Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment

Yue, Yumei; Wang, K.L.; Zhang, Bo; Jiao, Quanjun; Liu, B.; Zhang, M.Y.

doi:10.1016/j.proenv.2012.01.078

Cited by 41 publications

(25 citation statements)

References 5 publications

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“…In addition, due to topographic heterogeneity (e.g., deep canyons, caves and rivers formed by geological events) and global warming, many endemic plants in karst areas tend to suffer from fragmentation of suitable habitats and reduction in species diversity [ 20 , 21 ]. Furthermore, the unique biota in karst areas has been sharply affected by recent human activities, such as large-scale logging, agricultural expansion, livestock overgrazing and fire, which have led to extensive water and soil erosion and serious land degradation in the form of rocky desertification, reducing the stability of the karst ecosystem [ 22 , 23 ]. Under increasing habitat pressure caused by both human disturbance and global climate change, many plant communities in the karst area have become rare or endangered, especially species endemic to the area [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Genetic Diversity and Structure Pattern of Indigofera Pseudotinctoria in Karst Habitats of the Wushan Mountains Using AFLP Markers

Fan

Zhang

et al. 2017

Molecules

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Indigofera pseudotinctoria Mats is an agronomically and economically important perennial legume shrub with a high forage yield, protein content and strong adaptability, which is subject to natural habitat fragmentation and serious human disturbance. Until now, our knowledge of the genetic relationships and intraspecific genetic diversity for its wild collections is still poor, especially at small spatial scales. Here amplified fragment length polymorphism (AFLP) technology was employed for analysis of genetic diversity, differentiation, and structure of 364 genotypes of I. pseudotinctoria from 15 natural locations in Wushan Montain, a highly structured mountain with typical karst landforms in Southwest China. We also tested whether eco-climate factors has affected genetic structure by correlating genetic diversity with habitat features. A total of 515 distinctly scoreable bands were generated, and 324 of them were polymorphic. The polymorphic information content (PIC) ranged from 0.694 to 0.890 with an average of 0.789 per primer pair. On species level, Nei’s gene diversity (Hj), the Bayesian genetic diversity index (HB) and the Shannon information index (I) were 0.2465, 0.2363 and 0.3772, respectively. The high differentiation among all sampling sites was detected (FST = 0.2217, GST = 0.1746, G’ST = 0.2060, θB = 0.1844), and instead, gene flow among accessions (Nm = 1.1819) was restricted. The population genetic structure resolved by the UPGMA tree, principal coordinate analysis, and Bayesian-based cluster analyses irrefutably grouped all accessions into two distinct clusters, i.e., lowland and highland groups. The population genetic structure resolved by the UPGMA tree, principal coordinate analysis, and Bayesian-based cluster analyses irrefutably grouped all accessions into two distinct clusters, i.e., lowland and highland groups. This structure pattern may indicate joint effects by the neutral evolution and natural selection. Restricted Nm was observed across all accessions, and genetic barriers were detected between adjacent accessions due to specifically geographical landform.

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Section: Introductionmentioning

confidence: 99%

Analysis of Genetic Diversity and Structure Pattern of Indigofera Pseudotinctoria in Karst Habitats of the Wushan Mountains Using AFLP Markers

Fan

Zhang

et al. 2017

Molecules

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“…Fractional vegetation cover (FVC) can respond to the surface vegetation status directly and is an important quantitative index of vegetation and ecosystem changes [2][3][4]. The FVC commonly represents a Normalized Difference Vegetation Index (NDVI) derived vegetation parameter that is defined as the ratio of the vertical projection area, which includes plants' branches, stalks and leaves, to the total vegetation area [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011

Zhao

et al. 2014

Remote Sensing

149

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Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, USGS global land cover characteristics data and HWSD soil type data with a modified dimidiate pixel model, which considered vegetation and soil types and mixed pixels decomposition. The evaluation of the robustness and accuracy of the GIMMS FVC with MODIS FVC and Validation of Land European Remote sensing Instruments (VALERI) FVC show high reliability. Trends of the annual FVC max and FVC mean datasets in the last 30 years were reported by the Mann-Kendall method and Sen's slope estimator. The results indicated that global FVC change was 0.20 and 0.60 in a year with obvious seasonal variability. All of the continents in the world experience a change in the annual FVC max and FVC mean , which represents biomass production, except for Oceania, which exhibited a significant increase based on a significance level of p = 0.001 with the Student's t-test. Global annual maximum and mean FVC growth rates are 0.14%/y and 0.12%/y, respectively. The trends of the annual FVC max and FVC mean based on pixels also illustrated that the global vegetation had turned green in the last 30 years. A significant trend on the p = 0.05 level was found for 15.36% of the GIMMS FVC max pixels on a global scale (excluding permanent OPEN ACCESSRemote Sens. 2014, 6 4218 snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVC mean similarly produced a total of 16.64% significant pixels with 2.28% with a negative trend and 14.36% with a positive trend. The North Frigid Zone represented the highest annual FVC max significant increase (p = 0.05) of 25.17%, which may be caused mainly by global warming, Arctic sea-ice loss and an advance in growing seasons. Better FVC predictions at large regional scales, with high temporal resolution (month) and long time series, would advance our ability to understand the characteristics of the global FVC changes in the last 30 years and predict the response of vegetation to global climate change.

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“…There are a number of vegetation-cover-related variables that have proven to be valid criteria for assessing desertification risk and its evolution such as fractional vegetation cover (FVC) [ 28 , 29 ], net primary productivity (NPP) [ 13 , 30 ], and vegetation rain use efficiency (RUE) [ 31 , 32 ]. Among these vegetation indicators, FVC can easily be interpreted from remote-sensing observations.…”

Section: Methodsmentioning

confidence: 99%

Geographical Detector-Based Identification of the Impact of Major Determinants on Aeolian Desertification Risk

Zhang

et al. 2016

PLoS ONE

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Arid and semi-arid areas in North China are facing the challenge of a rising aeolian desertification risk (ADR) due to the intertwined effects of complex natural processes and intensified anthropogenic activities. An accurate quantitative assessment of the relationship between ADR and its determinants is beneficial for understanding the driving mechanisms of aeolian desertification and for controlling future desertification. Previous studies have failed to quantify the relative role of determinants driving ADR and have been limited in assessing their interactive impacts. In this study, a spatial variance analysis-based geographical detector methodology is used to quantify the effects of geological, physical, and human factors on the occurrence of ADR in an area characterized by mountains and hills in northern China. It is found that soil type, precipitation, and wind velocity are the major determinants of ADR, which implies that geological and physical elements (e.g., soil attribute) and climatic factors (e.g., precipitation and wind velocity) rather than human activities have played a greater role in the incidence of ADR. Particularly, the results show that the interaction of various determinants causes significant non-linearly enhanced impacts on the ADR. The findings of our study will assist local inhabitants and policy makers in developing measures for wind prevention and sand control to mitigate the effects of desertification in the region.

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Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment

Cited by 41 publications

References 5 publications

Analysis of Genetic Diversity and Structure Pattern of Indigofera Pseudotinctoria in Karst Habitats of the Wushan Mountains Using AFLP Markers

Analysis of Genetic Diversity and Structure Pattern of Indigofera Pseudotinctoria in Karst Habitats of the Wushan Mountains Using AFLP Markers

Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011

Geographical Detector-Based Identification of the Impact of Major Determinants on Aeolian Desertification Risk

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