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This paper uses slope, distance,visual probability and remarkableness as evaluation indices to study urban landscape visual sensitivity in the Zhongshan District of Dalian. The results show that areas of high sensitivity account for 14.12% of the total area, these areas should be protected. Moderate sensitivity areas occupy 2.66%, urban landscape transformation should be carried out properly. Low-sensitivity areas accounted for only 0.74%, the utilization of vegetation and the construction of public facilities can be carried out in these areas. Areas classified as insensitive account for 82.48%, this is a potential zone where the urban landscape can be developed. [ABSTRACT FROM AUTHOR]

Based on the features of environment and the fact of successful artificial afforestation, the paper points out that the restoration of vegetation in Arid-hot valleys along the Jinshajiang River should be implemented mainly by planting sparse — arbor/ shrub/ tussock Combination. Four ways to restore vegetation in the area are given in the paper. [ABSTRACT FROM AUTHOR]

Soil moisture is an important indicator to describe soil conditions, and can also provide information on crop water stress and yield estimation. The combination of vegetation index (VI) and land surface temperature (LST) can provide useful information on estimation soil moisture status at regional scale. In this paper, the Huang-huai-hai (HHH) plain, an important food production area in China was selected as the study area. The potential of Temperature-Vegetation Dryness Index (TVDI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data in assessing soil moisture was investigated in this region. The 16-day composite MODIS Vegetation Index product (MOD13A2) and 8-day composite MODIS temperature product (MOD11A2) were used to calculate the TVDI. Correlation and regression analysis was carried out to relate the TVDI against in-situ soil moisture measurements data during the main growth stages of winter wheat/summer maize. The results show that a significantly negative relationship exists between the TVDI and in-situ measurements at different soil depths, but the relationship at 10-20 cm depth (R2 = 0.43) is the closest. The spatial and temporal patterns in the TVDI were also analysed. The temporal evolution of the retrieved soil moisture was consistent with crop phenological development, and the spatial distribution of retrieved soil moisture accorded with the distribution of precipitation during the whole crop growing seasons. The TVDI index was shown to be feasible for monitoring the surface soil moisture dynamically during the crop growing seasons in the HHH plain. [ABSTRACT FROM AUTHOR]

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April-September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982-2003 and in 24.1% over the first half period, 1982-1992. In addition, few areas were shown to have negative trends during these periods. In 1993-2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May-August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions. [ABSTRACT FROM AUTHOR]

Based on the theory of recreational experience and landscape character classification systems, we propose that recreation potential includes aesthetic service potential and suitable environment provision potential. These two potentials are determined by landscape features including topography, land cover, vegetation and climate. Taking Sanjiangyuan as an example, we analyse spatiotemporal changes in both potentials from 1978 to 2012. In Sanjiangyuan, aesthetic quality has deteriorated, but the recreational environment has become more suitable owing to climate change. These changes may decrease ecotourism and increase mass tourism, which would further increase pressure on the environment. [ABSTRACT FROM AUTHOR]

In order to quantitatively evaluate the intensity of FT erosion, eight typical factors, including annual FT cycle days, precipitation, average diurnal phase-changed water content, rainfall erosivity during the FT period, wind field intensity during the FT period, slope, aspect, and vegetation, were introduced to establish an improved evaluation method of FT erosion in the TRSR, which had better applicability in TRSR with an overall precision of 93%. Results showed that FT erosion was widely distributed in the TRSR, with zones of slight, mild, and moderated erosion being the most widely distributed. During 2000–2015, a slight improvement can be observed in the condition of FT erosion over the whole study region. Vegetation coverage was the dominant factor affecting the intensity of FT erosion in the zones with sparse vegetation or bare land, whereas the climate factors played an important role in high vegetation coverage area. Vegetation coverage played a dominant role in affecting the FT erosion intensity in zones with 0.3 ≤ VC < 0.6, whereas the terrain and climate factors played an more important role in areas with sparse vegetation (VC < 0.3) or high vegetation coverage (VC ≥ 0.6). Meanwhile, slope was of great importance in affecting the process of FT erosion in zones with slopes of >18°. [ABSTRACT FROM AUTHOR]

