg,et al.  Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data , 2020, GIScience & Remote Sensing.

[19]  Liviu Theodor Ene,et al.  Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data , 2014 .

[20]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[21]  Jianhua Gong,et al.  UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis , 2015, Remote. Sens..

[22]  P. Adler,et al.  Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements , 2019, Forests.

[23]  Timothy A. Warner,et al.  Implementation of machine-learning classification in remote sensing: an applied review , 2018 .

[24]  E. Honkavaara,et al.  Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft , 2018 .

[25]  Ben G. Weinstein,et al.  Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks , 2019, bioRxiv.

[26]  Konstantinos P. Ferentinos,et al.  Deep learning models for plant disease detection and diagnosis , 2018, Comput. Electron. Agric..

[27]  S. Koptur,et al.  New findings on the pollination biology of Mauritia flexuosa (Arecaceae) in Roraima, Brazil: linking dioecy, wind, and habitat. , 2013, American journal of botany.

[28]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[29]  Moussa Sofiane Karoui,et al.  Palm Trees Counting in Remote Sensing Imagery Using Regression Convolutional Neural Network , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[30]  Erxue Chen,et al.  Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data , 2019, Remote. Sens..

[31]  Alexandre Siminski,et al.  Espécies Nativas da Flora Brasileira de Valor Econômico Atual ou Potencial , 2011 .

[32]  Maitiniyazi Maimaitijiang,et al.  Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning , 2019, Sensors.

[33]  Su Zhang,et al.  A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents , 2017, Remote. Sens..

[34]  Wuming Zhang,et al.  Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation , 2020, Remote. Sens..

[35]  Francisco Herrera,et al.  Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning , 2019, Remote. Sens..

[36]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Bogdan Zagajewski,et al.  Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images , 2017 .

[38]  Lin Liu,et al.  Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models , 2018, Remote. Sens..

[39]  Nicolas Brown,et al.  Mapping dead forest cover using a deep convolutional neural network and digital aerial photography , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[40]  Eija Honkavaara,et al.  Successional stages and their evolution in tropical forests using multi-temporal photogrammetric surface models and superpixels , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[41]  Nuno Silva,et al.  Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities , 2020, Remote. Sens..

[42]  Sergio Marconi,et al.  Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks , 2019, Remote. Sens..

[43]  Tal Hassner,et al.  Precise Detection in Densely Packed Scenes , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Juha Hyyppä,et al.  Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle , 2019, Forests.

[45]  Gregory Asner,et al.  Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data , 2012, Remote. Sens..

[46]  Guillermo Kemper,et al.  Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning , 2018, Forests.

[47]  S. Reutebuch,et al.  A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods , 2006 .

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

[49]  Heikki Saari,et al.  Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity , 2018, Remote. Sens..

[50]  Jennifer A. Holm,et al.  Population Dynamics of the Dioecious Amazonian Palm Mauritia flexuosa: Simulation Analysis of Sustainable Harvesting , 2008 .

[51]  Michele Dalponte,et al.  Tree Species Classification in Boreal Forests With Hyperspectral Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[53]  Ramanathan Sugumaran,et al.  Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object-Oriented Approach , 2008, Sensors.

[54]  Naoto Yokoya,et al.  Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges , 2019, Remote. Sens..

[55]  B. Apostol,et al.  Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery. , 2019, The Science of the total environment.

[56]  M. P. Gilmore,et al.  The socio-cultural importance of Mauritia flexuosa palm swamps (aguajales) and implications for multi-use management in two Maijuna communities of the Peruvian Amazon , 2013, Journal of Ethnobiology and Ethnomedicine.

[57]  M. Peñuela,et al.  The palm Mauritia flexuosa, a keystone plant resource on multiple fronts , 2019, Biodiversity and Conservation.

[58]  Raul Queiroz Feitosa,et al.  Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs , 2019, Sensors.

[59]  Nilton Nobuhiro Imai,et al.  A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery , 2020, ISPRS Journal of Photogrammetry and Remote Sensing.

[60]  Wenge Ni-Meister,et al.  Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data , 2019, Remote. Sens..

[61]  Juha Hyyppä,et al.  Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2018, Remote. Sens..

[62]  Jonathan Li,et al.  Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery , 2019, Remote. Sens..

[63]  Juha Hyyppä,et al.  Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2017, Remote. Sens..

[64]  Eija Honkavaara,et al.  Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level , 2015, Remote. Sens..

[65]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[66]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[67]  Aditya Khamparia,et al.  A systematic review on deep learning architectures and applications , 2019, Expert Syst. J. Knowl. Eng..

[68]  Maggi Kelly,et al.  Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks , 2018, Drones.

[69]  Moses Azong Cho,et al.  Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system , 2012 .

[70]  Weijia Li,et al.  Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images , 2016, Remote. Sens..

[71]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.