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144 results for “Activity Detection”

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zenodo48/100

Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction

Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>. Data are stored in a netcdf format  and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension. The included ipython notebooks provide examples and explanations for using these laboratory data.

opencc-by-4.0Mar 2020View details →

zenodo48/100

The stellar parameters and the quantities of the residual emissions of the detected active stars in the LAMOST-K2 survey

The full Table 1 in Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey (Xiang, Gu & Cao, 2022, MNRAS, 514, 4781; <a href="https://arxiv.org/abs/2206.07257">arXiv:2206.07257</a>). The columns are LAMOST obsid, K2 ID, Teff, logg, [Fe/H], EW_res_Halpha, EW_res_Ca II 8498, EW_res_Ca II 8542, EW_res_Ca II 8662, log F_Halpha, log F_Ca, log R'_Halpha, log R'_Ca. The chromospheric emissions were detected and measured with the spectral subtraction technique, which removes the inactive template spectra (photospheric contribution) from the observed stellar spectra. In this work, we used the variational autoencoder neural networks to efficiently generate the proper template spectra in a data-driven manner. More details can be found in the associated paper (<a href="https://arxiv.org/abs/2206.07257">https://arxiv.org/abs/2206.07257</a>). The demo code can be found on GitHub (<a href="https://github.com/xylib/vae-for-spectroscopic-survey">https://github.com/xylib/vae-for-spectroscopic-survey</a>).

opencc-by-4.0Jul 2022View details →

zenodo48/100

Data and model for detecting spam activity on academic articles

With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. This paper analyzes how Twitter bots interact with scholarly articles on the platform. Spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world. In this paper, we determined whether bots are disseminating a given scholarly article based on analyzing the relationship between Twitter bots and several research factors. We developed and tested several supervised machine-learning classification models to tackle this problem. Through our analysis, we also identified that scholarly articles in health and human science are more prone to bot activity than other research areas.

opencc-by-4.0Jul 2022View details →

zenodo44/100

Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection

This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.

opencc-by-4.0Apr 2024View details →

zenodo40/100

RealVAD: A Real-world Dataset for Voice Activity Detection

RealVAD: A Real-world Dataset for Voice Activity Detection The task of automatically detecting “Who is Speaking and When” is broadly named as Voice Activity Detection (VAD). Automatic VAD is a very important task and also the foundation of several domains, e.g., human-human, human-computer/ robot/ virtual-agent interaction analyses, and industrial applications. RealVAD dataset is constructed from a YouTube video composed of a panel discussion lasting approx. 83 minutes. The audio is available from a single channel. There is one static camera capturing all panelists, the moderator and audiences. Particular aspects of RealVAD dataset are: <ul> <li> It is composed of panelists with different nationalities (British, Dutch, French, German, Italian, American, Mexican, Columbian, Thai). This aspect allows studying the effect of ethnic origin variety to the automatic VAD.</li> <li> There is a gender balance such that there are four female and five male panelists.</li> <li> The panelists are sitting in two rows and they can be gazing audience, other panelists, their laptop, the moderator or anywhere in the room while speaking or not-speaking. Therefore, they were captured not only from frontal-view but also from side-view varying based on their instant posture and head orientation.</li> <li> The panelists are moving freely and are doing various spontaneous actions (e.g., drinking water, checking their cell phone, using their laptop, etc.), resulting in different postures.</li> <li> The panelists’ body parts are sometimes partially occluded by their/other's body part or belongings (e.g., laptop).</li> <li> There are also natural changes of illumination and shadow rising on the wall behind the panelists in the back row.</li> <li> Especially, for the panelists sitting in the front row, there is sometimes background motion occurring when the person(s) behind them moves.</li> </ul> The annotations includes: <ul> <li> The upper body detection of nine panelists in bounding box form.</li> <li> Associated VAD ground-truth (speaking, not-speaking) for nine panelists.</li> <li> Acoustic features extracted from the video: MFCC and raw filterbank energies.</li> </ul> All info regarding the annotations are given in the ReadMe.txt and Acoustic Features README.txt files. When using this dataset for your research, please cite the following paper in your publication: <ol> <li>C. Beyan, M. Shahid and V. Murino, "RealVAD: A Real-world Dataset and A Method for Voice Activity Detection by Body Motion Analysis", in IEEE Transactions on Multimedia, 2020.</li> </ol>

opencc-by-4.0Jul 2020View details →

dryad40/100

Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics

Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.

