Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

doi:10.3390/s18020373

Cited by 35 publications

(25 citation statements)

References 97 publications

(113 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The diverse climate, complex topography, and various ethnicities lead to a complex geography and landscape with the dominant land cover types of rice paddy, crops, grassland, wetland, urban, forest, bare land, and mangrove. have been effectively combined with Landsat for urban mapping [54,55] with the mosaics of Advanced Land Observing Satellite-2 Phased Arrayed L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) for mangrove and forest monitoring [56,57]. Integration of multiple optical and radar sensors with different electromagnetic spectra can recognize various land cover features better than a single sensor [58,59].…”

Section: Study Areamentioning

confidence: 99%

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

et al. 2018

View full text Add to dashboard Cite

Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies.

show abstract

Section: Study Areamentioning

confidence: 99%

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Monitoring of forests is one of the relevant and practical tasks effectively carried out on the basis of recent satellite optical-electronic multispectral (MS) and hyperspectral (HS) means (Boyd et al 2005;Banskota et al 2014;Transon et al 2017). MS and HS data referencing spectra of woody vegetation and algorithms of data processing in (semi)automatic mode allow separation of forest from non-forest lands (Connette et al 2016;Zhou et al 2018); prediction of forest inventory characteristics (McRoberts et al 2007); and detection of forest damage due to fires, degradation, and pathological changes (Grigorieva 2014;Lausch et al 2016;Brovkina et al 2017).…”

Section: Introductionmentioning

confidence: 99%

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

Grigorieva¹,

Brovkina²,

Saidov³

2020

Silva Fenn.

View full text Add to dashboard Cite

This study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectralâtemporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time.

show abstract

“…Window size selection depends on the spatial resolution of the image and the relationship between features (such as canopy size) [16] and texture indices to be calculated [38]. In fact, the spatial characteristics of specific land cover types cannot be exploited sufficiently if the window is too small.…”

Section: The Optimal Window Sizes For Textural Features Calculationmentioning

confidence: 99%

Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

Zhang

Huang

et al. 2020

Forests

View full text Add to dashboard Cite

The expansion of rubber (Hevea brasiliensis) plantations has been a critical driver for the rapid transformation of tropical forests, especially in Thailand. Rubber plantation mapping provides basic information for surveying resources, updating forest subplot information, logging, and managing the forest. However, due to the diversity of stand structure, complexity of the forest growth environment, and the similarity of spectral characteristics between rubber trees and natural forests, it is difficult to discriminate rubber plantation from natural forest using only spectral information. This study evaluated the validity of textural features for rubber plantation recognition at different spatial resolutions using GaoFen-1 (GF-1), Sentinel-2, and Landsat 8 optical data. C-band Sentinel-1 10 m imagery was first used to map forests (including both rubber plantations and natural forests) and non-forests, then the pixels identified as forests in the Sentinel-1 imagery were compared with GF-1, Sentinel-2, and Landsat 8 images to separate rubber plantations and natural forest using two different approaches: a method based on spectral information characteristics only and a method combining spectral and textural features. In addition, we extracted textural features of different window sizes (3 × 3 to 31 × 31) and analyzed the influence of window size on the separability of rubber plantations and natural forests. Our major findings include: (1) the suitable texture extraction window sizes of GF-1, Sentinel-2, and Landsat 8 are 31 × 31, 11 × 11 to 15 × 15, and 3 × 3 to 7 × 7, respectively; (2) correlation (COR) is a robust textural feature in remote sensing images with different resolutions; and (3) compared with classification by spectral information only, the producer’s accuracy of rubber plantations based on GF-1, Sentinel-2, and Landsat 8 was improved by 8.04%, 9.44%, and 8.74%, respectively, and the user’s accuracy was increased by 4.63%, 4.54%, and 6.75%, respectively, when the textural features were introduced. These results demonstrate that the method combining textural features has great potential in delineating rubber plantations.

show abstract

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Cited by 35 publications

References 97 publications

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

Contact Info

Product

Resources

About