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Editorial for the Special Issue "Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery"

Xiuliang Jin, Zhenhai Li, Clement Atzberger
2020 Remote Sensing  
This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery.  ...  The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems.  ...  Acknowledgments: The Guest Editors would like to thank the authors who contributed to this Special Issue and the reviewers who dedicated their time and provided the authors with valuable and constructive  ... 
doi:10.3390/rs12060940 fatcat:2dvq5kvlhrfgpjmalwuvexixl4

High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

Wang Li, Zheng Niu, Rong Shang, Yuchu Qin, Li Wang, Hanyue Chen
2020 International Journal of Applied Earth Observation and Geoinformation  
Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity.  ...  available canopy height (H canopy ) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data.  ...  Fig. 7 . 7 Canopy height maps (250-m) predicted by (a) deep-learning model and (b) random forest model using Sentinel satellite data.  ... 
doi:10.1016/j.jag.2020.102163 fatcat:oyktavb235e55eoddhd6ri7eka

Country-wide high-resolution vegetation height mapping with Sentinel-2 [article]

Nico Lang, Konrad Schindler, Jan Dirk Wegner
2019 arXiv   pre-print
Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon, respectively Switzerland.  ...  A deep convolutional network was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height.  ...  Technically, this amounts to regressing canopy height from monocular (multi-spectral) images, using known tree heights as reference data.  ... 
arXiv:1904.13270v1 fatcat:ij5i7wevnzh5lnp6kcxf2q2jw4

Editorial Summary, Remote Sensing Special Issue "Advances in Remote Sensing for Global Forest Monitoring"

Erkki Tomppo, Guangxing Wang, Jaan Praks, Ronald E. McRoberts, Lars T. Waser
2021 Remote Sensing  
The need for timely, spatially, and thematically accurate information regarding forests is increasing because of the key role of forests in the global carbon balance and sustainable social, economic, ecological  ...  , and cultural development [...]  ...  Multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers were used.  ... 
doi:10.3390/rs13040597 fatcat:5gqmfqf3ojagtid7pd7eyi2m7q

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

Tien Pham, Naoto Yokoya, Dieu Bui, Kunihiko Yoshino, Daniel Friess
2019 Remote Sensing  
We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and  ...  A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data.  ...  Pereira, Kampel, and Cunha-Lignon [76] showed that ALOS PALSAR L-band imagery can be used to characterize tree and canopy height, as well as the DBH of mangrove forests with satisfactory results.  ... 
doi:10.3390/rs11030230 fatcat:xgxftojh5vdfhgkpqcwehgbkwa

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Peter Surový, Karel Kuželka
2019 Forests  
, including variable temporal and spectral resolutions.  ...  The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial  ...  ) and multi-temporal Landsat-8 images (L8).  ... 
doi:10.3390/f10030273 fatcat:fzowinbsvfdwtnpe45f6yfzu6a

A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests

Heather Grybas, Russell G. Congalton
2021 Remote Sensing  
Mid- to late spring imagery produced the highest accuracies, potentially due to high spectral heterogeneity between species and homogeneity within species during this time.  ...  The multi-temporal classification approach significantly improved accuracy; however, there was no significant benefit when more than three dates were used.  ...  Acknowledgments: The authors would like to acknowledge Jacob Dearborn for his assistance with reference data collection. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13132631 fatcat:rxpm4cbjyvgqva7ocsjg4evs44

Chimera: A Multi-Task Recurrent Convolutional Neural Network for Forest Classification and Structural Estimation

Tony Chang, Brandon Rasmussen, Brett Dickson, Luke Zachmann
2019 Remote Sensing  
, spectral, and temporal resolutions.  ...  Here, we present a new model, based on a deep learning architecture, that performs both classification and regression concurrently, thereby consolidating what was previously several independent tasks and  ...  Wall-to-wall forest structural maps can also be used as inputs for fire modeling applications that depend on canopy cover, canopy height, canopy crown bulk density, and crown base height to determine fire  ... 
doi:10.3390/rs11070768 fatcat:l7xbhn7kijecfagdhh6yfzynia

