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Liu J, Liu B, Wu L, Miao H, Liu J, Jiang K, Ding H, Gao W, Liu T. Prediction of land use for the next 30 years using the PLUS model's multi-scenario simulation in Guizhou Province, China. Sci Rep 2024; 14:13143. [PMID: 38849508 PMCID: PMC11161487 DOI: 10.1038/s41598-024-64014-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 06/04/2024] [Indexed: 06/09/2024] Open
Abstract
Land use changes significantly impact the structure and functioning of ecosystems. The current research focus lies in how to utilize economic and policy instruments to regulate conflicts among stakeholders effectively. The objective is to facilitate rational planning and sustainable development of land utilization resources. The PLUS model integrates a rule-based mining method for land expansion analysis and a CA model based on multi-type stochastic seeding mechanism, which can be used to mine the driving factors of land expansion and predict the patch-level evolution of land use landscapes. Using the PLUS model, a simulation was conducted to study the future land use distribution in the research area over the next 30 years. Based on land use data from Guizhou Province in 2000, 2010, and 2020, a total of 16 driving factors were selected from three aspects: geographical environment, transportation network, and socio-economic conditions. Four scenarios, namely natural development, urban development, ecological conservation, and farmland rotection, were established. Comparative analysis of the simulated differences among the various scenarios was performed. (1) The overall accuracy of the land use simulation using the PLUS model in the study area was 0.983, with a Kappa coefficient of 0.972 and a FoM coefficient of 0.509. The research accuracy meets the simulation requirements. (2) Through the simulation of four different scenarios, the study investigated the land use changes in Guizhou Province over the next 30 years. Each scenario exhibited distinct impacts on land utilization. Comprehensive comparison of the different simulation results revealed that the farmland protection scenario aligns with the sustainable development goals of the research area. Currently, there is a relative scarcity of research on land use simulation, particularly in model application, for Guizhou Province. This study aims to provide a reference for the rational planning of land resources and high-quality urban construction in Guizhou, promoting the high-quality economic development in tandem with advanced ecological and environmental protection.
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Affiliation(s)
- Juncong Liu
- College of Eco-Environment Engineering, Engineering Research Center of Green and Low-Carbon Technology for Plastic Application, Guizhou Minzu University, Guiyang, 550025, China
| | - Bangyu Liu
- College of Architectural Engineering, Research Center of Solid Waste Pollution Control and Recycling, Guizhou Minzu University, Guiyang, 550025, China.
| | - Linjing Wu
- College of Eco-Environment Engineering, Engineering Research Center of Green and Low-Carbon Technology for Plastic Application, Guizhou Minzu University, Guiyang, 550025, China
| | - Haiying Miao
- College of Eco-Environment Engineering, Engineering Research Center of Green and Low-Carbon Technology for Plastic Application, Guizhou Minzu University, Guiyang, 550025, China
| | - Jiegang Liu
- College of Eco-Environment Engineering, Engineering Research Center of Green and Low-Carbon Technology for Plastic Application, Guizhou Minzu University, Guiyang, 550025, China
| | - Ke Jiang
- College of Eco-Environment Engineering, Engineering Research Center of Green and Low-Carbon Technology for Plastic Application, Guizhou Minzu University, Guiyang, 550025, China
| | - Hu Ding
- Institute of Surface-Earth SystemScience, School of Earth System Science, Tianjin University, Tianjin, 300072, China
| | - Weichang Gao
- Upland Flue-Cured Tobacco Quality & Ecology Key Laboratory of CNTC, Guizhou Academy of Tobacco Science, Guiyang, 550081, China
| | - Taoze Liu
- College of Eco-Environment Engineering, Engineering Research Center of Green and Low-Carbon Technology for Plastic Application, Guizhou Minzu University, Guiyang, 550025, China.
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Li X, Zhao H, Wu D, Liu Q, Tang R, Li L, Xu Z, Lyu X. SLMFNet: Enhancing land cover classification of remote sensing images through selective attentions and multi-level feature fusion. PLoS One 2024; 19:e0301134. [PMID: 38743645 PMCID: PMC11093330 DOI: 10.1371/journal.pone.0301134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/08/2024] [Indexed: 05/16/2024] Open
Abstract
Land cover classification (LCC) is of paramount importance for assessing environmental changes in remote sensing images (RSIs) as it involves assigning categorical labels to ground objects. The growing availability of multi-source RSIs presents an opportunity for intelligent LCC through semantic segmentation, offering a comprehensive understanding of ground objects. Nonetheless, the heterogeneous appearances of terrains and objects contribute to significant intra-class variance and inter-class similarity at various scales, adding complexity to this task. In response, we introduce SLMFNet, an innovative encoder-decoder segmentation network that adeptly addresses this challenge. To mitigate the sparse and imbalanced distribution of RSIs, we incorporate selective attention modules (SAMs) aimed at enhancing the distinguishability of learned representations by integrating contextual affinities within spatial and channel domains through a compact number of matrix operations. Precisely, the selective position attention module (SPAM) employs spatial pyramid pooling (SPP) to resample feature anchors and compute contextual affinities. In tandem, the selective channel attention module (SCAM) concentrates on capturing channel-wise affinity. Initially, feature maps are aggregated into fewer channels, followed by the generation of pairwise channel attention maps between the aggregated channels and all channels. To harness fine-grained details across multiple scales, we introduce a multi-level feature fusion decoder with data-dependent upsampling (MLFD) to meticulously recover and merge feature maps at diverse scales using a trainable projection matrix. Empirical results on the ISPRS Potsdam and DeepGlobe datasets underscore the superior performance of SLMFNet compared to various state-of-the-art methods. Ablation studies affirm the efficacy and precision of SAMs in the proposed model.
