Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

被引:18
|
作者
Abera, Temesgen Alemayehu [1 ,2 ]
Vuorinne, Ilja [1 ,2 ]
Munyao, Martha [1 ,3 ]
Pellikka, Petri K. E. [1 ,2 ,4 ]
Heiskanen, Janne [1 ]
机构
[1] Univ Helsinki, Dept Geosci & Geog, POB 68, FI-00014 Helsinki, Finland
[2] Univ Helsinki, Inst Atmospher & Earth Syst Res, Fac Sci, POB 4, FI-00014 Helsinki, Finland
[3] Kenya Wildlife Serv, POB 40241, Nairobi 00100, Kenya
[4] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
关键词
Taita Taveta; land cover; reference database; machine learning; Sentinel-1; Sentinel-2;
D O I
10.3390/data7030036
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Taita Taveta County (TTC) is one of the world's biodiversity hotspots in the highlands with some of the world's megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 +/- 2.3% and it is freely accessible for land use planners, conservation managers, and researchers.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Optimization of land cover mapping through improvements in Sentinel-1 and Sentinel-2 image dimensionality and data mining feature selection for hydrological modeling
    Fragoso-Campon, Laura
    Quiros, Elia
    Gutierrez Gallego, Jose Antonio
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2021, 35 (12) : 2493 - 2519
  • [22] Exploring optimal integration schemes for Sentinel-1 SAR and Sentinel-2 multispectral data in land cover mapping across different atmospheric conditions
    Pratama, Bimo Adi Satrio
    Danoedoro, Projo
    Arjasakusuma, Sanjiwana
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2024, 34
  • [23] Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region
    De Luca, Giandomenico
    Silva, Joao M. N.
    Di Fazio, Salvatore
    Modica, Giuseppe
    EUROPEAN JOURNAL OF REMOTE SENSING, 2022, 55 (01) : 52 - 70
  • [24] Optimization of land cover mapping through improvements in Sentinel-1 and Sentinel-2 image dimensionality and data mining feature selection for hydrological modeling
    Laura Fragoso-Campón
    Elia Quirós
    José Antonio Gutiérrez Gallego
    Stochastic Environmental Research and Risk Assessment, 2021, 35 : 2493 - 2519
  • [25] Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery
    Malinowski, Radek
    Lewinski, Stanislaw
    Rybicki, Marcin
    Gromny, Ewa
    Jenerowicz, Malgorzata
    Krupinski, Michal
    Nowakowski, Artur
    Wojtkowski, Cezary
    Krupinski, Marcin
    Kraetzschmar, Elke
    Schauer, Peter
    REMOTE SENSING, 2020, 12 (21) : 1 - 27
  • [26] Integrating Sentinel-2 Derivatives to Map Land Use/Land Cover in an Avocado Agro-Ecological System in Kenya
    King’ori E.W.
    Abdel-Rahman E.M.
    Obade P.
    Mudereri B.T.
    Adan M.
    Landmann T.
    Tonnang H.E.Z.
    Dubois T.
    Remote Sensing in Earth Systems Sciences, 2023, 6 (3-4) : 224 - 238
  • [27] War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images
    Aimaiti, Yusupujiang
    Sanon, Christina
    Koch, Magaly
    Baise, Laurie G.
    Moaveni, Babak
    REMOTE SENSING, 2022, 14 (24)
  • [28] Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine
    Carrasco, Luis
    O'Neil, Aneurin W.
    Morton, R. Daniel
    Rowland, Clare S.
    REMOTE SENSING, 2019, 11 (03)
  • [29] Soil Salinity Mapping of Plowed Agriculture Lands Combining Radar Sentinel-1 and Optical Sentinel-2 with Topographic Data in Machine Learning Models
    Tola, Diego
    Satge, Frederic
    Pillco Zola, Ramiro
    Sainz, Humberto
    Condori, Bruno
    Miranda, Roberto
    Yujra, Elizabeth
    Molina-Carpio, Jorge
    Hostache, Renaud
    Espinoza-Villar, Raul
    REMOTE SENSING, 2024, 16 (18)
  • [30] A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants
    Fundisi, Emmanuel
    Tesfamichael, Solomon G.
    Ahmed, Fethi
    EUROPEAN JOURNAL OF REMOTE SENSING, 2022, 55 (01) : 372 - 387