Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

Wang, Wuli; Jiang, Yan; Wang, Ge; Li, Zhongwei; Liu, Baodi

doi:10.3390/rs14143486

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…To deal with this problem, we have two options, one is to add additional data sources [57], and the other is to extract more complex features. Without additional data sources, spectral confusion can be reduced by more comprehensive features (combined with spectral, spatial, and texture features) [11,58]. Table 1 provides a summary of the strengths and limitations of these extraction units, with the relevant literature.…”

Section: Object-based Methodsmentioning

confidence: 99%

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Shao

Cheng

et al. 2023

Remote Sensing

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Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.

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Section: Object-based Methodsmentioning

confidence: 99%

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Shao

Cheng

et al. 2023

Remote Sensing

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“…However, with the deepening of research in recent years, compared with desertification in other parts of northern China, some scholars believe that the change in the amount of water resources transported downstream and controlled by the glaciers and snow in the Qilian Mountains, namely, water supply in the source area, is the key factor for the formation and evolution of sand dunes and the desertification in the Hexi Corridor (Xie et al, 2004). In addition, some studies have suggested that the weakening of large-scale wind activity in northern China and even the NH and the acceleration of regional water cycle (increasing water supply) jointly restrict the dune dynamics and desertification process in the Hexi area (Wang et al, 2002;Wang et al, 2008a;2008b). Taking a further comprehensive view, the above-mentioned formation mechanisms of dune landforms, whether on local scale, regional scale, or even hemispheric scale, can be attributed essentially to a natural mechanism, that is, the sand dunes in the Hexi Corridor are of natural origin.…”

Section: The Formation Mechanism Of Sand Dunes In the Hexi Corridormentioning

confidence: 99%

The recent evolution of dune landforms and its environmental indications in the mid-latitude desert area (Hexi Corridor)

Zhu

2022

J. Geogr. Sci.

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The history of dune landform changes and dust activity at mid-latitudes is a good archive for exploring environmental changes and related landscape response. In this study, the dynamic changes, material sources, dust activity history and the influencing factors of typical sand dunes in the Hexi Corridor were comprehensively analyzed from the aspects of aeolian geomorphology, grain-size sedimentology, geochemistry and climatology. The results show that in the past half century, the typical crescent-shaped dunes and chains of crescent-shaped dunes in the study area have moved or swayed greatly, with an average speed ranging from 0.8 m/a (Dunhuang) to 6.2 m/a (Minqin). The dynamic changes of sand dunes are mainly affected by annual precipitation, annual average wind speed and annual gale days, which indicates that climate is the primary influencing factor of dune landform changes. The three-stage grain-size curve model of dune sands is obviously different from that of gobi sediments (two-stage), revealing the "immaturity" of the latter in sedimentology, while the former has experienced efficiently aeolian differentiation and non-local origin. The comprehensive evidences of paleogeography, sedimentology and geochemistry reveal that the source materials of sand dunes are mainly alluvial/proluvial and palaeo-fluvial sediments, including clastic sediments in the denudation/erosion zones of the north and south piedmonts. Indicators such as the proportion of surface fine particles, the coverage of surface salt crusts, and the content of erodible sandy materials indicate that the western gobi areas are not the main source areas of wind-blown dust in the central and eastern parts of the Hexi Corridor. The spatial distribution of the movement direction of sand dunes is similar to that of the regional dominant wind direction, which indicates that the difference in the dynamic evolution of dune landforms between the east and west of the Hexi Corridor should be controlled by the regional-scale wind system, that is, controlled by the dynamic mechanism rather than the difference in material sources. The warming and humidification of the Hexi climate is a synchronous response to the global warming and the strengthening of the Asian Summer Monsoon. It is also the main reason for the reduction of dust storms in the study area, which means that a potential inverse desertification process exists in the Hexi Corridor during the

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“…Land desertification is a major ecological problem facing arid and semi-arid regions worldwide [1]. It often occurs in mid-latitude regions, directly or indirectly affects more than 100 countries worldwide, involves more than one billion people, causes losses of up to USD 10 billion per year, and is ranked as one of the most serious environmental and social problems in the world [2,3]. According to the statistics released by the United Nations regarding land desertification, the problem of land desertification faced by China Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

A New Remote Sensing Desert Vegetation Detection Index

Song,

Lu,

Ding

et al. 2023

Remote Sensing

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Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.

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Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

Cited by 11 publications

References 38 publications

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

The recent evolution of dune landforms and its environmental indications in the mid-latitude desert area (Hexi Corridor)

A New Remote Sensing Desert Vegetation Detection Index

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