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Affiliation(s)
- Xin Li
- College of Computer and Information, Hohai University, Nanjing, Jiangsu, China
| | - Hejing Zhao
- Water History Department, China Institute of Water Resources and Hydropower Research, Beijing, China
- Research Center on Flood and Drought Disaster Reduction of Ministry of Water Resource, China Institute of Water Resources and Hydropower Research, Beijing, China
| | - Dan Wu
- Information Engineering Center, Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission of the Ministry of Water Resources, Zhengzhou, Henan, China
- Key Laboratory of Yellow River Sediment Research, MWR (Ministry of Water Resources), Zhengzhou, Henan, China
- Henan Engineering Research Center of Smart Water Conservancy, Yellow River Institute of Hydraulic Research, Zhengzhou, Henan, China
| | - Qixing Liu
- Information Engineering Center, Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission of the Ministry of Water Resources, Zhengzhou, Henan, China
- Key Laboratory of Yellow River Sediment Research, MWR (Ministry of Water Resources), Zhengzhou, Henan, China
- Henan Engineering Research Center of Smart Water Conservancy, Yellow River Institute of Hydraulic Research, Zhengzhou, Henan, China
| | - Rui Tang
- Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Linyang Li
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, China
| | - Zhennan Xu
- College of Computer and Information, Hohai University, Nanjing, Jiangsu, China
| | - Xin Lyu
- College of Computer and Information, Hohai University, Nanjing, Jiangsu, China
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Nguyen XC, Jang S, Noh J, Khim JS, Lee J, Kwon BO, Wang T, Hu W, Zhang X, Truong HB, Hur J. Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models. MARINE POLLUTION BULLETIN 2024; 202:116307. [PMID: 38564820 DOI: 10.1016/j.marpolbul.2024.116307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.
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Affiliation(s)
- Xuan Cuong Nguyen
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang 550000, Viet Nam; Department of Environment and Energy, Sejong University, Seoul 05006, South Korea
| | - Suhyeon Jang
- Department of Environment and Energy, Sejong University, Seoul 05006, South Korea
| | - Junsung Noh
- Department of Environment and Energy, Sejong University, Seoul 05006, South Korea
| | - Jong Seong Khim
- School of Earth and Environmental Sciences & Research Institute of Oceanography, Seoul National University, Seoul 08826, South Korea
| | - Junghyun Lee
- Department of Environmental Education, Kongju National University, Gongju 32588, South Korea
| | - Bong-Oh Kwon
- Department of Marine Biotechnology, Kunsan National University, Kunsan 54150, Republic of Korea
| | - Tieyu Wang
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China
| | - Wenyou Hu
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Hai Bang Truong
- Optical Materials Research Group, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City 700000, Viet Nam; Faculty of Applied Technology, School of Technology, Van Lang University, Ho Chi Minh City 70000, Viet Nam
| | - Jin Hur
- Department of Environment and Energy, Sejong University, Seoul 05006, South Korea.
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Mao W, Jiao L. Land-use intensification dominates China's land provisioning services: From the perspective of land system science. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 356:120541. [PMID: 38479280 DOI: 10.1016/j.jenvman.2024.120541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/20/2024] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
A pressing challenge to global sustainability is meeting the escalating needs of a growing population while safeguarding land resources from degradation. In recent decades, China's rapid growth, expanding population, urban sprawl, and diminishing high-quality farmland have presented a compelling case suitable for exploring solutions and challenges related to this critical issue. Therefore, there is an urgent need for comprehensive and detailed information regarding land systems. Here, we developed the first fine-scale dataset of the China Land System at a spatial resolution of 1 km, covering the period from 2000 to 2015. By leveraging this comprehensive land information, we identified five primary types of land systems and their respective subsystems, thereby delineating distinct patterns of human-environmental interaction. Land system dynamics followed diverse developmental trajectories characterized by incremental shifts toward more functionally centralized systems. Land use intensification played a significant role in increasing the population capacity and food production in China, contributing nearly 93.94% and 84.99%, respectively. In contrast, land cover changes accounted for only 4.69% and 11.43%, respectively. These findings underscore the tendency of previous studies to overestimate the impact of land cover change and underestimate the influence of land use intensification in meeting the growing demands of land-based production. This study emphasizes the importance of transcending traditional land cover-based approaches and integrating land systems into land representation and global land change scenario simulations to promote sustainability.
