Früher|Nächster

Studie zur Extraktion von Langzeit-Terrassenfeldern basierend auf Landsat-Bildern und dem LandTrendr-Algorithmus – am Beispiel des San du-Flussbeckens in der Provinz Gansu

  • role： First author第一作者
  • Affiliation：

    School of Computer Science, China University of Geosciences, Wuhan 430078, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：liyifan202401@163.com
  • Introduction：黎一帆，研究方向为遥感信息智能提取。E-mail： liyifan202401@163.com
LI Yifan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Computer Science, China University of Geosciences, Wuhan 430078, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：wubf@aircas.ac.cn
  • Introduction：吴炳方，研究方向为农业与生态遥感。E-mail： wubf@aircas.ac.cn
WU Bingfang12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

TIAN Fuyou2,  
  • Affiliation：

    Soil and Water Conservation Monitoring Center of the Ministry of Water Resources, Beijing 100053

CHANG Shengwei4,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHAO Dan23,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHANG Miao23,  
  • Affiliation：

    School of Computer Science, China University of Geosciences, Wuhan 430078, China

FAN Runyu1,  
  • Affiliation：

    Qilu Aerospace Information Research Institute, Ji'nan 250132

HU Shuo5

Resümee

Terrassenfelder sind als wichtige landwirtschaftliche Landschaft von großer Bedeutung für die Steigerung der Erträge und den Boden- und Wasserschutz. Zeitlich-räumliche Veränderungsdaten der Terrassenfelder sind eine wichtige Grundlage zur Bewertung der Produktionseffizienz und der ökologischen und hydrologischen Effekte der Terrassen, sind jedoch schwer zu erlangen. Dieser Artikel stellt eine Methode zur Extraktion von Terrassenfeldern aus Langzeit-Zeitreihendaten basierend auf einem Veränderungserkennungsalgorithmus vor. Unter Verwendung von langzeitlichen Satellitenbildserien der Landsat-Reihe mit Fusion des panchromatischen Bands wurden die Daten von Landsat 7 ETM+ und Landsat 8 OLI normalisiert, um Störungen und Verzerrungen zwischen verschiedenen Sensoren zu entfernen; basierend auf der Verteilungskarte der Terrassen von 2020 wurde mit dem datengestützten LandTrendr-Veränderungserkennungsalgorithmus die zeitliche und räumliche Verteilung der Terrassen im Einzugsgebiet des San du-Flusses von 2001 bis 2020 extrahiert und die Beziehung zwischen Terrassenfläche und Getreideertrag analysiert. Die Ergebnisse zeigen, dass die Gesamtüberwachungsgenauigkeit der zeitlichen Terrassen im Einzugsgebiet des San du-Flusses 82,50% beträgt und der F1-Score 0,84 erreicht. Die Gesamtfläche der Terrassen im Einzugsgebiet des San du-Flusses ist im Jahr 2020 im Vergleich zu 2001 um 397 km² oder 45,27% gewachsen. Vor 2015 erreichte die Wachstumsrate der Terrassen ihren Höhepunkt, danach verlangsamte sich das Wachstum, aber der allgemeine Trend ist weiterhin steigend. Es besteht eine sehr enge Beziehung zwischen der Terrassenfläche und der Getreideproduktion (R²=0,94). Durch die Fusion der Landsat-Langzeit-Satellitendaten mit dem LandTrendr-Veränderungserkennungsalgorithmus konnten genaue Informationen zur zeitlich-räumlichen Verteilung der Terrassen gewonnen werden, die die dynamische Entwicklung der Terrassenbauwerke in zeitlicher und räumlicher Dimension sowie die Produktionseffizienz effektiv aufzeigen.

Schlüsselwort

Terrassenkartierung; sequenzielle Extraktion; Landsat; Veränderungserkennung; LandTrendr

