Uncertainties in classification system conversion and an analysis of inconsistencies in global land cover products

In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex mountain-oasis-desert system and quantitatively discussed the uncertainties in classification system conversion. This is the first study to compare these products based on terrain and to quantitatively study the uncertainties in classification system conversion. The inconsistencies and uncertainties decreased from high to low levels of aggregation (IGBP-17 to TC) and from mountain to desert areas, indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. The overall areal inconsistency in the comparison of the FROM-GLC and GlobCover 2009 datasets is the smallest among the three pairs, but the smallest overall spatial inconsistency was observed between the FROM-GLC and MODISLC. The GlobCover 2009 had the largest conversion uncertainties due to mosaic land cover definition, with values up to 23.9%, 9.68% and 0.11% in mountainous, oasis and desert areas, respectively. The FROM-GLC had the smallest inconsistency, with values less than 4.58%, 1.89% and 1.2% in corresponding areas. Because the FROM-GLC dataset uses a hierarchical classification scheme with explicit attribution from the second level to the first, this system is suggested for producers of map land cover products in the future

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management

Changes in plant species richness distribution in Tibetan alpine grasslands under different precipitation scenarios

Species richness is the core of biodiversity-ecosystem functioning (BEF) research. Nevertheless, it is difficult to accurately predict changes in plant species richness under different climate scenarios, especially in alpine biomes. In this study, we surveyed plant species richness from 2009 to 2017 in 75 alpine meadows (AM), 199 alpine steppes (AS), and 71 desert steppes (DS) in the Tibetan Autonomous Region, China. Along with 20 environmental factors relevant to species settlement, development, and survival, we first simulated the spatial pattern of plant species richness under current climate conditions using random forest modelling. Our results showed that simulated species richness matched well with observed values in the field, showing an evident decrease from meadows to steppes and then to deserts. Summer precipitation, which ranked first among the 20 environmental factors, was further confirmed to be the most critical driver of species richness distribution. Next, we simulated and compared species richness patterns under four different precipitation scenarios, increasing and decreasing summer precipitation by 20% and 10%, relative to the current species richness pattern. Our findings showed that species richness in response to altered precipitation was grassland-type specific, with meadows being sensitive to decreasing precipitation, steppes being sensitive to increasing precipitation, and deserts remaining resistant. In addition, species richness at low elevations was more sensitive to decreasing precipitation than to increasing precipitation, implying that droughts might have stronger influences than wetting on species composition. In contrast, species richness at high elevations (also in deserts) changed slightly under different precipitation scenarios, likely due to harsh physical conditions and small species pools for plant recruitment and survival. Finally, we suggest that policymakers and herdsmen pay more attention to alpine grasslands in central Tibet and at low elevations where species richness is sensitive to precipitation changes

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Vegetation response to climate zone dynamics and its impacts on surface soil water content and albedo in China

Extensive research has focused on the response of vegetation to climate change, including potential mechanisms and resulting impacts. Although many studies have explored the relationship between vegetation and climate change in China, research on spatiotemporal distribution changes of climate regimes using natural vegetation as an indicator is still lacking. Further, limited information is available on the response of vegetation to shifts in China's regional climatic zones. In this study, we applied Mann-Kendall, and correlation analysis to examine the variabilities in temperature, precipitation, surface soil water, normalised difference vegetation index (NDVI), and albedo in China from 1982 to 2012. Our results indicate significant shifts in the distribution of Koppen-Geiger climate classes in China from 12.08% to 18.98% between 1983 and 2012 at a significance level of 0.05 (MK). The percentage areas in the arid and continental zones expanded at a rate of 0.004%/y and 0.12%/y, respectively, while the percentage area in the temperate and alpine zones decreased by -0.05%/y and - 0.07%/y. Sensitivity fitting results between simulated and observed changes identified temperature to be a dominant control on the dynamics of temperate (r(2)= 0.98) and alpine (r(2)= 0.968) zones, while precipitation was the dominant control on the changes of arid (r(2) = 0.856) and continental (r(2) = 0.815) zones. The response of the NDVI to albedo infers a more pronounced radiative response in temperate (r = -0.82, pPeer reviewe

Response of hydrological processes to input data in high alpine catchment : an assessment of the Yarkant River basin in China

