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Yogurt is a popular product worldwide partly because of the health-promoting effects of the probiotics that it contains. Probiotics with high survivability constitute a promising direction for fortified yogurt products. This study aimed to prepare Bifidobacterium breve –loaded yogurt with the bacteria surviving transit to the lower part of small intestine or colon. Bifidobacterium breve beads were prepared through an ion-crosslinking method using low methoxyl pectin as the encapsulating material. Features such as encapsulation efficiency and stability during storage and passage through the simulated gastrointestinal tract were studied in vitro. A commercial starter was used for yogurt fermentation, and B. breve with or without encapsulation was added as a probiotic supplement with the starter or 3 to 4 h after fermentation. The effects of B. breve beads on yogurt characteristics were evaluated after different fermentation processes: BC, milk fermented with marketed yogurt starter; UBFF, unencapsulated B. breve added to fresh milk and then fermented; EBFF, encapsulated B. breve added to fresh milk and then fermented; UBAF, unencapsulated B. breve added after fermentation with the starter; and EBAF, encapsulated B. breve beads added 3 to 4 h after fermentation with the starter. Evaluation was based on texture, electronic nose, and electronic tongue analyses. The particle size analysis of B. breve beads showed that they were uniform, mostly spherical, 1 to 1.5 mm in diameter with encapsulating efficiency higher than 99%. Following treatment with the simulated gastrointestinal tract conditions, the number of B. breve decreased by 1.76 and 4.82 log cfu/g for B. breve beads and unencapsulated B. breve , respectively. The EBAF group showed the lowest viscosity (2,235.67 cP) at d 0, and the lower postfermentation degree was reflected by the slow increase in yogurt viscosity. All groups kept a relatively stable pH during storage. The cohesiveness values of the EBAF and UBAF groups were significantly higher than those of the other groups. The trends in texture changes within the BC, UBFF, and EBFF groups were similar, and the UBAF and EBAF groups showed similar trends. In conclusion, B. breve beads showed good stability in vitro and improved yogurt characteristics by increasing the survival rate of the encapsulated cells. Good compatibility of low methoxyl pectin beads with yogurt was also observed. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is one of the most important parameters for determining grassland canopy conditions. LAI controls numerous biological and physical processes in grassland ecosystems. Remote-sensing techniques are effective for estimating grassland LAI at a regional scale. Comparison of LAI inversion methods based on remote sensing is significant for accurate estimation of LAI in particular areas. In this study, we developed and compared two inversion models to estimate the LAI of a temperate meadow steppe in Hulunbuir, Inner Mongolia, China, based on HJ-1 satellite data and field-measured LAI data. LAI was measured from early June to late August in 2013, obtained from 326 sampling data. The back propagation (BP) neural network method proved better than the statistical regression model for estimating grassland LAI, the accuracy of the former being 82.8%. We then explored the spatio-temporal distribution in LAI ofStipa baicalensisRoshev. in the meadow steppe of Hulunbuir, including cut, grazed, and fenced plots. The LAI in the cut and grazed plots reflected the growth variations inS. baicalensisRoshev. However, because of the obvious litter layer, the LAI in the fenced plots was underestimated. [ABSTRACT FROM PUBLISHER]

