Combating land degradation through human efforts: Ongoing challenges for sustainable development of global drylands.

Drylands, as highly vulnerable ecosystems, support environmental functions and human well-being. Nevertheless, widespread land degradation and desertification present significant global and regional environmental challenges, with limited consensus on their area and degree. This study used time-series vegetation productivity and meteorological data from 2000 to 2020 to quantify global land degradation trends and driving factors in drylands. The results show a notable restoration of land degradation in drylands worldwide, with the area of improved land exceeding the degraded area by 1.4 times, although the threat of degradation persists. India and China emerge as pioneers in effective land improvement strategies, offering valuable experiences for other regions. Combined effects, as quantitatively distinguished by our established model, dominate the degradation and improvement processes. Notably, human activities play a decisive role in influencing land degradation trends, with the potential for either exacerbation or reversal. This study provides new perspectives on environmental health and human activities from global and regional observations. Finally, our research provides scientific support for desertification control and contributes to the overall advancement of the SDGs globally.

Promoting Surface Electric Conductivity for High-Rate LiCoO2.

The cathode materials work as the host framework for both Li+ diffusion and electron transport in Li-ion batteries. The Li+ diffusion property is always the research focus, while the electron transport property is less studied. Herein, we propose a unique strategy to elevate the rate performance through promoting the surface electric conductivity. Specifically, a disordered rock-salt phase was coherently constructed at the surface of LiCoO2 , promoting the surface electric conductivity by over one magnitude. It increased the effective voltage (Veff ) imposed in the bulk, thus driving more Li+ extraction/insertion and making LiCoO2 exhibit superior rate capability (154 mAh g-1 at 10 C), and excellent cycling performance (93 % after 1000 cycles at 10 C). The universality of this strategy was confirmed by another surface design and a simulation. Our findings provide a new angle for developing high-rate cathode materials by tuning the surface electron transport property.

Identification of Typical Ecosystem Types by Integrating Active and Passive Time Series Data of the Guangdong-Hong Kong-Macao Greater Bay Area, China.

The identification of ecosystem types is important in ecological environmental assessment. However, due to cloud and rain and complex land cover characteristics, commonly used ecosystem identification methods have always lacked accuracy in subtropical urban agglomerations. In this study, China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was taken as a study area, and the Sentinel-1 and Sentinel-2 data were used as the fusion of active and passive remote sensing data with time series data to distinguish typical ecosystem types in subtropical urban agglomerations. Our results showed the following: (1) The importance of different features varies widely in different types of ecosystems. For grassland and arable land, two specific texture features (VV_dvar and VH_diss) are most important; in forest and mangrove areas, synthetic-aperture radar (SAR) data for the months of October and September are most important. (2) The use of active time series remote sensing data can significantly improve the classification accuracy by 3.33%, while passive time series remote sensing data improves by 4.76%. When they are integrated, accuracy is further improved, reaching a level of 84.29%. (3) Time series passive data (NDVI) serve best to distinguish grassland from arable land, while time series active data (SAR data) are best able to distinguish mangrove from forest. The integration of active and passive time series data also improves precision in distinguishing vegetation ecosystem types, such as forest, mangrove, arable land, and, especially, grassland, where the accuracy increased by 21.88%. By obtaining real-time and more accurate land cover type change information, this study could better serve regional change detection and ecosystem service function assessment at different scales, thereby supporting decision makers in urban agglomerations.

Assessing impacts of the Ecological Retreat project on water conservation in the Yellow River Basin.

The Ecological Retreat (ER) project significantly impacts water conservation functions in the Yellow River Basin (YRB). However, studies on the impact and benefit of different ER modes on ecological water effects still lack systematic and integral disclosure. In our study, CNLUCC (China land use/cover data sets) and the Markov transition matrix were used to simulate land pattern changes from 2000 to 2018. Water yield was chosen as an indicator of water conservation to explore the impact of the ER project by using the water yield module of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. After the ER project, the land pattern changed significantly because massive cropland was transformed into forest and grassland. Thus, the total water yields of the YRB increased by 46.32 × 103 hm3 from 2000 to 2018, and the overall water yield benefit was 12.39% larger than the water loss. Forest rehabilitation (FR) showed the highest average water yield capacity, while grassland restoration (GR) exhibited the most incredible total water conservation benefit. Wetland recovery (WLR) manifested a great capacity to improve average water yield, but its total benefit was far less than FR and GR, while waterbody recovery (WBR) showed a negative impact. The results indicate that FR and GR were effective restoration methods, and WLR showed great potential to improve water yield. By using the random forest and principal components analysis, precipitation (PRE), evaporation (AET and ET0), and variation of forest and grassland proved to be the most critical driving indicators affecting water yield changes. Additionally, the correlation and sensitivity between root depth (R_depth) and water yield indicate that increasing R_Depth can also enhance water conservation. The ER project provides a valuable restoration model for water yield and water conservation benefits. The results can provide theoretical support for eco-hydrology and land ecological restoration studies.

Assessment of the impact of the Poplar Ecological Retreat Project on water conservation in the Dongting Lake wetland region using the InVEST model.