opencc-zeroOct 2023View details →

zenodo40/100

Real-bogus scores for active anomaly detection

Data description for <a href="https://arxiv.org/abs/2409.10256">Semenikhin et al., 2024</a> The dataset consists of the following files: feature_snad4_r_100.dat contains light curve feature data for objects, where each object is represented by 54 feature values. These values are encoded as little-endian single-precision IEEE-754 floating-point numbers (32-bit floats). Feature names are listed in the plain text file feature_snad4_r_100.name, with one name per line. sid_snad4_r_100.dat contains ZTF DR object identifiers, encoded as little-endian 64-bit unsigned integers. exp_feature_snad4_r_100.dat contains the same features as feature_snad4_r_100.dat, but with an additional column representing the real-bogus classifier prediction. Each object in this file corresponds to 55 features: the original 54 features plus 1 additional feature. Feature names for this file are provided in exp_feature_snad4_r_100.name. The files sid_snad4_r_100.dat, feature_snad4_r_100.dat, and exp_feature_snad4_r_100.dat share the same object order. Below is a sample Python script for accessing the data using NumPy: <code>import numpy as np</code> <code># Load object IDs</code> <code>oid     =  np.memmap('sid_snad4_r_100.dat', mode='c', dtype=np.uint64)</code> <code># Load features and reshape</code> <code>feature = np.memmap('feature_snad4_r_100.dat', mode='c', dtype=np.float32).reshape(oid.shape[0], -1)</code> <code># Print dataset information</code> <code>print(f'Number of objects: {len(oid)}')</code> <code>print(f'Features shape: {feature.shape}')</code>

opencc-by-4.0Nov 2024View details →

zenodo40/100

Figure 1. Response curves for percent acetyl-coenzyme A in Detecting the effect of ACCase-targeting herbicides on ACCase activity utilizing a malachite green colorimetric functional assay

Figure 1. Response curves for percent acetyl-coenzyme A carboxylase (ACCase) activities of resistant and susceptible Digitaria ciliaris biotypes in response to the increasing concentrations of the ACCase-targeting herbicides, sethoxydim, clethodim,fluazifop-p-butyl, and pinoxaden.The response was modeled based on the log rate of ACCase-targeting herbicides to create equal spacing between rates using least-squares fit regression of ACCase activity to the non-treated check. Means are represented by differing symbols for each biotype, and regression equation models are represented by differing line types for each biotype. Vertical bars represent the standard errors of the means (n = 6). Digitaria ciliaris biotypes: R1 and R2, resistant; S, susceptible. The concentration of ACCase-targeting herbicides required to cause 50% inhibition of ACCase activity (IC50) was calculated from concentration-response curves. CI, confidence interval.

opencc-by-4.0Aug 2021View details →

zenodo40/100

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 11. Activated Neuro-Symbols for Detecting that a Person walks around in the Room

With the example of Figure 11, also the function of feedback connections can be explained. According to the existing feedforward connections, the neuro-symbol “object stands” would be activated together with the neuro-symbol “object moves” whenever the neuro-symbols “motion” and “object moves” are active, because it is activated by a subset of the neuro-symbols that activate the neuro-symbol “object moves”. This activation would however be undesired in this concrete case. For this reason, an inhibitory feedback connection exists from the neuro-symbol “object moves” to the neuro-symbol “object stands” that inhibits the activation of the neuro-symbol “object stands”.

opencc-by-4.0Oct 2013View details →

zenodo40/100

Noninvasive detection of macrophage activation with single-cell resolution through machine learning

