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Land
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Current Advances in Remote Sensing for Arid Land Ecology and...
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Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 19494

Special Issue Editors

Dr. Alim Samat

E-Mail Website
Guest Editor
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: remote sensing image processing & application; arid land resource & environment remote sensing; urban remote sensing
Prof. Dr. Anming Bao

E-Mail Website
Guest Editor
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: application of remote sensing and geographic information system; resource and environment remote sensing; remote sensing for water resource
Dr. Jingjie Wang

E-Mail Website
Guest Editor
College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Interests: arid land resource & environment remote sensing; eco-hydrology remote sensing
Prof. Dr. Zhi Li

E-Mail Website
Guest Editor
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: drought; eco-hydrological process; climate change; land surface processes; water resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Xuemei Li

E-Mail Website
Guest Editor
Faculty of Geoinformatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: remote sensing phenology; regional climate change; ecological and environmental remote sensing
Dr. Ali Darvishi Boloorani

E-Mail Website
Guest Editor
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Interests: applied geoinformatics for climate-related changes modeling of the earth; spatial-temporal analysis of climate change; water issues; agriculture; land degradation; drought; air pollution and dust storms
Special Issues, Collections and Topics in MDPI journals
Dr. Mukhiddin Juliev

E-Mail Website
Guest Editor
Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Qori Niyoziy Street, 39, Tashkent 100000, Uzbekistan
Interests: natural hazardssoil; soil erosion; hydrology; geomorphology; RS&GIS

Special Issue Information

Dear Colleagues,

Arid and semiarid lands encompass approximately 30–40% of the Earth’s surface. These areas have harsh climatic conditions and are also under significant pressure to produce food and fibers for their rapidly increasing populations, which involve a wide range of land utilization and management regimes that could result in a reduction in arid and semiarid ecosystem quality and socioeconomic development. Hence, it is of great importance to understand the effects and responses of arid and semiarid landscape diversity and dynamics on the local and regional climate, environment, atmosphere, surface energy balance, carbon exchange, and sustainable development. In this regard, this Special Issue of Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment encourages the submission of review and research papers exploring remote sensing data, products, techniques, monitoring, and assessment of the arid and semiarid land environment and predictions of future climate change risks. In particular, topics that fall within the following themes (but not limited to them) are welcomed:

  • Big remote sensing data for arid and semiarid lands;
  • Advanced machine learning techniques for land use/cover (LUCC);
  • Vegetation, water and wetland dynamics;
  • Disaster monitoring and evaluation (e.g., drought, flood, sand and dust storms);
  • Environment and climate change;
  • Land surface phenology;
  • Vegetation and soil parameter retrieval.

Dr. Alim Samat
Prof. Dr. Anming Bao
Dr. Jingjie Wang
Prof. Dr. Zhi Li
Dr. Xuemei Li
Dr. Ali Darvishi Boloorani
Dr. Mukhiddin Juliev
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big remote sensing data
  • image classification
  • land use land cover
  • disaster monitoring
  • parameter retrieval
  • climate change
  • land surface penology

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Published Papers (10 papers)

