Dynamics of algal blooms in typical low-latitude plateau lakes: Spatiotemporal patterns and driving factors.

The alpine lakes distributed on the plateau are crucial for the hydrological, and biogeochemical cycle, and also serve as a guarantee for regional economic development and human survival. However, under the influence of human interference and climate fluctuations, lakes are facing problems of eutrophication and subsequent algal blooms (ABs) with acceleration, and the development and driving factors of this phenomenon need to be considered as a whole. In this study, ten lakes located on the Yunnan-Guizhou Plateau were selected as the study area to analyze the spatiotemporal distribution of ABs and possible controlling forces.

Monitoring vegetation sensitivity to drought events in China.

The frequency and severity of drought events have increased over the decades under the influence of global warming. Continued drought increases the risk of vegetation degradation. Many studies have investigated the responses of vegetation to drought but rarely from the perspective of drought events.

A Low-Cost Approach for Batch Separation, Identification and Quantification of Microplastics in Agriculture Soil.

An increasing trend of research on microplastics (MPs) pollution in soil requires plenty of accurate data on MPs occurrence in soil samples. Efficient and economical methods of obtaining MP data are in development, especially for film MPs. We focused on MPs originating from agricultural mulching films (AMF) and presented an approach that can separate MPs in batches and identify them quickly.

[Developing biomass estimation models of young trees in typical plantation on the Qinghai-Tibet Plateau, China.].

Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity.

Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia.

The increasing frequency and intensity of droughts in a warming climate are likely to exacerbate adverse impacts on ecosystems, especially for water-limited regions such as Central Asia. A quantitative understanding of the impacts of drought on vegetation is required for drought preparedness and mitigation. Using the Global Inventory Modeling and Mapping Studies NDVI3g data and Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2015, we evaluate the vegetation vulnerability to drought in Central Asia based on a copula-based probabilistic framework and identify the critical regions and periods.

Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover.

Vegetation growth is influenced not only by climate variability but also by its past states. However, the differences in the degree of the climate variability and past states affecting vegetation growth over seasons are still poorly understood, particularly given the cumulative climate effects. Relying on the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2014, the vegetation growing season was decomposed into three periods (sub-seasons) - green-up (GSgp), maturity (GSmp), and senescence (GSsp) - following a phenology-based definition.

Monitoring land degradation and assessing its drivers to support sustainable development goal 15.3 in Central Asia.

Land degradation has become one of the most critical environmental and socioeconomic issues in the world, particularly in Central Asia. Moreover, the realization of Land Degradation Neutrality (LDN) in Central Asia faces enormous challenges in achieving the global Sustainable Development Goal 15.3 (SDG 15.

Assessing vegetation stability to climate variability in Central Asia.

The dramatic climate change has far-reaching impacts on vegetation in drylands such as Central Asia. Recent attempts to assess vegetation stability to short-term climate variability often account solely for vegetation sensitivity or resilience but ignore the composite effects of these two indicators. Meanwhile, our understanding of the vegetation stability at the seasonal scale remains insufficient.

Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan.

The expansion of urban areas due to population increase and economic expansion creates demand and depletes natural resources, thereby causing land use changes in the main cities. This study focuses on land cover datasets to characterize impervious surface (urban area) expansion in select cities from 1993 to 2017, using supervised classification maximum likelihood techniques and by quantifying impervious surfaces. The results indicate an increasing trend in the impervious surface area by 35% in Bishkek, 75% in Osh, and 15% in Jalal-Abad.

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta.

Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood.

Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990-2030).

Land use/cover (LCLU) is considered as one of the most serious environmental challenges that threatens developed and less developed countries. LCLU changes' monitoring using the integration of remote sensing (RS) and geographical information systems (GIS) and their predicting using an artificial neural network (ANN) in the western part of the Tarim River Basin (Aksu), north-western Xinjiang-China, from 1990 to 2030 have been investigated first time through satellite imageries available. The imageries of 1990, 2000, 2005, 2010, and 2015 were downloaded from GLCF and USGS websites.

Identifying climate change impacts on water resources in Xinjiang, China.

Water resources have an important role in maintaining ecological fuctions and sustaining social and economic development. This is especially true in arid and semi-arid areas, where climate change has a large impact on water resources, such as in Xinjiang, China. Using a combination of precipitation and temperature bias correction methods, we analyzed projected changes in different hydrological components in nine high-alpine catchments distributed in Xinjiang using the Soil and Water Assessment Tool (SWAT).

Disentangling the relative impacts of climate change and human activities on arid and semiarid grasslands in Central Asia during 1982-2015.