References

  1. 1.
    Arnáez J, Lana-Renault N, Lasanta T, Ruiz-Flaño P and Castroviejo J. 2015. Effects of farming terraces on hydrological and geomorphological processes. A review. Catena, 128: 122-134
  2. 2.
    Berhane T M, Lane C R, Wu Q S, Anenkhonov O A, Chepinoga V V, Autrey B C and Liu H X. 2017. Comparing pixel- and object-based approaches in effectively classifying wetland-dominated landscapes. Remote Sensing, 10(1): 46
  3. 3.
    Cao B W, Yu L, Naipal V, Ciais P, Li W, Zhao Y Y, Wei W, Chen D, Liu Z and Gong P. 2021. A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine. Earth System Science Data, 13(5): 2437-2456
  4. 4.
    Chen G, Zhong C R, Li M Y, Yu Z, Liu X Y and Jia M M. 2022. Disturbance of mangrove forests in Guangxi Beilun Estuary during 1990-2020. National Remote Sensing Bulletin, 26(6): 1112-1120
  5. 5.
    de Jong S M, Shen Y C, de Vries J, Bijnaar G, van Maanen B, Augustinus P and Verweij P. 2021. Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. International Journal of Applied Earth Observation and Geoinformation, 97: 102293
  6. 6.
    De Marzo T, Pflugmacher D, Baumann M, Lambin E F, Gasparri I and Kuemmerle T. 2021. Characterizing forest disturbances across the Argentine Dry Chaco based on Landsat time series. International Journal of Applied Earth Observation and Geoinformation, 98: 102310
  7. 7.
    Deng C X, Zhang G Y, Liu Y J, Nie X D, Li Z W, Liu J Y and Zhu D M. 2021. Advantages and disadvantages of terracing: a comprehensive review. International Soil and Water Conservation Research, 9(3): 344-359
  8. 8.
    Duan W S, Chen Y P, Wang L, Huang N, He Y H Z, Zhang C S, Zhang Y J, Zhou Q and Niu Z. 2023. Information extraction of temporal and spatial distribution of short-rotation plantations in Guangxi Zhuang Autonomous Region. National Remote Sensing Bulletin, 27(11): 2617-2627
  9. 9.
    Gelabert P J, Rodrigues M, de La Riva J, Ameztegui A, Sebastià M T and Vega-Garcia C. 2021. LandTrendr smoothed spectral profiles enhance woody encroachment monitoring. Remote Sensing of Environment, 262: 112521
  10. 10.
    Kennedy R E, Yang Z Q and Cohen W B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment, 114(12): 2897-2910
  11. 11.
    Kennedy R E, Yang Z Q, Gorelick N, Braaten J, Cavalcante L, Cohen W B and Healey S. 2018. Implementation of the LandTrendr algorithm on Google earth engine. Remote Sensing, 10(5): 691
  12. 12.
    Li K K, Yang J Y, Wang J Y, Wang Z, Zeng Y, Borrelli P, Hubacek K, Hu Y C, Xu B D, Fang N F, Zeng C, Zhou Z H and Shi Z H. 2024. Human-altered soil loss dominates nearly half of water erosion in China but surges in agriculture-intensive areas. One Earth, 7(11): 2008-2018
  13. 13.
    Liang X Y, Jin X B, Yang X H, Xu W Y, Lin J H and Zhou Y K. 2021. Exploring cultivated land evolution in mountainous areas of Southwest China, an empirical study of developments since the 1980s. Land Degradation and Development, 32(2): 546-558
  14. 14.
    Liu S C, Du K C, Zheng Y J, Chen J, Du P J and Tong X H. 2023. Remote sensing change detection technology in the Era of artificial intelligence: inheritance, development and challenges. National Remote Sensing Bulletin, 27(9): 1975-1987
  15. 15.
    Liu X H, He B L, Li Z X, Zhang J L, Wang L and Wang Z. 2011. Influence of land terracing on agricultural and ecological environment in the loess plateau regions of China. Environmental Earth Sciences, 62(4): 797-807
  16. 16.
    Liu Z H, Wang W J, Ballantyne A, He H S, Wang X G, Liu S G, Ciais P, Wimberly M C, Piao S, Yu K L, Yao Q C, Liang Y, Wu Z W, Fang Y T, Chen A P, Xu W R and Zhu J J. 2023. Forest disturbance decreased in China from 1986 to 2020 despite regional variations. Communications Earth and Environment, 4(1): 15
  17. 17.
    Liu Z L, Ma X Y, Jiao Y M, Wang L and Wei J F. 2025. Quantification and spatiotemporal variation characteristics of soil and water conservation service functions in Hani Terraces from 1990 to 2020. Research of Soil and Water Conservation, 32(3): 312-320, 342
  18. 18.
    Lu Y H, Li X B, Xin L J, Song H F and Wang X. 2023. Mapping the terraces on the Loess Plateau based on a deep learning-based model at 1.89 m resolution. Scientific Data, 10(1): 115
  19. 19.
    Mao Z R, Che X R, Chen X K and Gao Y J. 2025. Spatial and temporal evolution characteristics of landscape pattern in the core heritage area of Hani Terrace. Journal of Chinese Urban Forestry, 23(1): 119-126
  20. 20.
    Murillo-Sandoval P J, Gjerdseth E, Correa-Ayram C, Wrathall D, Van Den Hoek J, Dávalos L M and Kennedy R. 2021. No peace for the forest: rapid, widespread land changes in the Andes-Amazon region following the Colombian civil war. Global Environmental Change, 69: 102283
  21. 21.