Data related to the article "Noninvasive detection of macrophage activation with single-cell resolution through machine learning". The package contains 2 folders: - RawData:   This package contains raw data and examples of processing to extract the              variables employed to train and assess the models. - Variables: This package contains the extracted data from the various experiments              showed in the article.

opencc-by-sa-4.0Mar 2018View details →

zenodo40/100

Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning

The EAVISE Open Source Face Detection Dataset consists of several items that were used to generate the improved frontal face detection model using LBP features and AdaBoost for OpenCV3.2. <ul> <li>The annotations of the FDDB dataset, converted to the OpenCV format for doing a correct evaluation.</li> <li>The final trained model (IterativeHardPositives+ model) which is included in the OpenCV 3.2 framework.</li> </ul>

opencc-by-4.0Feb 2017View details →

zenodo40/100

Wrist-worn sensor validation for heart rate variability and electrodermal activity detection in a stressful driving environment

The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.

opencc-by-sa-4.0Jun 2023View details →

zenodo40/100

CEP-based Activity Detection Services generated from IoT Data

Data set accompanying the paper "Data-driven Generation of Services for IoT-based Online Activity Detection" submitted to the International Conference on Service-Oriented Computing (ICSOC) 2023. The data set has 6 automatically generated activity detection services (*.siddhi files) for 6 different types of activities executed by 6 different production stations in a smart factory. The *.siddhi files have to be deployed to and activated on an instance of the <a href="http://siddhi.io">Siddhi</a> complex event processing platform. The data set includes the corresponding low-level IoT data for each activity (activity signature) that was used to generate the activity detection service (*.txt files). It also includes a visual representation of the activity signature for each type of activity (*.png files). To test the activity detection services, the *.txt files have to be read line by line, each line has to be send as one MQTT message to an MQTT broker and topic that the activity detection service is also listening to (standard: localhost).

opencc-by-4.0Jun 2023View details →

dryad40/100

Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics

Open the record for dataset details and reuse information.

publicDec 2023View details →

zenodo36/100

Data set for "Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task"

Data set for: Vavladeli A, Daigle T, Zeng H, Crochet S, Petersen CCH (2020) Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task. FUNCTION 1: zqaa008. doi: 10.1093/function/zqaa008 There are 2 files in this upload: 1. The file named "2020_Vavladeli_FUNCTION.pdf" is the Open Access pdf file of the manuscript published in FUNCTION. 2. The file named "Vavladeli_data_code.zip" (~2 GB) is a zipped version of a folder named "Vavladeli_data_code" (~2 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and two '.mat' data files. In order to run the analysis of the data set, you need to execute the '.m' file with the corresponding figure name.

opencc-by-4.0Jul 2020View details →

zenodo36/100

Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'

Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison. The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did. The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>. The licences valid for the elements of this repository are discussed under point "3. Licenses" below. 1. Structure The repository contains the following items: <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> We now focus on the structure of the file CODE_AND_RESULTS.zip. It contains the following items: <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> Inside the folder "new_methods," the following sub-folders are provided: <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> In our experiments, we used two datasets: "WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license. The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>]. 2. References [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, IEEE Access 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>.    [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>. [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, ISPRS Journal of Photogrammetry and Remote Sensing 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>.    [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>.    [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>. 3. Licenses The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip": <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE". 4. Contact 1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn Institute of Applied Optimization,    School of Artificial Intelligence and Big Data,    Hefei University, South Campus 2, Jinxiu Dadao 99,    Hefei Economic and Technological Development Area,    Shushan District, Hefei 230601, Anhui, China

openmit-licenseJan 2021View details →

zenodo36/100

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Background: The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.   Methods: First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis. Results: The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR. Conclusions: The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/ distinction of muscle activity from background noise in sEMG signals.

