Limited research has been published on land changes and their driving mechanisms in Central Asia, but this area is an important ecologically sensitive area. Supported by Google Earth Engine (GEE), ...this study used Landsat satellite imagery and selected the random forest algorithm to perform land classification and obtain the annual land cover datasets of Central Asia from 2001 to 2017. Based on the temporal datasets, the distributions and dynamic trends of land cover were summarized, and the key factors driving land changes were analyzed. The results show that (1) the obtained land datasets are reliable and highly accurate, with an overall accuracy of 0.90 ± 0.01. (2) Grassland and bareland are the two most prominent land cover types, with area proportions of 45.0% and 32.9% in 2017, respectively. Over the past 17 years, bareland has displayed an overall reduction, decreasing by 2.6% overall. Natural vegetation (grassland, forest, and shrubland), cultivated land, water bodies and wetlands have displayed increasing trends at different rates. (3) The amount of precipitation and degree of drought are the driving factors that affect natural vegetation. The changes in cultivated land are mainly affected by precipitation and anthropogenic drivers. The effects of increasing urban populations and expanding industrial development are the factors driving the expansion of urban regions. The advantages and uncertainties arising from the land mapping and change detection method and the complexity of the driving mechanisms are also discussed.
Understanding how complex urban factors affect the Urban Heat Island (UHI) is crucial for assessing the impacts of urban planning and environmental management on the thermal environment. This paper ...investigates the relationships between two-dimensional (2D) and three-dimensional (3D) factors and land surface temperatures (LST) within the Olympic Area of Beijing in different seasons, using the boosted regression tree (BRT) model. The BRT model captures the specific contributions of each urban factor to LST in each season and across a continuum of magnitudes for this factor. The results show that these relationships are complex and highly nonlinear. The four most common dominant factors are the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), a gravity index for parks (GPI), and average building height (BH). The most important factor in spring is NDBI, with a 45.5% contribution rate. In the other seasons, NDVI is the dominant factor, with contributions of 40% in summer, 21% in autumn, and 19% in winter. NDVI has an overall negative impact on LST in spring and summer, with a quadratic nonlinear decreasing curve, but a positive one in autumn and winter. The 2D land-use variables are most strongly related to LST in summer and spring, but 3D building-related variables have stronger impacts in colder weather. The Sky View Factor (SVF), a 3D measure of urban morphology, has also strong impacts in summer and winter. Both a building-based and a DSM-based SVFs are computed. The latter accounts for buildings, bridges, and trees. In contrast to a building-based SVF, the DSM-based SVF reduces LST when it varies between 0 and 0.75, reflecting the effects of high-density tree canopies that increase shades and evapotranspiration while blocking sky view. The marginal effect curves produced by the BRT are often characterized by thresholds. For instance, the maximal NDVI effect in summer takes place when NDVI = 0.7, suggesting that a very intense green coverage is not necessary to achieve maximal thermal results. Implications for urban planning and environmental management are outlined, including the increased use of evergreen trees that provide thermal benefits in both summer and winter.
•Both 2D and 3D urban indicators explain the urban heat island (UHI).•Two measures of the Sky View Factor (SVF) are related to the UHI.•The boosted regression tree model captures factor importance and marginal effects.•The effects of urban factors on the UHI are captured across the different seasons.•Some urban planning implications of the results are outlined for different scenarios.
