State Key Laboratory of Desert and Oasis Ecology

Convolutional neural networks (CNN) have attracted tremendous attention in the remote sensing community due to its excellent performance in different domains. Especially for remote sensing scene classification, the CNN-based methods have brought a great breakthrough. However, it is not feasible to fully design and train a new CNN model for remote sensing scene classification, as this usually requires a large number of training samples and high computational costs. To alleviate these limitations of fully training a new model, some work attempts to use the pretrained CNN models as feature extractors to build feature representation of scene images for classification and has achieved impressive results. In this scheme, how to construct feature representation of scene image via the pretrained CNN model becomes the key process. Existing studies paid a little attention to build more discriminative feature representation by exploring the potential benefits of multilayer features from a single CNN model and different feature representations from multiple CNN models. To this end, this paper presents a fusion strategy to build the feature representation of the scene images by integrating multilayer features of a single pretrained CNN model, and extends it to a framework of multiple CNN models. For these purposes, a multiscale improved Fisher kernel coding method is used to build feature representation of the scene images on convolutional layers, and a feature fusion approach based on two feature subspace learning methods [principal component analysis (PCA)/spectral regression kernel discriminant analysis and PCA/spectral regression kernel locality preserving projection] is proposed to construct final fused features for scene classification. For validation and comparison purposes, the proposed approaches are evaluated with two challenging high-resolution remote sensing datasets and shows the competitive performance compared with existing state-of-the-art baselines such as fully trained CNN models, fine tuning CNN models, and other related works.

Grazing intensity (GI) is a major determining factor that controls the functioning of rangelands and the overall nutrient cycle. The Teltele rangeland is used for communal grazing area by the local pastorals; however, to date, there is no documented study data about the impact of GI. The objective of this study was to evaluate the impacts of grazing intensity on selected soil properties in the Teltele rangeland, Ethiopia. Soil samples were collected from different GI sites using different elevation gradient and soil depth from both open grazing and bush-encroached grazing land sand-assessed soil properties. Grazing intensity, elevation, and soil depth significantly ( p < 0.05) affected both soils’ physical and chemical properties but rangeland types had no significant effect. The correlation analysis of soil characteristics with the principal component analysis axes showed significant variation. The highly weighted and correlated properties under principal component 1 (PC1) were electrical conductivity, organic carbon, total nitrogen, available phosphorus, and potassium, and under principal component 2, sand and bulk density with equal loaded value (r = −0.998), clay and silt, with silt (0.962) a more loaded one. Soil pH (0.743) demonstrated a significant ( p < 0.05) positive correlation with sodium (−0.960) at PC1 (r = 0.610). Based on our results, we recommend further model-based studies on spatial–temporal change of soil properties due to impact of grazing intensity, combined with GIS and remote sensing data to be developed for sustainable rangeland management.

ABSTRACT Using brackish and saline water is one of the most effective ways to solve the problem of water resource shortage. To rationally exploit and make use of brackish and saline water resources, the effects of drip irrigation underneath a plastic film with different salinities of irrigation water on soil salt content and cotton growth were studied at the Aksu National Field Research Station of Agro‐ecosystem in Xinjiang from 2008 to 2009. The results showed that the soil salt increases before the first irrigation of the growing period, then generally decreases during the irrigation period, and increases after the final irrigation of the growing period and before winter irrigation. In extremely arid areas, the leaching effect of rainfall is weakened. When cotton is irrigated with saline water of salinity greater than 2·24 g l −1 , the salt is accumulated at the main cotton root zone. The effect of salinity of irrigation water on cotton growth is a gradual process and is highlighted during the boll‐opening period. Leaf area index, root area index, root length density, and root weight density all decrease with an increase of salinity of irrigation water and soil salt content in the root zone. The root weight density and root length density of treatments with brackish or saline water are far less than those of treatment with fresh water. After sowing, with the increase of time, the leaf area index changes from small to large, reaches its peak at the flowering‐bolling stage, and then reverts from large to small; however, root area index, average cotton root length density, and root weight density of the 0‐ to 50‐cm soil layer continuously increase throughout the entire process. During the boll‐opening period, the vitality of the cotton root remains strong. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud contamination is one of the major barriers for wider applications of MODIS snow cover products. This study presents a cloud-removal approach, through multiday backward replacements based on Terra and Aqua daily MODIS snow cover products (MOD10A1 and MYD10A1), to generate a series of daily cloud-free snow cover products for advanced applications (MODMYD_MC). The products are evaluated using in situ snow depth data measured during 2000 to 2010 at 53 weather stations in the Heilongjiang Province, northeast China. The results show that the annual mean cloud covers of MOD10A1, MYD10A1, MODMYD_DC (the daily combination of MOD10A1 and MYD10A1), and MODMYD_MC are 50%, 54%, 35%, and 0%, mean snow covers are 6%, 6%, 10%, and 19%, and their mean agreements of snow cover mapping are 42%, 40%, 51%, and 91%, respectively. The snow-covered days (SCDs) derived from MODMYD_MC are also in good agreement (91%) with those obtained from in situ observations. The MODMYD_MC snow cover images are then used to investigate the detailed variation of snow cover in the XiaoXing’AnLing watershed. The snow-covered area in the watershed has an increasing trend in the recent decade, with the minimum present in the 2002 (hydrologic year) and the maximum present in 2010. The plains with lower elevation show shorter SCD but larger interannual variations than in the mountainous areas. This study indicates that MODMYD_MC can be applied to monitor the spatiotemporal variations of snow cover in northeast China and elsewhere in the world.