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Affiliation(s)
- Wenjing Mao
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China; Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079, China.
| | - Limin Jiao
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China; Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079, China.
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Shohan AAA, Hang HT, Alshayeb MJ, Bindajam AA. Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:29048-29070. [PMID: 38568310 DOI: 10.1007/s11356-024-33091-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 03/21/2024] [Indexed: 05/01/2024]
Abstract
Rapid urbanisation has led to significant environmental and climatic changes worldwide, especially in urban heat islands where increased land surface temperature (LST) poses a major challenge to sustainable urban living. In the city of Abha in southwestern Saudi Arabia, a region experiencing rapid urban growth, the impact of such expansion on LST and the resulting microclimatic changes are still poorly understood. This study aims to explore the dynamics of urban sprawl and its direct impact on LST to provide important insights for urban planning and climate change mitigation strategies. Using the random forest (RF) algorithm optimised for land use and land cover (LULC) mapping, LULC models were derived that had an overall accuracy of 87.70%, 86.27% and 93.53% for 1990, 2000 and 2020, respectively. The mono-window algorithm facilitated the derivation of LST, while Markovian transition matrices and spatial linear regression models assessed LULC dynamics and LST trends. Notably, built-up areas grew from 69.40 km2 in 1990 to 338.74 km2 in 2020, while LST in urban areas showed a pronounced warming trend, with temperatures increasing from an average of 43.71 °C in 1990 to 50.46 °C in 2020. Six landscape fragmentation indices were then calculated for urban areas over three decades. The results show that the Largest Patch Index (LPI) increases from 22.78 in 1990 to 65.24 in 2020, and the number of patches (NP) escalates from 2,531 in 1990 to an impressive 10,710 in 2020. Further regression analyses highlighted the morphological changes in the cities and attributed almost 97% of the LST variability to these urban patch dynamics. In addition, water bodies showed a cooling trend with a temperature decrease from 33.76 °C in 2000 to 29.69 °C in 2020, suggesting an anthropogenic influence. The conclusion emphasises the urgent need for sustainable urban planning to counteract the warming trends associated with urban sprawl and promote climate resilience.
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Affiliation(s)
- Ahmed Ali A Shohan
- Department of Architecture and Planning, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia
| | - Hoang Thi Hang
- Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, India.
| | - Mohammed J Alshayeb
- Department of Architecture and Planning, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia
| | - Ahmed Ali Bindajam
- Department of Architecture and Planning, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia
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Mariye M, Jianhua L, Maryo M, Tsegaye G, Aletaye E. Remote sensing and GIS-based study of land use/cover dynamics, driving factors, and implications in southern Ethiopia, with special reference to the Legabora watershed. Heliyon 2024; 10:e23380. [PMID: 38148827 PMCID: PMC10750153 DOI: 10.1016/j.heliyon.2023.e23380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 12/28/2023] Open
Abstract
This paper investigates the trends, drivers, and consequences of LULC changes in Legabora watershed, Ethiopia, by utilizing remote sensing and geographic systems. Landsat Maltispectiral scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images of years 1976, 1991, 2001, and 2022, respectively, were used to study the dynamics of LULC. Essential image pre-processing steps were carefully carried out to correct distortions caused by sensor limitations. Eight main LULC categories were identified based on supervised image categorization methods and the maximum likelihood classification algorithm.The findings of change detection and cross-tabulation matrix demonstrate that there has been a significant increase in the area of cropland 345.1 ha/year, settlement 5.9 ha/year, forest 38.2 ha/year, and degraded lands 2.56 ha/year, respectively, over the period between 1976 and 2022. In contrast, considerable decreases were observed in grasslands (-248 ha/year) and shrublands (-144 ha/year), whereas other LULC categories augmented. The results revealed that the overall accuracy rates stood at 88.3 %, 88.4 %, and 85.6 % for 1976, 1991, and 2022, respectively. The overall kappa coefficient demonstrated values of 0.86 %, 0.86 %, and 0.83 % for the same period. Surveyed respondents perceived population growth, settlement, agricultural expansion, and infrastructure development as the most noticeable drivers of these LULC changes. In contrast, deforestation, land degradation, lack of livestock fodder, and biodiversity loss were identified as the main consequences of LULC changes. The factors and implications addressed in this study may be helpful tool for the formulation and implementation of evidence-based land use policies and strategies within in the study area and elsewhere.
