Application of Multi-Temporal and Multisource Satellite Imagery in the Study of Irrigated Landscapes in Arid Climates

被引:2
|
作者
Bulawka, Nazarij [1 ]
Orengo, Hector A. [1 ,2 ]
机构
[1] Catalan Inst Class Archaeol, Landscape Archaeol Res Grp GIAP, Placa Rovellat S-N, Tarragona 43003, Spain
[2] Catalan Inst Res & Adv Studies ICREA, Passeig Lluis Co 23, Barcelona 08010, Spain
关键词
irrigation; landscape archaeology; remote sensing; TanDEM-X; Sentinel-1; Sentinel-2; LANDSAT; 5; Google Earth Engine; GOOGLE EARTH ENGINE; VEGETATION INDEXES; SETTLEMENT; PLANT; SET; MAP;
D O I
10.3390/rs16111997
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features.
引用
收藏
页数:25
相关论文
共 50 条
  • [31] Detecting aquatic vegetation changes in Taihu Lake, China using multi-temporal satellite imagery
    Ma, Ronghua
    Duan, Hongtao
    Gu, Xiaohong
    Zhang, Shouxuan
    SENSORS, 2008, 8 (06) : 3988 - 4005
  • [32] Feature Selection and Analysis of Powdery Mildew of Winter Wheat based on Multi-Temporal Satellite Imagery
    Chen, Dongmei
    Zhang, Jingcheng
    Yuan, Lin
    8TH INTERNATIONAL CONFERENCE ON INTERNET MULTIMEDIA COMPUTING AND SERVICE (ICIMCS2016), 2016, : 251 - 254
  • [33] MAPPING ORCHARDS ON PLAIN TERRAINS USING MULTI-TEMPORAL MEDIUM-RESOLUTION SATELLITE IMAGERY
    Yuan, H. L.
    Ma, R. H.
    Luo, J. H.
    APPLIED ENGINEERING IN AGRICULTURE, 2015, 31 (03) : 351 - 362
  • [34] Multi-temporal Landsat imagery and MSAVI index for monitoring rangeland degradation in arid ecosystem, case study of Biskra (southeast Algeria)
    Belhadj, Amina
    Boulghobra, Nouar
    Allache, Fatma Demnati
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (06)
  • [35] Multi-temporal Landsat imagery and MSAVI index for monitoring rangeland degradation in arid ecosystem, case study of Biskra (southeast Algeria)
    Amina Belhadj
    Nouar Boulghobra
    Fatma Demnati Allache
    Environmental Monitoring and Assessment, 2023, 195
  • [36] Analyzing multi-temporal satellite imagery and stakeholders' perceptions to have an insight into how forest co-management is changing the protected area landscapes in Bangladesh
    Islam, Kazi Nazrul
    Rahman, Mohammad Mahfuzur
    Jashimuddin, Mohammed
    Hossain, Mohammad Mosharraf
    Islam, Kamrul
    Al Faroque, Mohiuddin
    FOREST POLICY AND ECONOMICS, 2019, 101 : 70 - 80
  • [37] Crop discrimination using multi-temporal SAR imagery
    Tso, B
    Mather, PM
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1999, 20 (12) : 2443 - 2460
  • [38] A MULTI-SCALE, MULTI-TEMPORAL ANALYSIS OF NDVI IN BURNED LANDSCAPES
    Gupta, Vaibhav
    Reinke, Karin
    Jones, Simon
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 2118 - 2121
  • [39] Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery
    Demirel, Nuray
    Emil, M. Kemal
    Duzgun, H. Sebnem
    INTERNATIONAL JOURNAL OF COAL GEOLOGY, 2011, 86 (01) : 3 - 11
  • [40] SELF-SUPERVISED REPRESENTATION LEARNING ENHANCES BROAD AREA SEARCH IN MULTI-TEMPORAL SATELLITE IMAGERY
    Stephens, Tom
    Corley, Isaac
    Gould, Adrian
    Polakiewicz, Anthony
    McVicar, David
    Torres, Carlos
    Colangelo, Rose
    Aguilar-Simon, Mario
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 5337 - 5340