Investigating natural drivers of vegetation coverage variation using MODIS imagery in Qinghai, China

Zhou, Ling; Lyu, Aifeng

doi:10.1007/s40333-015-0016-1

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…Moreover, in recent decades -especially after reform and opening up in China -reclamation, construction of reservoirs, afforestation, and other human activities have intensified. Hence the trend variations of flow in the Yellow River Basin may be affected by anthropogenic impacts such as reservoirs [28], water consumption for irrigation, and increasing forest cover [29][30]. Similar analysis can also be seen in previous studies [28].…”

Section: Trendsupporting

confidence: 70%

Trends, Abrupt Changes, and Periodicity of Streamflow in Qinghai Province, the Northeastern Tibetan Plateau, China

Chang¹,

He²,

Li³

et al. 2018

Pol. J. Environ. Stud.

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The Qinghai-Tibet Plateau is a more sensitive area of climate change and fragile zone of ecological environment. Qinghai Province is an important component of the Qinghai-Tibet Plateau, and the study of streamflow in the province is of great significance for understanding the situation of water resources. Based on hydrological data of hydrological stations in Qinghai, the spatiotemporal variations of streamflow from 1956 to 2013 were analyzed by Mann-Kendall analysis and Morlet wavelet analysis. The results indicated that the annual streamflow of the Yangtze River basin, Qaidam basin, Qinghai Lake basin, and Lantsang River basin showed significantly increasing trends during 1956-2013, while the Yellow River basin showed no significant trends or downward trends. Changes in precipitation, glacier melt, and snowmelt caused by climate warming might be a major driver of changes of streamflow in the source regions of the Yangtze River and Lantsang River basins. The trend variations of flow in the Yellow River basin may be affected by precipitation, evaporation caused by increased temperature, and anthropogenic impacts. The abrupt changes of streamflow at Tuotuohe, Delingha, and Nachitai stations were mainly in the early 2000s, while the abrupt changes of streamflow in Zhimenda station, stations in the Yellow River basin, Qinghai Lake basin, and Lantsang River basin appeared primarily in the early 1960s and the early 2000s. Most of the streamflow in Qinghai Province was abundant after about 2007, and the dominant periods with low frequency fluctuation (about 12 years) and inter-decadal (about 44 years) oscillations in Zhimenda and Tangnaihai stations and low frequency fluctuation (about five years) in other stations.

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Section: Trendsupporting

confidence: 70%

Trends, Abrupt Changes, and Periodicity of Streamflow in Qinghai Province, the Northeastern Tibetan Plateau, China

Chang¹,

He²,

Li³

et al. 2018

Pol. J. Environ. Stud.

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“…The Cover and Management factor (C): the vegetation cover management factor C represents the effects of plants, crop sequence and other cover surface on soil erosion by water. The value of C is defined as the ratio of soil loss from a certain kinds of land surface cover conditions (28).…”

Section: Impact On Soil Erosion By Water 1) Estimation Of Soil Erosiomentioning

confidence: 99%

Assessment of the World Largest Afforestation Program: Success, Failure, and Future Directions

Zheng

Wang

et al. 2017

Preprint

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The Three-North Afforestation Program (TNAP), initiated in 1978 and scheduled to be completed in 2050, is the world's largest afforestation project and covers 4.07 x 10 6 km 2 (42.4%) of China. We systematically assessed goals and outcomes of the first 30 years of the TNAP using high-resolution remote sensing and ground survey data.With the 30-year net reduction was 13.0% (4.05×10 6 ha), with 8.0% being accounted for by afforestation in areas with only slight, prior desertification. In addition to its direct impacts, the TNAP has enhanced people's awareness of environmental protection and attracted consistent attention and long-term commitment from the Chinese government to the restoration and protection of fragile ecosystems in the vast ThreeNorth region. The significant decline in forest quality, limited success in reducing desertification, and low coverage of shelterbelts are aspects of the TNAP in need of re-assessment, and additional ca. 34 billion dollars will be needed to ensure the completion of the TNAP.

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“…At present, many traditional sensor data (such as MODIS and Landsat data, etc.) have been applied to desertification monitoring, but they can only capture the integrated vegetation cover information within the pixel [5], are unable to provide more specific information about desertification. The development of unmanned aerial vehicles (UAVs) can solve this problem.…”

Section: Introductionmentioning

confidence: 99%

“…The development of unmanned aerial vehicles (UAVs) can solve this problem. Compared with satellite remote sensing, UAVs with the high resolution image have rich geometric texture information [5] and have the ability to capture detailed information. UAV with high resolution is rarely reported in desert vegetation mapping at home and abroad, especially at home.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach of Combining Random Forest with Texture Analysis and VDVI for Desert Vegetation Mapping Based on UAV RGB Data

Zhou

Sharma

et al. 2021

Remote Sensing

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Desert vegetation is an important part of arid and semi-arid areas, which plays an important role in preventing wind and fixing sand, conserving water and soil, maintaining the balanced ecosystem. Therefore, mapping the vegetation accurately is necessary to conserve rare desert plants in the fragile ecosystems that are easily damaged and slow to recover. In mapping desert vegetation, there are some weaknesses by using traditional digital classification algorithms from high resolution data. The traditional approach is to use spectral features alone, without spatial information. With the rapid development of drones, cost-effective visible light data is easily available, and the data would be non-spectral but with spatial information. In this study, a method of mapping the desert rare vegetation was developed based on the pixel classifiers and use of Random Forest (RF) algorithm with the feature of VDVI and texture. The results indicated the accuracy of mapping the desert rare vegetation were different with different methods and the accuracy of the method proposed was higher than the traditional method. The most commonly used decision rule in the traditional method, named Maximum Likelihood classifier, produced overall accuracy (76.69%). The inclusion of texture and VDVI features with RGB (Red Green Blue) data could increase the separability, thus improved the precision. The overall accuracy could be up to 84.19%, and the Kappa index with 79.96%. From the perspective of features, VDVI is less important than texture features. The texture features appeared more important than spectral features in desert vegetation mapping. The RF method with the RGB+VDVI+TEXTURE would be better method for desert vegetation mapping compared with the common method. This study is the first attempt of classifying the desert vegetation based on the RGB data, which will help to inform management and conservation of Ulan Buh desert vegetation.

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Investigating natural drivers of vegetation coverage variation using MODIS imagery in Qinghai, China

Cited by 11 publications

References 31 publications

Trends, Abrupt Changes, and Periodicity of Streamflow in Qinghai Province, the Northeastern Tibetan Plateau, China

Trends, Abrupt Changes, and Periodicity of Streamflow in Qinghai Province, the Northeastern Tibetan Plateau, China

Assessment of the World Largest Afforestation Program: Success, Failure, and Future Directions

A Hybrid Approach of Combining Random Forest with Texture Analysis and VDVI for Desert Vegetation Mapping Based on UAV RGB Data

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