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• 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]

Changes in land use and landscapes have a direct impact on the regional eco-environment. It is of great importance to understand the change pattern of land use, landscapes, and their mechanism on the ecological quality, especially ecologically fragile areas. The northern sand-prevention belt (NSPB) is an important ecologically fragile area in China, which has a large influence on the ecological security of the entire country. Based on the land use data of the NSPB in 2000, 2010, and 2018, we studied the spatio-temporal characteristics of land-use change and change in landscape patterns. The ecological quality represented by the remote sensing-based desertification index (RSDI) was calculated using satellite images. The effects of land use and landscape patterns on RSDI were analyzed by geographic detector and geographically weighted regression. Important results include the following: (1) Land-use change in the study area was high during 2000–2010 but slower in 2010–2018. Grassland was the largest land-use type in the NSPB, and varied greatly in terms of total change and spatial location. The major change was the conversion between dense and moderate grass, with 64,860 km2 of dense grass turning into moderate grass, and 48,505 km2 changing the other way. (2) Among the four landscape metrics, patch density, area-weighted mean fractal dimension, and edge density increased, whereas the aggregation index decreased, which indicated that the landscape was developing towards heterogeneity, fragmentation, complexity, and aggregation. Spatially, the landscape metrics presented a strip distribution in the east of the NSPB. (3) The effects of various land-use types on ecological quality, from high to low, were unused land, woodland, dense grass, cropland, moderate grass, built-up land, sparse grass, and waterbody. The areas where the ecological quality was greatly affected by the landscape patterns were concentrated in the agro-pastoral ecotone and the forest-steppe ecotone. The results of this study reveal the trends of land use and landscape patterns in the NSPB over 18 years and can help to understand their mechanism on ecological quality, which is of significance for the management of this area. • LUC in the NSPB was active from 2000 to 2010 and weakened from 2010 to 2018. • The landscape was developing towards heterogeneity, fragmentation, complexity, and aggregation. • The effects of land use and landscape on ecological quality were studied by GD and GWR. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future. [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]

• The RSDI is suitable for monitoring aeolian desertification. • The areas with significant restoration (34.1%) exceeded those with significant deterioration (6%) in the NSPB. • In the areas with significant restoration, 57.4% of the land was mainly affected by human activities. The northern sand-prevention belt (NSPB) is the key area for sand control in China, and the various ecological projects conducted there are important to the Chinese strategy for ecological security. In this paper, a new remote sensing-based desertification index (RSDI) based on principal component analysis was constructed from four aspects of vegetation greenness, surface moisture, soil grain size, and surface radiation. The overall evaluation accuracy of the index was 89.2%, and the kappa coefficient was 0.80, indicating high sensitivity to different degrees of aeolian desertification and suitability for multiscale research. The coefficient of variation, Mann–Kendall test, Theil–Sen median trend analysis, and residual analysis were used to analyze the spatiotemporal changes and driving forces of the RSDI in the NSPB from 2000 to 2020. The RSDI was used to compare aeolian desertification in different subregions, land use types, and ecological project areas. The important results are as follows: (1) the trend of the average RSDI was downward, but it increased significantly in 2008–2009 and 2013–2014; (2) the RSDI was characterized by relatively high volatility in 28.9% and moderate volatility in 27.1% of the area; (3) the areas with significant restoration (34.1%) greatly exceeded those with significant deterioration (6%), whereas 59.9% of the total area was stable; and (4) within the area with significant restoration, 57.4% was primarily affected by human activities, and 42.4% was primarily affected by climate change; however, most of the area with significant deterioration (71.1%) was affected by human activities. In general, the degree of aeolian desertification in the NSPB has decreased in the past 20 years and its ecological quality has continued to recover. However, unreasonable human activities still need to be reduced, and the ecological management of areas under serious threat of desertification needs to be strengthened. [ABSTRACT FROM AUTHOR]

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]

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]

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 was found that the agricultural land expanded significantly, desert reduced from 1993 to 2019 in the oasis region in Hotan River Basin, China. • The key indicators of the coupling system of vegetation–climate, soil-adjusted vegetation index, albedo, surface temperature, and evapotranspiration were calculated and mapped their spatial-temporal dynamics. • We investigated how the different landscape patterns altered the interaction between vegetation and climate in the desert oasis ecosystem and quantified the effects with structural equation model. In the arid region, the oasis is characterized by a desert substance and a mosaic of vegetation. The biophysical processes and interactions between vegetation and the local climate in this kind of region are determined by the macroscopic structure of the ecosystems, i.e. landscape patterns. To understand how these landscape patterns impact regional hydro-heat coupling across space and evolve over time, we utilized remote sensing observational data and methods to examine the relationships among these factors. In this case, we focused on the oasis along the upstream of the Hotan River Basin in the Taklamakan Desert in Xinjiang of western China and employed the satellite imagery datasets of Landsat from 1993 to 2019 to investigate the dynamics of vegetation–climate factors. Based on the land use and cover change datasets, the landscape pattern metrics, including patch density (PD), contagion index (CONTAG), fractal dimension (FRAC), were calculated to measure the landscape features on the different aspects, i.e. the fragmentation, the connectivity, and the complexity. With the algorithm of land surface energy balance, the land surface indicators, including the soil-adjusted vegetation index (SAV I), albedo, surface irradiance temperature (T s), and evapotranspiration (ET), were calculated to represent the key process in the interaction of vegetation–climate. The temporal-spatial dynamics of the landscape patterns and the vegetation–climate metrics were mapped and demonstrated in a quantitative manner. The statistical results revealed that during the past decades, the agricultural land in the study area had significantly increased by 17 %. Grassland and shrubs had also expanded, while the desert area decreased from 82.57 % to 78.82 % of the total area, with an overall reduction rate of 1.4 %/10a. It was found the study area was getting warmer and dryer based on the general trends of T s and ET observed during the period of 1993–2019. The agricultural land had the highest PD and CONTAG , and the lowest FRAC. The agricultural land had the lowest T s and the highest ET. The results of structural equation model identified the decoupling effects of PD and CONTAG on the regional hydro-heat environment, while confirmed that FRAC had positive impact on the coupling between T s and ET. Our study bridged the landscape pattern with the regional vegetation–climate interaction and provided the suggestions for the landscape planning and management for a more sustainable arid region. [Display omitted] [ABSTRACT FROM AUTHOR]

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d ( f s t a r f m 8 ); (2) the estimation accuracy based on 8d composite datasets ( R M S E ¯ = 11.1d) is better than daily fusion datasets ( R M S E ¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The R M S E ¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation. [ABSTRACT FROM AUTHOR]

[Display omitted] • A selection strategy for base-period images of spatio-temporal fusion was proposed. • The spatio-temporal inconsistency between field samples and images was reduced. • The estimation accuracy of grassland aboveground biomass was improved. The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China. [ABSTRACT FROM AUTHOR]