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Research

22 pages, 18324 KiB  
Article
Spatial Downscaling of Precipitation Data in Arid Regions Based on the XGBoost-MGWR Model: A Case Study of the Turpan–Hami Region
by Huanhuan He, Jinjie Wang, Jianli Ding and Lei Wang
Land 2024, 13(4), 448; https://doi.org/10.3390/land13040448 - 31 Mar 2024
Cited by 3 | Viewed by 1242
Abstract
Accurate and reliable precipitation data are important for analyzing regional precipitation distribution, water resource management, and ecological environment construction. Due to the scarcity of meteorological stations in the Turpan–Hami region, precipitation observation conditions are limited, and it is difficult to obtain precipitation data. [...] Read more.
Accurate and reliable precipitation data are important for analyzing regional precipitation distribution, water resource management, and ecological environment construction. Due to the scarcity of meteorological stations in the Turpan–Hami region, precipitation observation conditions are limited, and it is difficult to obtain precipitation data. Firstly, the applicability of TRMM 3B43v7, GPM_3IMERGM 06, and CMORPH CDR satellite precipitation data for the Turpan–Hami Region was evaluated, and the products with better applicability were selected. Next, the Extreme Gradient Boosting Algorithm (XGBoost) and the Shapley Additive Explanations for Machine Learning (SHAP) model were combined to carry out a feature importance analysis on the climate factors affecting precipitation (mean temperature, actual evapotranspiration, wind speed, cloud cover), from which climate factors with a greater influence on precipitation were selected. Combined with climate factors, normalized difference vegetation index (NDVI), slope, aspect, and elevation as explanatory variables, a Multi-Scale Geographically Weighted Regression (MGWR) model was constructed to obtain the monthly precipitation data of 1 km spatial resolution in the Turpan–Hami area from 2001 to 2020. Finally, the spatiotemporal distribution characteristics and changing trend of precipitation in the Turpan–Hami region from 2001 to 2020 were analyzed. The results show that (1) GPM_3IMERGM 06 satellite precipitation data exhibits good applicability in the Turpan–Hami region. (2) The precision verification of the downscaling results from a monthly scale and an annual scale shows that the accuracy and spatial resolution of the data are improved after downscaling. (3) From 2001 to 2020, the precipitation in the Turpan–Hami region showed an insignificantly increasing trend. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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14 pages, 18833 KiB  
Article
The Oasisization Process Promotes the Transformation of Soil Organic Carbon into Soil Inorganic Carbon
by Junhu Tang, Lu Gong, Xinyu Ma, Haiqiang Zhu, Zhaolong Ding, Yan Luo and Han Zhang
Land 2024, 13(3), 336; https://doi.org/10.3390/land13030336 - 6 Mar 2024
Cited by 1 | Viewed by 1323
Abstract
The dynamic fluctuations in the soil organic carbon (SOC) stock, a fundamental part of the terrestrial ecosystem’s carbon stock, are critical to preserving the global carbon balance. Oases in arid areas serve as critical interfaces between oasis ecosystems and deserts, with land use [...] Read more.
The dynamic fluctuations in the soil organic carbon (SOC) stock, a fundamental part of the terrestrial ecosystem’s carbon stock, are critical to preserving the global carbon balance. Oases in arid areas serve as critical interfaces between oasis ecosystems and deserts, with land use changes within these oases being key factors affecting soil organic carbon turnover. However, the response of the soil SOC-CO2-SIC (soil inorganic carbon) micro-carbon cycle to oasis processes and their underlying mechanisms remains unclear. Five land-use types in the Alar reclamation area—cotton field (CF), orchard (OR), forest land (FL), waste land (WL), and sandy land (SL)—were chosen as this study’s research subjects. Using stable carbon isotope technology, the transformation process of SOC in the varieties of land-use types from 0 to 100 cm was quantitatively analyzed. The results showed the following: (1) The SOC of diverse land-use types decreased with the increase in soil depth. There were also significant differences in SIC-δ13C values among the different land-use types. The PC(%) (0.73 g kg−1) of waste land was greatly higher than that of other land-use types (p < 0.05) (factor analysis of variance). (2) The CO2 fixation in cotton fields, orchards, forest lands, and waste land primarily originates from soil respiration, whereas, in sandy lands, it predominantly derives from atmospheric sources. (3) The redundancy analysis (RDA) results display that the primary influencing factors in the transfer of SOC to SIC are soil water content, pH, and microbial biomass carbon. Our research demonstrates that changes in land use patterns, as influenced by oasis processes, exert a significant impact on the conversion from SOC to SIC. This finding holds substantial significance for ecological land use management practices and carbon sequestration predictions in arid regions, particularly in the context of climate change. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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22 pages, 7595 KiB  