The Poplar Ecological Retreat (PER) project is a significant environmental protection initiative implemented to protect the Dongting Lake wetlands ecosystem in China, and the ecological impacts of this project have gradually become a hot topic. In this study, water conservation was selected as an indicator of ecosystem function to explore the impact of the project by using the water yield module of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and geographic statistical methods. Water yield and land-use changes were quantitatively evaluated, and significant changes in the wetland landscape configuration occurred after the PER project because forest beach, moss marshland and mudflat areas were transformed into a poplar-retreat area. Thus, the proportion of the poplar-retreat area and average water yield increased. By using the principal component and cluster analysis methods, climate change and landscape patterns were shown to be the dominant driving factors affecting water conservation under stable meteorological conditions. Moreover, the landscape heterogeneity that resulted from the landscape patterns had a greater impact than climate change, which means that reducing human activities can enhance water conservation. Additionally, the correlation between landscape heterogeneity and water yield indicated that reducing landscape fragmentation and improving aggregation also benefit water conservation. Therefore, the PER project is successful at enhancing ecological functions and protecting wetland environments, and it represents a good example of maintaining ecological sustainability despite local economic performance restrictions. This project serves as an important reference for the establishment of sustainable wetland ecological policies by national governments, and the results can provide theoretical support for landscape ecology and eco-hydrology research.

Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China.

Desert vegetation plays significant roles in securing the ecological integrity of oasis ecosystems in western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation cover is necessary to guide management practices on land desertification and research into the mechanisms driving vegetation recession. In this study, nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates are investigated through comparing the performance of linear and nonlinear spectral mixture models with different endmembers applied to field spectral measurements of two types of typical desert vegetation, namely, Nitraria shrubs and Haloxylon. The main results were as follows. (1) The correct selection of endmembers is important for improving the accuracy of vegetation cover estimates, and in particular, shadow endmembers cannot be neglected. (2) For both the Nitraria shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture Model (KNSMM) with nonlinear parameters was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon plots since the nonlinear spectral mixture effects were more obvious for this vegetation type. (3) The vegetation canopy structure (planophile or erectophile) determines the strength of the nonlinear spectral mixture effects. Therefore, no matter for Nitraria shrubs or Haloxylon, the non-linear spectral mixing effects between the photosynthetic / non-photosynthetic vegetation and the bare soil do exist, and its strength is dependent on the three-dimensional structure of the vegetation canopy. The choice of linear or nonlinear spectral mixture models is up to the consideration of computational complexity and the accuracy requirement.

Estimation for sparse vegetation information in desertification region based on Tiangong-1 hyperspectral image.

In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared (NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. R2 of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.

[Accuracy comparison of BJ-1, HJ and Landsat data in the retrieval of grassland vegetation coverage, leaf area index and above ground biomass].

Domestic satellites BJ-1, HJ and the most widely used satellite Landsat were selected to systematically compare their abilities and differences on the estimation of the biophysical parameters of grassland in sandstorm source region in Beijing and Tianjin, with the combination of field-measured fractional coverage, leaf area index and aboveground biomass data. The result shows: (1) In terms of the surface reflectance, HJ-1B and Landsat have a higher correlation with biophysical parameters in red band, compared with BJ-1, while BJ-1's near infra-red band was obviously superior to HJ-1B and Landsat, (2) with respect to the vegetation indices, Landsat performed best, HJ-1B was the second, and BJ-1 was the worst, (3) compared with vegetation indices, multiple regression model can raise the estimation accuracy, BJ-1 based model improved significantly, while Landsat and HJ-1B based models were less obvious. Among them, the highest accuracy was acquired for leaf area index estimation through the BJ-1 based model (R2 = 0.61, RMSEP = 0.15). In general, domestic satellites have their own unique features, which remain a huge potential to be further tapped.

[Estimation of sparse vegetation coverage in arid region based on hyperspectral mixed pixel decomposition].

Based on Hyperion hyperspectral image data, the image-derived shifting sand, false-Gobi spectra, and field-measured sparse vegetation spectra were taken as endmembers, and the sparse vegetation coverage (< 40%) in Minqin oasis-desert transitional zone of Gansu Province was estimated by using fully constrained linear spectral mixture model (LSMM) and non-constrained LSMM, respectively. The results showed that the sparse vegetation fraction based on fully constrained LSMM described the actual sparse vegetation distribution. The differences between sparse vegetation fraction and field-measured vegetation coverage were less than 5% for all samples, and the RMSE was 3.0681. However, the sparse vegetation fraction based on non-constrained LSMM was lower than the field-measured vegetation coverage obviously, and the correlation between them was poor, with a low R2 of 0.5855. Compared with McGwire's corresponding research, the sparse vegetation coverage estimation in this study was more accurate and reliable, having expansive prospect for application in the future.

[Preliminary research on environmental characteristics of oasis-desert ecotone].

Taking Minqin Oasis in the downstream of Shiyanghe Rivers as example, the shrub characteristics of sand dune, which was closely related to the vegetation of the interface between the outside of oasis and desert, was studied in this paper. The change of vegetation coverage and dune density indicated that the vegetation coverage in the ecotone was obviously higher than that in the outside of oasis and in drift desert. There existed an area outside oasis that was obviously affected by human activies and 0-600 m away from the outside of oasis. Meanwhile, the appearing position of area outside oasis was different in different place. The naked area ratio of dune in the area outside oasis was the biggest, so that the area outside oasis was the main resource of drift sand resulting in desertification of oasis. Over extracting ground water in oasis district resulted in a forming of dropping funnel of ground water there, and resulted in a fluid of ground water from desert to oasis. Ultimately, human activites aggravated those processes to some extent.