Linkages between Atmospheric Circulation, Weather, Climate, Land Cover and Social Dynamics of the Tibetan Plateau

The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. In recent decades, the TP has undergone significant changes due to climate and human activities. Since the 1980s anthropogenic activities, such as the stocking of livestock, land cover change, permafrost degradation, urbanization, highway construction, deforestation and desertification, and unsustainable land management practices, have greatly increased over the TP. As a result, grasslands have undergone rapid degradation and have altered the land surface which in turn has altered the exchange of heat and moisture properties between land and the atmosphere. But gaps still exist in our knowledge of land-atmosphere interactions in the TP and their impacts on weather and climate around the TP, making it difficult to understand the complete energy and water cycles over the region. Moreover, human, and ecological systems are interlinked, and the drivers of change include biophysical, economic, political, social, and cultural elements that operate at different temporal and spatial scales. Current studies do not holistically reflect the complex social-ecological dynamics of the Tibetan Plateau. To increase our understanding of this coupled human-natural system, there is a need for an integrated approach to rendering visible the deep interconnections between the biophysical and social systems of the TP. There is a need for an integrative framework to study the impacts of sedentary and individualized production systems on the health and livelihoods of local communities in the context of land degradation and climate change. To do so, there is a need to understand better the spatial variability and landscape patterns in grassland degradation across the TP. Therefore, the main goal of this dissertation is to contribute to our understanding of the changes over the land surface and how these changes impact the plateau\u27s weather, climate, and social dynamics. This dissertation is structured as three interrelated manuscripts, which each explore specific research questions relating to this larger goal. These manuscripts constitute the three primary papers of this dissertation. The first paper documents the significant association of surface energy flux with vegetation cover, as measured by satellite based AVHRR GIMMS3g normalized difference vegetation index (NDVI) data, during the early growing season of May in the western region of the Tibetan Plateau. In addition, a 1°K increase in the temperature at the 500 hPa level was observed. Based on the identified positive effects of vegetation on the temperature associated with decreased NDVI in the western region of the Tibetan Plateau, I propose a positive energy process for land-atmosphere associations. In the second paper, an increase in Landsat-derived NDVI, i.e., a greening, is identified within the TP, especially during 1990 to 2018 and 2000 to 2018 time periods. Larger median growing season NDVI change values were observed for the Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grassland regions, in comparison to the other three regions studied. Land degradation is prominent in the lower and intermediate hillslope positions in comparison to the higher relative topographic positions, and change is more pronounced in the eastern Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grasslands. Geomorphons were found to be an effective spatial unit for analysis of hillslope change patterns. Through the extensive literature review presented in third paper, this dissertation recommends using critical physical geography (CPG) to study environmental and social issues in the TP. The conceptual model proposed provides a framework for analysis of the dominant controls, feedback, and interactions between natural, human, socioeconomic, and governance activities, allowing researchers to untangle climate change, land degradation, and vulnerability in the Tibetan Plateau. CPG will further help improve our understanding of the exposure of local people to climate and socio-economic and political change and help policy makers devise appropriate strategies to combat future grassland degradation and to improve the lives and strengthen livelihoods of the inhabitants of the TP

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets

For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which reference information was available, the root mean squared error (RMSE) of winter crop acreage at sub-pixel level was 10%. When combined with current and future sensors, such as MODIS and Sentinel-3, the unmixing of AVHRR data can help in the building of an extended time series of crop distributions and cropping patterns dating back to the 80s.JRC.H.4-Monitoring Agricultural Resource

How are Interannual Variations of Land Surface Phenology in the Highland Pastures of Kyrgyzstan Modulated by Terrain, Snow Cover Seasonality, and Climate Oscillations? An Investigation Using Multi-Source Remote Sensing Data

