Effect of the ecological water conveyance project on environment in the Lower Tarim River, Xinjiang, China

The dynamic response of groundwater level is examined in traverse and lengthways directions. Take the Yinsu section for an example, we have simulated groundwater levels before and after water-conveyance every time and calculated the incidence of groundwater on the both sides of the river. It is noted that the effect keeps growing with the water-delivery times increasing, from 570 m after the first times to 3,334 m after the eighth times. In addition, this paper involves the temporal response of the natural vegetation to water conveyance, vegetation coverage, planted-species number, dominant position and species diversity from 2002 to 2006. The findings indicate that the positive influence of ecological water conveyance project (EWCP) on the ecosystem in the Lower Tarim River is a long-term process. In this paper, we try to calculate water required for recovery of damaged ecosystem by using data available. This project is likely the base of research on water demand and the reference of measures for research on ecological water conveyance effect.     

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.     

Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds?=?330 %), land-use intensity (odds?=?103 %), low soil quality (odds?=?49 %), slope (odds?=?29 %), and salinity of the groundwater (odds?=?26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.     

The Natural Vegetation Responses to the Groundwater Change Resulting from Ecological Water Conveyances to the Lower Tarim River

This paper takes the ecological water conveyance project (EWCP) that transfers water from the Bosten Lake, to Daxihaizi Reservoir, and finally to the Taitema Lake as a case study to analyze the dynamic change of the groundwater depth, the vegetation responses to the elevation of the groundwater depth as well as the relationship between the groundwater depth and the natural vegetation. The results from many years’ monitoring in field indicate: (1) the groundwater depth has been elevating gradually with the increase in the times of watering and the elevation range has been expanding continuously in the lower reaches of Tarim River. Correspondingly, the natural vegetation has a favorable response to the elevation of the groundwater depth. The change of the natural vegetation has accordance with that of the groundwater depth. Such facts not only show that groundwater is a key factor to the growth of the native vegetation but also prove it is feasible that the degraded ecosystem can be restored and protected by the EWCP; (2) the results of analysis of the spatial-temporal response of the natural vegetation to watering reveals that the beneficial influence of the EWCP on the ecosystem in the lower Tarim River is a long-term process; (3) in terms of the function and structure of ecosystem after watering in the lower reaches of Tarim River, the EWCP does not still reach the goal of ecological restoration at a large spatial scale at present. Based on such monitoring results, some countermeasures and suggestions for the future restoration strategy are proposed so as to provide a theoretical basis for restoring and protecting the ecosystem in Tarim River, and meanwhile it can also provide some scientific references for implementing the similar ecological projects in other areas.     

Growing stock and woody biomass assessment in Asola-Bhatti Wildlife Sanctuary,Delhi, India

Biomass is an important entity to understand the capacity of an ecosystem to sequester and accumulate carbon over time. The present study, done in collaboration with the Delhi Forest Department, focused on the estimation of growing stock and the woody biomass in the so-called lungs of Delhi—the Asola-Bhatti Wildlife Sanctuary in northern Aravalli hills. The satellite-derived vegetation strata were field-inventoried using stratified random sampling procedure. Growing stock was calculated for the individual sample plots using field data and species-specific volume equations. Biomass was estimated from the growing stock and the specific gravity of the wood. Among the four vegetation types, viz. Prosopis juliflora, Anogeissus pendula, forest plantation and the scrub, the P. juliflora was found to be the dominant vegetation in the area, covering 23.43 km² of the total area. The study revealed that P. juliflora forest with moderate density had the highest (10.7 m³/ha) while A. pendula forest with moderate density had the lowest (3.6 m³/ha) mean volume. The mean woody biomass was also found to be maximum in P. juliflora forest with moderate density (10.3 t/ha) and lowest in A. pendula forest with moderate density (3.48 t/ha). The total growing stock was estimated to be 20,772.95 m³ while total biomass worked out to be 19,366.83 t. A strong correlation was noticed between the normalized difference vegetation index (NDVI) and the growing stock (R ²?=?0.84)/biomass (R ²?=?0.88). The study demonstrated that growing stock and the biomass of the woody vegetation in Asola-Bhatti Wildlife Sanctuary could be estimated with high accuracy using optical remote sensing data.     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale

Both the net primary productivity (NPP) and the normalized difference vegetation index (NDVI) are commonly used as indicators to characterize vegetation vigor, and NDVI has been used as a surrogate estimator of NPP in some cases. To evaluate the reliability of such surrogation, here we examined the quantitative difference between NPP and NDVI in their outcomes of vegetation vigor assessment at a landscape scale. Using Landsat ETM+ data and a process model, the Boreal Ecosystem Productivity Simulator, NPP distribution was mapped at a resolution of 90 m, and total NDVI during the growing season was calculated in Heihe River Basin, Northwest China in 2002. The results from a comparison between the NPP and NDVI classification maps show that there existed a substantial difference in terms of both area and spatial distribution between the assessment outcomes of these two indicators, despite that they are strongly correlated. The degree of difference can be influenced by assessment schemes, as well as the type of vegetation and ecozone. Overall, NDVI is not a good surrogate of NPP as the indicators of vegetation vigor assessment in the study area. Nonetheless, NDVI could serve as a fairish surrogate indicator under the condition that the target region has low vegetation cover and the assessment has relatively coarse classification schemes (i.e., the class number is small). It is suggested that the use of NPP and NDVI should be carefully selected in landscape assessment. Their differences need to be further evaluated across geographic areas and biomes.     

1982—2022年黄土高原归一化植被指数变化的时空特征

长时间地表植被指数变化序列构建与分析是生态环境监测领域的重要内容。以我国生态工程建设重点地区——黄土高原为研究区，采用时间序列的方差匹配方法，融合了2套卫星遥感的归一化植被指数(NDVI)数据产品(GIMMS 3g和MODIS),建立了覆盖1982—2022年的黄土高原暖季(5—9月)NDVI数据集，揭示了其间黄土高原植被覆盖变化的时空特征。研究发现：黄土高原暖季NDVI呈现“先慢后快”的增加趋势，转折点大致出现在2002年，1982—2002年暖季NDVI增速仅为0.01/(10 a),2003—2022年增速高达0.06/(10 a),其中十八大以来增速尤为显著；暖季NDVI快速增加区域主要位于黄土高原中部，并向东北、西南方向延展，与“退耕还林(草)”重点区域范围基本一致；在黄土高原南部、东部和青海省东部一带，暖季NDVI呈缓慢下降趋势。过去40年间黄土高原NDVI增加与生态工程建设关系密切。     

Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest

Forest disturbances around the world have the potential to alter forest type and cover, with impacts on diversity, carbon storage, and landscape composition. These disturbances, especially fire, are common and often large, making ground investigation of forest recovery difficult. Remote sensing offers a means to monitor forest recovery in real time, over the entire landscape. Typically, recovery monitoring via remote sensing consists of measuring vegetation indices (e.g., NDVI) or index-derived metrics, with the assumption that recovery in NDVI (for example) is a meaningful measure of ecosystem recovery. This study tests that assumption using MODIS 16-day imagery from 2000 to 2010 in the area of the Colorado’s Routt National Forest Hinman burn (2002) and seedling density counts taken in the same area. Results indicate that NDVI is rarely correlated with forest recovery, and is dominated by annual and perennial forb cover, although topography complicates analysis. Utility of NDVI as a means to delineate areas of recovery or non-recovery are in doubt, as bootstrapped analysis indicates distinguishing power only slightly better than random. NDVI in revegetation analyses should carefully consider the ecology and seasonal patterns of the system in question.     

