Land-Use and Land-Cover Changes in Cottbus City and Spree-Neisse District, Germany, in the Last Two Decades: A Study Using Remote Sensing Data and Google Earth Engine

被引:2
作者
Ahmed, Rezwan [1 ]
Zafor, Md. Abu [2 ]
Trachte, Katja [1 ]
机构
[1] Brandenburg Univ Technol Cottbus Senftenberg, Dept Atmospher Proc, D-03046 Cottbus, Germany
[2] Bangladesh Army Int Univ Sci & Technol BAIUST, Dept Civil Engn, Comilla 3501, Bangladesh
关键词
LULC; machine learning; Landsat; Google Earth Engine; supervised; unsupervised; CLASSIFICATION; CITIES; FOREST; AREA;
D O I
10.3390/rs16152773
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region's various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022).
引用
收藏
页数:16
相关论文
共 77 条
[1]   Comparison of Classification Algorithms for Detecting Typical Coastal Reclamation in Guangdong Province with Landsat 8 and Sentinel 2 Images [J].
Ai, Bin ;
Huang, Ke ;
Zhao, Jun ;
Sun, Shaojie ;
Jian, Zhuokai ;
Liu, Xiaoding .
REMOTE SENSING, 2022, 14 (02)
[2]  
Alshari E A., 2021, Global Transitions Proceedings, V2, P8, DOI DOI 10.1016/J.GLTP.2021.01.002
[3]   Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery [J].
Amani, Meisam ;
Mahdavi, Sahel ;
Kakooei, Mohammad ;
Ghorbanian, Arsalan ;
Brisco, Brian ;
Delancey, Evan ;
Toure, Souleymane ;
Reyes, Eugenio Landeiro .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 :10314-10335
[4]   Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review [J].
Amani, Meisam ;
Ghorbanian, Arsalan ;
Ahmadi, Seyed Ali ;
Kakooei, Mohammad ;
Moghimi, Armin ;
Mirmazloumi, S. Mohammad ;
Moghaddam, Sayyed Hamed Alizadeh ;
Mahdavi, Sahel ;
Ghahremanloo, Masoud ;
Parsian, Saeid ;
Wu, Qiusheng ;
Brisco, Brian .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2020, 13 (13) :5326-5350
[5]  
Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027
[6]   Statistical features for land use and land cover classification in Google Earth Engine [J].
Becker, Willyan Ronaldo ;
Lo, Thiago Berticelli ;
Johann, Jerry Adriani ;
Mercante, Erivelto .
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2021, 21
[7]  
boomtown.de, The Boomtown-BOOMTOWN COTTBUS
[8]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[9]   Dynamic World, Near real-time global 10 m land use land cover mapping [J].
Brown, Christopher F. ;
Brumby, Steven P. ;
Guzder-Williams, Brookie ;
Birch, Tanya ;
Hyde, Samantha Brooks ;
Mazzariello, Joseph ;
Czerwinski, Wanda ;
Pasquarella, Valerie J. ;
Haertel, Robert ;
Ilyushchenko, Simon ;
Schwehr, Kurt ;
Weisse, Mikaela ;
Stolle, Fred ;
Hanson, Craig ;
Guinan, Oliver ;
Moore, Rebecca ;
Tait, Alexander M. .
SCIENTIFIC DATA, 2022, 9 (01)
[10]  
Caruana R., 2006, P 23 INT C MACH LEAR, P161, DOI [10.1145/1143844.1143865, DOI 10.1145/1143844.1143865]