In the semiarid, continental climates of montane Central Asia, with its constant moisture deficit and low relative humidity, agropastoralism constitutes the foundation of the rural economy. In Kyrgyzstan, an impoverished, landlocked republic in Central Asia, herders of the highlands practice vertical transhumance—the annual movement of livestock to higher elevation pastures to take advantage of seasonally available forage resources. Dependency on pasture resource availability during the short mountain growing season makes herds and herders susceptible to changing weather and climate patterns. This dissertation focuses on using remote sensing observations over the highland pastures in Kyrgyzstan to address five interrelated topics: (i) changes in snow cover and its seasonality from 2002 through 2016; (ii) pasture phenology from the perspective of land surface phenology using multi-scale data from 2001 through 2017; (iii) relationships between snow cover seasonality and subsequent land surface phenology; (iv) terrain effects on the snow-phenology interrelations; and (v) the influence of atmospheric teleconnections on modulating the relationships between snow cover seasonality, growing season duration, and pasture phenology. Results indicate that more territory has been experiencing earlier snow onset than earlier snowmelt, and around equivalent areas with longer and shorter duration of snow seasons. Significant shifts toward earlier snow onset (snowmelt) occurred in western and central (eastern) Kyrgyzstan, and significant duration of the snow season shortening (extension) across western and eastern (northern and southwestern) Kyrgyzstan. Below 3400 m, there was a general trend of significantly earlier snowmelt, and this area of earlier snowmelt was 15 times greater in eastern than western rayons. In terms of land surface phenology, there was a predominant and significant trend of increasing peak greenness, and a significant positive relationship between snow covered dates and modeled peak greenness. While there were more negative correlations between snow cover onset and peak greenness, there were more positive correlations between snowmelt timing and peak greenness, meaning that a longer snow cover season increased the amplitude of peak greenness. The amount of thermal time (measured in accumulated growing degree-days) to reach peak greenness was significantly negatively correlated both with the number of snow covered dates and the snowmelt date. Thus, more snow covered dates translated into fewer growing degree-days accumulated to reach peak greenness in the subsequent growing season. Terrain features influenced the timing of snowmelt more strongly than the number of snow covered dates. Slope was more important than aspect, but the strongest effect appeared from the interaction of aspect and the steepest slopes. The influence of atmospheric teleconnection arising from climate oscillation modes was marginal at the spatial resolutions of this study. Thermal time accumulation could be largely explained with Partial Least Squares (PLS) regression models by elevation and snow cover metrics. However, explanation of peak greenness was less susceptible to terrain and snow cover variables. This research study provides a comprehensive evaluation of the spatial variation of interannual phenology in the highland pastures that serve as the foundation of rural Kyrgyz economy. Finally, it contributes to a better understanding of recent environmental changes in remote highland Central Asia

