Analysis of Land Cover Changes in Northern Vietnam Using High Resolution Remote Sensing Data

Hoàng, Thanh Tùng; Nasahara, Kenlo Nishida; Katagi, Jin

doi:10.1007/978-3-319-68240-2_9

Cited by 3 publications

(9 citation statements)

References 27 publications

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“…Nevertheless, these researches mapped land cover for smaller areas (<900 km 2 ) compared with the current study (94,000 km 2 ). Note that this study corroborated the research by Hoang et al [66], which used the combination of multisensor data (i.e., Landsat 5 and 8 and ASTER-VA version) for analyzing land cover in Northern Vietnam that demonstrated the potential of the fusion of multiple sensors for mapping heterogeneous landscapes. However, our maps are better than their maps in term of the overall accuracy and spatial resolution (90.5% and 10 m vs. 81.0% and 15 m).…”

Section: Discussionsupporting

confidence: 87%

“…A land cover and land use category system was established to identify the dominant land cover types for certain purposes in the study region [62][63][64]. Based on the local knowledge and the Land Cover Classification System [65], this research used nine land cover types including water, urban & built-up, paddy, other crops, grassland, barren, forest, and mangrove (Table 1); we also referred to a past paper [66] in order to produce a consistent land cover map for larger scales.…”

Section: Classification Scheme and Reference Data Designmentioning

confidence: 99%

“…Even though the fusion of remote sensing data has been applied in recent LULC mapping studies [49][50][51]66], our study has made significant contributions to the field. As in the above discussions, Central Vietnam, a cloudy area, has been a challenging area for mapping with LULC, especially with high spatial resolution, due to the scarcity of data availability, which can be customized by our approach.…”

Section: Potential Application and Future Workmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Discussionsupporting

confidence: 87%

Section: Classification Scheme and Reference Data Designmentioning

confidence: 99%

Section: Potential Application and Future Workmentioning

confidence: 99%

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

et al. 2018

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“…Hashimoto et al (2013) proposed this approach [41] and used it to produce land use/land cover maps with higher overall accuracy than those produced using other algorithms, such as maximum likelihood classification and support vector machines. The approach was applied to produce high-resolution land use and land cover maps for all of Japan [43,44] and Vietnam [45,46]. The resulting products were published by JAXA as an open land-cover dataset [47].…”

Section: Classification Methodsmentioning

confidence: 99%

“…In this study, we utilized the Land Cover Classification System of the Food Agriculture Organization [48], which was used in recent land-cover mapping studies for Vietnam [45,46]. Forests are defined as areas larger than 0.5 ha which is predominantly covered by woody vegetation with at least 10% cover.…”

Section: Collection Of Reference Datamentioning

confidence: 99%

JAXA Annual Forest Cover Maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and Auxiliary Data

et al. 2019

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Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015-2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam's forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale. order to enhance active forest management and the implementation of the Reducing Emissions from Deforestation and Forest Degradation (REDD+) framework.The development of satellite missions such as Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel, and Advanced Land Observation Satellite (ALOS) has facilitated the mapping of changes on the Earth's land cover. It is possible to extract information about forests from global land-cover datasets such as the 1 km Global Land Cover Dataset for the year 2000 (GLC2000) [7], the 500 m MODIS Land Cover Type (MCD12Q1) [8], the 300 m MERIS Global Land Cover Service (GlobCover) [9], and the 30 m Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) [10]. However, these products focus not only on forests but also on other land cover categories, and they are different in spatial resolutions. It can, therefore, be challenging to obtain accurate information on the forest category from these products. Some satellite-derived global forest datasets have been developed, such as the MODIS vegetation continuous fields product [11], the global tree cover products [5,12], and the Japan Aerospace Exploration Agency (JAXA) forest/non-forest products [13]. However, difficulties remain regarding how to directly apply the results of these global land cover maps to national reports due to uncertainties in the estimation of forest area and change in forest area at a national scale [14][15][16] and the fact that forest definition is different in many parts of the w...

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New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring

et al. 2020

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Highly detailed and accurate forest maps are important for various applications including forest monitoring, forestry policy, climate change, and biodiversity loss. This study demonstrates a comprehensive and geographically transferable approach to produce a 12 category high-resolution land use/land cover (LULC) map over mainland Vietnam in 2016 by remote sensing data. The map included several natural forest categories (evergreen broadleaf, deciduous (mostly deciduous broadleaf), and coniferous (mostly evergreen coniferous)) and one category representing all popular plantation forests in Vietnam such as acacia (Acacia mangium, Acacia auriculiformis, Acacia hybrid), eucalyptus (Eucalyptus globulus), rubber (Hevea brasiliensis), and others. The approach combined the advantages of various sensor data by integrating their posterior probabilities resulting from applying a probabilistic classifier (comprised of kernel density estimation and Bayesian inference) to each datum individually. By using different synthetic aperture radar (SAR) images (PALSAR-2/ScanSAR, PALSAR-2 mosaic, Sentinel-1), optical images (Sentinel-2, Landsat-8) and topography data (AW3D30), the resultant map achieved 85.6% for the overall accuracy. The major forest classes including evergreen broadleaf forests and plantation forests had a user’s accuracy and producer’s accuracy ranging from 86.0% to 95.3%. Our map identified 9.55 × 106 ha (±0.16 × 106 ha) of natural forests and 3.89 × 106 ha (±0.11 × 106 ha) of plantation forests over mainland Vietnam, which were close to the Vietnamese government’s statistics (with differences of less than 8%). This study’s result provides a reliable input/reference to support forestry policy and land sciences in Vietnam.

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Analysis of Land Cover Changes in Northern Vietnam Using High Resolution Remote Sensing Data

Cited by 3 publications

References 27 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

JAXA Annual Forest Cover Maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and Auxiliary Data

New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring

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