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  1. Article ; Online: Spatial and spectral analysis of fairy circles in Namibia on a landscape scale using satellite image processing and machine learning analysis

    Noy, Klil / Silver, Micha / Pesek, Ondrej / Yizhaq, Hezi / Marais, Eugène / Karnieli, Arnon

    International Journal of Applied Earth Observation and Geoinformation. 2023, p.103377-

    2023  , Page(s) 103377–

    Abstract: Fairy circles (FCs) are a unique phenomenon characterized by circular patches, 4-10 m in diameter, of bare soil within a vegetated matrix. This project aimed to study the spatial and spectral characteristics of FCs on a landscape scale in Namibia. The ... ...

    Abstract Fairy circles (FCs) are a unique phenomenon characterized by circular patches, 4-10 m in diameter, of bare soil within a vegetated matrix. This project aimed to study the spatial and spectral characteristics of FCs on a landscape scale in Namibia. The specific objectives of this research are (1) processing satellite observations to explore the FCs distributions by applying statistical analysis and deep machine learning algorithms; (2) analyzing the FCs' geometric attributes to retrieve their spatial patterns regarding topographic features nearby. The FCs were classified within 25 km² by processing 15 input layers through a convolutional neural network (CNN) model. The layers include four WorldView2 spectral bands, derived vegetation, biocrust, and mineral indices, and textural characteristics. The FCs' geometry was extracted, and spatial autocorrelation was performed. By labeling 1600 FCs and using the CNN model, 14536 FCs were mapped with 0.97% accuracy and a binary cross-entropy loss function value of only 0.01. Field measurements and laboratory analysis justified the need to use spectral indices for the model. Unique elongated FCs, clustered by hotspot analysis, were quantified and mapped along watercourses in alluvial fans with notable connectivity. On a landscape scale that has not yet been studied, spatial and spectral analyses became possible only with valuable remote sensing retrievals, deep statistical analysis, and machine learning algorithms.
    Keywords autocorrelation ; biological soil crusts ; geometry ; landscapes ; neural networks ; remote sensing ; satellites ; spatial data ; spectral analysis ; topography ; vegetation ; Namibia ; CNN model ; Spectral indices ; Texture variables ; Spatial autocorrelation
    Language English
    Publishing place Elsevier B.V.
    Document type Article ; Online
    Note Pre-press version ; Use and reproduction
    ISSN 1569-8432
    DOI 10.1016/j.jag.2023.103377
    Database NAL-Catalogue (AGRICOLA)

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  2. Article: A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield

    Noy, Klil / Ohana-Levi, Noa / Panov, Natalya / Silver, Micha / Karnieli, Arnon

    Science of the total environment. 2021 June 20, v. 774

    2021  

    Abstract: Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and ... ...

    Abstract Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p < 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 > CI > 0.64 and 0.64 > CI > 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.
    Keywords Landsat ; algorithms ; arid lands ; biological soil crusts ; dry environmental conditions ; remote sensing ; statistical analysis ; time series analysis
    Language English
    Dates of publication 2021-0620
    Publishing place Elsevier B.V.
    Document type Article
    Note NAL-AP-2-clean
    ZDB-ID 121506-1
    ISSN 1879-1026 ; 0048-9697
    ISSN (online) 1879-1026
    ISSN 0048-9697
    DOI 10.1016/j.scitotenv.2021.145154
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 74/341: Show issues

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  3. Article ; Online: A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield.

    Noy, Klil / Ohana-Levi, Noa / Panov, Natalya / Silver, Micha / Karnieli, Arnon

    The Science of the total environment

    2021  Volume 774, Page(s) 145154

    Abstract: Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and ... ...

    Abstract Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p < 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 > CI > 0.64 and 0.64 > CI > 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.
    MeSH term(s) Egypt ; Satellite Imagery ; Spatio-Temporal Analysis
    Language English
    Publishing date 2021-02-06
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 121506-1
    ISSN 1879-1026 ; 0048-9697
    ISSN (online) 1879-1026
    ISSN 0048-9697
    DOI 10.1016/j.scitotenv.2021.145154
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 949: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article: Cascading effects of sand stabilization on pathogen communities: Connecting global and local processes

    Halle, Snir / Garrido, Mario / Noy, Klil / Messika, Irit / Kedem, Hadar / Cohen, Carmit / Ytzhak, Koren / Siegal, Zehava / Shenbrot, Georgy / Abramsky, Zvika / Ziv, Yaron / Karnieli, Arnon / Hawlena, Hadas

    Global ecology and biogeography. 2022 Feb., v. 31, no. 2

    2022  

    Abstract: AIM: To advance our understanding of the mechanisms that mediate the relationships between global climatic and anthropogenic processes and pathogen occurrence, it is crucial to evaluate the exact pathways connecting the ecological mediators and the ... ...

    Abstract AIM: To advance our understanding of the mechanisms that mediate the relationships between global climatic and anthropogenic processes and pathogen occurrence, it is crucial to evaluate the exact pathways connecting the ecological mediators and the pathogen responses across spatial and temporal heterogeneities at various scales. We investigated the pathways connecting these two types of heterogeneities in sand stabilization that were created by contrasting forces of various human activities and long‐term droughts, and pathogen occurrence in host populations. The considered candidate ecological mediators were various components of host community structure, arthropod vector traits, and the pathogen occurrence in these vectors. LOCATION: North‐western Negev Desert's sands in Israel. TIME PERIOD: 1982–2018. MAJOR TAXA STUDIED: Gerbillus andersoni, Gerbillus floweri, Gerbillus gerbillus, Mycoplasma, Bartonella, Synosternus cleopatrae. METHODS: We combined information from satellite images, 36 years of rodent censuses, a natural experiment, and causal modelling. RESULTS: We found evidence that the spatial heterogeneity in sand biocrusts is largely correlated with structural differences between host communities, especially at medium spatial scales. Pathogen sampling, followed by causal modelling, suggested that the cascading effect of sand stabilization on pathogen occurrence is mainly mediated through changes in host community structure and vector burdens. Importantly, we found that structural changes in the same host community can simultaneously amplify and dilute different pathogens. MAIN CONCLUSIONS: These findings suggest that global processes can translate into local processes, where the importance of the mediation effects depend on the magnitude of environmental heterogeneity. These mediation effects can benefit some organisms while adversely affecting others.
    Keywords Bartonella ; Gerbillus ; Mycoplasma ; Synosternus cleopatrae ; arthropods ; biogeography ; biological soil crusts ; community structure ; humans ; pathogen occurrence ; pathogens ; rodents ; sand ; sand stabilization ; satellites ; spatial variation ; Israel
    Language English
    Dates of publication 2022-02
    Size p. 215-232.
    Publishing place John Wiley & Sons, Ltd
    Document type Article
    Note JOURNAL ARTICLE
    ZDB-ID 2021283-5
    ISSN 1466-8238 ; 1466-822X ; 0960-7447
    ISSN (online) 1466-8238
    ISSN 1466-822X ; 0960-7447
    DOI 10.1111/geb.13423
    Database NAL-Catalogue (AGRICOLA)

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