The Three Rivers Source Region, in the central Qinghai-Tibet Plateau, has a sensitive and fragile ecological environment. Adverse changes in climate and human activities have degraded the grassland ...ecosystems. To mitigate or reverse the degradation, alleviate rural poverty, and stimulate economic development, ecological restoration projects have been implemented. In the present study, our goal was to assess the ecological and socioeconomic effects of these programs based on land-use change, grassland NPP, and household surveys. Household data were collected using structured questionnaires in 11 villages from three counties, with average elevation above 3773 m asl. We found that the grassland degradation had been mitigated, especially after the implementation of ecological restoration programs since 2005 in a regional nature reserve in Qinghai Province. Household income depends strongly on the region's natural resources, so the grassland ecosystems are still at risk of unsustainable use. A household that understood the effects of the ecological restoration programs and had received training to participate in the programs was more willing to participate in future programs. Our findings suggest that for successful restoration, it is essential to help residents of the study area thoroughly understand the ecological restoration programs and learn the restoration techniques before implementation of such programs. This is because the participation of the residents depended strongly on both their income and their satisfaction with the ecological restoration programs.
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•Grassland degradation has been mitigated.•The pastoral's livelihoods still mainly rely on the natural resources of grassland.•Pastoral's cognition plays key effects in the successful implementation of the programs.
Understanding the relationship between urban land structure and land surface temperatures (LST) is important for mitigating the urban heat island (UHI). This paper explores this relationship within ...central Beijing, an area located within the 2nd Ring Road. The urban variables include the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Build-up Index (NDBI), the area of building footprints, the area of main roads, the area of water bodies and a gravity index for parks that account for both park size and distance. The data are captured over 8 grids of square cells (30 m, 60 m, 90 m, 120 m, 150 m, 180 m, 210 m, 240 m). The research involves: (1) estimating land surface temperatures using Landsat 8 satellite imagery, (2) building the database of urban variables, and (3) conducting regression analyses. The results show that (1) all the variables impact surface temperatures, (2) spatial regressions are necessary to capture neighboring effects, and (3) higher-order polynomial functions are more suitable for capturing the effects of NDVI and NDBI.
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•Land uses and parks simultaneously explain the urban heat island (UHI).•A gravity index measures distance and size effect of parks on surface temperatures.•Spatial regressions are used to account for spatial autocorrelation.•The effects of the vegetation and building indices NDVI and NDBI are nonlinear.•The results can be used in urban planning to mitigate the UHI at fine scales.
Land-use and land-cover changes have important effects on ecology, human systems, the environment, and policy at both global and regional scales. Thus, they are closely related to human activities. ...The extraction of more details about land-use change and grassland degradation is necessary to achieve future sustainable development in Inner Mongolia. The current study presents the patterns and processes of land-use changes over space and time, while also analyzing grassland degradation that is based on an analysis of land-use changes using a transition matrix, the Markov chain model and Moran’s I index, and a combination of long-time-scale remote sensing data as the data source. The major results indicate the following. (1) In 1990–2015, 13% (123,445 km2) of the total study area, including eight land-use types, changed. Woodland increased the most and moderate grassland decreased the most. (2) Grassland degradation, which occupied 2.8% of the total area of Inner Mongolia, was the major land-use conversion process before 2000, while, after 2000, 8.7% of the total area was restored; however, grassland degradation may still be the major ecological issue in Inner Mongolia. (3) Environmental protection policies show a close relationship with land-use conversion.
•Before 2000, human disturbance was the dominant driver of land degradation in eight out of twelve counties.•After 2000, urbanisation became the dominant driver of land degradation in nine ...counties.•The impact of human disturbance and water availability decreased after 2000, suggesting that policies have taken effect.•The Hasse diagram technique highlights dominant drivers at county level and extracts useful information for policy-making.
Land degradation occurs in all kinds of landscapes over the world, but the drivers of land degradation vary from region to region. Identifying these drivers at the appropriate spatial scale is an essential prerequisite for developing and implementing appropriate area-specific policies. In this study, we investigate nine different driving factors in three categories: human disturbance, water condition, and urbanisation. Using partial order theory and the Hasse diagram technique, we analyse the temporal and spatial dynamics of these drivers and identify the major drivers of land degradation at the county level in the Xilingol League, China. Our findings indicate that: (i) in eight out of the region’s 12 counties, human disturbance was the dominant driver responsible for land degradation up to 2000, followed by water conditions, while urbanisation was the dominant driver in only four counties; (ii) the effects resulting from human disturbance and water availability decreased after 2000, while urbanisation became the dominant driver for land degradation in seven counties. The influence of human disturbance in this region has decreased, which suggests that ecological protection policies that were designed to control population and livestock numbers have worked as intended for this region. However, land degradation has continued and new policy measures are required to ease the effect of urbanisation.
