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The Grain for Green Project (GGP) has considerably improved the vegetation cover of the Loess Plateau, as well as changed the carbon and water coupling process of local vegetation to a certain extent. Water use efficiency (WUE) is a crucial measure for evaluating ecosystem responses to global climate change and is a key indicator of the carbon–water coupling between terrestrial ecosystems and the environment. A comprehensive understanding of the impact of vegetation reconstruction on WUE on the Loess Plateau is of great significance to the vegetation growth and contribution to sustainable of the Loess Plateau. In recent years, scholars have gained a more comprehensive understanding of the distribution and drivers of WUE on the Loess Plateau. However, through the study of carbon and water coupling in the Loess Plateau, it is found that the effects of different vegetation restoration levels on WUE are still to be studied in depth in terms of spatial and temporal heterogeneity and long timeseries. In this paper, we analyzed the trends of Normalized Difference vegetation cover (NDVI) and WUE from 2001 to 2010 and 2011 to 2020, respectively, to research at the WUE of the vegetation in this area in relation to vegetation restoration. It was found that the Loess Plateau's vegetation WUE rose from 2001 to 2020 at a rate of 0.023 g C kg−1 H2O per year, and that the increase from 2011 to 2020 was more significant than the growth from 2000 to 2010. The Loess Plateau's area with a growing trend in vegetation water use rate increased from 77.12% in 2001–2010 to 88.63% in 2011–2020, with the majority of the increased area occurring in the northeastern Inner Mongolia region. After 20 years of the reforestation project, the area where NDVI and WUE increased simultaneously accounted for 71.54% of the Loess Plateau, the area where NDVI increased but WUE decreased accounted for 10.95% of the Loess Plateau, and the area where NDVI increased but WUE decreased accounted for 7.15% of the Loess Plateau. The correlation between temperature precipitation and WUE was not significant for the whole Loess Plateau, further indicating that the increase in vegetation cover was the main reason for the increase in vegetation water efficiency. Therefore, the effect of vegetation cover on WUE should be fully considered when vegetation restoration is carried out on the Loess Plateau. [ABSTRACT FROM AUTHOR]

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[Objective] The present paper systematically studied the relationship between vegetation cover and surface temperature, and the temperature effect of forest land in small area. [Method] Based on the remote sensing image data of Wuchuan County in Inner Mongolia from 2000 to 2015 and the distribution map of public welfare forest in the corresponding years, the vegetation coverage and surface temperature data were extracted based on the pixel dichotomy model and the radiation transfer equation. Through the spatial analysis function of Arcgis10. 2, the changes of vegetation cover and surface temperature and the temperature effect of forest land in Wuchuan County were analyzed, and the relationship between them was studied by contribution value calculation and correlation analysis. [Result] The vegetation coverage of Wuchuan County was good, the low and middle coverage accounted for the main position, followed by high coverage. The vegetation development was good in 15 years, and the vegetation area of high coverage increased by 522. 13 km² . The surface temperature decreased year by year, and the temperature difference decreased to 17. 2 °C at the end of the study, and the development was stable. The effect of vegetation cover on surface temperature is mainly reflected in the middle and high coverage. There is a significant negative relationship between vegetation cover and surface temperature. Forest resources in Wuchuan County can effectively reduce surface temperature. The temperature difference between forest land and non-forest land is obvious, and the cooling effect of forest land decreases with the increase of distance. Effective cooling range is O -150 meters. Forest land has the best cooling effect, followed by shrub land and sparse woodland. [Conclusion] The development of vegetation coverage in Wuchuan County is good, the surface temperature is controlled, and the forest land a certain cooling effect [ABSTRACT FROM AUTHOR]