• Remote sensing was used to monitor plateau wetland ecological degradation. • A composite wetland degradation index was constructed to assess wetland degradation quantitatively. • We analyzed ecological degradation process with three administrative levels. The Zoige Plateau Wetland is the largest high-altitude swamp wetland in China. It plays a key role in ecological balance and the global change. Climate change and improper utilization have consequently resulted in decreased wetland area, disrupted ecosystem structure, and wetland function degradation and function loss. The Zoige Plateau Wetland thus faces severe ecological degradation problems. In this study, selecting the Zoige Plateau Wetland as the study area, remote sensing was used to track the ecological degradation process during 2000–2015 from three aspects (area degradation, structure degradation and function degradation) and at three administrative levels (study area, county and key township). To assess the wetland degradation quantitatively, an analytic hierarchy process (AHP) was used to construct a composite wetland degradation index based on five indicators: wetland area change rate, landscape fractal dimension, landscape fragmentation, vegetation biomass and vegetation coverage. The results showed the following. (1) In terms of area change rate, the total wetland area had continuously decreased during 2000–2015 compared to that in the 1990s, and 2010 was the most significant. The frequency of negative change in the wetland area reached 87.5% in all four counties and across four time periods (2000, 2005, 2010 and 2015), and 85% at the township level. At the spatial scale, Hongyuan County had the smallest wetland area change rate. (2) In terms of structure change, the variations in landscape fractal dimension and landscape fragmentation generally exhibited similar trends at different levels. Specifically, the ecosystem structure was seriously affected in 2000, then gradually recovered in 2005 and 2010, and was again under aggravated interference in 2015. (3) In terms of function change, the above-ground biomass yield in the northeastern region was higher than that in the southern region. A low value was detected in 2000, and then the value increased and remained essentially the same in 2005 and 2010. However, the value decreased in 2015, indicating deteriorated vegetation condition. The vegetation coverage in the study area remained steady, without deterioration in a particular year. (4) In terms of the whole study area, the wetland degradation grades in the four periods were mild (2000), moderate (2005), mild (2010) and mild (2015). In brief, the wetland degradation was the most severe in 2005. Over the 2000–2015 period, the spatial degradation pattern of the Zoige Plateau Wetland progressed from the northeast to the southwest. The model built in this study could be widely applicable. And this study has also some reference value for monitoring and management of wetland degradation by remote sensing. [ABSTRACT FROM AUTHOR]

Simple Summary: Human activities have been found to be the major factor driving the changes in terrestrial ecosystems. However, large uncertainties remain in assessing the impact of human activities on terrestrial ecosystems. In consequence, we established a quantitative indicator based on terrestrial potential and actual net primary productivity (NPP), as well as the forage harvest NPP, to better reveal the spatiotemporal changes of grassland ecosystems in response to human activities in eastern Inner Mongolia, and to further explore the relationship between human activities and grassland degradation in typical ecologically fragile areas. The quantitative indicator showed that NPP loss induced by human activities is weakening in the study area, and a positive relationship between human activities and grassland degradation was found. The results of this study will provide a deeper and more comprehensive understanding of the grassland degradation influenced by human activities, and a method reference for similar research. Global enhanced human activities have deeply influenced grassland ecosystems. Quantifying the impact of human activities on grasslands is crucial to understanding the grassland dynamic change mechanism, such as grassland degradation, and to establishing ecosystem protection measures. In this study, potential net primary productivity (PNPP), actual NPP (ANPP), and the forage harvest NPP (HNPP) were employed to establish the human activities index (HAI) to reveal the spatiotemporal changes of the effects of human activities on grassland ecosystems in eastern Inner Mongolia from 2000 to 2017, and to further explore the relationship between human activities and grassland degradation. The results showed that the total average PNPP, ANPP, and HNPP of grasslands in eastern Inner Mongolia were 187.2 Tg C yr−1, 152.3 Tg C yr−1, and 8.9 Tg C yr−1, respectively, during the period of 2000 to 2017. The HAI exhibited a clear decreasing trend during the study period, with annual mean values ranging from 0.75 to 0.47, which indicates that the NPP loss induced by human activities is weakening, and this trend is dominated by the difference between potential NPP and actual NPP. About 42.4% of the study area was non-degraded grassland, and the declining grassland degradation index (GDI) indicated that the degradation grade in eastern Inner Mongolia improved from moderate to light degradation. A positive relationship was found between HAI and GDI. This relationship was more significant in Xilingol League, which is a typical ecologically fragile area, than that in Xing'an League and Hulunbuir City. [ABSTRACT FROM AUTHOR]

