scholarly journals Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah

2020 ◽  
Author(s):  
David M. Makori ◽  
Elfatih M. Abdel-Rahman ◽  
Tobias Landmann ◽  
Onisimo Mutanga ◽  
John Odindi ◽  
...  
Keyword(s):  
Cost Effective ◽  
Landscape Scale ◽  
Flowering Plants ◽  
Aerial Imagery ◽  
Data Sets ◽  
Pollination Services ◽  
Multispectral Data ◽  
Independent Test ◽  
Honeybee Health ◽  
Sentinel 2

AbstractPollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees’ foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.

scholarly journals Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions

2019 ◽  
Vol 11 (24) ◽  
pp. 2947 ◽  
Author(s):  
Daniel Žížala ◽  
Robert Minařík ◽  
Tereza Zádorová
Keyword(s):  
Prediction Accuracy ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Hyperspectral Data ◽  
Landsat 8 ◽  
Prediction Ability ◽  
Multispectral Data ◽  
Multispectral Remote Sensing ◽  
Data Source ◽  
Sentinel 2

The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale.

Peatland restoration age (Scotland, UK) can be better reproduced by a classification model based on Sentinel-2 than with high resolution aerial imagery

2020 ◽  
Author(s):  
Rebekka Artz ◽  
Jonathan Ball ◽  
Catherine Smart ◽  
Gillian Donaldson-Selby ◽  
Neil Cowie ◽  
...  
Keyword(s):  
Landscape Scale ◽  
Aerial Imagery ◽  
Classification Model ◽  
Vegetation Composition ◽  
National Scale ◽  
Model Uncertainties ◽  
Peatland Restoration ◽  
Natural Climate ◽  
Main Model ◽  
Sentinel 2

<p>Damage to peatland globally causes significant contributions to the current net greenhouse gas emissions and pose a further future risk as such damaged peatlands are vulnerable to future climatic stress. Globally, peatland restoration efforts are rapidly increasing in scale as natural climate solutions, yet relatively little effort has been it into effective monitoring of landscape scale restoration projects. We developed a classification model that uses remote observations (Sentinel-2 or national scale aerial imagery from Getmapping) to detect restoration efficacy by training it against a dataset from a chronosequence of spatially collocated peatland restoration sites that had previously been converted to plantation forestry. The Sentinel-2 based model greatly outperformed the aerial imagery-based model (RGB and IR, 25 and 50 cm, respectively). Adding slope to the classification improved kappa by less than 0.02. Prediction of the starting (forestry) and target (restored) state was very robust, and both recent and the oldest restoration sites were spatially well predicted. The main model uncertainties lie with sites of intermediate age, where on-the-ground restoration trajectories based on vegetation composition also differ the most, and with sites where additional layers of management after the initial restoration management have been applied.</p>

scholarly journals A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

2021 ◽  
Vol 11 (23) ◽  
pp. 11486
Author(s):  
Shahab Ud Din ◽  
Khan Muhammad ◽  
Muhammad Fawad Akbar Khan ◽  
Shahid Bashir ◽  
Muhammad Sajid ◽  
...  
Keyword(s):  
Principal Component ◽  
Cost Effective ◽  
Thermal Infrared ◽  
Google Earth ◽  
Engineering Properties ◽  
Petrographic Analysis ◽  
Landsat 8 ◽  
Khyber Pakhtunkhwa ◽  
Multispectral Data ◽  
Sentinel 2

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

Monitoring Bee Diversity in Natural Systems – Novel Aerial and Classical Ground Methods to Evaluate Biotic and Abiotic Indicators

2021 ◽  
Author(s):  
Vincenzo Di Pietra ◽  
Paolo Dabove ◽  
Yael Mandelik ◽  
Yael Mishael ◽  
Karmit Levy ◽  
...  
Keyword(s):  
Community Composition ◽  
Cost Effective ◽  
Field Work ◽  
Ecological Monitoring ◽  
Natural Ecosystems ◽  
Pollination Services ◽  
Multispectral Data ◽  
Natural Systems ◽  
Plant Groups ◽  
Bee Diversity