opencc-by-4.0Dec 2020View details →

dryad36/100

Miniaturization eliminates detectable impacts of drones on bat activity

A new way to survey wildlife populations may be possible with advancements in drones, or unmanned aerial vehicles (UAVs) that render aerial technology more accessible and promote surveying in inapproachable habitats. However, it remains unclear whether UAV disturbance deters animals, which would make this method inaccurate for data collection and hazardous to wildlife welfare. This study addresses the viability of UAV use for wildlife research by measuring the effects of UAV flight on acoustic bat detection and comparing bat activity in response to varying UAV models. Depending on the way UAVs effect bat detection rate, it may be possible to identify whether wildlife surveys should be done with UAVs and the drone models best suited for this purpose. The results reveal that larger and louder UAVs deterred significantly more bats, and the smallest and quietest model had no effect on bat detection. Indeed, drone noise was positively correlated with drone size, but drone size had little effect on the range of frequencies emitted. While detecting bats with small and quiet UAVs may be possible, complications still arise with acoustic detection and the species-specific effects of drone flight. The reliability of automatic identification with the acoustic detecting software is limited, as over a quarter of detections were triggered by non-bat noises yet still classified as bats (25.99%). Overall, using drones for wildlife detection should be approached with caution, as this study illustrates that some drones deter and disturb wild bats. If drones are used in wildlife habitat, consider flying smaller and quieter models, which are significantly less disturbing. Otherwise, large and loud drones will likely deter more animals and skew the results of the survey.

opencc-zeroJan 2022View details →

zenodo36/100

Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: code and processed data

Manuscript, figure, and analysis code and processed data for the Groza et al (2022) preprint.