In recent decades, climate change and human activities have severely affected grasslands in Central Asia. Grassland regulation and sustainability in this region require an accurate assessment of the effects of these two factors on grasslands. Based on the abrupt change analysis, linear regression analysis and net primary productivity (NPP), the spatiotemporal patterns of grassland ecosystems in Central Asia during 1982-2015 were studied.

Spatiotemporal transition of institutional and socioeconomic impacts on vegetation productivity in Central Asia over last three decades.

Central Asia experienced substantial institutional and socioeconomic changes during the last few decades, especially the Soviet Union collapse in 1991. It remains unclear how these profound changes impacted vegetation productivity across space and time. This study used the satellite-derived normalized difference vegetation index (NDVI) and gridded climate data to examine the institutional and socioeconomic impacts on vegetation productivity in Central Asia in 1982-2015.

Monitoring land sensitivity to desertification in Central Asia: Convergence or divergence?

In Central Asia, desertification risk is one of the main environmental and socioeconomic issues; thus, monitoring land sensitivity to desertification is an extremely urgent issue. In this study, the combination of convergence patterns and desertification risk is advanced from a technical perspective. Furthermore, the environmentally sensitive area index (ESAI) method was first utilized to monitor the risk of desertification in Central Asia.

Spatial and temporal characteristics of droughts in Central Asia during 1966-2015.

In drought-prone regions like Central Asia, drought monitoring studies are paramount to provide valuable information for drought risk mitigation. In this paper, the spatiotemporal drought characteristics in Central Asia are analyzed from 1966 to 2015 using the Climatic Research Unit (CRU) dataset. Drought events, as well as their frequency, duration, severity, intensity and preferred season, are studied by using the Run theory and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month, 6-month, and 12-month timescales.

Analysis of spatial-temporal variation in NPP based on hydrothermal conditions in the Lancang-Mekong River Basin from 2000 to 2014.

Against the background of global climate change, spatial-temporal variation in net primary productivity (NPP) has attracted much attention. To analyze NPP spatial-temporal variation within the context of changes in hydrothermal conditions, the Vegetation Photosynthesis Model (VPM) is used to elucidate the mathematical relationship between NPP and hydrothermal conditions. Based on this spatial-temporal pattern of NPP and hydrothermal conditions in the Lancang-Mekong River Basin, regression statistics, an empirical model of land evaporation, and the water and thermal product index (K) are used to evaluate correlations between NPP and hydrothermal conditions in terms of their distribution pattern and interaction.

Vegetation dynamics and responses to climate change and human activities in Central Asia.

Knowledge of the current changes and dynamics of different types of vegetation in relation to climatic changes and anthropogenic activities is critical for developing adaptation strategies to address the challenges posed by climate change and human activities for ecosystems. Based on a regression analysis and the Hurst exponent index method, this research investigated the spatial and temporal characteristics and relationships between vegetation greenness and climatic factors in Central Asia using the Normalized Difference Vegetation Index (NDVI) and gridded high-resolution station (land) data for the period 1984-2013. Further analysis distinguished between the effects of climatic change and those of human activities on vegetation dynamics by means of a residual analysis trend method.

Using hyperspectral indices to measure the effect of mine dust on the growth of three typical desert plants.

To examine the influence of coal dust from mining on vegetative growth, three typical plants from near an open-pit coalmine in an arid region were selected, and their spectral signals were determined. The present study was conducted near the Wucaiwan open-pit coalmine in the East Junggar Basin in Xinjiang. We extracted nineteen vegetation indices and examined their correlation with the dust flux.

[Discussion on hyperspectral index for the estimation of cotton canopy water content].

Proper vegetation indices have decisive influences on the precision of hyperspectral estimation models for surface parameters. In the present paper, in order to find the proper hyperspectral indices for cotton canopy water content estimation, two water parameters for cotton canopy water content (EWT(canopy), equivalent water thickness; VWC, vegetation water content) and corresponding hyperspectra data were analyzed. A rigorous search procedure was used to determine the best index predictors of cotton canopy water.

[A remote sensing band simulation approach based on image spectral library].

The authors proposed an image spectral library based band simulation method. Firstly, the authors clustered the reference image which has the same class composition with the target image by using its pixel spectrum similarity. Secondly, the authors fetched sample from the reference image base on the former cluster image, and then built the image spectral library.

[Coverage extraction and up-scaling of sparse desert vegetation in arid area].

Five kinds of remote sensing inversion models, i.e., linear spectral un-mixing model, sub-pixel un-mixing model, maximal gradient difference model, and two modified maximal gradient difference models, were used to derive f(c) from remote sensing data, and the results were compared with those measured in field, aimed to select appropriate model for deriving the data of the coverage of sparse desert vegetation in arid area.