Article
Seasonal Drought Dynamics and the Time-Lag Effect in the MU Us Sandy Land (China) Under the Lens of Climate Change
by Fuqiang Wang, Ruiping Li, Sinan Wang, Huan Wang, Yanru Shi, Yin Zhang, Jianwei Zhao and Jinming Yang
Land 2024, 13(3), 307; https://doi.org/10.3390/land13030307 - 29 Feb 2024
Cited by 1 | Viewed by 1063
Abstract
Sand prevention and control are the main tasks of desertification control. The MU Us Sandy Land (MUSL), one of China’s four main deserts, frequently experiences droughts and has a very fragile biological environment. Climate change is the main factor leading to drought, and [...] Read more.
Sand prevention and control are the main tasks of desertification control. The MU Us Sandy Land (MUSL), one of China’s four main deserts, frequently experiences droughts and has a very fragile biological environment. Climate change is the main factor leading to drought, and it may result in more serious drought situations in the future. The Temperature Vegetation Dryness Index (TVDI) was established using land surface temperature and normalized difference vegetation index data. In this paper, we investigate spatial and temporal change characteristics, future change trends, and the time-lag effect of TVDI on climate factors at different scales in MUSL from 2001 to 2020 using Sen + Mann–Kendall trend analysis, Hurstexponent, partial correlation analysis, and lag analysis methods. The results show that (1) the overall drought shows a spatial characteristic of gradually alleviating from west to east (TVDI = 0.6). A significant drying trend dominated 38.5% of the pixels in the fall (Z = 1.99), and a highly significant drying trend dominated the rest of the three seasons (Z average = 2.95) and the whole year (Z = 3.47). (2) In the future, dry autumn, winter, and the whole year will be dominated by continuous drying, and spring and summer will mainly change from dry to wet. The main relationships between winter TVDI and temperature (−0.06) and precipitation (−0.07) were negative, while evapotranspiration (0.18) showed a positive correlation. The six land use types in spring, summer, fall, and the whole year were primarily non-significantly positively correlated with temperature and evapotranspiration. (3) At the seasonal scale, the sensitive factors in spring and autumn were opposite, with spring TVDI responding quickly to precipitation (0.3 months) and being less sensitive to temperature (1.8 months) and evapotranspiration (2 months). At the interannual scale, desert land TVDI was most sensitive to precipitation (2.6 months) and least responsive to temperature (3 months). Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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28 pages, 7325 KiB  
Article
Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)
by Wenbo Li, Alim Samat, Jilili Abuduwaili and Wei Wang
Land 2024, 13(2), 222; https://doi.org/10.3390/land13020222 - 10 Feb 2024
Cited by 2 | Viewed by 1582
Abstract
Considering climate change and increasing human impact, ecological quality and its assessment have also received increasing attention. Taking the Irtysh River Basin as an example, we utilize multi-period MODIS composite imagery to obtain five factors (greenness, humidity, heat, dryness, and salinity) to construct [...] Read more.
Considering climate change and increasing human impact, ecological quality and its assessment have also received increasing attention. Taking the Irtysh River Basin as an example, we utilize multi-period MODIS composite imagery to obtain five factors (greenness, humidity, heat, dryness, and salinity) to construct the model for the amended RSEI (ARSEI) based on the Google Earth Engine platform. We used the Otsu algorithm to generate dynamic thresholds to improve the accuracy of ARSEI results, performed spatiotemporal pattern and evolutionary trend analysis on the results, and explored the influencing factors of ecological quality. Results indicate that: (1) The ARSEI demonstrates a correlation exceeding 0.88 with each indicator, offering an efficient approach to characterizing ecological quality. The ecological quality of the Irtysh River Basin exhibits significant spatial heterogeneity, demonstrating a gradual enhancement from south to north. (2) To