opencc-by-4.0Sep 2021View details →

dryad36/100

Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019

1. PUBLICATION CORRESPONDING TO THESE DATA Sloan, Sean*; Locatelli, Bruno; Andela, Niels; Cattau, Megan E.; Gaveau, David; Tacconi, Luca. 2022 'Declining Severe Fire Activity on Managed Lands in Equatorial Asia'. Communications Earth & Environment. DOI: 10.1038/s43247-022-00522-6. *Corresponding author email: sean.sloan@viu.ca 2. ABSTRACT OF THE DATA The GIS data and corresponding attribute data described here pertain to discrete fire events, their severity, and their ignitions, as derived on the basis of daily MODIS Collection 6 MCD14ML active-fire detections (AFDs). Data on fire events and their ignitions are provided separately, as two data files. These data files on fire events and ignitions may however be linked to each other by the data user. Fire-event severity is quantified per fire event and reported in the data file for fire events. A fire event is a cluster of MODIS Collection 6 MCD14ML active-fire detections (AFDs) wherein each AFD has a spatial (<=1-km) and temporal (<=4-day) proximity to another AFD in the same fire event, inferring thus a relational co-occurrence amongst AFDs in time and space. In other words, a fire event is considered a likely occasion of burning wherein all constituent AFDs are related to each other in time and space, either directly (as for proximate AFDs) or indirectly (as in the case of a large area of fire activity that spread progressively over time and space from an initial source). Each fire event has a designated ignition AFD, being the AFD of the fire event with the earliest detection date. A given fire event can have more than one ignition AFD if the ignitions all share same earliest detection date. The ignition AFD(s) is the nominal initial source of the burning described by the corresponding fire event. All other, non-ignition AFDs of a fire event are deemed its 'propagation' AFDs, since these AFDs follow from the ignitions, temporally and spatially. See Figure 4 in the publication by Sloan et al. for an illustration of the geography of fire events and their ignition AFDs. Fire events and their ignitions were derived from standard science-quality MODIS Collection 6 MCD 14ML AFD data, commonly referred to as fire 'hotspot' data. Data were detected by both the Terra and Aqua satellite sensors daily for Indonesia between July 2002 and December 2019. Information on these input data are provide by the two citations below. The publication of Sloan et al. provides methodological details on how the MODIS Collection 6 MCD 14ML AFD data were processed into discrete fire events and ignitions. EarthData. MODIS Collection 6 Active-Fire Detections standard scientific data (MCD14ML), NASA EarthData, https://earthdata.nasa.gov/firms (2019). Giglio, L., Schroeder, W. & Justice, C. O. The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31-41, (2016). 3. DATA FILES Two data files are distributed here – one for discrete fire events, and another for the ignition AFDs of each fire event. The data files are provided in a GIS-compatible format, and also as a generic text format, as described below. 3.1 GIS VERSION Data files in GIS-compatible format are provided as 'feature classes' within an ArcGIS file geodatabase 'Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb'. These data files can be viewed and manipulated using either ArcGIS Desktop or ArcGIS Pro software. There is one feature class for fire events, and another file for ignitions. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_joins_sp_LC This file pertains to fire events. All AFDs of a given fire event are included, without differentiation as to whether the AFDs are ignition AFDs or other (propagation) AFDs. Fire events are assigned unique ID values and basic attribute data. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_igs_sp_LC This file pertains to ignitions. Only ignition AFDs are included for a given fire event. Fire events corresponding to the ignitions are assigned unique ID values and basic attribute data. 3.2 CSV TEXT VERSION Both data files are also supplied as comma-separated value (CSV) text files for viewing and manipulation in non-GIS software, such as Excel, text editors, or any statistical software. The text files can also be read into various GIS software. CSV-formatted files have the same file name and attribute fields as the corresponding GIS-formatted data files. These CSV-formatted data files (as well as the GIS-formatted data files) include attribute data on the latitude and the longitude of each AFD. Attribute field names are included as the first row of values in a CSV file. No 'text qualifiers' like quotations (" ") or inverted commas ('') are used to designate text/string values within the CSV file. Text values appear directly between commas in the CSV data file, e.g., …,Kalimantan_Southern,… . Note two points of caution for working with these CSV data: i) Microsoft Excel may be used for a partial view of the data file nfire4_all_spatial_fire_2002_2019_joins_sp_LC.csv, but it is not recommended for working with this data file. This is because the number of records/rows in this csv file slightly exceeds that maximum that may be read by Excel, which is just over 1 million. This limitation does not apply to the other csv file, however. ii) The GIS-formatted data files employ 'null values' in their attribute tables, and so the corresponding 'values' in the CSV-formatted data files are similarly null. For null values, no value whatsoever is ascribed, not even 0. In the syntax of a CSV file (apparent upon opening the file in any text editor like Microsoft Notepad), a null value is denoted by two consecutive commas without any value, text, or space between them. If a CSV file were opened in Excel, a cell assigned a null value would be blank, not 0 or otherwise. This denotes the correct transcription of the GIS-formatted data. This feature will not impede the correct reading of these CSV data by whatever software. Users are made aware of this feature merely to ensure the proper input of these data into whatever software. 