Grassland aboveground biomass is crucial for evaluating grassland desertification, degradation, and grassland and livestock balance. Given the lack of understanding of mechanical processes and limited simulation accuracy for grassland aboveground biomass estimation, especially at the regional scale, this study investigates a new method combining remote sensing data assimilation technology and a grassland process-based model to estimate regional grassland biomass, focusing on improving the simulation accuracy by modeling and revealing the mechanism interpretability of grassland growth processes. Xilinhot City of Inner Mongolia was used as the study area. The ModVege model was selected as the grass dynamic simulation model. A likelihood function was constructed composed of the LAI, grassland aboveground biomass, and daily measurements wherein the accumulated temperature reached ST2 (the temperature sum defining the end of reproductive growth). Then, the Markov chain Monte Carlo (MCMC) methodology was adapted to calibrate the ModVege model by maximizing the likelihood function. The time-series LAI from MOD15A3H was assimilated into the ModVege model, and the model parameters ST2 and BMGV0 (initial biomass and green vegetative tissues, respectively) were optimized at a 500 m pixel scale based on the four-dimensional variational method (4DVar) method. Compared with August 15th, the RMSE and MAPE of aboveground biomass were 242 kg/ha and 10%, respectively, after calibration. Data assimilation improved this accuracy, with the RMSE decreasing to 214 kg/ha. Overall, the aboveground grassland biomass of Xilinhot City shows spatial distribution patterns of high value in the northeast and low value in the central and southeast areas. Generally, the method implemented in this study provides an important reference for the aboveground biomass estimation of regional grassland. [ABSTRACT FROM AUTHOR]

Grassland is not only an important landscape of the Beijing–Tianjin sandstorm source control project area, but also a significant object of the Beijing–Tianjin sandstorm source control project. By taking the situation in 2000 as the base of comparison and using the established grassland vegetation growth model, the monitoring and evaluation of grassland vegetation dynamic variation in the project area from 2000 to 2010 was conducted based on MODIS 16 days NDVI data. The conclusions are as follows: (1) The comparative result of average growth between each year from 2001 to 2010 and the base year was on the good side in general; the grassland growth was good both in the early and later periods of grassland growth peak season than in the first years of the project implementation, indicating that the implementation of the Beijing–Tianjin sandstorm source project has significantly improved the growth conditions of grassland vegetation; (2) With regard to the annual dynamic variation of grassland growth, the area proportions of the grasslands, of which the average grassland growth was on the good side, fluctuated and increased slightly with the time changes in general. The area proportions of the grasslands, of which the average grassland growth was on the bad side, fluctuated and decreased in general. The area proportions of the grasslands with normal growth showed an increasing overall trend; (3) From the regional perceptive on four zones, including the northern arid grassland desertification control zone, Hunshandake sandy land control zone, the farming-pastoral area of desertified land control zone, and the water conservation zone of Yanshan hills and mountains, except that the grassland growth in the farming-pastoral area of desertified land control zone was bad, the average growth of other three zones was good each year from 2001 to 2010 compared with the base year. (4) In respect of space, the regions with big grassland growth variation in the project area were concentrated in the western and eastern sections of the northern arid grassland desertification control zone and the western section of Hunshandake sandy land control zone. The grassland growth variation in the water conservation zone of Yanshan hills and mountains and the farming-pastoral area of desertified land control zone were relatively stable. On one hand, the conclusions of this paper can evaluate the effectiveness of the project control, on the other hand, it can also provide scientific basics to grassland management departments, facilitate the rational utilization of grassland, and preserve the regional ecological balance. [ABSTRACT FROM AUTHOR]

Abstract: State and local governments in China have implemented a series of grassland protection policies to address the problem of grassland degradation. In 2003, Ningxia was the first province to implement a province-wide grazing ban. The effect of this ban is contentious at all levels of government and has become a topic of public concern. Grassland desertification is the most direct indicator of the effect of the grazing ban. We selected 14 counties and cities in north-central Ningxia as the study area. A desertification classification and grading system for Ningxia's grassland was then designed based on fieldwork and expert review. Using the Spectral Mixture Analysis (SMA) and decision-tree methods, we interpreted Landsat TM/ETM+ images of the study area during four years: 1993, 2000, 2006 and 2011. The following results were obtained: from 1993 to 2011, the area of desertified grassland in north-central Ningxia decreased gradually from 8702km2 in 1993 to 7485km2 in 2011, a decrease of 13.98%; the degree of desertification gradually decreased from 3573km2 of severely desertified grassland in 1993 to 1450km2 in 2011, a decrease of 59.41%; desertified grassland vegetation was restored rapidly during 2000–2006 and 2006–2011, reducing the total area of desertified grassland annually by 1.87 and 0.61%, respectively; finally, the area of severely desertified grassland decreased annually by 5.78 and 6.28% during 2000–2006 and 2006–2011, respectively. These results show that the region-wide grazing ban, together with other ecological engineering measures, has helped reverse desertification and promote the restoration of grassland vegetation. [Copyright &y& Elsevier]