Most studies of input data used in hydrological models have focused on flow; however, point discharge data negligibly reflect deviations in spatial input data. To study the effects of different input data sources on hydrological processes at the catchment scale, eight MIKE SHE models driven by station-based data (SBD) and remote sensing data (RSD) were implemented. The significant influences of input variables on water components were examined using an analysis of the variance model (ANOVA) with the hydrologic catchment response quantified based on different water components. The results suggest that compared with SBD, RSD precipitation resulted in greater differences in snow storage in the different elevation bands and RSD temperatures led to more snowpack areas with thinner depths. These changes in snowpack provided an appropriate interpretation of precipitation and temperature distinctions between RSD and SBD. For potential evapotranspiration (PET), the larger RSD value caused less plant transpiration because parameters were adjusted to satisfy the outflow. At the catchment scale, the spatiotemporal distributions of sensitive water components, which can be defined by the ANOVA model, indicate that this approach is rational for assessing the impacts of input data on hydrological processes

Ecohydrology in water-limited environment using quantitative remote sensing - the Heihe River basin (China) case

Water-limited environments exist on all continents of the globe and they cover more than 30% of the Earth’s land surface. The eco-environments of these regions tend to be fragile and they are changing in a dramatic way through processes like land desertification, shrinking of oases, groundwater depletion, and soil erosion. These are either human induced or results of a changing climate. Implications of these changes for both the regional hydrologic cycle and the vegetation have been documented. Since these changes occur over a wide range of scales in space and time, remote sensing methods are needed to monitor the land surface characteristics, to observe changes in vegetation and hydrological states, and to compare these with predictions from hydrological models. It is widely accepted that remote sensing methods offer the ability to acquire spatially continuous measurements over large areas. Remote sensing can also help to visualize complex processes because the spatial data can be captured regularly over time. China is one of several countries with large arid and semi-arid areas. The Heihe River basin, situated in the arid inland of northwestern China, is one of the areas severely affected by ecoenvironmental degradation and recovery. The problem of the degraded environment is due to overexploitation of surface and ground water leading to shrinking of oases, including the decline and death of natural vegetation, and the lowering of the groundwater table. Exhaustive (over-)use of water resources is the main cause of land degradation in the lower reaches of the basin, called the Ejina oasis. The whole Heihe River basin is therefore selected as study area in this thesis to analyze the long-term eco-environmental changes. What happens in this river basin is likely to have a growing influence on regional hydrological cycles, even affecting human life. Effective management of eco-environmental problems in this critical zone of water-limited conditions will provide scientific evidence for protecting and improving the eco-environment in these Chinese northwestern arid regions, eventually resulting in land improvement. Studies on quantifying the relationship between the vegetation and the water resources are a critical step in developing an ecohydrological approach to resources management in order to minimize environmental degradation. Remote sensing measurements can help us to better understand the effects of changes in water management on hydrological processes and their subsequent feedback to the eco-environment at the regional scale. Remote sensing methods can also provide information to quantify heterogeneity and change at a large scale. Therefore, the main objective of this thesis is to develop a methodology for the quantitative assessment of eco-environmental changes at a large scale in arid regions by integrating remote sensing methods in ecohydrological approaches. Chapter 1 outlines the significance of quantitative assessment of eco-environmental changes using remote sensing methods and applying them for ecohydrology in northwestern China, resulting in the specific research objectives of this thesis. Chapter 2 quantifies both the vertical and horizontal distribution of vegetation in the Qilian Mountains area, representing the upper reaches of the Heihe River basin, based on MODIS NDVI images from the year 2000 - 2006. Our analysis reveals that elevation and aspect are two important impact factors for the vertical distribution of vegetation in a mountainous area. The NDVI increases with the elevation and reaches a maximum value at a certain elevation threshold, and then decreases as the elevation increases beyond this threshold. The optimal vegetation growth is on the shady side of the mountains because of less evapotranspiration. The best combination of temperature and precipitation is assessed providing good conditions for vegetation growth. Chapter 3 presents an efficient method to estimate the regional annual evapotranspiration (ET) based on the SEBS algorithm (Surface Energy Balance System) in the Zhangye basin, representing the middle reaches of the Heihe River basin. The method proposed is a combination of the daily SEBS results and data collected by meteorological stations. The result shows that the annual ET increased gradually during the period 1990-2004 and the main impact factor on the long-term increase of annual ET was the vegetation change. The accuracy of the ET result is validated using a water balance for the whole watershed and the validation reveals that the SEBS algorithm can be used to effectively estimate annual ET in the Zhangye basin. Chapter 4 establishes the quantitative relationship between the runoff of the Heihe River and the long-term vegetation change of the Ejina oasis, located in the lower reaches of the Heihe River. In this part, two time periods are distinguished corresponding to before and after the implementation of a new water allocation scheme in the Heihe River basin. The GIMMS NDVI and MODIS NDVI data sets are used to quantify the long-term change of the oasis vegetation in the first period 1989-2002 and the second period 2000-2006, respectively. The vegetation change shows a decreasing trend from 1989 to 2002 and an increasing trend between 2000 and 2006. Good relation between the runoff of the river and the vegetation growth are found at both stages and the time lag of the observed hysteresis effect of the runoff of the river on the oasis vegetation is one year. In addition, the yearly smallest water amount which sustains the demand of the eco-environment of the Ejina area is estimated to be 4×108 m3 based on MODIS images. Chapter 5 explores a method to quantify the effect of the groundwater depth on the vegetation growth in the year 2000 in the oasis area by combining MODIS NDVI with groundwater observation data. The result demonstrates that the groundwater depth suitable for vegetation growth in this region ranges from 2.8 to 5 m, depending on species composition. Hardly any vegetation growth occurs when the groundwater depth is below 5 m because the rooting depth of the occurring species is limited and cannot maintain adequate water supplies to their canopies when the water depth is below 5 m. The situation changes after implementation of the new water allocation scheme since 2000. The mean NDVI increased and the annual conversion of bare land into vegetated land is about 38 km2 per year during the period 2000 – 2008. It reflects a potential recovery of the eco-environment of the Ejina area. Chapter 6 comprises the main conclusions and the outlook for possible improvements in future research. The main contribution of this study is the successful integration of remote sensing with ecohydrology in quantifying the relationship between water resources and vegetation occurrence at large scale. It provides a methodology to evaluate the long-term vegetation change and the water resources impact using remote sensing data in water-limited areas. The approach of vegetation dynamics, runoff and groundwater impacts presented in this thesis serves as a sound foundation for predicting the effects of future environmental changes. <br/