4. DATA STRUCTURE / GEOGRAPHY The GIS-formatted data files are 'point data', i.e., they map the geography of AFDs as individual 'points', in keeping with how these MODIS MCD14ML AFD data were originally structured. For the GIS-formatted data files, each record/row in its corresponding attribute tables corresponds geographically to single AFD 'point', regardless of whether that AFD belongs to a fire event comprised of many AFDs. In the parlance of GIS files, the files depict 'single-part' point features. The unique ID field [nfireID2] serves to denote the fire event to which a given AFD belongs. Similarly, for the CSV-formatted data files, each record/row of values corresponds to a single AFD. There are 1,232,377 records for the data file 'nfire4_all_spatial_fire_2002_2019_joins_sp_LC'. There are 720795 records for the data file 'nfire4_all_spatial_fire_2002_2019_igs_sp_LC'. 5. ATTRIBUTE FIELDS In the data files, while some attribute fields pertain to the individual AFD as the unit of observation (e.g., the land-cover class coincident with the AFD), other attribute fields correspond to the larger 'fire event' to which the individual AFD belongs (e.g., the total duration of fire activity for the fire event). Accordingly, for certain attribute fields pertaining to the fire event as a whole, their values will appear 'duplicated' in the data file amongst those individual AFDs (records) that constitute the fire event in question. Whether a given attribute field pertains to the individual AFD or to its constituent fire event is denoted below for each field. Each AFD is assigned a unique ID field denoting its constituent fire event, [nfireID2]. This field is consistent between both data files, so that attribute data for a given fire event may be 'matched' to attribute data for its corresponding ignition AFD(s), and vice versa, on the basis of the common value of the field [nfireID2]. Note that many attributes below are as originally defined/measured by the input MCD 14ML data, or are derived directly thereof. 5.1 DATASET nfire4_all_spatial_fire_2002_2019_joins_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> MIN_CONFIDE </td> <td> Fire Event </td> <td> The minimum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MAX_CONFIDE </td> <td> Fire Event </td> <td> The maximum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MEAN_CONFIDE </td> <td> Fire Event </td> <td> The mean detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> STD_CONFIDE </td> <td> Fire Event </td> <td> The standard deviation of detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> SUM_FRP </td> <td> Fire Event </td> <td> The sum total of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_FRP </td> <td> Fire Event </td> <td> The mean of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MIN_FRP </td> <td> Fire Event </td> <td> The minimum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MAX_FRP </td> <td> Fire Event </td> <td> The maximum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_LATITUD </td> <td> Fire Event </td> <td> The mean latitude of all AFDs in the fire event. </td> </tr> <tr> <td> MEAN_LONGITU </td> <td> Fire Event </td> <td> The mean longitude of all AFDs in the fire event. </td> </tr> <tr> <td> MIN_ACQ_DAT </td> <td> Fire Event </td> <td> The minimum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MAX_ACQ_DAT </td> <td> Fire Event </td> <td> The maximum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> MAX_acq_mnt </td> <td> Fire Event </td> <td> The month in which the latest AFD of the fire event was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> MAX_yday </td> <td> Fire Event </td> <td> The detection day of year of the latest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> RANGE_yday </td> <td> Fire Event </td> <td> The number of days of duration of the fire event, defined as [MAX_yday]-[MIN_yday] </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> AF_Count </td> <td> Fire Event </td> <td> Number of AFDs in the fire event. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> Island </td> <td> AFD </td> <td> Numerical values coding for major Indonesian islands/region: 1000000=Sumatra; 2000000=Kalimantan; 3000000=Sulawesi; 4000000=Papua. </td> </tr> <tr> <td> FRP_Days </td> <td> Fire Event </td> <td> The severity of the fire event, as defined by Sloan et al., equal to [Sum_FRP] * ([Range_yday]+1). </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event </td> </tr> <tr> <td> CCI_LC2002 </td> <td> </td> <td> The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> </td> </tr> <tr> <td> CCI_LC2004 </td> <td> </td> </tr> <tr> <td> CCI_LC2005 </td> <td> </td> </tr> <tr> <td> CCI_LC2006 </td> <td> </td> </tr> <tr> <td> CCI_LC2007 </td> <td> </td> </tr> <tr> <td> CCI_LC2008 </td> <td> </td> </tr> <tr> <td> CCI_LC2009 </td> <td> </td> </tr> <tr> <td> CCI_LC2010 </td> <td> </td> </tr> <tr> <td> CCI_LC2011 </td> <td> </td> </tr> <tr> <td> CCI_LC2012 </td> <td> </td> </tr> <tr> <td> CCI_LC2013 </td> <td> </td> </tr> <tr> <td> CCI_LC2014 </td> <td> </td> </tr> <tr> <td> CCI_LC2015 </td> <td> </td> </tr> <tr> <td> CCI_LC2016 </td> <td> </td> </tr> <tr> <td> CCI_LC2017 </td> <td> </td> </tr> <tr> <td> CCI_LC2018 </td> <td> </td> </tr> <tr> <td> CCI_LC2019 </td> <td> </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.2 DATASET nfire4_all_spatial_fire_2002_2019_igs_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file. </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event. </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> CCI_LC2002 </td> <td> AFD </td> <td> The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2004 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2005 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2006 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2007 