evaluate the ecological quality of the Irtysh River Basin, the ARSEI was utilized, exposing a stable condition with slight fluctuations. In the current research context, the ecological quality of the Irtysh River Basin watershed area is projected to continuously enhance in the future. This is due to the constant ecological protection and management initiatives carried out by countries within the basin. (3) Precipitation, soil pH, elevation, and human population are the main factors influencing ecological quality. Due to the spatial heterogeneity, the driving factors for different ecological quality classes vary. Overall, the ARSEI is an effective method for ecological quality assessment, and the research findings can provide references for watershed ecological environment protection, management, and sustainable development. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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23 pages, 23654 KiB  
Article
Detecting Long-Term Series Eco-Environmental Quality Changes and Driving Factors Using the Remote Sensing Ecological Index with Salinity Adaptability (RSEISI): A Case Study in the Tarim River Basin, China
by Wen Chen, Jinjie Wang, Jianli Ding, Xiangyu Ge, Lijing Han and Shaofeng Qin
Land 2023, 12(7), 1309; https://doi.org/10.3390/land12071309 - 28 Jun 2023
Cited by 7 | Viewed by 1718
Abstract
Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this [...] Read more.
Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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18 pages, 9611 KiB  
Article
Responses of Vegetation Phenology to Urbanisation and Natural Factors along an Urban-Rural Gradient: A Case Study of an Urban Agglomeration on the Northern Slope of the Tianshan Mountains
by Gulbakram Ahmed, Mei Zan, Pariha Helili and Alimujiang Kasimu
Land 2023, 12(5), 1108; https://doi.org/10.3390/land12051108 - 22 May 2023
Cited by 3 | Viewed by 1418
Abstract
Understanding the responses of vegetation phenology to natural and human disturbances is essential for better understanding ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer data and products were used together with other relevant data to analyse vegetation phenological responses to urbanisation and natural [...] Read more.
Understanding the responses of vegetation phenology to natural and human disturbances is essential for better understanding ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer data and products were used together with other relevant data to analyse vegetation phenological responses to urbanisation and natural factors in the major urban agglomerations of the Urumqi-Changji, Shihezi-Manasi, and Wusu-Kuidun-Dushanzi regions on the Urban Agglomeration on the Northern Slope of the Tianshan Mountains (UANSTM). Vegetation phenology distributed along an urban-rural gradient showed distinct variability, with start of growing season (SOS), end of growing season (EOS), and growing season length (GSL) occurring earlier, later, and longer, respectively, in urban areas than those in suburban and rural areas. In the Urumqi-Changji region, the earliest SOS, the later EOS, and the longest GSL occurred. Surface urban heat island intensity (SUHII) was most pronounced in the Urumqi-Changji region, with a heat island intensity of 1.77–3.34 °C. Vegetation phenology was influenced by both urbanisation and natural factors, whose contributions were 44.2% to EOS and 61.8% to SOS, respectively. The results of this study emphasise the importance of quantifying the vegetation phenological responses to human disturbances, including climate change, along the urban-rural gradient on the UANSTM. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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20 pages, 8140 KiB  
Article
Spatiotemporal Changes and Driving Force Analysis of Land Sensitivity to Desertification in Xinjiang Based on GEE
by Yazhou Zhao, Shengyu Li, Dazhi Yang, Jiaqiang Lei and Jinglong Fan
Land 2023, 12(4), 849; https://doi.org/10.3390/land12040849 - 8 Apr 2023
Cited by 6 | Viewed by 2263
Abstract
Land desertification profoundly affects economic and social development, thus necessitating a collective response. Regional land control planning needs to assess the land sensitivity to desertification across different regions. In this study, we selected 12 factors from soil, vegetation, climate, and terrain aspects to [...] Read more.