Study of vegetation cover change and its driving forces

The dynamic change of vegetation cover exerts significant influences on the energetic and chemical circulation worldwide. Systematically monitoring the global vegetation cover change is critical to promote a better understanding of the basic biogeochemical processes, and their possible feedbacks to the global climate system. It is of great practical value to study dynamic vegetation variation related to climate change, human activities, and natural factors to explore the underlying relationships between vegetation cover change and its driving forces and the responding mechanisms of vegetation cover to the variability of the driving forces. Vegetation degradation is continually proceeding worldwide, but the degradation situation is more serious in developing countries than in developed countries. China is the largest developing country, and it has been experiencing significant socio-economic development, rapid urban expansion, and sharp population growth in eastern China in particular after launching the program of reform and opening-up termed "Socialism with Chinese Characteristics" in China in 1978. The unprecedented socio-economic development, urban expansion, and population growth have led to land use and land cover change, soil fertility decline, vegetation degradation, water contamination, and biodiversity loss in eastern China. Eastern China, a place with a highly developed socioeconomic status than other regions of China, covers seven provinces (e.g., Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong) and three municipalities (e.g., Beijing, Tianjin, and Shanghai) with an area of about 1.0277 million km2. It is of critical importance for monitoring the dynamic vegetation variation on multi-spatiotemporal scales, exploring the underlying relationship between vegetation cover change and its driving forces (e.g., climate forces, topographic forces, and socio-economic forces), and investigating the time lag effects of vegetation variation in response to climate variables (e.g., precipitation and temperature) in eastern China from 2001 to 2016. To achieve the objectives of this study, the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (NDVI) time series with a 250 m spatial resolution and a 16-day temporal resolution, monthly meteorological data from meteorological (automatic) base station , Digital Elevation Model data with a 30 m spatial resolution, socio-economic statistical data, and the map of land use types, gross domestic product, and population density in 2000 and 2015 with an 1 km spatial resolution, and the vector map of eastern China at city level were used. A set of mathematical methods such as the maximum value composite method, linear regression analysis, rescaled range analysis, coefficient of variation, Person’s correlation coefficient, t-test, and spatial analysis methods (e.g., surface analysis and overlap analysis) were applied in this study. This study aims at monitoring the dynamic change of vegetation cover and investigating the relationship between vegetation cover and its driving forces on multiple spatiotemporal scales in eastern China from 2001 to 2016. The objectives of this study are fulfilled and the main findings and new results of this study are summarized in following. The overall annual NDVI displays a distinctive spatial heterogeneity across eastern China, presenting a gradient decrease from the south to the north of eastern China. The spatial distribution of NDVI in spring, summer, and autumn follows a similar pattern, but the overall NDVI value is higher in summer than in spring and autumn. Our calculation indicated that, during the past 16 years, the vegetation cover had gradually increased in eastern China with a magnitude of 0.0003 year-1. Areas with a greening trend and areas with a browning trend account for 49% and 33% of the study area, respectively. Spatially, we found that the browning areas are mainly distributed in city centers and the three economic zones and its surrounding areas. Considering the vegetation variation on seasonal scale, NDVI performs an increasing trend in spring and autumn but a decreasing trend in summer. In this study, we detected that areas expected to show consistency accounting for a larger proportion when compared with the areas expected to show anti-consistency on annual scale, while an opposite phenomenon was found on seasonal scale. In terms of the future changing trend of vegetation cover, areas with certain vegetation degradation will be larger than areas with certain vegetation improvement for eastern China both on annual and seasonal scales in the future. Estimating the vegetation stability on the basis of variation of coefficient, we found that the vegetation cover is relatively stable in the south of the study area, but it fluctuated wildly in the north of the study area. Our calculation suggested that temperature can be considered as the dominant climate factor controlling the vegetation growth in eastern China. The relationship is more pronounced between NDVI and temperature than between NDVI and precipitation both on annual and seasonal scales in eastern China for the study period. Moreover, the relationship between NDVI and precipitation is higher in autumn than in spring and summer, while the response of NDVI to temperature is stronger in spring than in autumn, followed by in summer. In this study, we observed, spatially, the overall maximum correlation coefficients between NDVI and precipitation as well as NDVI and temperature are basically higher in the north and lower in the south of the study area both on annual and seasonal scales. Temporally, on annual scale, the NDVI shows no lag time to changes in temperature but a 1-month lag time to precipitation variation. On seasonal scale, the maximum responses of NDVI to changes in precipitation and temperature establish 1-month longer in summer than in spring and autumn. Spatially, the lag time for maximum NDVI response to precipitation and temperature gradually increase from the north to the south of the study area. Elevation is regarded to be a dominant factor affecting the vertical distribution of vegetation cover. Our findings indicated that both the vegetation cover and vegetation stability increase with the elevation increase and reach its peak at an elevation of about 500 m. The vegetation degradation is more serious at the elevation range of 0 to 100 m than at higher elevation ranges. It is worth noticing that, in this study, our result is against our initial assumptions that the vegetation growth on the north-facing slope is better than the vegetation growth on the south-facing slope. However, we found that the vegetation cover, vegetation cover change, and vegetation stability show no statistical difference on the south-facing slope and north-facing slope. Similar to the responding mechanisms between the elevation-vegetation cover and elevation-vegetation stability, the vegetation cover and vegetation stability show a gradient upward trend with slope range increase. Furthermore, the proportion of the areas with a greening trend shows a “humped” pattern with the slope range increase, and it reaches the peak at the slope range of 6° to 15°. Our findings indicated that vegetation degradation is generally attributed to socio-economic development, urban expansion, and population growth, particularly in Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong. However, implementing large-scale reforestation and afforestation programs such as the Natural Forest Conservation Program, Three-North Shelter Forest Program, Beijing and Tianjin Sandstorm Source Controlling Program, and Grain for Green Program contribute to the vegetation greening phenomenon since 1978, in Liaoning, Beijing, Shandong, and Hebei in particular. We further observed that, spatially, the dynamic change of vegetation cover is negatively coupled with socio-economic development, urban expansion, and population growth. Areas with a high-speed socio-economic development, rapid urban expansion, and sharp population growth are along with severe vegetation degradation and strong vegetation oscillation spatially