This study investigated the influence of climatic variables on the spatio-temporal variation of vegetation growth using normalized difference vegetation index (NDVI) data and climate data from 2000 to 2013 in the Northeast China Transect. Partial correlation and linear regression methods were applied to quantify the response of the growing season NDVI to climatic variables. Gradient analysis was used to investigate how the response changes across the precipitation gradient over the transect. The results show that, at the spatial scale, NDVI increases with precipitation in grassland, and the spatial sensitivity is 0.001/mm. At the temporal scale, grassland NDVI is less correlated with precipitation in wet areas where precipitation exceeds a threshold of 250 mm. The temporal sensitivity of grassland NDVI to precipitation is 0.0003–0.0006/mm. Positive correlations between NDVI and temperature dominate in forest areas, and forest NDVI is sensitive to temperature by 0.06–0.12/°C. [ABSTRACT FROM PUBLISHER]

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI. [ABSTRACT FROM PUBLISHER]

We used a regionally modified global vegetation model (BIOME4-Tibet) to simulate biome distribution on the Tibetan Plateau under current climate conditions derived from regional meteorological observations. The bioclimatic limits (mean temperatures of the coldest and warmest months, minimum temperature, growing degree-days on 5 °C and 0 °C bases) for some key alpine plant functional types (temperate deciduous and conifer trees, boreal deciduous and conifer trees, desert woody plants, tundra shrubs, cold herbaceous plants, and lichens/forbs) were redefined based on regional vegetation-climate relationships. Modern vegetation maps confirmed that the BIOME4-Tibet model does a better job of simulating biome patterns on the plateau (gridcell agreement 52%) than the original BIOME4 model (35%). This improved model enhanced our ability to simulate temperate conifer forest, cool conifer and mixed forest, evergreen taiga, temperate xerophytic shrubland, temperate grassland and desert, and steppe and shrub tundra biomes, but made a negligible or reduced difference to the prediction of temperate deciduous forest, warm-temperate mixed forest, and three tundra biomes (erect dwarf-shrub tundra, prostrate dwarf-shrub tundra, and cushion forb, lichen, and moss tundra). Future modification of the vegetation model, by increasing the number of shrub and herb plant functional types, re-parameterization of more precise bioclimatic constraints, and improved representation of soil, permafrost, and snow processes, will be needed to better characterize the distribution of alpine vegetation on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Climate change has a large impact on vegetation dynamics. A series of statistical analyses were employed to demonstrate the relationship between Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data with an 8 × 8 km resolution and meteorological data, during the period 1982-2005. Rainfall has a great impact on vegetation with varying time lags. The sensitivity of NDVI to the threshold of accumulated temperature varies regionally. To identify a 'best factor' for each meteorological station simple and partial correlation analyses were carried out. Multiple correlation analysis was used to validate the association between the two climatic factors and monthly maximum NDVI (MNDVI). This study led to the conclusion that good correlations between MNDVI and two climatic factors are prevalent in China. It also indicated that the 'best factors' for some regions identified by partial correlation analysis are better than those selected by simple correlation analysis. The partial correlation coefficients of MNDVI and each climate factor were calculated to describe the singular influence of each meteorological variable. The results indicated that the impact of other variables on vegetation should be considered in the 'best factor' selection for one climatic variable. Temperature has a significant positive influence on vegetation growth in China. Precipitation is the most important climatic factor that closely correlates with MNDVI, particularly in arid and semi-arid environments. However, in some wet regions, precipitation is not a limiting factor on vegetation growth. A trend analysis was carried out to study climate change and its impacts on vegetation. The annual accumulated temperature had an increasing trend in China during 1982-2005. Temperature increases had different influences on vegetation dynamics in different parts of China. The results coincided with those of the multiple and partial correlation analysis. [ABSTRACT FROM AUTHOR]