The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. ...With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time–series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 106 ha, of which the cumulative forest disturbance area reached 1.12 × 106 ha, and the cumulative forest recovery area reached 0.55 × 106 ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large–scale and high–precision dynamic monitoring of the forests in Russia’s Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested.
Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human ...activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.
Land-use and land-cover change (LUCC) are currently contested topics in the research of global environment change and sustainable change. Identifying the historic land-use change process is important ...for the new economic development belt (the Zhujiang–Xijiang Economic Belt, ZXEB). During this research, based on long-time-series land-use and land-cover data, while using a combination of a transition matrix method and Markov chain model, the authors derive the patterns, processes, and spatial autocorrelations of land-use and land-cover changes in the ZXEB for the periods 1990–2000 and 2000–2017. Additionally, the authors discuss the spatial autocorrelation of land-use in the ZXEB and the major drivers of urbanization. The results indicate the following: (1) The area of cropland reduces during the two periods, and woodland decreases after the year 2000. The woodland is the most stable land-use type in both periods. (2) Built-up land expansion is the most important land-use conversion process; the major drivers of built-up land expansion are policy intervention, GDP (gross domestic product), population growth, and rural population migration. (3) Transition possibilities indicate that after 2000, most land-use activities become stronger, the global and local Moran’s I of all land-use types show that the spatial autocorrelations have become more closely related, and the spatial autocorrelation of built-up land has become stronger. Policies focus on migration from rural to urban, and peri-urban development is crucial for future sustainable urbanization.
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•A fully unrecrystallized microstructure was achieved by the composite micro-alloying.•A novel low-Zn-Mg-content Al-7.61Zn-1.62 Mg-2.17Cu-0.12Zr-0.04Ti-0.28Y (wt.%) alloy was ...developed with a UTS of ∼ 700 MPa and 6.8 % elongation.•New insights into Al8Cu4Y phase in aluminum alloys were given.
Composite micro-alloying is an important technique for developing cost-effective, high-performance aluminum alloys. Here, the effect of combined addition of Zr, Ti and rare-earth Y on the microstructure and tensile properties of an Al-7.6Zn-1.6 Mg-2.1Cu (wt.%) alloy is systematically investigated by means of X-ray diffraction, optical microscopy, scanning electron microscopy, transmission electron microscopy and room-temperature unixal tensile tests. After adding 0.12 %Zr, 0.04 %Ti and 0.28 %Y, L12-Al3Zr/(Al,Zn)3Zr, AlMgZnTiCuFe and (Al,Zn)8Cu4Y phases were induced; the as-T6 treated microstructure of the base alloy changed from fully recrystallized to fully unrecrystallized, accompanied by strong 〈100〉 + 〈111〉 fiber textures along extrusion direction. As a result, the Al-Zn-Mg-Cu-Zr-Ti-Y alloy with low Zn and Mg contents exhibits an ultimate tensile strength of 692 MPa, yield strength of 647 MPa (∼36 % higher than the base alloy) and 6.8 % elongation. The main strengthening mechanisms responsible for this high yield strength are Orowan dislocation bypassing strengthening (∼449 MPa), fiber texture strengthening, and dispersion strengthening. Moreover, the synergistic strengthening effect of rare-earth Y and transitional element(s) Zr and/or Ti was revealed, which may be related to the formation(s) of nanoscale network-structured Al8Cu4Y and/or L12-Al3(Zr,Y) phase(s). The massive formation of the Al8Cu4Y network structure requires a narrow process window.
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