There is consistent evidence of vegetation greening in Central Asia over the past four decades. However, in the early 1990s, the greening temporarily stagnated and even for a time reversed. In this study, we evaluate changes in the normalized difference vegetation index (NDVI) based on the long-term satellite-derived remote sensing data systems of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI from 1981 to 2013 and MODIS NDVI from 2000 to 2020 to determine whether the vegetation in Central Asia has browned. Our findings indicate that the seasonal sequence of NDVI is summer > spring > autumn > winter, and the spatial distribution pattern is a semicircular distribution, with the Aral Sea Basin as its core and an upward tendency from inside to outside. Around the mid-1990s, the region’s vegetation experienced two climatic environments with opposing trends (cold and wet; dry and hot). Prior to 1994, NDVI increased substantially throughout the growth phase (April–October), but this trend reversed after 1994, when vegetation began to brown. Our findings suggest that changes in vegetation NDVI are linked to climate change induced by increased CO2. The state of water deficit caused by temperature changes is a major cause of the browning turning point across the study area. At the same time, changes in vegetation NDVI were consistent with changes in drought degree (PDSI). This research is relevant for monitoring vegetation NDVI and carbon neutralization in Central Asian ecosystems.

Ammonia-oxidizing Nitrososphaeria are among the most abundant archaea on Earth and have profound impacts on the biogeochemical cycles of carbon and nitrogen. In contrast to these well-studied ammonia-oxidizing archaea (AOA), deep-branching non-AOA within this class remain poorly characterized because of a low number of genome representatives. Here, we reconstructed 128 Nitrososphaeria metagenome-assembled genomes from acid mine drainage and hot spring sediment metagenomes. Comparative genomics revealed that extant non-AOA are functionally diverse, with capacity for carbon fixation, carbon monoxide oxidation, methanogenesis, and respiratory pathways including oxygen, nitrate, sulfur, or sulfate, as potential terminal electron acceptors. Despite their diverse anaerobic pathways, evolutionary history inference suggested that the common ancestor of Nitrososphaeria was likely an aerobic thermophile. We further surmise that the functional differentiation of Nitrososphaeria was primarily shaped by oxygen, pH, and temperature, with the acquisition of pathways for carbon, nitrogen, and sulfur metabolism. Our study provides a more holistic and less biased understanding of the diversity, ecology, and deep evolution of the globally abundant Nitrososphaeria.

Spatial variation of phenology is a central feature of global change research. Satellite remote sensing is used for continental to global monitoring due to the limitations of long-term field observations of plant phenology. A threshold method was used to estimate the start of the season, length of the season, maximum normalized difference vegetation index (NDVI), and integral NDVI for selected tree species using remote sensing based NDVI data acquired by the VEGETATION instrument on board Satellite Pour l’Observation de la Terre (SPOT VGT NDVI). Afterward, the spatial patterns in the satellite-derived phenological metrics for four dominant tree species (i.e., beech, birch, pine, and spruce) across Europe were characterized. The results indicate that: (1) The SOS occurs 1.6–2.9 days later and the average LOS is 2.7–3 days shorter per 1 deg of latitude increase from south to north. (3) The SOS occurs 0.7–1.8 days later and the LOS was 0.6–2 days shorter per 100-m increase in altitude for the four species. (4) The SOS and LOS across Europe are well correlated with the mean annual air temperature (1°C correlates with a 4.5-day advance in the SOS and a 7-day extension in the LOS). Our research is the first one to characterize the spatial and temporal variations of phenology for different tree species across Europe using remote sensing.