Integrating spatial continuous wavelet transform and normalized difference vegetation index to map the agro-pastoral transitional zone in Northern China

The agro-pastoral transitional zone (APTZ) in Northern China is one of the most important ecological barriers of the world. The commonly-used method to identify the spatial distribution of ATPZ is to apply a threshold rule on climatic or land use indicators. This approach is highly subjective, and the quantity standards vary among the studies. In this study, we adopted the spatial continuous wavelet transform (SCWT) technique to detect the spatial fluctuation in normalized difference vegetation index (NDVI) sequences, and as such identify the APTZ. To carry out this analysis, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI 1-month data (MODND1M) covering the period 2006–2015 were used. Based on the spatial variation in NDVI, we identified two sub-regions within the APTZ. The temporal change of APTZ showed that although vegetation spatial pattern changed annually, certain areas appeared to be stable, while others showed higher sensitivity to environmental variance. Through correlation analysis between the dynamics of APTZ and precipitation, we found that the mean center of the APTZ moved toward the southeast during dry years and toward the northwest during humid years. By comparing the APTZ spatial pattern obtained in the present study with the outcome following the traditional approach based on mean annual precipitation data, it can be concluded that our study provides a reliable basis to advance the methodological framework to identify accurately transitional zones. The identification framework is of high importance to support decision-making in land use management in Northern China as well as other similar regions around the world

Vegetation response to extreme climate events on the Mongolian Plateau from 2000 to 2010

Climate change has led to more frequent extreme winters (aka, dzud) and summer droughts on the Mongolian Plateau during the last decade. Among these events, the 2000–2002 combined summer drought–dzud and 2010 dzud were the most severe on vegetation. We examined the vegetation response to these extremes through the past decade across the Mongolian Plateau as compared to decadal means. We first assessed the severity and extent of drought using the Tropical Rainfall Measuring Mission (TRMM) precipitation data and the Palmer drought severity index (PDSI). We then examined the effects of drought by mapping anomalies in vegetation indices (EVI, EVI2) and land surface temperature derived from MODIS and AVHRR for the period of 2000–2010. We found that the standardized anomalies of vegetation indices exhibited positively skewed frequency distributions in dry years, which were more common for the desert biome than for grasslands. For the desert biome, the dry years (2000–2001, 2005 and 2009) were characterized by negative anomalies with peak values between �1.5 and �0.5 and were statistically different (P \u3c 0:001) from relatively wet years (2003, 2004 and 2007). Conversely, the frequency distributions of the dry years were not statistically different (p \u3c 0:001) from those of the relatively wet years for the grassland biome, showing that they were less responsive to drought and more resilient than the desert biome. We found that the desert biome is more vulnerable to drought than the grassland biome. Spatially averaged EVI was strongly correlated with the proportion of land area affected by drought (PDSI \u3c �1) in Inner Mongolia (IM) and Outer Mongolia (OM), showing that droughts substantially reduced vegetation activity. The correlation was stronger for the desert biome (R2 D 65 and 60, p \u3c 0:05) than for the IM grassland biome (R2 D 53, p \u3c 0:05). Our results showed significant differences in the responses to extreme climatic events (summer drought and dzud) between the desert and grassland biomes on the Plateau