Indicating appropriate groundwater tables for desert river-bank forest at the Tarim River, Xinjiang, China

Based on data collected over 2 years of monitoring the lower reaches of the Tarim River, the groundwater table depth was divided into six classes; 0 to 2 m, 2 to 4 m, 4 to 6 m, 6 to 8 m, 8 to 10 m, >10 m. We investigated the vegetation in this area to measure the influence of groundwater table depth on plant diversity and species ecological niche. The results indicated that plant diversity was highest at the 2 to 4 m groundwater table depth, followed by that at 4 to 6 m, and then that at 0 to 2 m. When the groundwater depth dropped to below 6 m, species diversity decreased dramatically, and the slope of Hill's index tended to level off. The ecological niche of the major species in this area initially expanded as the groundwater level dropped. The widest niche appeared at the 4 to 6 m groundwater table depth and gradually narrowed with deepening groundwater. Ecological niche analysis also revealed that the 4 to 6 m groundwater table depth was associated with the lowest degree of niche overlap and the richest variety of species. Our findings indicate that in the lower reaches of the Tarim River, the groundwater table depth must be a minimum of 6 m for vegetation restoration; it should be maintained at 2 to 4 m in the vicinity of the water path, and at 4 to 6 m for the rest of this arid area.     

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T _s) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–T _s space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.     

Weed mapping in cotton using ground-based sensors and GIS

Site-specific weed management presupposes the careful monitoring and mapping of weed infestation areas. Cut-edge sensor technologies coupled with geographical information systems (GIS) provide the means for reliable decision-making concerning weed management even in sub-field level. In present research, two different spectral sensing systems were engaged in order to digitally map weed patches as grown in four different cotton fields in Central Greece. The systems used were a set of two Crop Circle multispectral sensors ACS-430 and a digital camera Nikon D300S. The spaces between cotton rows were scanned and photographed with the two systems accordingly. Raw recorded data were stored and analyzed in GIS environment producing spatially interpolated maps of red-edge normalized difference vegetation index (NDVI) and weed cover percentage values. Both mapping approaches were satisfactorily related to weed distribution as occurred in the fields; however, the photographic method tended to underestimate weed populations. Correlation of red-edge NDVI and weed cover values, at the points where photographs were taken, as revealed by Pearson’s correlation coefficient was high (r?>?0.83) and statistically significant at the 0.01 level. A first-degree linear equation adequately modeled (R²?>?0.7) the between value pair relations, strengthening the validity of the two methodologies in spatially monitoring weed patches. The methodologies and the technologies used in the study can be used for yearly mapping weed flora in cotton cultivation and potentially constitute a means of rationalizing herbicide application in terms of doses and spatio-temporal decision-making.     

Land Cover Changes as a Result of Environmental Restrictions on Nitrate Leaching in Dairy Farming

Nitrate leaching forms an important environmental problem because it causes pollution of groundwater and surface water, and adds to already problematic eutrophication. This study analyses the impact of reductions in nitrate leaching on land cover decisions of dairy farms, of which the activities make an important contribution to nitrate leaching. As the level of nitrate leaching depends on groundwater depth as well as on the supply of nitrogen, spatial variation in groundwater levels will cause a spatial variation in land cover under restrictions on nitrate leaching. A non-linear partial optimisation model for the economic and ecological aspects of the problem were used to show how land cover and dairy farms' financial balances change when nitrate losses are reduced. The model is spatially explicit, and describes nitrate leakage and yields of maize and grass as a function of groundwater depth, including the effects of various grazing systems. The model analyses the decisions of a risk neutral agent who minimises costs under the following constraints: (i) production, feed requirements and mass balances for fodder; (ii) constraints for nitrate leaching. Economic costs are attributed to increased costs of fodder and processing of manure when nitrate restrictions are tightened. An important result of the study is the variation in compliance costs and land cover for maize and grass production brought about by spatial variation in groundwater depth. While the effects are negligible for some shallow groundwater classes, it is extremely difficult in other classes – if not impossible – to obtain the EU standard of maximum admissible losses of 34 kg N ha^–1 at low costs. The study shows an important reduction in land cover by maize.     

