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  1. Article: Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

    Rinawan, Fedri Ruluwedrata / Tateishi, Ryutaro / Raksanagara, Ardini Saptaningsih / Agustian, Dwi / Alsaaideh, Bayan / Natalia, Yessika Adelwin / Raksanagara, Ahyani

    ISPRS international journal of geo-information. 2015 Nov. 23, v. 4, no. 4

    2015  

    Abstract: Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease ... ...

    Abstract Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease patterns (DDPs) and their association with DDP. A Supervised Minimum Distance classifier was applied to 653 training data from image object segmentations: PR (81 polygons), FR (50), and non-roof (NR) class (522). Ground validation of 272 pixels (52 for PR, 51 for FR, and 169 for NR) was done using a global positioning system (GPS) tool. Getis-Ord score pattern analysis was applied to 1154 dengue disease incidence with address-approach-based data with weighted temporal value of 28 days within a 1194 m spatial radius. We used ordinary least squares (OLS) and geographically weighted regression (GWR) to assess spatial association. Our findings showed 70.59% overall accuracy with a 0.51 Kappa coefficient of the roof classification images. Results show that DDPs were found in hotspot, random, and dispersed patterns. Smaller PR size and larger FR size showed some association with increasing DDP into more clusters (OLS: PR value = −0.27; FR = 0.04; R2 = 0.076; GWR: R2 = 0.76). The associations in hotspot patterns are stronger than in other patterns (GWR: R2 in hotspot = 0.39, random = 0.37, dispersed = 0.23).
    Keywords Aedes ; dengue ; disease incidence ; global positioning systems ; habitats ; least squares
    Language English
    Dates of publication 2015-1123
    Size p. 2586-2603.
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2655790-3
    ISSN 2220-9964
    ISSN 2220-9964
    DOI 10.3390/ijgi4042586
    Database NAL-Catalogue (AGRICOLA)

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  2. Article: Tropical forest mapping using a combination of optical and microwave data of ALOS

    Hoan, Nguyen Thanh / Tateishi, Ryutaro / Alsaaideh, Bayan / Ngigi, Thomas / Alimuddin, Ilham / Johnson, Brian

    International journal of remote sensing. 2013 Jan. 10, v. 34, no. 1

    2013  

    Abstract: It is difficult to monitor forests in tropical regions with frequent cloud cover using optical remote-sensing data. Adequate multi-temporal, high-resolution imagery is often not available. Microwave imagery is able to penetrate cloud cover, enabling ... ...

    Abstract It is difficult to monitor forests in tropical regions with frequent cloud cover using optical remote-sensing data. Adequate multi-temporal, high-resolution imagery is often not available. Microwave imagery is able to penetrate cloud cover, enabling imagery of the land surface to be recorded more frequently. This study seeks to improve tropical forest mapping by combining optical and microwave imagery, with one of the main objectives being the discrimination of planted and natural forests. First, multi-spectral Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images were used to create a forest and land-cover classification of the study area. Subsequently, ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) single-polarized and dual-polarized microwave images were used to generate forest and land-cover masks to be used in combination with the ALOS/AVNIR-2 classification. The overall accuracy of the ALOS/AVNIR-2 classification was 77%. When the ALOS/PALSAR masks were used in combination with the ALOS/AVNIR-2 classification, the overall accuracy increased to 88% with higher than 90% accuracy for the main forest classes.
    Keywords cloud cover ; image analysis ; land cover ; remote sensing ; synthetic aperture radar ; tropical forests ; tropics
    Language English
    Dates of publication 2013-0110
    Size p. 139-153.
    Publishing place Taylor & Francis
    Document type Article
    ZDB-ID 1497529-4
    ISSN 1366-5901 ; 0143-1161
    ISSN (online) 1366-5901
    ISSN 0143-1161
    DOI 10.1080/01431161.2012.709329
    Database NAL-Catalogue (AGRICOLA)

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  3. Article: Production of global land cover data – GLCNMO

    Tateishi, Ryutaro / Uriyangqai, Bayaer / Al-Bilbisi, Hussam / Ghar, Mohamed Aboel / Tsend-Ayush, Javzandulam / Kobayashi, Toshiyuki / Kasimu, Alimujiang / Hoan, Nguyen Thanh / Shalaby, Adel / Alsaaideh, Bayan / Enkhzaya, Tsevengee / Gegentana / Sato, Hiroshi P

    International journal of digital earth. 2011 Jan. 1, v. 4, no. 1

    2011  

    Abstract: Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping ...

    Abstract Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and expert's comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.
    Keywords Internet ; Landsat ; data collection ; ice ; land cover ; moderate resolution imaging spectroradiometer ; seasonal variation ; snow ; trees
    Language English
    Dates of publication 2011-0101
    Size p. 22-49.
    Publishing place Taylor & Francis Group
    Document type Article
    ISSN 1753-8955
    DOI 10.1080/17538941003777521
    Database NAL-Catalogue (AGRICOLA)

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