GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
نویسندگان
چکیده
Abstract. Over past decades, a lot of global land-cover products have been released; however, these still lack map with fine classification system and spatial resolution simultaneously. In this study, novel 30 m for the year 2015 (GLC_FCS30-2015) was produced by combining time series Landsat imagery high-quality training data from GSPECLib (Global Spatial Temporal Spectra Library) on Google Earth Engine computing platform. First, were developed applying rigorous filters to CCI_LC (Climate Change Initiative Global Land Cover) MCD43A4 NBAR (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, local adaptive random forest model built each 5∘×5∘ geographical tile using multi-temporal spectral texture features corresponding data, GLC_FCS30-2015 product containing types generated tile. Lastly, validated three different validation systems (containing details) 44 043 samples. The results indicated that achieved an overall accuracy 82.5 % kappa coefficient 0.784 level-0 (9 basic types), 71.4 0.686 UN-LCCS (United Nations Cover Classification System) level-1 (16 LCCS 68.7 0.662 level-2 (24 types). comparisons against other (CCI_LC, MCD12Q1, FROM_GLC, GlobeLand30) provides more details than CCI_LC-2015 MCD12Q1-2015 greater diversity FROM_GLC-2015 GlobeLand30-2010. They also showed best 59.1 GlobeLand30-2010 75.9 %. Therefore, it is concluded first dataset 16 as well 14 detailed regional types) high at m. in paper are free access https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).
منابع مشابه
An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery
This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning a...
متن کاملLand Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data
Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. The MODIS NDVI data were first fused with Landsat data via the Spa...
متن کاملLand Cover Classification Using Landsat TM Imagery in the Tropical Highlands: The Influence of Anisotropic Reflectance
Despite the tremendous attention given to conservation projects in the Neotropics, few published studies have documented remote sensing studies in tropical highland areas. Even fewer publications have addressed the use of topographic normalization methods in these regions. This article discusses the influence of anisotropic reflectance patterns on land cover classification for two study areas c...
متن کاملComparing the Capability of Sentinel 2 and Landsat 8 Satellite Imagery in Land Use and Land Cover Mapping Using Pixel-based and Object-based Classification Methods
Introduction: Having accurate and up-to-date information on the status of land use and land cover change is a key point to protecting natural resources, sustainable agriculture management and urban development. Preparing the land cover and land use maps with traditional methods is usually time and cost consuming. Nowadays satellite imagery provides the possibility to prepare these maps in less ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Earth System Science Data
سال: 2021
ISSN: ['1866-3516', '1866-3508']
DOI: https://doi.org/10.5194/essd-13-2753-2021