High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

Abstract

This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets-with a sample size of 390-showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.

Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam

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Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

“…Also, some recent studies on tropical forest monitoring are based on L-band data (e.g., [16,17]). Due to the large amount of papers using Sentinel-2 for different land cover applications, only a limited number of selected land cover classifications can be listed here, which include: vegetation mapping [18,19], agricultural applications [20,21], water related applications [22] and forest mapping [23][24][25][26][27]. Pre-processing is a crucial part of the whole processing chain as errors or missing steps greatly influence the quality of intermediate and final results.…”

Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI)

“…Remote sensing data have been extensively employed for vegetation mapping in various environments due to its ability to discriminate broad scales of land cover types. For vegetation monitoring in urban and rural regions, aerial photography and satellite imaging have been used [ 3 , 4 , 5 , 6 ]. Urban vegetation cover is much more fragmented than natural vegetation (i.e., forest, rangeland), making accurate extraction of vegetation cover more complex and challenging.…”