The spatial distribution of vegetation in the Qilian Mountain area was quantified with remote sensing data. The MODIS NDVI values for June, July, August and September are the best indicators for the vegetation growth during a year in this area and thus were used in this study. The results obtained by analysing NDVI data for seven years from 2000 to 2006 clearly indicated that elevation and aspect, as a proxy for precipitation and temperature, are two very important factors for the vertical distribution of vegetation in Qilian Mountain area. In the Qilian Mountain area the vegetation growth is optimal between the elevations of 3200 m and 3600 m, with NDVI values larger than 0.50 and a peak value of >0.56 around 3400 m. It is the combination of plentiful precipitation and suitable land surface temperature that provides less soil moisture stress and thus suitable conditions for vegetation growth in this range of elevations. The optimal vegetation growth is found in the shady slope between NW340° to NE70° with the largest NDVI value (>0.56) within the elevation range of 3200 m and 3600 m. The methodology developed in this study should be useful for similar ecological studies on vegetation distribution. [ABSTRACT FROM AUTHOR]

The change history of vegetation cover and its relations to growing season precipitation (GSP) and average growing season temperature (AGST) in the source region of the Yellow River (SRYR) during 1990-2000 was retrieved based on the 1 km Advanced Very High-Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and meteorological records. The results show an overall warming and drying trend of the climate and a common degradation tendency of the ecosystem, with a greening trend in higher rugged regions. The pixel-by-pixel correlations between NDVI and climate factors indicate that a decrease in GSP mainly affects ecosystems with low precipitation and worse vegetation condition, and superimposes on the effects of increasing AGST which further deteriorate the climate background of these ecosystems. However, the positive correlations between AGST and NDVI in some higher/rugged regions suggest that the raising temperature can ameliorate vegetation growth conditions in these areas. Comparison and combination of the results of three change detection algorithms, i.e. post-classification comparison (PCC), principal components analysis (PCA) and a newly developed multi-temporal image difference (MTID) method, show that the integration of different methods can give a more comprehensive understanding of vegetation changes than any single method. [ABSTRACT FROM AUTHOR]

This study attempts to model alpine tundra vegetation dynamics in a tundra region in the Qinghai Province of China in response to global warming. We used Rasterbased cellular automata and a Geographic Information System to study the spatial and temporal vegetation dynamics. The cellular automata model is implemented with IDRISI's Multi-Criteria Evaluation functionality to simulate the spatial patterns of vegetation change assuming certain scenarios of global mean temperature increase over time. The Vegetation Dynamic Simulation Model calculates a probability surface for each vegetation type, and then combines all vegetation types into a composite map, determined by the maximum likelihood that each vegetation type should distribute to each raster unit. With scenarios of global temperature increase of 1 to 3°C, the vegetation types such as Dry Kobresia Meadow and Dry Potentilla Shrub that are adapted to warm and dry conditions tend to become more dominant in the study area. [ABSTRACT FROM AUTHOR]

The extreme alpine environment around the mid-Yalong Zangbo River on the Tibetan plateau supports a unique avifauna. To understand the structure and habitat selection of this poorly known bird assemblage, we quantified abundance of the birds in four representative types of vegetation and a human settlement inside a shrub-covered alpine valley (29°27'N, 91°40'E, 4000-5200 m a.s.l.) in the Lhasa mountains, Tibet, from September 2004 to January 2005. A total of 40 species of birds was recorded, six of which were summer breeders, one vagrant autumn migrant, 29 permanent residents, and four local wintering migrants. Latitudinally migrating taxa were absent. Of the 33 wintering species, 70% were alpine specialists. Patterns of distribution of species and individuals differed across habitats. The highest bird species richness was found in Rose-Barberry shrub where vegetation was most complex, and fruiting and seeding plants abundant. From autumn to winter, birds tended to aggregate in the human settlement for foods supplemented by local Buddhists; as a consequence this habitat contained the highest individual density. In terms of trophic guilds, plant-eating birds were predominant in composition (65.8-75.8%) and numerically abundant (89.8-96.7%) in all the habitats. We concluded that vegetation diversity and food supply significantly influence species composition and individual abundance of the alpine bird community. For long-term survival of the unique bird assemblage, we argue for protection of the shrub vegetation as it is difficult to regenerate in the harsh high-altitude conditions. [ABSTRACT FROM AUTHOR]

