Land Cover Mapping Using Sentinel-1 Time-Series Data and Machine-Learning Classifiers in Agricultural Sub-Saharan Landscape

被引:12
|
作者
Dahhani, Sara [1 ]
Raji, Mohamed [1 ]
Hakdaoui, Mustapha [1 ]
Lhissou, Rachid [2 ]
机构
[1] Hassan II Univ Casablanca, Fac Sci Ben Msik, POB 7955, Casablanca, Morocco
[2] Inst Natl Rech Sci, Ctr ETE, 490 Couronne, Quebec City, PQ GIK 9A9, Canada
关键词
SAR data; PCA; K-D tree KNN; time series; land use; random forest; synthetic aperture radar; CLASSIFICATION; FOREST;
D O I
10.3390/rs15010065
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper shows the efficiency of machine learning for improving land use/cover classification from synthetic aperture radar (SAR) satellite imagery as a tool that can be used in some sub-Saharan countries that experience frequent clouds. Indeed, we aimed to map the land use and land cover, especially in agricultural areas, using SAR C-band Sentinel-1 (S-1) time-series data over our study area, located in the Kaffrine region of Senegal. We assessed the performance and the processing time of three machine-learning classifiers applied on two inputs. In fact, we applied the random forest (RF), K-D tree K-nearest neighbor (KDtKNN), and maximum likelihood (MLL) classifiers using two separate inputs, namely a set of monthly S-1 time-series data acquired during 2020 and the principal components (PCs) of the time-series dataset. In addition, the RF and KDtKNN classifiers were processed using different tree numbers for RF (10, 15, 50, and 100) and different neighbor numbers for KDtKNN (5, 10, and 15). The retrieved land cover classes included water, shrubs and scrubs, trees, bare soil, built-up areas, and cropland. The RF classification using the S-1 time-series data gave the best performance in terms of accuracy (overall accuracy = 0.84, kappa = 0.73) with 50 trees. However, the processing time was relatively slower compared to KDtKNN, which also gave a good accuracy (overall accuracy = 0.82, kappa = 0.68). Our results were compared to the FROM-GLC, ESRI, and ESA world cover maps and showed significant improvements in some land use and land cover classes.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics
    Kontgis, Caitlin
    Warren, Michael S.
    Skillman, Samuel W.
    Chartrand, Rick
    Moody, Daniela I.
    2017 9TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTITEMP), 2017,
  • [2] Agricultural Land Cover Mapping based on Sentinel-1 Coherence Time-Series
    Nikaein, Tina
    Iannini, Lorenzo
    Dekker, Paco Lopez
    13TH EUROPEAN CONFERENCE ON SYNTHETIC APERTURE RADAR, EUSAR 2021, 2021, : 517 - 520
  • [3] Illigal irrigation Mapping in Oases Using Optical and radar Time-Series Data and Machine-Learning Classifiers
    Kassouk, Zeineb
    Akacha, Ferdaws
    Chebbi, Wafa
    Habaieb, Hamadi
    Saad, Atfa
    Chabaane, Zohra Lili
    2024 IEEE 7TH INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES, SIGNAL AND IMAGE PROCESSING, ATSIP 2024, 2024, : 348 - 353
  • [4] LAND COVER MAPPING USING SENTINEL-1 SAR DATA
    Abdikan, S.
    Sanli, F. B.
    Ustuner, M.
    Calo, F.
    XXIII ISPRS CONGRESS, COMMISSION VII, 2016, 41 (B7): : 757 - 761
  • [5] Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model
    Xu, Lu
    Zhang, Hong
    Wang, Chao
    Wei, Sisi
    Zhang, Bo
    Wu, Fan
    Tang, Yixian
    REMOTE SENSING, 2021, 13 (19)
  • [6] Paddy Rice Mapping in Hainan Island Using Time-Series Sentinel-1 SAR Data and Deep Learning
    Shen, Guozhuang
    Liao, Jingjuan
    REMOTE SENSING, 2025, 17 (06)
  • [7] Time-series classification of Sentinel-1 agricultural data over North Dakota
    Whelen, Tracy
    Siqueira, Paul
    REMOTE SENSING LETTERS, 2018, 9 (05) : 411 - 420
  • [8] MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN
    Sari, I. L.
    Weston, C. J.
    Newnham, G. J.
    Volkova, L.
    GEOINFORMATION WEEK 2022, VOL. 48-4, 2023, : 319 - 325
  • [9] Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel
    Schulz, Dario
    Yin, He
    Tischbein, Bernhard
    Verleysdonk, Sarah
    Adamou, Rabani
    Kumar, Navneet
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 178 : 97 - 111
  • [10] Evaluation of Using Sentinel-1 and-2 Time-Series to Identify Winter Land Use in Agricultural Landscapes
    Denize, Julien
    Hubert-Moy, Laurence
    Betbeder, Julie
    Corgne, Samuel
    Baudry, Jacques
    Pottier, Eric
    REMOTE SENSING, 2019, 11 (01)