Extracellular polymeric substances (EPS) are important components of activated sludge and play an important role in removing heavy metals. The interaction of soluble EPS (SEPS)/bound EPS (BEPS) with Pb(II) was investigated using excitation—emission matrix (EEM) fluorescence spectroscopy and infra-red spectrometry. One protein-like fluorescence peak (peak A) was identified from the EEM spectra of SEPS and BEPS, and one aromatic protein peak (peak B) was observed in the EEM spectra of SEPS. the interaction of Pb(II) with SEPS was governed by collision sorption, while the binding of Pb(II) to BEPS was due to complexation. Functional groups of proteins, polysaccharides, lipids, and uronic acid were involved in Pb(II) adsorption to SEPS/BEPS. The organic acids in SEPS are also responsible for binding Pb(II) to SEPS. Other groups such as the phosphate group in the fingerprint zone also participated in binding of Pb(II) to EPS. There were no significant differences in the values of binding constants and conditional stability constants between SEPS and BEPS.

This paper identifies the driving forces of CO 2 emissions from 1990 to 2014 in Xinjiang's transport sector based on the logarithmic mean divisia index (LMDI) method. Then we introduce the decoupling index to further quantitatively analyze the delinking indicators on the transport sector's growth and environmental pressures. The results indicate that: 1) CO 2 emissions increased significantly with an average annual growth rate of 8.7%. On the contrary, energy intensity has declined constantly over the study period. 2) Economic growth, population size, industrial structure, internal structural and energy mix have proven to contribute to CO 2 emissions increases. Moreover, economic growth plays a critical role in the increment with a contribution of 13.23 million tons, followed by population size and internal structure. 3) Xinjiang's transport witnessed a fluctuating decoupling progress with weak decoupling as the theme. In particular, the decoupling state moved from weak decoupling in 1991-2000 with short-term volatility to weak decoupling in 2001-2010. However, the coupling relationship was strengthened during 2011-2014. 4) Energy intensity is the most important factor for explaining the dissociation in Xinjiang's transport sector. However, internal structural, industrial structure, and population size has turned out to be the obstacles in decoupling progress.

Airborne hyperspectral remote sensing data provide rapid, non-destructive, and near laboratory quality reflectance spectra for mineral mapping and lithological discrimination, thereby ushering an innovative era of remote sensing. In this study, NEO HySpex cameras, which comprise 504 spectral channels in the spectral ranges of 0.4–1.0 μm and 1.0–2.5 μm, were mounted on a delta wing XT-912 aircraft. The designed flexibility and modular nature of the HySpex aircraft hyperspectral imaging system made it relatively easy to test, transport, install, and remove the system multiple times before the acquisition flights. According to the design fight plan, including the route distance, length, height, and flight speed, we acquired high spectral and spatial resolutions airborne hyperspectral images of Yudai porphyry Cu (Au, Mo) mineralization in Kalatag District, Eastern Tianshan terrane, Northwest China. By comparing the features of the HySpex hyperspectral data and standard spectra data from the United States Geological Survey database, endmember pixels of spectral signatures for most alteration mineral assemblages (goethite, hematite, jarosite, kaolinite, calcite, epidote, and chlorite) were extracted. After a HySpex data processing workflow, the distribution of alteration mineral assemblages (iron oxide/hydroxide, clay, and propylitic alterations) was mapped using the random forest (RF) algorithm. The experiments demonstrated that the workflow for processing data and RF algorithm is feasible and active, and show a good performance in classification accuracy. The overall classification accuracy and Kappa classification of alteration mineral identification were 73.08 and 65.73%, respectively. The main alteration mineral assemblages were primarily distributed around pits and grooves, consistent with field-measured data. Our results confirm that HySpex airborne hyperspectral data have potential application in basic geology survey and mineral exploration, which provide a viable alternative for mineral mapping and identifying lithological units at a high spatial resolution for large areas and inaccessible terrains.