<p>Bees provide essential pollination services to natural ecosystems and agricultural crops. However, managed and wild (unmanaged) bee populations are in decline worldwide. In order to better manage and restore bee populations, long-term monitoring programs are required. Direct bee monitoring is costly, labor intensive, and requires high expertise. Therefore, cost-effective indicators for bee diversity and community composition are essential.<br>Here we propose to test the cost-efficacy of novel aerial techniques along with classical ground methods to collect biotic and a-biotic indicators of bee diversity and community composition. We will couple classical ecological monitoring approach with advanced photogrammetric tools, in order to develop a multi-scale and multi-temporal platform for monitoring bees. To this end, we formed a complementary, interdisciplinary research group of a pollination ecologist, soil chemists, environmental engineer, geomatics engineer, and topography surveyot. The study will include field work in two complimentary study systems in central Israel, light sandy vs heavy vertisol soils. In each study system we will concurrently conduct bee, flower, bee nesting substrates and soil surveys using classical tools/approaches, as well as apply advanced photogrammetric tools, based on RGB images, with thermal, multispectral data. The indicative ability for bee diversity and community composition of the different biotic and a-biotic measures collected, will be tested using advances statistical tools. Our findings may be instructive to other insects and plant groups, thus provide a novel generic approach towards the ecological monitoring of terrestrial systems.</p>

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Future of Personalized Medicine in Oncology: A Systems Biology Approach

2010 ◽  
Vol 28 (16) ◽  
pp. 2777-2783 ◽  
Author(s):  
Ana Maria Gonzalez-Angulo ◽  
Bryan T.J. Hennessy ◽  
Gordon B. Mills
Keyword(s):  
Systems Biology ◽  
Patient Outcomes ◽  
Cost Effective ◽  
Molecular Medicine ◽  
Multidimensional Data ◽  
Data Sets ◽  
Therapeutic Modalities ◽  
Molecular Tests ◽  
Therapeutic Decision Making ◽  
The Masses

The development of cost-effective technologies able to comprehensively assess DNA, RNA, protein, and metabolites in patient tumors has fueled efforts to tailor medical care. Indeed validated molecular tests assessing tumor tissue or patient germline DNA already drive therapeutic decision making. However, many theoretical and regulatory challenges must still be overcome before fully realizing the promise of personalized molecular medicine. The masses of data generated by high-throughput technologies are challenging to manage, visualize, and convert to the knowledge required to improve patient outcomes. Systems biology integrates engineering, physics, and mathematical approaches with biologic and medical insights in an iterative process to visualize the interconnected events within a cell that determine how inputs from the environment and the network rewiring that occurs due to the genomic aberrations acquired by patient tumors determines cellular behavior and patient outcomes. A cross-disciplinary systems biology effort will be necessary to convert the information contained in multidimensional data sets into useful biomarkers that can classify patient tumors by prognosis and response to therapeutic modalities and to identify the drivers of tumor behavior that are optimal targets for therapy. An understanding of the effects of targeted therapeutics on signaling networks and homeostatic regulatory loops will be necessary to prevent inadvertent effects as well as to develop rational combinatorial therapies. Systems biology approaches identifying molecular drivers and biomarkers will lead to the implementation of smaller, shorter, cheaper, and individualized clinical trials that will increase the success rate and hasten the implementation of effective therapies into the clinical armamentarium.

scholarly journals 3-D visualisation of palaeoseismic trench stratigraphy and trench logging using terrestrial remote sensing and GPR – a multiparametric interpretation