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2008 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2009 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2010 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2011 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2012 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2013 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2014 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2015 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2016 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2017 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2018 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2019 </td> <td> AFD </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.3. LAND-COVER ATTRIBUTE DATA As noted in Section 5.1 and Section 5.2, the nominal values of the attribute fields 'CCI_LC####' correspond to column 1 of the table below. These hierarchical values, and their corresponding land-cover classes labels in column 2 of the table, pertain to the land-cover classification of the Copernicus Climate Change Initiative Land-Cover Product of the European Space Agency. This classification has an annual temporal resolution and 300-meter spatial resolution. Pertinent citations for these land-cover data are below: ESA. Annual land-cover product, 1992 to 2019/present, based on MERIS 300-m and ancillary SPOT, AVHRR, Sentinel-3 and PROB-V satellite data. European Space Agency (ESA) European Centre for Medium-Range Weather Forecasts (ECMFW) Copernicus Climate Change Service (C3S) Climate Change Initiative (CCI), <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview</a>; <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download.php">http://maps.elie.ucl.ac.be/CCI/viewer/download.php</a>; <a href="http://www.esa-landcover-cci.org/">http://www.esa-landcover-cci.org/</a> (2020). Pérez-Hoyos, A., Rembold, F., Kerdiles, H. & Gallego, J. Comparison of global land cover datasets for cropland monitoring. Remote Sensing 9, (2017). Columns 3 and 4 in the table below illustrate how the original land-cover classes of the Copernicus Climate Change Initiative Land-Cover Product were reclassified for analysis in Sloan et al. <table class="MsoNormalTable"> <tbody> <tr> <td> 1. CCI-LC Class Value </td> <td> 2. CCI-LC Class Label </td> <td> 3. New Class Value for Sloan et al. </td> <td> 4. New Class Label for Sloan et al. </td> </tr> <tr> <td> 0 </td> <td> No Data </td> <td> 0 </td> <td> No Data </td> </tr> <tr> <td> 10 </td> <td> Cropland, rainfed </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 11 </td> <td> Herbaceous cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 12 </td> <td> Tree or shrub cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 20 </td> <td> Cropland, irrigated or post‐flooding </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 30 </td> <td> Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%) </td> <td> 2 </td> <td> Mosaic Cropland </td> </tr> <tr> <td> 40 </td> <td> Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) </td> <td> 3 </td> <td> Mosaic Natural Veg </td> </tr> <tr> <td> 50 </td> <td> Tree cover, broadleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 60 </td> <td> Tree cover, broadleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 61 </td> <td> Tree cover, broadleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 62 </td> <td> Tree cover, broadleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 70 </td> <td> Tree cover, needleleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 71 </td> <td> Tree cover, needleleaved, evergreen, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 72 </td> <td> Tree cover, needleleaved, evergreen, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 80 </td> <td> Tree cover, needleleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 81 </td> <td> Tree cover, needleleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 82 </td> <td> Tree cover, needleleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 90 </td> <td> Tree cover, mixed leaf type (broadleaved and needleleaved) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 100 </td> <td> Mosaic tree and shrub (>50%) / herbaceous cover (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 110 </td> <td> Mosaic herbaceous cover (>50%) / tree and shrub (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 120 </td> <td> Shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 121 </td> <td> Evergreen shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 122 </td> <td> Deciduous shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 130 </td> <td> Grassland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 140 </td> <td> Lichens and mosses </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 150 </td> <td> Sparse vegetation (tree, shrub, herbaceous cover) (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 151 </td> <td> Sparse tree (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 152 </td> <td> Sparse shrub (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 153 </td> <td> Sparse herbaceous cover (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 160 </td> <td> Tree cover, flooded, fresh or brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 170 </td> <td> Tree cover, flooded, saline water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 180 </td> <td> Shrub or herbaceous cover, flooded, fresh/saline/brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 190 </td> <td> Urban areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 200 </td> <td> Bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 201 </td> <td> Consolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 202 </td> <td> Unconsolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 210 </td> <td> Water bodies </td> <td> 8 </td> <td> Other </td> </tr> </tbody> </table>

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