Groundwater-dependent vegetation (GDV) is useful as an indicator of watertable depth and water availability in north-western China. Nitrogen (N) is an essential limiting resource for growth of GDV. To elucidate how leaf N allocation and partitioning influence photosynthesis and photosynthetic N-use efficiency (PNUE), three typical GDV species were selected, and their photosynthesis, leaf N allocation and partitioning were investigated in the Taklamakan Desert. The results showed that Karelinia caspica (Pall.) Less. and Peganum harmala L. had lower leaf N content, and allocated a lower fraction of leaf N to photosynthesis. However, they were more efficient in photosynthetic N partitioning among photosynthetic components. They partitioned a higher fraction of the photosynthetic N to carboxylation and showed higher PNUE, whereas Alhagi sparsifolia Shap. partitioned a higher fraction of the photosynthetic N to light-harvesting components. For K. caspica and P. harmala, the higher fraction of leaf N was allocated to carboxylation and bioenergetics, which led to a higher maximum net photosynthetic rate, and therefore to a higher PNUE, water-use efficiency (WUE), respiration efficiency (RE) and so on. In the desert, N and water are limiting resources; K. caspica and P. harmala can benefit from the increased PNUE and WUE. These physiological advantages and their higher leaf-area ratio (LAR) may contribute to their higher resource-capture ability.

During the period of senescence of desert plant Alhagi sparsifolia Shap. the maximum photochemical quantum yield measured as variable to maximum fluorescence ratio (Fv/Fm) remained relatively high, although the number of active reaction centres per cross section (RCs) decreased significantly. The efficiency of electron acceptors beyond the primary quinone acceptor (QA) decreased. The effect of temperature and irradiance on photosystem activity was maximum after 6 d. Our results suggest that: 1) the down-regulation of photosystem activity was due to the decline of both RCs and electron acceptance between plastoquinone (PQ) and cytochrome (cyt) b6/f; 2) photosystem activity presented negative correlation with daily mean temperature, and 3) reduction of daily sunshine period and increase of temperature at noon can stimulate the speed of senescence.

为探明生态输水后地下水响应带范围及地下水恢复下生态需水量,以塔里木河下游大西海子水库至台特玛湖段为研究区,基于2000-2010年生态输水和地下水埋深分布特征,分析了塔里木河下游生态输水后两岸地下水位恢复状况,并借助遥感和地理信息系统技术对研究区生态需水量进行了研究。结果表明:塔河下游地下水位的抬升幅度与输水量的大小呈一定的正相关关系,并存在一定的时效性。2004-2010年地下水处于长期的负均衡状态,多年下降幅度明显。塔河下游英苏、喀尔达依、阿拉干和依干不及麻断面地下水响应幅度分别为1195、1050、2281 m和1000 m。历经11a输水后,塔里木河下游地下水总恢复需水量为7.06×10⁸ m³,其中,齐文阔尔河段为4.98×10⁸ m³,老塔里木河段为2.09×10⁸ m³,地下水恢复至生态水位4.5 m需要5-8a的时间。保护塔里木河下游大西海子以下所有天然植被面积(96114.09 hm²)的生态需水量为0.587×10⁸ m³,保护下游地下水响应带天然植被面积(41439.85 hm²)的生态需水量为0.21×10⁸ m³。

Understanding the Earth's energy cycle and water balance requires an understanding of the distribution of precipitation types and their total equivalent water budget estimation. The fine distribution of precipitation types over the contiguous United States (CONUS) is not yet well understood due to either unavailability or coarse resolution of previous satellite- and ground radar-based precipitation products that have difficulty in classifying precipitation. The newly available NOAA/National Severe Storms Laboratory ground radar network-based National Multi-Sensor Mosaic QPE (NMQ/Q2) System has provided precipitation rates and types at unprecedented high spatiotemporal resolution. Here, four years of 1 km/5 min observations derived from the NMQ are used to probe spatiotemporal distribution and characteristics of precipitation types (stratiform, convective, snow, tropical/warm (T/W), and hail) over CONUS, resulting in assessment of occurrence and volume contribution for these precipitation types through the four-year period, including seasonal distributions, with some radar coverage artifacts. These maps in general highlight the snow distribution over northwestern and northern CONUS, convective distribution over southwestern and central CONUS, hail distribution over central CONUS, and T/W distribution over southeastern CONUS. The total occurrences (contribution of total rain amount/volume) of these types are 72.88% (53.91%) for stratiform, 21.15% (7.64%) for snow, 2.95% (19.31%) for T/W, 2.77% (14.03%) for convective, and 0.24% (5.11%) for hail. This paper makes it possible to prototype a near seamless high-resolution reference for evaluating satellite swath-based precipitation type retrievals and also a potentially useful forcing database for energy-water balance budgeting and hydrological prediction for the United States.