Land desertification profoundly affects economic and social development, thus necessitating a collective response. Regional land control planning needs to assess the land sensitivity to desertification across different regions. In this study, we selected 12 factors from soil, vegetation, climate, and terrain aspects to calculate and evaluate Xinjiang’s land sensitivity to desertification, from 2001 to 2020, and analyzed its trends and drivers. The results indicated that the region is highly (22.93%) to extremely sensitive (34.63%) to desertification. Of these, deserts, Gobi lands, oasis–desert transitional zones, and the downstream of rivers are highly and extremely sensitive areas. Mountainous areas, oases, and along rivers are non- and mildly sensitive areas. Over the past two decades, most areas have experienced stability (45.07%) and a slight improvement of desertification (26.18%), while the Junggar Basin and Central Taklamakan Desert have seen slight and severe intensification trends, respectively. Climate-related indicators, such as surface temperature and potential evapotranspiration (PET), were identified as the most important drivers of changes in land sensitivity to desertification. Having an integrated water resource allocation and establishing the long-term monitoring of land sensitivity to desertification would have positive implications for desertification control. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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14 pages, 5721 KiB  
Article
Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
by Geer Cheng, Tiejun Liu, Sinan Wang, Yingjie Wu and Cunhou Zhang
Land 2023, 12(3), 583; https://doi.org/10.3390/land12030583 - 28 Feb 2023
Cited by 6 | Viewed by 2070
Abstract
The dynamics of plants’ carbon and water use efficiency and their responses to drought are crucial to the sustainable development of arid and semi-arid environments. This study used trend analysis and partial correlation analysis to examine the carbon use efficiency (CUE) and water [...] Read more.
The dynamics of plants’ carbon and water use efficiency and their responses to drought are crucial to the sustainable development of arid and semi-arid environments. This study used trend analysis and partial correlation analysis to examine the carbon use efficiency (CUE) and water use efficiency (WUE) of Inner Mongolia’s vegetation from 2001 to 2020. MODIS data for gross primary productivity (GPP), net primary productivity (NPP), potential evapotranspiration (PET), evapotranspiration (ET), drought severity index (DSI), and plant type were used. Altered trends were observed for drought during 2001–2020 in the study area. The results revealed that 98.17% of the research area’s drought trend was from dry to wet and 1.83% was from wet to dry, and the regions with decreased drought regions were broadly dispersed. In 2001–2020, CUE in Inner Mongolia declined by 0.1%·year−1, whereas WUE reduced by 0.008 g C·mm−1·m−2·year−1, but the total change was not significant. CUE decreased from west to east, whereas WUE increased from southwest to northeast. DSI and CUE had the highest negative connection, accounting for 97.96% of the watershed area, and 71.6% passed the significance test. The correlation coefficients of DSI and WUE were spatially opposite to those of CUE and DSI. In total, 54.21% of the vegetation cover exhibited a negative connection with DSI. The CUE and WUE of different vegetation types in Inner Mongolia were negatively correlated with the DSI index except for grasslands (GRA). Drought in Inner Mongolia mostly influenced the CUE of different plant types, which had a higher negative correlation than WUE. The study’s findings can inform climate change research on Inner Mongolia’s carbon and water cycles. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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20 pages, 4694 KiB  
Article
Assessment of Water Yield and Water Purification Services in the Arid Zone of Northwest China: The Case of the Ebinur Lake Basin
by Xilinayi Duolaiti, Alimujiang Kasimu, Rukeya Reheman, Yimuranzi Aizizi and Bohao Wei
Land 2023, 12(3), 533; https://doi.org/10.3390/land12030533 - 22 Feb 2023
Cited by 8 | Viewed by 2251
Abstract
Assessing how land-use changes will affect water-producing ecosystem services is particularly important for water resource management and ecosystem conservation. In this study, the InVEST model and geographical detector were used to assess the water ecosystem service functions of the Ebinur Lake Basin and [...] Read more.