基于RS和GIS技术的贵州省植被生态环境监测分析

为阐明贵州省植被生态环境变化的整体状况,基于RS和GIS技术,应用美国国家航空航天局最新的全球植被指数变化研究数据（GIMMS）,通过计算月归一化植被指数（NDVI）变化率,并对研究区一元线性回归模拟,分析了贵州省1982年-2003年的地表植被覆盖。结果表明：22年来,研究区植被覆盖呈增加趋势,表明贵州省植被生态环境向好的方向发展;贵州省平均植被覆盖在春季和秋季呈上升趋势,夏季和冬季呈下降趋势,其中春季对植被覆盖总变化量的贡献最大;植被覆盖程度增减因区域不同而异,变化程度呈增加的区域主要位于贵,ki-I省的中部地区;变化程度呈减小的区域分布在贵州省的四周边缘。     

Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography

We assess the feasibility of using airborne imagery for Buffel grass detection in Australian arid lands and evaluate four commonly used image classification techniques (visual estimate, manual digitisation, unsupervised classification and normalised difference vegetation index (NDVI) thresholding) for their suitability to this purpose. Colour digital aerial photography captured at approximately 5 cm of ground sample distance (GSD) and four-band (visible–near-infrared) multispectral imagery (25 cm GSD) were acquired (14 February 2012) across overlapping subsets of our study site. In the field, Buffel grass projected cover estimates were collected for quadrates (10 m diameter), which were subsequently used to evaluate the four image classification techniques. Buffel grass was found to be widespread throughout our study site; it was particularly prevalent in riparian land systems and alluvial plains. On hill slopes, Buffel grass was often present in depressions, valleys and crevices of rock outcrops, but the spread appeared to be dependent on soil type and vegetation communities. Visual cover estimates performed best (r ² 0.39), and pixel-based classifiers (unsupervised classification and NDVI thresholding) performed worst (r ² 0.21). Manual digitising consistently underrepresented Buffel grass cover compared with field- and image-based visual cover estimates; we did not find the labours of digitising rewarding. Our recommendation for regional documentation of new infestation of Buffel grass is to acquire ultra-high-resolution aerial photography and have a trained observer score cover against visual standards and use the scored sites to interpolate density across the region.     

Zoning for eco-geological environment before mining in Yushenfu mining area,northern Shaanxi,China

Zoning for the eco-geological environment (EGE) aims to protect and improve the regional ecological environment. It is the basis for evaluating the ecological characteristics of a mining area prior to mining activities and has the purpose of implementing water-preserved mining according to zoning type. In this study, four EGE types were proposed following field investigation in the Yushenfu mining area: oasis type with phreatic water and bottomland in desert (OTPWBD), oasis type with surface water and valley river (OTSWVR), loess gully type with surface runoff (LGTSR), and regional deep groundwater enrichment type (RDGET). Nine EGE evaluation indices were selected: rainfall, evaporation capacity, Luohe formation thickness, surface elevation, Sara Wusu aquifer water abundance, surface lithology, topography, slope, and normalized difference vegetation index (NDVI). Remote sensing technology and geographic information systems were first used to generate the evaluation index thematic maps. Then, the weight of each evaluation index was determined based on an analytic hierarchy process (AHP). Third, the index weight was used to form an improved weighted fuzzy C s clustering algorithm, and EGE zones were assigned using the MATLAB computing platform. For comparison, the AHP was also adopted for EGE zoning and a map of zoning differences was obtained. Finally, EGE field surveys of typical mines were carried out, which verified that EGE zoning using fuzzy clustering was accurate and reasonable.     

Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data

The estimation of vegetation coverage is essential in the monitoring and management of arid and semi-arid sandy lands. But how to estimate vegetation coverage and monitor the environmental change at global and regional scales still remains to be further studied. Here, combined with field vegetation survey, multispectral remote sensing data were used to estimate coverage based on theoretical statistical modeling. First, the remote sensing data were processed and several groups of spectral variables were selected/proposed and calculated, and then statistically correlated to measured vegetation coverage. Both the single- and multiple-variable-based models were established and further analyzed. Among all single-variable-based models, that is based on Normalized Difference Vegetation Index showed the highest R (0.900) and R ² (0.810) as well as lowest standard estimate error (0.128024). Since the multiple-variable-based model using multiple stepwise regression analysis behaved much better, it was determined as the optimal model for local coverage estimation. Finally, the estimation was conducted based on the optimal model and the result was cross-validated. The coefficient of determination used for validation was 0.867 with a root-mean-squared error (RMSE) of 0.101. The large-scale estimation of vegetation coverage using statistical modeling based on remote sensing data can be helpful for the monitoring and controlling of desertification in arid and semi-arid regions. It could serve for regional ecological management which is of great significance.     