The Tarim and Konqi Rivers in western China have experienced dramatic changes in streamflow and riparian vegetation due to climatic variability, land cover change, and water management including interbasin water transfers. To assess the extent and evolution of vegetation dynamics along these rivers, we use Landsat and MODIS images for land cover classification, spectral mixture analysis, and landscape phenology analysis. From 1998 to 2011, agriculture nearly tripled in extent, from 1376 to 3742 km2. Natural riparian vegetation persisted in aggregate but experienced losses (to agriculture) in some areas while expanding into barren land elsewhere. Spectral mixture analysis suggests that interbasin water transfers from the Konqi to the Tarim River increased near-channel riparian vegetation on the Tarim at the expense of vegetation on the Konqi. A time-series of MODIS images reveals a pattern of increasing and decreasing greenness across the region, including loss of vegetation in distal regions that were formerly subject to sporadic seasonal flooding but now are cut off from their water supply due to water management. These results suggest that satellite remote sensing may play a valuable role in monitoring the effects of changing land use and hydrology on riparian systems in Central Asia and other arid regions.

The Yabello rangeland is a semi-arid area in Borana, Ethiopia that is facing great degradation challenges. Increasing infestation of vegetation cover, over grazing and high seasonal variation have significantly affected the herbage composition and biomass in the Yabello rangeland. This study focused on assessing the effect of vegetation cover, grazing and season on both herbage composition and biomass in the Yabello rangeland. An experiment was conducted using randomized plots of 1 m × 1 m. Sites were selected based on vegetation cover type and grazing variation, and seasonal impacts were also assessed. Data on herbage composition, height and mass with respect to those parameters were analyzed using SAS statistical software version 9.1 (SAS Institute, 2001) and Microsoft Excel. A total of 26 grass species were recorded and Chloris roxburghiana Chrysopogon aucheri and Chrysopogon aucheri grass species showed the highest average single species cover height and biomass production, for all the sites among all parameters. As a result, those grass species are highly recommended for the rehabilitation of degraded rangeland in the study area. This study also showed that vegetation cover type grazing and seasonal variation were the key factors in determining herbage species composition, height and biomass production. Finally, we recommended that sustainable management which controls bush vegetation cover and balances grazing levels is essential for sustainable herbage production and biodiversity conservation in the area.

WRKY transcription factors are one of the largest families in plants, playing important roles in regulating plant immunity. Malus sievesii has abundant genetic diversity and can offer various and high-quality gene resources. In this study, 112 putative MsWRKY proteins were identified from a full-length transcriptome of M. sieversii during the Valsa canker disease (caused by Valsa mali). The MsWRKY proteins were phylogenetically divided into three groups (I–III). Motif compositions of the MsWRKY proteins were clustered and fifteen conserved motifs were observed. Expression pattern analysis showed that thirty-four MsWRKY transcripts strongly responded to the V. mali infection, demonstrating that MsWRKY transcripts might play different roles during the response. Functional identifications were subsequently conducted with transient expressions, demonstrating that MsWRKY16, MsWRKY21, MsWRKY70, MsWRKY74 and MsWRKY85 positively regulated the resistant response. Besides, the MsWRKY21, MsWRKY70 and MsWRKY85 were dramatically induced by salicylic acid (SA), methyl-jasmonate acid (MeJA) and 1-aminocyclopropane-1-carboxylate (ACC), indicating that they play important roles in the regulatory resistance of V. mali infection. This work provides a comprehensive understanding of the WRKY family in M. sieversii and will build a foundation for future research of the potential disease resistances MsWRKY transcripts.