Assessing how land-use changes will affect water-producing ecosystem services is particularly important for water resource management and ecosystem conservation. In this study, the InVEST model and geographical detector were used to assess the water ecosystem service functions of the Ebinur Lake Basin and analyze their relationship with land-use changes. The results show that in the past 25 years, the water yield of the study area showed a trend of a strong yield at first and then a weaker one; there was a relatively large water yield in the west and southeast regions of the basin. The order of water yield for different land-use types is as follows: forest land > grassland > water area > unused land > crop land > construction land. After 2010, the output load of nitrogen and phosphorus increased; thus, the water purification ability weakened. The main land-use types in areas that demonstrate a large change rate in water purification capacity in the basin are cultivated land and construction land. Changes in the two water ecosystem services were associated with land-use changes. Geodetector analysis results further validated this conclusion. This study proposes a viable, replicable framework for land-use decisions in ecologically fragile watersheds. This study not only helps to gain insight into urban growth patterns in the study area but also helps to inform different land-use stakeholders. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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14 pages, 2199 KiB  
Article
Microbial Community Structure and Predictive Functional Analysis in Reclaimed Soil with Different Vegetation Types: The Example of the Xiaoyi Mine Waste Dump in Shanxi
by Dong Zhao, Huping Hou, Haiya Liu, Chen Wang, Zhongyi Ding and Jinting Xiong
Land 2023, 12(2), 456; https://doi.org/10.3390/land12020456 - 10 Feb 2023
Cited by 8 | Viewed by 2598
Abstract
We explored the characteristics of soil bacterial communities and their ecological functions under different types of vegetation reclamation in open-pit mines on the Loess Plateau, which is the guiding significance for the selection of vegetation and the improvement of restoration effect in mining [...] Read more.
We explored the characteristics of soil bacterial communities and their ecological functions under different types of vegetation reclamation in open-pit mines on the Loess Plateau, which is the guiding significance for the selection of vegetation and the improvement of restoration effect in mining areas. The research object was to reclaim the soil of the aluminum mine waste dump in Xiaoyi County, Shanxi. The soil characteristics were measured under different types of vegetation reclamation. The soil bacterial community under different vegetation reclamation was measured using the 16S rRNA gene high-throughput sequencing technology. The ecological function was predicted using the PICRUSt method. The correlation between soil physical and chemical properties and bacterial community structure and function was analyzed. From the results, (1) the bacterial compositions of the reclaimed soil samples were 33 phyla, 90 classes, 121 orders, 207 families, 298 genera, and 140 species. The abundance and diversity of the soil microbial community showed the rule of yellow rose > lespedeza and sweet wormwood herb > alfalfa. (2) Proteobacteria were the dominant bacteria in alfalfa and sweet wormwood herb samples, accounting for 36.09–43.36%. Proteobacteria and actinobacteria were the dominant bacteria in the yellow rose and lespedeza samples accounted for 53.34–53.39%. α-Proteobacteria, actinobacteria, and β-proteobacteria were the dominant bacteria of the four vegetation types. The relative abundance of the α-proteobacteria and β-proteobacteria was positively correlated with soil organic carbon (SOC) and negatively correlated with soil total kalium (TK). Actinobacteria were positively correlated with available kalium (AK) and negatively correlated with SOC and total nitrogen (TN). (3) There was no difference in the primary functions of the soil bacterial community after the reclamation of different plants, and the main functions were metabolism, genetic information processing, and environmental information processing, with the function abundance accounting for 81.52%. (4) The abundance of functional genes in the metabolism of other amino acids, folding, sorting, and degradation and glycan biosynthesis and metabolism were relatively rich in the rhizosphere soil of yellow rose. The abundance of functional genes in signal molecules and interaction, transport, and catabolism in the rhizosphere soil of lespedeza was the highest. The abundance of functional genes in carbohydrate metabolism, translation, and energy metabolism in the rhizosphere soil of alfalfa was the highest. Therefore, there were significant differences in the structure and function of rhizosphere soil microbial communities among yellow rose, lespedeza, sweet wormwood herb, and alfalfa, and they were also affected by the soil properties. Hence, we concluded that the differences and diversity of soil microbial structure and function can help select plants for the sustainable development of soil remediation in mining areas. Full article
(This article belongs to the Special Issue Current Advances in Remote Sensing for Arid Land Ecology and Environment Change Monitoring and Risk Assessment)
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