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/

The spatial distribution and temporal variation of desert riparian forests and their influencing factors in the downstream Heihe River basin, China

Desert riparian forests are the main restored vegetation community in Heihe River basin. They provide critical habitats and a variety of ecosystem services in this arid environment. Since desert riparian forests are also sensitive to disturbance, examining the spatial distribution and temporal variation of these forests and their influencing factors is important to determine the limiting factors of vegetation recovery after long-term restoration. In this study, field experiment and remote sensing data were used to determine the spatial distribution and temporal variation of desert riparian forests and their relationship with the environmental factors. We classified five types of vegetation communities at different distances from the river channel. Community coverage and diversity formed a bimodal pattern, peaking at the distances of 1000 and 3000 m from the river channel. In general, the temporal normalized difference vegetation index (NDVI) trend from 2000 to 2014 was positive at different distances from the river channel, except for the region closest to the river bank (i.e. within 500 m from the river channel), which had been undergoing degradation since 2011. The spatial distribution of desert riparian forests was mainly influenced by the spatial heterogeneity of soil properties (e.g. soil moisture, bulk density and soil particle composition). Meanwhile, while the temporal variation of vegetation was affected by both the spatial heterogeneity of soil properties (e.g. soil moisture and soil particle composition) and to a lesser extent, the temporal variation of water availability (e.g. annual average and variability of groundwater, soil moisture and runoff). Since surface (0–30 cm) and deep (100–200 cm) soil moisture, bulk density and the annual average of soil moisture at 100 cm obtained from the remote sensing data were regarded as major determining factors of community distribution and temporal variation, conservation measures that protect the soil structure and prevent soil moisture depletion (e.g. artificial soil cover and water conveyance channels) were suggested to better protect desert riparian forests under climate change and intensive human disturbance

Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data

The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area

Improved atmospheric modelling of the oasis-desert system in Central Asia using WRF with actual satellite products

Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only possible solution to the limitation; however, it is impractical for long-period simulations due to the limited satellite products available before 2000 and the extremely time- and labor-consuming processes involved. In this study, we used the Weather Research and Forecasting (WRF) model with observed estimates of land use (LU), albedo, Leaf Area Index (LAI), and green Vegetation Fraction (VF) datasets from satellite products to examine which terrestrial datasets have a great impact on simulating water and heat conditions over heterogeneous oasis-desert systems in the northern Tianshan Mountains. Five simulations were conducted for 1-31 July in both 2010 and 2012. The decrease in the root mean squared error and increase in the coefficient of determination for the 2 m temperature (T2), humidity (RH), latent heat flux (LE), and wind speed (WS) suggest that these datasets improve the performance of WRF in both years; in particular, oasis effects are more realistically simulated. Using actual satellite-derived fractional vegetation coverage data has a much greater effect on the simulation of T2, RH, and LE than the other parameters, resulting in mean error correction values of 62%, 87%, and 92%, respectively. LU data is the primary parameter because it strongly influences other secondary land surface parameters, such as LAI and albedo. We conclude that actual LU and VF data should be used in the WRF for both weather and climate simulations

Spatio-Temporal Patterns of Water Table and Vegetation Status of a Deserted Area

Understanding groundwater-vegetation interactions is crucial for sustaining fragile environments of desert areas such as the Horqin Sandy Land (HSL) in northern China. This study examined spatio-temporal variations in the water table and the associated vegetation status of a 9.71 km2 area that contains meadowland, sandy dunes, and intermediate transitional zones. The depth of the water table and hydrometeorologic parameters were monitored and Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were utilized to assess the vegetation cover. Spatio-temporal variations over the six-year study period were examined and descriptive groundwater-vegetation associations developed by overlaying a water table depth map onto a vegetation index map derived from MODIS. The results indicate that the water table depends on the local topography, localized geological settings, and human activities such as reclamation, with fluctuations occurring at annual and monthly scales as a function of precipitation and potential evapotranspiration. Locations where the water table is closer to the surface tend to have more dense and productive vegetation. The water table depth is more closely associated with vegetative density in meadowlands than in transitional zones, and only poorly associated with vegetation in sandy dunes

Mapping Changes in Fractional Vegetation Cover on the Namib Gravel Plains with Satellite-Retrieved Land Surface Emissivity Data

Monitoring changes in vegetation cover over time is crucial for understanding the spatial distribution of rainfall, as well as the dynamics of plants and animals in the Namib desert. Traditional vegetation indices have limitations in capturing changes in vegetation cover within water-limited ecosystems like the Namib gravel plains. Spectral emissivity derived from thermal infrared remote sensing has recently emerged as a promising tool for distinguishing between bare ground and non-green vegetation in arid environments. This study investigates the potential of satellite-derived emissivities for mapping changes in fractional vegetation cover across the Namib gravel plains. Analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) band 29 (λ = 8.55 µm) emissivity time series from 2001 to 2021, our findings demonstrate the ability of both Normalized Difference Vegetation Index (NDVI) and emissivity to detect sudden vegetation growth on the gravel plains. Emissivity additionally allows monitoring the extent of desiccated grass over several years after a rainfall event. Our results support a relationship between the change in fractional vegetation cover, the amount of rainfall and emissivity change magnitude. Information from NDVI and emissivity therefore provide complementary information for assessing vegetation in arid environments