Potential evapotranspiration (PET) is the capacity of the sub-surface evapotranspiration process, which is determined by weather and climate conditions. As an important component of the surface energy balance and hydrological cycle, PET determines hydrothermal transport in surface ecosystems and is an important factor in regional water resource evaluation, water use efficiency, and drought prediction. Most of the existing studies have focused on the impact of PET on the ecological environment and regional climate, providing limited information on the characteristics of the regional distribution of potential evapotranspiration itself and the associated drivers. In this study, we use the Penman-Monteith (P–M) model to calculate the PET in Akmola Oblast, combined with relevant climate data, partial correlation analysis, and structural equation modelling (SEM) to investigate the spatial and temporal distribution characteristics of PET in the study area and its driving factors, as well as the influence of meteorological activity on PET after the implementation of the Green Ring Project in the capital area of Kazakhstan. The results of the study show that: (1) The PET in Akmola State presented a decreasing trend from 1991 to 2021, with a multi-year average value of 835.87 mm. There is large heterogeneity in the spatial distribution of PET, being significantly higher in the southwestern and northeastern regions of the study area than in the central region. (2) Simple and partial correlation analyses indicate that most of the correlations between meteorological and PET were significant, with strong spatial heterogeneity in the number of biased relationships between different meteorological activity and PET. The spatial characteristics of the correlations between PET and Srad (Solar radiation), VS (wind speed), and MAT (Mean annual temperature) were similar, with the strongest correlations observed in the southwestern part of Akmola State. Furthermore, the spatial distribution of the correlations between PET and SWC (soil water content) and ST (soil temperature) was similar, with stronger correlations in the central part of the study area than elsewhere. (3) The SEM demonstrated that the main drivers of PET change across the study area are Srad (0.59) and VS (0.37). In the metropolitan area, MAP (mean annual precipitation) is also a major driver of PET change, due to the implementation of the Green Ring Project, which has increased vegetation cover and improved the local environment. The results of this study highlight the impact of climate change on PET in Akmola Oblast, Kazakhstan, contributing to a better understanding of PET evolution and providing guidance for water management planning.

Introduction: Ultraviolet (UV) radiation is believed to play a significant role in accelerating litter decomposition in water-limited ecosystems. Litter traits also influence the decomposition. However, the dominance of litter traits and ultraviolet radiation on litter decomposition in hyper-arid deserts (annual precipitation: potential evaporation < 0.05) with diverse species and seasonal variations remain unclear. Methods: To address this knowledge gap, we examined the decomposition of three dominant litter species ( Karelinia caspia , Alhagi sparsifolia , and Populus euphratica ) in the southern edge of the Taklimakan Desert, Northwest China. Results: Our results revealed that under UV radiation conditions, K. caspia , A. sparsifolia , and P. euphratica experienced mass losses of 45.4%, 39.8%, and 34.9%, respectively, and 20%, 22.2% and 17.4%, respectively under UV filtering treatment. Specifically, the loss rate of carbon and lignin under UV radiation, was 2.5 and 2.2 times higher than under UV filtering treatment, respectively. Conclusion: UV radiation did not dominate decomposition throughout the year in our study area, and the loss rate of litter traits was significantly higher in summer than in winter under UV radiation. Moreover, this photodegradation is related to the intensity of UV exposure, but not to precipitation or temperature. Surprisingly, species type had no significant effect on litter decomposition. However, when we applied a UV filtering treatment, we observed higher loss rates of nitrogen compared with the ambient treatment, suggesting the involvement of other spectra in the litter decomposition process. Overall, our findings elucidate that UV radiation is a crucial factor that affects litter mass loss. The magnitude of this effect mostly varies with the season rather than the species of litter.

Land use/cover changes(LUCC) have been regarded as a research hotspot in the field of global environment change.Quantitative evaluation on land use change is of great significance for sustainable utilization of local land resources.The Ebinur Lake watershed in Xinjiang Uygur Autonomous Region,as the study area,is a typical area of arid regions.Based on the LUCC data in 1970 and 2009,we used mathematical models and ArcGIS to analyze the change in land use/cover during this time period.The results show that the areas of cropland and construction land increased constantly from 1970 to 2007,with average annual rate of 5.21% and 5.62%,respectively.Accordingly,the areas of the other land use types decreased.The transformation between cropland and grassland,and between unused land and grassland were significant.The area of construction land experienced maximum changes and the water area minimum.Specifically,unused land,grassland,and cropland received most conversion areas from the other land use types,and unused land and grassland contributed most areas to the other types.