Land use/land cover change (LUCC) is the most direct driving force of landscape pattern change. The Zoige Plateau is a natural ecosystem with the largest high-altitude swamp wetland in China and its land use pattern has undergone great changes in recent years, but how the changes of each land use type affect the landscape pattern is uncertain. Here, we used the object-oriented method to extract land use information in 2015. Then, combined with land use data, the land use change characteristics from 2000 to 2015 were analyzed. We used the correlation analysis method to analyze the effects of land use changes on landscape pattern systematically. Three key conclusions were reached. (1) Land use information for the Zoige Plateau could be extracted with high accuracy by combining the object-oriented method and support vector machine (SVM). The overall accuracy was 93.2% and the Kappa coefficient was 0.889. (2) The comprehensive dynamic degree of land use was the highest from 2010 to 2015. From 2000 to 2015, the wetland area decreased the fastest because 57.05% of the wetlands were transferred out. Construction land increased the fastest, and the transferred in area from grassland and farmland were the main reason. (3) The effects of unused land, farmland, and construction land on the overall landscape pattern were stronger than that of the other types, among which farmland had the most significant impact (with a correlation coefficient of 0.959, p < 0.001). The change of unused land was the most highly significant factor associated with the landscape area pattern, and both the water body and unused land showed strong correlations with landscape shape pattern change. This suggested that the effects of land use types occupying a relatively small area on the landscape pattern were intensified. This study will provide guidance for the environmental management of local land resources and other natural ecosystem areas. [ABSTRACT FROM AUTHOR]

• The MP-Net model aggregates context information. • The MP-Net model reduces the feature losses. • The MP-Net model efficiently and precisely classifies crops. Accurate crop classification map is of great significance in various fields such as the survey of agricultural resource, the analysis of existing circumstance on land application, the yield estimation of crop and the disaster warning. The methods based on machine learning and deep learning are popularly used in crop classification and recognition of remote sensing images. However, the crop classification task based on neural networks still faces significant challenges due to the spatial and temporal distribution of crops and the inherent characteristics of remote sensing images. Therefore, this study proposes the multi-layer pyramid crop classification network (MP-Net) to solve the above problems. To reduce the feature loss during the crop extraction, the proposed model uses the pyramid pooling module to improve the ability of global information acquisition, and the information concatenation module to retain the upper features. Using the GF-6 and Sentinel-2 satellite data, the proposed model was tested in Erhai Lake Basin and Beian City. Compared with other five deep learning models, such as FCN, SegNet, U-Net, PSPNet and DeepLabv3+, the experimental results indicate that the proposed model achieves the highest accuracy in both study areas. Meanwhile, the proposed model has the advantages of short training time and high efficiency under the same running conditions. Overall, this study is beneficial to improve the efficiency and accuracy of crop classification task in the unbalanced temporal and spatial distribution. It also brings a feasible scheme for crop classification tasks in complex growing areas. The code has been publicly available at https://github.com/Xu-Chang-Hong/MP-Net. [ABSTRACT FROM AUTHOR]

Grassland vegetation growth directly reflects plant growth conditions, and growth processes are an important component of ecological status assessments of grasslands and can provide timely guidance for agricultural production. In this study, the Xilingol Grassland in Inner Mongolia was used as the study area, and a monitoring indicator system for the remote sensing of grassland vegetation growth was established based on 16 days of Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground sampling data, which were used to assess the suitability of the indicator system. A monitoring indicator system for the remote sensing of grassland vegetation that included the modified growth index (MGI)-normalized difference vegetation index (NDVI), MGI-enhanced vegetation index (EVI), growth index (GI)-NDVI, and GI-EVI was established by using the year 2000 as the base year, two vegetation indices and difference and normalized difference methods. A model for estimating the ground growth (g) was then constructed by using expert opinion scoring and the Analytic Hierarchy Process (AHP) to determine the weights of vegetation coverage (c), height (h), and yield (y) in the ground plots, with the model calculated as follows:g = 0.2543c + 0.1848 h + 0.5609y. Additionally, the ground growth value was calculated according to the ground growth model, and the values obtained from the remote-sensing indicators in the corresponding region were subjected to a correlation analysis based on this partition. The remote-sensing growth indices suitable for temperate steppe, meadow steppe, and desert steppe regions were GI-EVI, MGI-NDVI, and GI-NDVI, respectively. Finally, vegetation growth in the Xilingol Grassland was evaluated using the optimal remote-sensing GI for each area, and the results indicated that the areas with the greatest growth improvement occurred in the temperate steppe region followed by the meadow steppe region, whereas vegetation growth improvement was insignificant in the desert steppe region. The results of this study have important significance for the economic development and ecological environmental improvement of pastoral and semi-pastoral regions and can be used to determine the optimal use and scientific management of grasslands. [ABSTRACT FROM PUBLISHER]