A long time series (20 years) of Advanced Very High Resolution Radiometer (AVHRR) data with 8 km resolution are used to monitor vegetation cover change in the Heihe river basin. Linear regression is used to characterize the trends in vegetation cover change. The yearly Maximum Normalized Difference Vegetation Index (MNDVI) in the Heihe river basin elicits an explicit inter‐annual change in the period 1982–2001. An increase in MNDVI occurs in mid‐basin oasis mainly, while a decrease in MNDVI is mainly observed in the mountainous and Alxa's natural oasis regions of the Heihe river basin. Precipitation is the major climate driving force for vegetation cover changes in the Heihe River Basin. The MNDVI is sensitive to precipitation and its sensitivity decreases with increasing precipitation. Precipitation has a higher effect on the mountainous regions' vegetation cover than on the oasis regions' vegetation cover. Precipitation also elicits a lag effect on the MNDVI with a phase of one year at a yearly scale mainly in the mountainous regions of the Heihe River Basin. In the Heihe River Basin, the temperature has a slight and positive effect on the oasis regions' vegetation cover and a slight and negative effect on the mountainous regions' vegetation cover. [ABSTRACT FROM AUTHOR]

In the Loess Plateau of China, soil water has three ecological properties: high infiltration capacity, high storage capacity and availability to deep plant roots. Soil desiccation is the most serious problem for forest vegetation in the Loess Plateau. Arid soils are the result of intensified soil desiccation caused by disturbances in plant succession, which constitute the ecological foundation of soil water. The negative effects of the arid soil layer on surface water infiltration for recharging underground water are discussed in terms of ecological hydrology. The arid soil layer disrupts the link between surface water and underground water and prevents vertical precipitation infiltration from supplementing underground water. Forest vegetation has a significant runoff-retaining efficiency that reduces total runoff from forest areas leading to low surface and ground runoff which affect the water cycle on a watershed scale. [ABSTRACT FROM AUTHOR]

We developed a method for integrated analysis of multi-source data for vegetation classification at the continental scale, and applied it to China. Multi-temporal 1 km NOAA Advanced Very High Resolution Radiometer (AVHRR) Holdridge's life zone system and its vegetation-climate classification indices such as bio-temperature (BT), potential evapotranspiration rate (PER) and precipitation (P) correspond better with undisturbed vegetation types all over the world. We generated 1 km images of BT, PER and P using the quantitative model of Holdridge's life zone system with climate data of China. They were processed with principal component analysis (PCA) to produce an ancillary image. This image and 12 monthly images of maximum Normalized Difference Vegetation Index (NDVI) values at 1 km resolution were input into an ISODATA clustering algorithm to carry out a vegetation classification. As a result, 47 information classes were obtained. Seasonal NDVI parameters derived through time series analysis (TSA) of the NDVI temporal profile and a set of quantitative vegetation-climate parameters of Holdrige's life zone model were synthetically utilized to label information classes. In this method, climate, terrain and spectral data were integrated; separability between vegetation types and classification accuracy were improved. A total of 47 land cover classes were obtained. Validation data collected in the field using GPS indicated that an overall classification accuracy of 71.4% was reached, an 8.1% improvement to the map derived only from multi-temporal NDVI images. To compare our results with the International Geosphere-Biosphere Programme (IGBP) DISCover land cover dataset, we aggregated our land cover classes according to the IGBP classification system. The overall classification accuracy for the aggregated vegetation map from our classification results improved IGBP land cover map from 75.5% to 86.3%. [ABSTRACT FROM AUTHOR]