Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

被引:69
|
作者
Ruetschi, Marius [1 ]
Small, David [2 ]
Waser, Lars T. [1 ]
机构
[1] Swiss Fed Inst Forest Snow & Landscape Res WSL, Dept Land Change Sci, Zurcherstr 111, CH-8903 Birmensdorf, Switzerland
[2] Univ Zurich, RSL, Winterthurerstr 190, CH-8057 Zurich, Switzerland
关键词
SAR; C-band backscatter; Sentinel-1; windthrow; storm damage; forestry; change detection; windthrow index; mixed temperate forest; DIGITAL SURFACE MODELS; RADAR BACKSCATTER; SEASONAL-CHANGES; FOREST STRUCTURE; STORM DAMAGE; BARK BEETLES;
D O I
10.3390/rs11020115
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha-as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer's accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user's accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user's needs.
引用
收藏
页数:23
相关论文
共 50 条
  • [21] Simple method for identification of forest windthrows from Sentinel-1 SAR data incorporating PCA
    Lazecky, Milan
    Wadhwa, Sweety
    Mlcousek, Marek
    Sousa, Joaquim J.
    INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS / INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT / INTERNATIONAL CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES 2020 (CENTERIS/PROJMAN/HCIST 2020), 2021, 181 : 1154 - 1161
  • [22] Estimating vegetation water content from Sentinel-1 C-band SAR data over savanna and grassland ecosystems
    Bernardino, Paulo N.
    Oliveira, Rafael S.
    Van Meerbeek, Koenraad
    Hirota, Marina
    Furtado, Mariana N.
    Sanches, Isabela A.
    Somers, Ben
    ENVIRONMENTAL RESEARCH LETTERS, 2024, 19 (03)
  • [23] COPERNICUS SENTINEL-1 NEXT GENERATION MISSION: ENHANCED C-BAND DATA CONTINUITY
    Torres, Ramon
    Geudtner, Dirk
    Davidson, Malcolm
    Bibby, David
    Traver, Ignacio Navas
    Hernandez, Ana Isabel Garcia
    Laduree, Gregory
    Poupaert, Jelle
    Cossu, Mario
    Touveneau, Marie
    Graham, Stefan
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 4717 - 4719
  • [24] The PSIG procedure to Persistent Scatterer Interferometry (PSI) using X-band and C-band Sentinel-1 data
    Cuevas-Gonzalez, Maria
    Devanthery, Nuria
    Crosetto, Michele
    Monserrat, Oriol
    Crippa, Bruno
    SAR IMAGE ANALYSIS, MODELING, AND TECHNIQUES XV, 2015, 9642
  • [25] Change detection in a series of Sentinel-1 SAR data
    Nielsen, Allan A.
    Conradsen, Knut
    Skriver, Henning
    Canty, Morton J.
    2017 9TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTITEMP), 2017,
  • [26] Pre-processing of Sentinel-1 C-band SAR images based on incidence angle correction for dark target detection
    Gouveia, Nelson De Almeida
    Alves, Fabio Correa
    Pereira, Luciana De Oliveira
    REMOTE SENSING LETTERS, 2019, 10 (10) : 939 - 948
  • [27] Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection
    Mondini, Alessandro C.
    Santangelo, Michele
    Rocchetti, Margherita
    Rossetto, Enrica
    Manconi, Andrea
    Monserrat, Oriol
    REMOTE SENSING, 2019, 11 (07)
  • [28] An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data
    Li, Yu
    Martinis, Sandro
    Plank, Simon
    Ludwig, Ralf
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2018, 73 : 123 - 135
  • [29] Displacement monitoring and modelling of a high-speed railway bridge using C-band Sentinel-1 data
    Huang, Qihuan
    Crosetto, Michele
    Monserrat, Oriol
    Crippa, Bruno
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2017, 128 : 204 - 211
  • [30] FIRST EXPERIENCES WITH ACTIVE C-BAND RADAR REFLECTORS AND SENTINEL-1
    Gisinger, C.
    Eineder, M.
    Brcic, R.
    Balss, U.
    Gruber, T.
    Oikonomidou, X.
    Heinze, M.
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 1165 - 1168