Desertification causes not only a reduction of vegetation cover and surface moisture but also the expansion of sandy land. Considering that the modified soil adjusted vegetation index (MSAVI) is linearly related to canopy cover, albedo plays a significant role in the surface energy balance, and the sandy feature index (SFI) can identify sandy land. This paper proposed a desertification monitoring index (DMI) based on MSAVI, albedo and SFI to accurately monitor desertification. In addition, three classification methods (quantile grading, the susceptibility index segmentation method, and Jenks Natural Breaks) were used to divide the DMI into non-desertification, mild desertification, moderate desertification, and severe desertification. The relative exponential entropy model was adopted to select the optimal method for desertification classification. Spatiotemporal distributions of desertification in Duolun County were finally observed from 2005 to 2020. The results show that (1) although MSAVI, albedo and SFI have their own advantages in monitoring desertification, a single indicator is not suitable for various land uses. The DMI can combine the advantages of MSAVI, albedo and SFI in desertification monitoring and is more consistent with the real desertification levels. (2) Compared with quantile grading and susceptibility index segmentation methods, Jenks Natural Breaks has the smallest relative exponential entropy (2.124), with an overall classification accuracy of 88.21%, indicating that Jenks Natural Breaks is an optimal method for desertification monitoring. (3) Although approximately 1708.60 km2 of desertification areas have improved from 2005 to 2020, accounting for 44.23% of the whole area, the remaining areas are still under fluctuating or degraded conditions. This shows that the local government has made some progress in desertification protection, but the areas under fluctuating or degraded conditions still require protection or management. • Constructing the desertification monitoring index (DMI) by integrating multidimensional spatial information. • Using relative exponential entropy model to evaluate desertification classification methods. • Desertification protection categories were divided into four regions, and management strategies were suggested. [ABSTRACT FROM AUTHOR]

High-resolution Corona imagery acquired by the United States through spy missions in the 1960s presents an opportunity to gain critical insight into historic land cover conditions and expand the timeline of available data for land cover change analyses, particularly in regions such as Northern China where data from that era are scarce. Corona imagery requires time-intensive pre-processing, and the existing literature lacks the necessary detail required to replicate these processes easily. This is particularly true in landscapes where dynamic physical processes, such as aeolian desertification, reshape topography over time or regions with few persistent features for use in geo-referencing. In this study, we present a workflow for georeferencing Corona imagery in a highly desertified landscape that contained mobile dunes, shifting vegetation cover, and a few reference points. We geo-referenced four Corona images from Inner Mongolia, China using uniquely derived ground control points and Landsat TM imagery with an overall accuracy of 11.77 m, and the workflow is documented in sufficient detail for replication in similar environments. [ABSTRACT FROM AUTHOR]

Soil salinization is a serious restrictive factor affecting sustainable agricultural development. In order to explore the effect of Fractional Vegetation Cover (FVC), we monitored soil salinization in sites different vegetation coverage in Jiefangzha Irrigation District in Inner Mongolia using satellite remote sensing. From May to August 2018, we carried out field sampling at different depths in each month, and calculated FVC and spectral covariates using GF‐1 satellite images in the corresponding sampling period. Based on the FVC division criteria for Inner Mongolia, we took the following steps: (a) setting up a control treatment A (the full data with undivided FVC, TA) and experimental treatments B (bare land, TB), C (mid‐low FVC, TC), D (mid FVC, TD) and E (high FVC, TE); (b) conducting the Best Subset Selection (BSS) for all spectral covariates at different depths of each treatment; and (c) constructing the Soil Salt Content (SSC) inversion models using partial least square regression (PLSR), Cubist, and Extreme Learning Machine (ELM). The results indicated that (a) classifying FVC could improve the stability and predictive ability of the models; (b) the performance of the three modeling methods were different (Cubist was the best, ELM next and PLSR the poorest); (c) the optimal inversion models for TB, TC and TE were constructed by Cubist at 0–20, 0–40 and 0–20 cm, and for TD was constructed by ELM at 0–60 cm, respectively. The results can provide references for soil salinization prevention and agricultural production in Jiefangzha Irrigation District and other areas with the similar vegetation cover. [ABSTRACT FROM AUTHOR]

Naturally growing vegetation often suffers from the effects of drought. There exists a vast number of drought indices (DI's) to assess the impact of drought on the growth of crops and naturally occurring vegetation. However, assessing the fitness of these indices for large areas with variable vegetation cover is often problematic because of the absence of adequate spatial information. In this study, we compared six DI's to NDVI (the normalized difference vegetation index), a common indicator of vegetation occurrence and health based on satellite-acquired reflectance data. The study area covers an aridity gradient from forests to deserts along a 2,400-km-long section across the Inner Mongolia Autonomous Region of China. On an annual timescale, standardized precipitation index (SPI) was the most appropriate in assessing drought in steppes and deserts. On a seasonal timescale, the self-calibrated Palmer drought severity index (scPDSI) displayed the greatest sensitivity during the summer, but not during the other seasons. On a monthly timescale, scPDSI demonstrated the greatest sensitivity to the various vegetation zones (i.e., forests, steppes, and deserts) in June and July. Further analysis indicated that summer drought had a lag-effect on vegetation growth, which varied from one to six months according to the specific vegetation cover. The mixed response of DI's to NDVI and the lag-effect in transitional vegetation on annual, seasonal, and monthly timescales were ascribed to differences in DI definition and the dominant plant species within the transitional cover. The current study has the potential to inform the drafting of selection criteria of DI's for the study of drought-related impact on naturally growing vegetation at timescales from month to year. [ABSTRACT FROM AUTHOR]