Assessment of phenol infiltration resilience in soil media by HYDRUS-1D transport model for a waste discharge site

The movement of contaminants through soil imparts a variety of geo-environmental problem inclusive of lithospheric pollution. Near-surface aquifers are often vulnerable to contamination from surface source if overlying soil possesses poor resilience or contaminant attenuation capacity. The prediction of contaminant transport through soil is urged to protect groundwater from sources of pollutants. Using field simulation through column experiments and mathematical modeling like HYDRUS-1D, assessment of soil resilience and movement of contaminants through the subsurface to reach aquifers can be predicted. An outfall site of effluents of a coke oven plant comprising of alarming concentration of phenol (4–12.2 mg/L) have been considered for studying groundwater condition and quality, in situ soil characterization, and effluent characterization. Hydrogeological feature suggests the presence of near-surface aquifers at the effluent discharge site. Analysis of groundwater of nearby locality reveals the phenol concentration (0.11–0.75 mg/L) exceeded the prescribed limit of WHO specification (0.002 mg/L). The in situ soil, used in column experiment, possess higher saturated hydraulic conductivity (K _S ?=?5.25?×?10^?4 cm/s). The soil containing 47 % silt, 11 % clay, and 1.54 % organic carbon content was found to be a poor absorber of phenol (24 mg/kg). The linear phenol adsorption isotherm model showed the best fit (R ²?=?0.977, RMSE?=?1.057) to the test results. Column experiments revealed that the phenol removal percent and the length of the mass transfer zone increased with increasing bed heights. The overall phenol adsorption efficiency was found to be 42–49 %. Breakthrough curves (BTCs) predicted by HYDRUS-1D model appears to be close fitting with the BTCs derived from the column experiments. The phenol BTC predicted by the HYDRUS-1D model for 1.2 m depth subsurface soil, i.e., up to the depth of groundwater in the study area, showed that the exhaustion point was reached within 12 days of elapsed time. This clearly demonstrated poor attenuation capacity of the soil to retard migration of phenol to the groundwater from the surface outfall site. Suitable liner, based on these data, may be designed to inhibit subsurface transport of phenol and thereby to protect precious groundwater from contamination.     

A method to the impact assessment of the returning grazing land to grassland project on regional eco-environmental vulnerability

The Chinese government has conducted the Returning Grazing Land to Grassland Project (RGLGP) across large portions of grasslands from western China since 2003. In order to explore and understand the impact in the grassland's eco-environment during the RGLGP, we utilized Projection Pursuit Model (PPM) and Geographic Information System (GIS) to develop a spatial assessment model to examine the ecological vulnerability of the grassland. Our results include five indications: (1) it is practical to apply the spatial PPM on ecological vulnerability assessment for the grassland. This methodology avoids creating an artificial hypothesis, thereby providing objective results that successfully execute a multi-index assessment process and analysis under non-linear systems in eco-environments; (2) the spatial PPM is not only capable of evaluating regional eco-environmental vulnerability in a quantitative way, but also can quantitatively demonstrate the degree of effect in each evaluation index for regional eco-environmental vulnerability; (3) the eco-environment of the Xianshui River Basin falls into the medium range level. The normalized difference vegetation index (NDVI) and land use cover and change (LUCC) crucially influence the Xianshui River Basin's eco-environmental vulnerability. Generally, in the Xianshui River Basin, regional eco-environmental conditions improved during 2000 and 2010. The RGLGP positively affected NDVI and LUCC structure, thereby promoting the enhancement of the regional eco-environment; (4) the Xianshui River Basin divides its ecological vulnerability across different levels; therefore our study investigates three ecological regions and proposes specific suggestions for each in order to assist in eco-environmental protection and rehabilitation; and lastly that (5) the spatial PPM established by this study has the potential to be applied on all types of grassland eco-environmental vulnerability assessments under the RGLGP and under the similar conditions in the Returning Agriculture Land to Forest Project (RALFP). However, when establishing an eco-environmental vulnerability assessment model, it is necessary to choose suitable evaluation indexes in accordance with regional eco-environmental characteristics.