Application of Sentinel-1 data in mapping land-use and land cover in a complex seasonal landscape: a case study in coastal area of Vietnamese Mekong Delta

Pham, Luan Hong; Pham, Lien T.H.; Dang, Thanh Duc; Tran, Dung Duc; Dinh, Toan Quang

doi:10.1080/10106049.2020.1869329

Cited by 14 publications

(4 citation statements)

References 58 publications

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“…Detailed and punctual information about vegetation phenology, on limited-size contexts, can be obtained by applying RS technology at ground level and by recording time-series imagery through specific digital cameras [43]; georeferenced information, on large-scale contexts, can be obtained using satellite observations, which provide high spatial-temporal resolution multispectral imagery (MSI) [38]. Most of the RS applications are dedicated to monitoring land use changes in order to investigate agricultural crop status [44][45][46][47], forest ecosystems [48,49] and water bodies [50][51][52]. Few RS applications seem to be devoted to support the tourism sector.…”

Section: Mapping Seasonal Landscape Changes Through Rsmentioning

confidence: 99%

Parametric Definition of Slow Tourism Itineraries for Experiencing Seasonal Landscapes. Application of Sentinel-2 Imagery to the Rural Paddy-Rice Landscape in Northern Italy

Scandiffio

2021

Sustainability

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Slow tourism is a growing phenomenon in Italy; it is assuming a key role in the definition of new strategies for sustainable tourism for the enhancement of landscape and cultural heritage, but also as a driver for the revitalization of marginalized and inner areas of the country. In this framework, the aesthetical phenomena related to seasonal landscape changes (e.g., autumn coloring foliage, spring blooming, controlled paddy-rice fields flooding) that occur in specific environments are emerging as new tourist destinations and are of major interest for the experiential tourism sector. This research shows a GIS-based method to draw up parametric slow tourism itineraries, which are defined according to seasonal landscape changes, by exploiting the high frequency of Sentinel-2 data acquisition. The algorithm defines parametric itineraries within the network of existing local roads by detecting the current landscape conditions through NDVI. The algorithm has been tested in the study area, within the historical agricultural landscape of paddy-rice fields in between Turin and Milan, where high scenic conditions related to the flooding occur over the spring season. This tool can support a range of end users’ decisions for the creation of a widespread tourist destination offer year-round, with the aim to promote more sustainable and balanced use of the places and reduce overpressures in the most frequented places.

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Section: Mapping Seasonal Landscape Changes Through Rsmentioning

confidence: 99%

Parametric Definition of Slow Tourism Itineraries for Experiencing Seasonal Landscapes. Application of Sentinel-2 Imagery to the Rural Paddy-Rice Landscape in Northern Italy

Scandiffio

2021

Sustainability

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“…The synergistic combination of optical and radar images maximizes their respective benefits, enhancing the ability to identify ground objects and improving information extraction accuracy. Notably, scholars [ 11 , 12 , 13 , 14 ] have validated that the integration of Sentinel-2 and Sentinel-1 images yields higher classification accuracy in surface information extraction, including wetland and agricultural land. Additionally, Zhang Hao et al [ 15 ] incorporated topographic data into the western region of the Loess Plateau to obtain relatively precise information about abandoned land, underscoring the importance of topographic data in classifying areas with complex topography.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Lilium davidii var. unicolor Planting Information Based on Deep Learning and Multi-Source Data

Shi,

Zhang,

Wang

2024

Sensors

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Accurate extraction of crop acreage is an important element of digital agriculture. This study uses Sentinel-2A, Sentinel-1, and DEM as data sources to construct a multidimensional feature dataset encompassing spectral features, vegetation index, texture features, terrain features, and radar features. The Relief-F algorithm is applied for feature selection to identify the optimal feature dataset. And the combination of deep learning and the random forest (RF) classification method is utilized to identify lilies in Qilihe District and Yuzhong County of Lanzhou City, obtain their planting structure, and analyze their spatial distribution characteristics in Gansu Province. The findings indicate that terrain features significantly contribute to ground object classification, with the highest classification accuracy when the number of features in the feature dataset is 36. The precision of the deep learning classification method exceeds that of RF, with an overall classification accuracy and kappa coefficient of 95.9% and 0.934, respectively. The Lanzhou lily planting area is 137.24 km2, and it primarily presents a concentrated and contiguous distribution feature. The study’s findings can serve as a solid scientific foundation for Lanzhou City’s lily planting structure adjustment and optimization and a basis of data for local lily yield forecasting, development, and application.

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“…The effective combination of optical and SAR datasets has shown advantages in vegetation classification [19]. Pham et al [20] tested S1 alone, fusion of S1 and S2 datasets and only S2 to assess the land cover (LC) classification in a complicated landscape of agricultural in the Vietnam coastal area utilizing RF and OBIA algorithms. The findings indicated that using S1 alone had a lower accuracy result, with an accuracy rate of 60% against 63% and 72% accuracy rate, for S2 alone, as well as fused S1 and S2, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

Rehman,

Zhang,

Sajjad

et al. 2024

Remote Sensing

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Generating orchards spatial distribution maps within a heterogeneous landscape is challenging and requires fine spatial and temporal resolution images. This study examines the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) satellite data of relatively high spatial and temporal resolutions for discriminating major orchards in the Khairpur district of the Sindh province, Pakistan using machine learning methods such as random forest (RF) and a support vector machine. A Multicollinearity test (MCT) was performed among the multi-temporal S1 and S2 variables to remove those with high correlations. Six different feature combination schemes were tested, with the fusion of multi-temporal S1 and S2 (scheme-6) outperforming all other combination schemes. The spectral separability between orchards pairs was assessed using Jeffries-Matusita (JM) distance, revealing that orchard pairs were completely separable in the multi-temporal fusion of both sensors, especially the indistinguishable pair of dates-mango. The performance difference between RF and SVM was not significant, SVM showed a slightly higher accuracy, except for scheme-4 where RF performed better. This study concludes that multi-temporal fusion of S1 and S2 data, coupled with robust ML methods, offers a reliable approach for orchard classification. Prospectively, these findings will be helpful for orchard monitoring, improvement of yield estimation and precision based agricultural practices.

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Application of Sentinel-1 data in mapping land-use and land cover in a complex seasonal landscape: a case study in coastal area of Vietnamese Mekong Delta

Cited by 14 publications

References 58 publications

Parametric Definition of Slow Tourism Itineraries for Experiencing Seasonal Landscapes. Application of Sentinel-2 Imagery to the Rural Paddy-Rice Landscape in Northern Italy

Parametric Definition of Slow Tourism Itineraries for Experiencing Seasonal Landscapes. Application of Sentinel-2 Imagery to the Rural Paddy-Rice Landscape in Northern Italy

Extraction of Lilium davidii var. unicolor Planting Information Based on Deep Learning and Multi-Source Data

Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

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