ABSTRACT Vegetation is sensitive to changes in the ecological environment in arid and semiarid regions, so information on the dynamics of vegetation cover changes can provide important information for ecological environmental protection and early warning of ecosystem degradation. With the SPOT/VEGETATION normalised difference vegetation index dataset of the typical semiarid land in Inner Mongolia (IM) during 1998-2008, this study applied an integrated statistical method combing asymmetric Gaussian filtering, seasonal Kendall test, R/S analysis, correlation analysis and regression analysis, to investigate the impact of climatic factors on trends in vegetation cover. The main findings are as follows: (1) Over the 1998-2008 period, the vegetation coverage is relatively stable in IM, with only 24.5% of the total area exhibiting a significant variation in cover. The spatial distribution of the vegetation cover change has the following regional characteristics: in the northeast forest region, the vegetation cover is stable; in the middle steppe region, significant changes are observed and in the southwest desert region, the vegetation exhibits significant degradation. (2) Normalised difference vegetation index time series in most regions of IM reveal a vegetation change trend. In the high vegetation covered regions, the change trend will be reversed, whereas in the low vegetation covered regions, the original change trend will be preserved. (3) Analysis of correlation coefficients and stepwise linear regression reveals relationships between vegetation change and climatic factors. Temperature and precipitation have a direct influence on vegetation change, acting as the main climatic driving forces for the regional vegetation evolution. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We map temporal-spatial variation of vegetation cover in the grassland of Hulun Buir in northern China by analyzing remotely sensed data from MODIS. Results from our study indicate that vegetation cover in the grassland steadily decreased during 2000–09, with relatively high variations in both spatial and temporal dimensions. Average vegetation fractional cover (PV) was 0.56 for this period, with a maximum of 0.64 in 2002 and a minimum of 0.43 in 2007 when a massive drought occurred. In the early 2000s, the cover was approximately ∼0.60, dropping to ∼0.52 in 2007–09. The percentage of both dense (0.50 ≤ PV < 0.70) and very dense (PV ≥ 0.70) categories of grass gradually decreased by 6.58% during 2000–09, while the area of grasslands following into the bare surface (PV < 0.15) and very sparse (0.15 ≤ PV < 0.25) categories expanded up to 3.84 × 106 square kilometers over these ten years. The western part of the grassland where temperate steppe prevails had lower vegetation cover than the eastern part where lowland meadow and mountain meadow dominate. This variation of vegetation helps to understand ecosystem degradation on the grassland discussed in several studies though precipitation shortage and intensive grazing activities are the driving forces for the vegetation variation. Therefore, monitoring of vegetation change on the grassland is imperative for making sound environmental policies. [ABSTRACT FROM PUBLISHER]

The rapid, accurate, and real-time estimation of crop coefficients at the farm scale is one of the key prerequisites in precision agricultural water management. This study aimed to map the maize crop coefficient (Kc) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the Kc is based on multispectral images of high spatial resolution taken using an unmanned aerial vehicle (UAV). The analysis was performed on five experimental plots using Kc values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the Kc, the fraction of vegetation cover (fc) derived from the normalized difference vegetation index (NDVI) was used to compare with field measurements, and the stress coefficients (Ks) calculated from two vegetation index (VI) regression models were compared. The results showed that the NDVI values under different levels of deficit irrigation had no significant difference in the reproductive stage but changed significantly in the maturation stage, with a decrease of 0.09 with 72% water applied difference. The fc calculated from the NDVI had a high correlation with field measurement data, with a coefficient of determination (R2) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (TCARI) to renormalized difference vegetation index (RDVI) and TCARI to soil-adjusted vegetation index (SAVI) were used, respectively, to establish two types of Ks regression models to retrieve Kc. Compared to the TCARI/SAVI model, the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc, with R2 and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to Kc calculated from on-site measurements, the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops. Overall, use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale. [ABSTRACT FROM AUTHOR]