Desertification is treated as an important and critical environmental hazard. In the face of increasingly serious grassland desertification, China has made great efforts to combat desertification and a series of key national ecological projects has been launched. However, accurate, timely, and effective monitoring and assessment are required to determine whether these projects work well. The Horqin sandy land represents the largest area of sandy land in China. In this article, the Naiman and Ongniud Banners were studied because these contain the main desertified grassland in Horqin. Next, a desertification classification and grading system was designed for the Horqin sandy land after conducting fieldwork. Based on spectral mixture analysis (SMA) and decision-tree methods, we interpreted Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images of the study area from four years: 1985, 1992, 2001, and 2013. Overall, the following results were obtained. The total area of desertified grassland in the Naiman and Ongniud Banners increased from 5979 km2in 1985 to 9195 km2in 1992 (an increase of 53.79%) and then decreased to 7828 km2in 2001 and to 6023 km2in 2013. The changes in the areas of desertified grassland with various degrees of desertification displayed the same trends as that of the total desertified grassland area. The severely desertified grassland expanded from 1872 km2in 1985 to 3723 km2in 1992 before shrinking to 2189 km2in 2013. The annual rates of expansion of desertified grassland during the periods 1985–1992, 1992–2001, and 2001–2013 were 7.68%, −1.65%, and −1.92%, respectively, and the corresponding expansion rates of severely desertified grassland were 14.12%, −3.48%, and −1.19%, respectively. Both the desertified grasslands and the areas with various degrees of desertification displayed significant expansion during the period 1985–1992. Since 1992, this trend has reduced. During the study period, the changes in temperature and precipitation did not benefit the reversal of grassland sandy desertification. Furthermore, the growing population and expansion of livestock production and farming inhibited such reversal. However, the results presented in this article suggest that a reversal in grassland sandy desertification has been occurring since 1992. The results indicate that ecological engineering measures have helped reverse desertification and have promoted the restoration of grassland vegetation. [ABSTRACT FROM PUBLISHER]

Knowledge about grassland biomass and its dynamics is critical for studying regional carbon cycles and for the sustainable use of grassland resources. In this study, we investigated the spatio-temporal variation of biomass in the Xilingol grasslands of northern China. Field-based biomass samples and MODIS time series data sets were used to establish two empirical models based on the relationship of the normalized difference vegetation index (NDVI) with above-ground biomass (AGB) as well as that of AGB with below-ground biomass (BGB). We further explored the climatic controls of these variations. Our results showed that the biomass averaged 99.01 Tg (1 Tg=1012 g) over a total area of 19.6×104 km2 and fluctuated with no significant trend from 2001 to 2012. The mean biomass density was 505.4 g/m2, with 62.6 g/m2 in AGB and 442.8 g/m2 in BGB, which generally decreased from northeast to southwest and exhibited a large spatial heterogeneity. The year-to-year AGB pattern was generally consistent with the inter-annual variation in the growing season precipitation (GSP), showing a robust positive correlation (R2=0.82, P<0.001), but an opposite coupled pattern was observed with the growing season temperature (GST) (R2=0.61, P=0.003). Climatic factors also affected the spatial distribution of AGB, which increased progressively with the GSP gradient (R2=0.76, P<0.0001) but decreased with an increasing GST (R2=0.70, P<0.0001). An improved moisture index that combined the effects of GST and GSP explained more variation in AGB than did precipitation alone (R2=0.81, P<0.0001). The relationship between AGB and GSP could be fit by a power function. This increasing slope of the GSP–AGB relationships along the GSP gradient may be partly explained by the GST–GSP spatial pattern in Xilingol. Our findings suggest that the relationships between climatic factors and AGB may be scale-dependent and that multi-scale studies and sufficient long-term field data are needed to examine the relationships between AGB and climatic factors. [ABSTRACT FROM AUTHOR]

