Land Use and Land Cover Change in Vientiane Area, Lao PDR Using Object-Oriented Classification on Multi-Temporal Landsat Data

Hue, Su Wah; Korom, Alexius; Seng, Yen Wah; Sihapanya, Vongphet; Phimmavong, Somvang; Phua, Mui How

doi:10.1166/asl.2017.10279

Cited by 10 publications

(6 citation statements)

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“…Luang Prabang Province was selected because of its long tradition of teak growing, while Vientiane Province was selected because of its proximity to the capital and the main national markets, which have stimulated teak plantation development in the area (Hue et al 2017). Four study villages were purposively selected for this research; three in Luang Prabang (Ban En, Ban Xiengloum, Ban Thinsom) and one in Vientiane (Phialat) (Figure 1) to represent variation in access to markets and extension support (Table 1).…”

Section: Research Areamentioning

confidence: 99%

By accident or by design? Influence of government policies on drivers and barriers of smallholder teak growing in Lao PDR

Arvola

Anttila

Hogarth

2018

Forests, Trees and Livelihoods

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This study analyses the impact of policy, legal and market conditions, as well as specific incentives on smallholders' interest and success in tree growing between 1990-2015 in Lao People's Democratic Republic (Lao PDR or Laos). A review of previous studies and legal and policy papers established the framework for this study, with primary data then collected from smallholders through semistructured interviews in four villages. The interview questions covered household socioeconomic features, land use, information on woodlots and their management, extension services, and perceptions on drivers and challenges of tree growing in the past and in the future. The findings indicate that high level policy objectives of promoting smallholder tree growing are weak in practice at the district and village levels, and the only incentive of any significance, namely land allocation for tree growing, has become ineffectual due to increased land scarcity and preference for other income sources. Current tree growers intend to mainly preserve their present plantation areas, although their interest to expand tree growing areas is weak, and one third of non-growers see tree growing as a potential livelihood diversification option. If the promotion of smallholder tree growing is to be improved, the land and forest policy-and associated legislation-requires thorough revision and simplification, extension services must be made available, and specific incentives developed to allow smallholders to access land and meet their specific needs.

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Section: Research Areamentioning

confidence: 99%

By accident or by design? Influence of government policies on drivers and barriers of smallholder teak growing in Lao PDR

Arvola

Anttila

Hogarth

2018

Forests, Trees and Livelihoods

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“…Remote sensing image fusion refers to the process of combining multi-source remote sensing according to certain rules to eliminate information redundancy in order to expand the application scope and effects [1][2][3][4]. High-quality fusion of remote sensing images entails more than simply combining data; it also implies improvement of the images' spatial resolution while retaining the original spectral information, thereby enhancing the reliability of interpretation [5].…”

Section: Introductionmentioning

confidence: 99%

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Chen¹,

Fei²,

Wang³

et al. 2022

Sustainability

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This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms.

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“…According to the recognition principle, pattern recognition can be divided into the following four recognition modes: pattern recognition based on structure, pattern recognition based on statistics, pattern recognition based on neural network, and fuzzy pattern recognition [28]. Among them, the statistical-based pattern recognition based on the Bayesian decision system based on the principle of statistical probability theory is the most classic recognition mode, which is widely used in the recognition and classification of surface objects in remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Classification of Land Cover Remote-Sensing Images Based on Pattern Recognition

Xie

Huang

2022

Scientific Programming

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With the development of remote sensing technology, remote sensing image data plays an active role in the dynamic monitoring of global resource changes and land cover utilization. Remote sensing image land cover classification is an important application direction of remote sensing data; how to further improve the accuracy of remote sensing image land cover classification is very important for the effective application of remote sensing data. The traditional remote sensing image land cover classification is mainly to classify remote sensing data according to the spectral data of ground objects. However, due to the complex environment of remote sensing images and the dynamic changes of the environment, traditional classification methods based only on pixel spectral data are often unable to achieve. A satisfactory classification result is achieved. In addition, some researchers have also proposed to combine pixel neighborhood texture information to supplement spectral feature data. Although the traditional classification method based on spectral features solves the problem of time-consuming visual interpretation, to a certain extent, due to the limited semantic expression ability and poor generalization ability of the design features, the classification accuracy is still not very satisfactory. This paper mainly studies the classification method of land cover remote sensing image based on pattern recognition. This paper is based on the experimental results of remote sensing data in Nanjing Yuhuatai District in 2018 and 2019. The ground resolution of the data is 2.5 meters. Data is projected, corrected, and equalized. Half of the images covering 43.75 square kilometers are used as training samples, and the remaining 50 square kilometers are used for detection. In the classification results of this IndianPines data, OA only increased by nearly 10% to 86.2%, AA increased by 13%, r was 82.77%, and Kappa coefficient was 0.84. In the classification results of Salinas data, both OA and AA increase by about 5%, and the optimization effect is not obvious.

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Land Use and Land Cover Change in Vientiane Area, Lao PDR Using Object-Oriented Classification on Multi-Temporal Landsat Data

Cited by 10 publications

References 0 publications

By accident or by design? Influence of government policies on drivers and barriers of smallholder teak growing in Lao PDR

By accident or by design? Influence of government policies on drivers and barriers of smallholder teak growing in Lao PDR

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Classification of Land Cover Remote-Sensing Images Based on Pattern Recognition

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