Solid Earth ◽  
2016 ◽  
Vol 7 (2) ◽  
pp. 323-340 ◽  
Author(s):  
Sascha Schneiderwind ◽  
Jack Mason ◽  
Thomas Wiatr ◽  
Ioannis Papanikolaou ◽  
Klaus Reicherter
Keyword(s):  
Seismic Hazard ◽  
Near Infrared ◽  
Normal Faults ◽  
Data Sets ◽  
Data Set ◽  
Backscatter Signal ◽  
Multispectral Data ◽  
Sedimentary Architecture ◽  
Hazard Assessments ◽  
Multispectral Analysis

Abstract. Two normal faults on the island of Crete and mainland Greece were studied to test an innovative workflow with the goal of obtaining a more objective palaeoseismic trench log, and a 3-D view of the sedimentary architecture within the trench walls. Sedimentary feature geometries in palaeoseismic trenches are related to palaeoearthquake magnitudes which are used in seismic hazard assessments. If the geometry of these sedimentary features can be more representatively measured, seismic hazard assessments can be improved. In this study more representative measurements of sedimentary features are achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of ISO (iterative self-organising) cluster analysis of a true colour photomosaic representing the spectrum of visible light. Photomosaic acquisition disadvantages (e.g. illumination) were addressed by complementing the data set with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D interpretation of attached 2-D ground-penetrating radar (GPR) profiles collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements, and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. This manuscript combines multiparametric approaches and shows (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GPR techniques, and (ii) how a multispectral digital analysis can offer additional advantages to interpret palaeoseismic and stratigraphic data. The multispectral data sets are stored allowing unbiased input for future (re)investigations.

scholarly journals Optimization through Bayesian Classification on the k-Anonymized Data

2012 ◽  
pp. 245-249
Author(s):  
L Mohana Tirumala ◽  
S. Srinivasa Rao
Keyword(s):  
Cost Effective ◽  
Privacy Preserving ◽  
Bayesian Classification ◽  
Data Sets ◽  
Data Set ◽  
Equal Importance ◽  
Cost Effective Approach ◽  
Effective Manner ◽  
Anonymized Data ◽  
Refinement Algorithm

Privacy preserving in Data mining & publishing, plays a major role in today networked world. It is important to preserve the privacy of the vital information corresponding to a data set. This process can be achieved by k-anonymization solution for classification. Along with the privacy preserving using anonymization, yielding the optimized data sets is also of equal importance with a cost effective approach. In this paper Top-Down Refinement algorithm has been proposed which yields optimum results in a cost effective manner. Bayesian Classification has been proposed in this paper to predict class membership probabilities for a data tuple for which the associated class label is unknown.

scholarly journals Reshaping Text Data for Efficient Processing on Amazon EC2

2011 ◽  
Vol 19 (2-3) ◽  
pp. 133-145
Author(s):  
Gabriela Turcu ◽  
Ian Foster ◽  
Svetlozar Nestorov
Keyword(s):  
Cost Effective ◽  
Performance Model ◽  
Small Data ◽  
Data Sets ◽  
Performance Measurements ◽  
File Size ◽  
Application Performance ◽  
Execution Plan ◽  
Small Data Sets ◽  
Amazon Ec2

Text analysis tools are nowadays required to process increasingly large corpora which are often organized as small files (abstracts, news articles, etc.). Cloud computing offers a convenient, on-demand, pay-as-you-go computing environment for solving such problems. We investigate provisioning on the Amazon EC2 cloud from the user perspective, attempting to provide a scheduling strategy that is both timely and cost effective. We derive an execution plan using an empirically determined application performance model. A first goal of our performance measurements is to determine an optimal file size for our application to consume. Using the subset-sum first fit heuristic we reshape the input data by merging files in order to match as closely as possible the desired file size. This also speeds up the task of retrieving the results of our application, by having the output be less segmented. Using predictions of the performance of our application based on measurements on small data sets, we devise an execution plan that meets a user specified deadline while minimizing cost.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Cost Effective ◽  
Spatiotemporal Analysis ◽  
Short Wave ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Data Volume ◽  
Sentinel 2

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

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