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 1012 g) over a total area of 66.01 × 104 km2between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R2 = 0.05,p > 0.05). (2) The mean AGB density was 28.9 g C m−2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R2 = 0.41,p< 0.001), while temperature seemed to be a minor factor (R2 = 0.14,p< 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R2 = 0.45,p< 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia. [ABSTRACT FROM PUBLISHER]

Abstract: A standard methodology is needed to recognize potentially suitable areas for sustainable bioenergy crop production. This facilitates better identification of promising crops and cropping systems, logistical and economic studies, and work needed to meet regulatory criteria. A possible approach is built upon three layers of internationally available spatial data: (1) degrading and abandoned areas, (2) potentially suitable land cover classes, (3) exclusion zones such as nature reserves and areas of high biodiversity. For China, areas identified as potentially suitable range from 1.2 to 6.0% of the national territory, depending on different levels of statistical confidence in degrading area status and allowable limits of terrestrial carbon. Verification on the ground showed that about 60% of points tested conformed to the remote suitability assessment in the scenario, which represents the results for the combination of all degrading areas and a terrestrial carbon stock limit of 200 t ha−1. A top-down approach is useful in framing potentially suitable locations, but a complementary bottom-up analysis is still required to ultimately identify areas for sustainable bio-fuel production. [Copyright &y& Elsevier]

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region. [ABSTRACT FROM AUTHOR]

The accumulation of heat and moderate precipitation are the primary factors that are used by grasslands to trigger a green-up date. The accumulated growing degree-days (AGDD) requirement over the preseason is an important indicator of the response of grassland spring phenology to climate change. This study adopted the Normalized Difference Phenology Index (NDPI), which derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), to extract annual green-up dates in the Hulun Buir grassland in China between 2001–2015. Our analysis indicated that the range (standard deviation) and trend for the green-up date were DOY (day of year) 104 to DOY 144 (10.6 days) and −2.0 days per decade. Nine point two percent of the study area had significant (p < 0.05) changes in AGDD requirements. The partial correlations between the AGDD requirements and chilling days (67.04%, pixels proportion) were negative and significant (p < 0.05). The partial correlations between the AGDD requirement and precipitation (28.87%) were positive and significant (p < 0.05). Finally, the partial correlation between the AGDD requirement and insolation (97.65%) were positive and significant (p < 0.05). The results of this study could reveal the response of vegetation to climate warming and contribute to improving the phenological mechanism model of different grassland types in future research. [ABSTRACT FROM AUTHOR]

Light detection and range (LiDAR) intensity is an important feature describing the characteristics of a target. The direct use of original intensity values has limitations for users, because the same objects may have different spectra, while different objects may have similar spectra in the overlapping regions of airborne LiDAR intensity data. The incidence angle and range constitute the geometric configuration of the airborne measurement system, which has an important influence on the LiDAR intensity. Considering positional shift and rotation angle deviation of the laser scanner and the inertial measurement unit (IMU), a new method for calculating the incident angle is presented based on the rigorous geometric measurement model for airborne LiDAR. The improved approach was applied to experimental intensity data of two forms from a RIEGL laser scanner system mounted on a manned aerial platform. The results showed that the variation coefficient of the intensity values after correction in homogeneous regions is lower than that obtained before correction. The overall classification accuracy of the corrected intensity data of the first form (amplitude) is significantly improved by 30.01%, and the overall classification accuracy of the corrected intensity data of second form (reflectance) increased by 18.21%. The results suggest that the correction method is applicable to other airborne LiDAR systems. Corrected intensity values can be better used for classification, especially in more refined target recognition scenarios, such as road mark extraction and forest monitoring. This study provides useful guidance for the development of future LiDAR data processing systems. [ABSTRACT FROM AUTHOR]