    Nyssen J, Clymans W, Poesen J, Vandecasteele I, De Baets S, Haregeweyn N, Naudts J, Hadera A, Moeyersons J, Haile M and Deckers J. 2009. How soil conservation affects the catchment sediment budget-a comprehensive study in the north Ethiopian highlands. Earth Surface Processes and Landforms, 34(9): 1216-1233
  22. 22.
    Pasquarella V J, Arévalo P, Bratley K H, Bullock E L, Gorelick N, Yang Z Q and Kennedy R E. 2022. Demystifying LandTrendr and CCDC temporal segmentation. International Journal of Applied Earth Observation and Geoinformation, 110: 102806
  23. 23.
    Roy D P, Kovalskyy V, Zhang H K, Vermote E F, Yan L, Kumar S S and Egorov A. 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185: 57-70
  24. 24.
    Schneibel A, Stellmes M, Röder A, Frantz D, Kowalski B, Haß E and Hill J. 2017. Assessment of spatio-temporal changes of smallholder cultivation patterns in the Angolan Miombo belt using segmentation of Landsat time series. Remote Sensing of Environment, 195: 118-129
  25. 25.
    Schönbrodt-Stitt S, Behrens T, Schmidt K, Shi X Z and Scholten T. 2013. Degradation of cultivated bench terraces in the Three Gorges Area: field mapping and data mining. Ecological Indicators, 34: 478-493
  26. 26.
    Senf C and Seidl R. 2021. Mapping the forest disturbance regimes of Europe. Nature Sustainability, 4(1): 63-70
  27. 27.
    Wang D W. 2016. Analysis on the effect and successful experience of terrace construction in Gangu County. Gansu Science and Technology, 32(19): 135-137
  28. 28.
    Wei Z Q, Han Y F, Li M Y, Yang K, Yang Y, Luo Y and Ong S H. 2017. A small UAV based multi-temporal image registration for dynamic agricultural terrace monitoring. Remote Sensing, 9(9): 904
  29. 29.
    Wu B F, Tian Y C and Li Q Z. 2004. GVG, a crop type proportion sampling instrument. Journal of Remote Sensing (in Chinese), 8(6): 570-580
  30. 30.
    Wu D, Li B and Yang A L. 2017. Estimation of tree height and biomass based on long time series data of landsat. Engineering of Surveying and Mapping, 26(6): 1-5
  31. 31.
    Xu Y, Yang B, Tang Q, Liu G B and Liu P L. 2011. Analysis of comprehensive benefits of transforming slope farmland to terraces on the Loess Plateau: a case study of the Yangou Watershed in Northern Shaanxi Province, China. Journal of Mountain Science, 8(3): 448-457
  32. 32.
    Yang B, Mao Y, Chen J, Liu J Q, Chen J and Yan K. 2023. Review of remote sensing change detection in deep learning: bibliometric and analysis. National Remote Sensing Bulletin, 27(9): 1988-2005
  33. 33.
    Yang Y J, Erskine P D, Lechner A M, Mulligan D, Zhang S L and Wang Z Y. 2018. Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm. Journal of Cleaner Production, 178: 353-362
  34. 34.
    Ye Z X, Yang K L, Lin Y W, Guo S J, Sun Y M, Chen X L, Lai R W and Zhang H X. 2023. A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images. Computers and Electronics in Agriculture, 209: 107822
  35. 35.
    Yin Q D, Liu C X and Tian Y. 2020. Detecting dynamics of vegetation disturbance in forest natural reserve using Landsat imagery and LandTrendr algorithm: the case of Chaisong and Taibaishan Natural Reserves in Shaanxi, China. Acta Ecologica Sinica, 40(20): 7343-7352
  36. 36.
    Yu M, Li Y B, Luo G J, Zhang H, Xu Q and Yu L M. 2022. Spatio-temporal evolution of rice terraces in mountainous areas of Southwest China: a case study of Xijiang and Jiabang townships, Miaoling. Geographical Research, 41(8): 2184-2202
  37. 37.
    Yue Z H, Sui K R, Liu P, Liu X J, Zhang Y M, Zhai R W and Wang W. 2017. Study on change rule of terracing crop yield of the mountain area in southern Ningxia. Soil and Water Conservation in China, (9): 56-58
  38. 38.
    Zhang X B, Wang Z Q, Xu H and Li R H. 2002. Analysis of increases in crop production and labor productivity by terracing of sloping fields in three counties of Shananxi and Gansu Provinces. Journal of Mountain Science, 20(5): 589-593
  39. 39.
    Zhang Y, Shi M C, Zhao X, Wang X J, Luo Z D and Zhao Y. 2017. Methods for automatic identification and extraction of terraces from high spatial resolution satellite data (China-GF-1). International Soil and Water Conservation Research, 5(1): 17-25
  40. 40.
    Zhang Z N, Jiao Y M and Yang S Q. 2025. Spatiotemporal variation characteristics and driving mechanisms of the five rice terraced heritage sites in southern China. Transactions of the Chinese Society of Agricultural Engineering, 41(1): 288-297
  41. 41.
    Zhu Z. 2017. Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 370-384
  42. 42.
    Zhu Z and Woodcock C E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144: 152-171
  43. 43.
    Zhu Z, Woodcock C E and Olofsson P. 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment, 122: 75-91

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website