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Ovidiu Csillik, John Cherbini, Robert Johnson, Andy Lyons, Maggi Kelly
2018 Drones  
To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees.  ...  Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to  ...  [35] used a combination of DSM local maxima and object-based image analysis (OBIA) method using UAV orthomosaic imagery for the estimation of olive tree crown parameters (tree height and crown diameter  ... 
doi:10.3390/drones2040039 fatcat:7gkpnswddfaehjfduug6l7oyem

Quantification of Carbon Sequestration in Urban Forests [article]

Levente J. Klein, Wang Zhou, Conrad M. Albrecht
2021 arXiv   pre-print
We present an approach to estimate the carbon storage in trees based on fusing multi-spectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species – key attributes  ...  We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from aerial imagery in order to determine the tree's biomass.  ...  Multi-spectral NAIP imagery (a) and its corresponding segmentation for individual tree crowns (b).  ... 
arXiv:2106.00182v2 fatcat:w77px4tnh5dd7owqmvfnicdfzi

Combining LiDAR metrics and Sentinel-2 imagery to estimate basal area and wood volume in complex forest environment via neural networks

Kamel Lahssini, Florian Teste, Karun Dayal, Sylvie Durrieu, Dino Ienco, Jean-Matthieu Monnet
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
To deal with this particular issue in the context of structure and biophysical variables estimation for forest characterization, we propose a new deep learning based fusion strategy to combine together  ...  In order to manage and fully exploit the available multi-modal information, we implement a two-branch late fusion deep learning architecture taking advantage of the specificity of each modality: on the  ...  the aim to assess the combination of LiDAR metric and multi-spectral/multi-temporal Sentinel-2 data.  ... 
doi:10.1109/jstars.2022.3175609 fatcat:svibkir4cnb33n2oxrbj4r4pje

AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION

K. Dilmurat, V. Sagan, S. Moose
2022 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Hyperspectral imagery-based canopy spectral & texture features and LiDAR point cloud-based canopy structure features were extracted and, along with their combination, were used as inputs for maize  ...  imagery-based canopy spectral and texture information with LiDAR-based canopy structure features outperformed the predictions when using a single sensor alone; (3)the H2O-AutoML framework presented to  ...  ACKNOWLEDGEMENT This work has been supported by the United Soybean Board (2120-152-0201), NSF(IOS-1339362), NSF/USDA (2020-67021-31530), NASA (80NSSC20M0100), and USGS AmericaView Grant (G18AP00077).  ... 
doi:10.5194/isprs-annals-v-3-2022-193-2022 fatcat:hekzype3svg2levcjzrdg6gfmu

Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping

Wenzhi Liao, Frieke Van Coillie, Lianru Gao, Liwei Li, Bing Zhang, Jocelyn Chanussot
2018 IEEE Access  
Deep learning has been widely used to fuse multi-sensor data for classification.  ...  (LiDAR) remote sensing data for tree species mapping in complex, closed forest canopies.  ...  A digital surface model (DSM) provides an estimate of the tree canopy height.  ... 
doi:10.1109/access.2018.2880083 fatcat:etaikage6rdpfgcp62skznsl2m

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

Bing Lu, Phuong D. Dao, Jiangui Liu, Yuhong He, Jiali Shang
2020 Remote Sensing  
The imaging platforms and sensors, together with analytic methods used in the literature, were discussed.  ...  Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming.  ...  Machine learning or deep learning is capable of processing multi-source and multi-type data [202] .  ... 
doi:10.3390/rs12162659 fatcat:bfoe3xuja5b27b7wuu5ak7tdpq

The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification

Andrew Mellor, Andrew Haywood, Christine Stone, Simon Jones
2013 Remote Sensing  
In this paper, we evaluate the performance of the Random Forest (RF) classifier, an ensemble learning algorithm that has recently shown promise using multi-spectral satellite sensor imagery for large area  ...  multi-sourced data using open-source software for the production of consistent and accurate forest cover maps across the full spectrum of Victorian sclerophyll forest types.  ...  and support in the implementation of the random forest classifier.  ... 
doi:10.3390/rs5062838 fatcat:cj7c4cqu4jda5ctohgnmte3y5e
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