Abandoned farmland detection using single-year satellite images in Japan

被引:0
|
作者
Kobayashi, Yoshihiko [1 ]
Kinoshita, Tsuguki [2 ]
机构
[1] Natl Agr & Food Res Org, Inst Agr Machinery, Saitama, Saitama, Japan
[2] Ibaraki Univ, Coll Agr, Mito, Ibaraki, Japan
关键词
normalized difference vegetation index reconstruction; PlanetScope; phenological profile; satellite constellation; support vector machine; AGRICULTURAL LAND ABANDONMENT; ENVIRONMENTAL SUSTAINABILITY; NDVI; RECULTIVATION; COVER;
D O I
10.1117/1.JRS.17.014517
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Mapping abandoned farmlands' location and spatial pattern is essential for rural planning. Monitoring small abandoned farmland based on dynamic farmland's phenology is challenging to due to conflict between spatial and temporal resolutions of conventional satellite missions. A unique approach that combines satellite constellation imagery with improved automatic radiometric normalization method for more temporally consistent reflectance were proposed. Applying it in Ami-town, Ibaraki Prefecture, Japan, abandoned farmlands were identified with 3-m resolution and a Kappa coefficient of 0.81 based on satellite constellation-based time-series data; however, the discrimination of the degree of abandonment was challenging. Unlike conventional abandoned farmland mapping approaches which targets spatially large area, our approach provides information at small, fragmented and abandoned farmland in East Asia that enables labor-saving monitoring of land use shifts.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Detection of roads from satellite images using optimal search
    Rianto, Yan
    Kondo, Shozo
    Talguk, Kim
    International Journal of Pattern Recognition and Artificial Intelligence, 2001, 14 (08) : 1008 - 1023
  • [22] Detection of forest disaster using satellite images with sematic segmentation
    Park, Seong Wook
    Lee, Yang Won
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXV, 2019, 11155
  • [23] Cemetery Detection Using Satellite Images in Google Earth Engine
    Rodrigo Suarez, Ranyart
    Villasenor, Elio
    PROCEEDINGS OF THE 2021 XXIII IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC 2021), 2021,
  • [24] WINDMILLS DETECTION USING DEEP LEARNING ON SENTINEL SATELLITE IMAGES
    Tertre, M.
    Laurencot, T.
    XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, 2022, 43-B3 : 197 - 203
  • [25] Change Detection in Satellite Images Using a Genetic Algorithm Approach
    Celik, Turgay
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2010, 7 (02) : 386 - 390
  • [26] Detection of roads from satellite images using optimal search
    Rianto, Y
    Kondo, S
    Kim, T
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2000, 14 (08) : 1009 - 1023
  • [27] Detection of Forest Ecosystem Disturbance Using Satellite Images and ISODATA
    Kim, Daesun
    Kim, Eun-Sook
    Lim, Jong-Hwan
    Lee, Yangwon
    KOREAN JOURNAL OF REMOTE SENSING, 2020, 36 (05) : 835 - 846
  • [28] Analysis of Various Change Detection Techniques Using Satellite Images
    Kotkar, Snehal R.
    Jadhav, B. D.
    2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING (ICIP), 2015, : 664 - 668
  • [29] Change Detection in Satellite Images using Self Organizing Maps
    Santos, Michael D. L.
    Shiguemori, Elcio H.
    Mota, Rodrigo L. M.
    Ramos, Alexandre C. B.
    2015 12TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY - NEW GENERATIONS, 2015, : 662 - 667
  • [30] Severe Thunderstorm Detection by Visual Learning Using Satellite Images
    Zhang, Yu
    Wistar, Stephen
    Li, Jia
    Steinberg, Michael A.
    Wang, James Z.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55 (02): : 1039 - 1052