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  1. Article ; Online: The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

    Nagihan Aslan / Dilek Koc-San

    ISPRS International Journal of Geo-Information, Vol 10, Iss 416, p

    2021  Volume 416

    Abstract: The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. ... ...

    Abstract The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.
    Keywords surface urban heat island (SUHI) ; normalized difference vegetation index (NDVI) ; soil-adjusted vegetation index (SAVI) ; modified normalized difference water index (MNDWI) ; index-based built-up index (IBI) ; Landsat ; Geography (General) ; G1-922
    Subject code 910
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article: Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform

    Koc-San, Dilek / Serdar Selim / Nagihan Aslan / Bekir Taner San

    Computers and electronics in agriculture. 2018 July, v. 150

    2018  

    Abstract: Tree counts and sizes are important information to apply to crop yield estimation and agricultural planning. Therefore, obtaining automatic extraction of trees, their locations, diameters, and counts from remotely sensed data is a challenging task. In ... ...

    Abstract Tree counts and sizes are important information to apply to crop yield estimation and agricultural planning. Therefore, obtaining automatic extraction of trees, their locations, diameters, and counts from remotely sensed data is a challenging task. In this study, a novel approach is proposed for the automatic extraction of citrus trees using unmanned aerial vehicle (UAV) multispectral images (MSIs) and digital surface models (DSMs). The tree boundaries were extracted by using sequential thresholding, Canny edge detection and circular Hough transform algorithms. The performance of the developed approach was assessed on three test areas that include different characteristics with regard to tree counts, diameters, densities and background covers. The proposed tree extraction procedure was applied to DSM that were generated from UAV images (Data Set 1), UAV MSIs (Data Set 2) and both of them together (Data Set 3). The accuracies of the obtained results were assessed using three different techniques that evaluate the tree extraction results according to the counts, areas and locations. The obtained results indicate the success of the developed approach with delineation accuracies that exceeded 80% for each test area using each data set. The most accurate results were obtained when Data Set 1 was used. Although Data Set 2 provides the lowest accuracies when compared with other data sets, the delineation accuracies are still high and can be used especially for counting trees and detecting tree locations.
    Keywords Citrus ; agricultural management ; algorithms ; crop yield ; data collection ; models ; multispectral imagery ; trees
    Language English
    Dates of publication 2018-07
    Size p. 289-301.
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 395514-x
    ISSN 0168-1699
    ISSN 0168-1699
    DOI 10.1016/j.compag.2018.05.001
    Database NAL-Catalogue (AGRICOLA)

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