How Much Variation in Land Surface Phenology can Climate Oscillation Modes Explain at the Scale of Mountain Pastures in Kyrgyzstan?

Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degree-days based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearman’s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30m resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia

Using Long Time Series of Satellite Remote Sensing Data to Assess the Impact of Climate and Anthropogenic Changes in the Mesopotamian Marshes, Iraq

In the recent past, the Mesopotamia region has been rich in all forms of biological diversity, characterized by a fertile living environment and natural habitats full of rare birds, wild animals, aquatic animals, and diverse plants. Its natural abundance and geographical location have allowed it to be break or transit point for millions of migratory birds from Russia to South Africa. It is a breeding ground for many species of Persian Gulf fish. Despite all this historical, environmental and economic richness, they have been neglected as a result of the combination of a number of human and climatic factors, which in 16 years (1988-2003) has modified them to a land where vegetation, water, and biodiversity have been clearly reduced. This is a great environmental loss, not only for West Asia but for the whole world. This dissertation explores the changes in the vegetation coverage and water bodies in the Mesopotamian marshes, Iraq over more than three decades (36 years) using different sources of satellite remote sensing datasets. Firstly, we utilized Normalized Difference Vegetation Index (NDVI) from the Land Long Term Data Record (LTDR) Version 5 which has a 0.05o x 0.05o in spatial resolution and daily temporal repeat to monitor the fluctuations of vegetation together with hydrological variables such precipitation, surface temperature, and evapotranspiration. In this research, we studied the impact of climate change and anthropogenic activities on vegetation and water coverage changes. Secondly, we compared Normalized Difference Vegetation Index from various satellite sensors - Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for 17 years. We selected this time series (2002-2018) to monitor the changes in vegetation area. The time series (2002-2018) is considered as a period of rehabilitation for the Mesopotamian marshes. Thirdly, as a result of human factors and local and regional climate changes, the marshes and Iraq are in general vulnerable to face a large number of dust storms annually. According to local sources (Iraq news) and National Aeronautics and Space Administration, the time period from June 29 to July 8, 2009, is considered the longest dust storm period in Iraq during last decade. In this research, we utilized the Moderate Resolution Imagining Spectroradiometer, surface reflectance daily data to calculate the Normalized Difference Dust Index. Additionally, brightness temperature data from Aqua thermal band 31 were used to separate sand on the ground from atmospheric dust. The main reasons for the degradation of the Mesopotamian marshes were due to anthropogenic activities. In the comparison research, we found that the NDVI derived from MODIS, AVHRR and Landsat sensors are correlated with high precision. This paper investigates the utility of combining low spatial resolution with frequent temporal repeat and long-term coverage and a high spatial resolution with infrequent temporal repeat and similar long-term coverage. This study also proves that we can use the low-resolution Advance Very High- resolution Radiometer data for studies on land cover change

QUANTIFICATION OF ERROR IN AVHRR NDVI DATA

Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. However temporal aggregation improved the precision of the data to a limited extent only. The research presented in this dissertation indicated that the NDVI change of ~0.03 to ~0.08 NDVI units in 10 to 20 years, frequently reported in recent literature, can be significant in some cases. However, unless spatially explicit uncertainty metrics are quantified for the specific spatiotemporal aggregation schemes used by these studies, the significance of observed differences between sites and temporal trends in NDVI will remain unknown