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Affiliation(s)
- Mehari Mariye
- Tongji University, College of Environmental Science & Engineering, Shanghai, 200092, China
| | - Li Jianhua
- Tongji University, College of Environmental Science & Engineering, Shanghai, 200092, China
| | - Melesse Maryo
- Ethiopian Biodiversity Institute home-based in Ethiopian Civil Service University, Addis Ababa, Ethiopia
| | - Gedion Tsegaye
- Tongji University, College of Environmental Science & Engineering, Shanghai, 200092, China
| | - Eskedar Aletaye
- City Government of Addis Ababa Environmental Protection Authority, Addis Ababa, Ethiopia
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Mumtaz F, Li J, Liu Q, Arshad A, Dong Y, Liu C, Zhao J, Bashir B, Gu C, Wang X, Zhang H. Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:166940. [PMID: 37690760 DOI: 10.1016/j.scitotenv.2023.166940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/13/2023] [Accepted: 09/07/2023] [Indexed: 09/12/2023]
Abstract
We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a - 0.80 % reduction over Uzbekistan and - 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000-2010) and a decrease (-0.19 % = -16,490.50 km2) in the 2nd period (2011-2020), with a notable decline over Kazakhstan (-0.54 % = 13,690 km2), Tajikistan (-0.18 % = 1483 km2), and Volgograd (-0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (-0.40 % = 10,261 km2) and Uzbekistan (-2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year-1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.
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Affiliation(s)
- Faisal Mumtaz
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Jing Li
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Qinhuo Liu
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Arfan Arshad
- Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74075, USA
| | - Yadong Dong
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China
| | - Chang Liu
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Zhao
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Barjeece Bashir
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenpeng Gu
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohan Wang
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hu Zhang
- State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute Chinese Academy of Science (AIRCAS), Beijing 100094, China
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Zhao S, Liu M, Tao M, Zhou W, Lu X, Xiong Y, Li F, Wang Q. The role of satellite remote sensing in mitigating and adapting to global climate change. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166820. [PMID: 37689189 DOI: 10.1016/j.scitotenv.2023.166820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/30/2023] [Accepted: 09/02/2023] [Indexed: 09/11/2023]
Abstract
Climate change has critical adverse impacts on human society and poses severe challenges to global sustainable development. Information on essential climate variables (ECVs) that reflects the substantial changes that have occurred on Earth is critical for assessing the influence of climate change. Satellite remote sensing (SRS) technology has led to a new era of observations and provides multiscale information on ECVs that is independent of in situ measurements and model simulations. This enhances our understanding of climate change from space and supports policy-making in combating climate change. However, it remains challenging to remotely retrieve ECVs due to the complexity of the climate system. We provide an update on the studies on the role of SRS in climate change research, specifically in monitoring and quantifying ECVs in the atmosphere (greenhouse gases, clouds and aerosols), ocean (sea surface temperature, sea ice melt and sea level rise, ocean currents and mesoscale eddies, phytoplankton and ocean productivity), and terrestrial ecosystems (land use and land cover change and carbon flux, water resource and hydrological hazards, solar-induced chlorophyll fluorescence and terrestrial gross primary production). The benefits and challenges of applying SRS in climate change studies are also examined and discussed. This work will help us apply SRS and recommend future SRS studies to mitigate and adapt to global climate change.
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Affiliation(s)
- Shaohua Zhao
- Satellite Environment Center, Ministry of Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing 100094, China
| | - Min Liu
- College of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450000, China
| | - Minghui Tao
- School of Geography and Information Engineering, China University of Geosciences, Wuhan 430000, China
| | - Wei Zhou
- Satellite Environment Center, Ministry of Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing 100094, China
| | - Xiaoyan Lu
- Guangxi Eco-Environmental Monitoring Centre, Nanning 530028, China
| | - Yujiu Xiong
- School of Civil Engineering, Sun Yat-Sen University, Zhuhai 519082, Guangdong, China; Center of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China.
| | - Feng Li
- School of Civil Engineering, Sun Yat-Sen University, Zhuhai 519082, Guangdong, China
| | - Qiao Wang
- Satellite Environment Center, Ministry of Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing 100094, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
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Perturbation-based oversampling technique for imbalanced classification problems. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01662-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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10
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Rong T, Zhang P, Zhu H, Jiang L, Li Y, Liu Z. Spatial correlation evolution and prediction scenario of land use carbon emissions in China. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101802] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Modeling Spatiotemporal Patterns of Land Use/Land Cover Change in Central Malawi Using a Neural Network Model. REMOTE SENSING 2022. [DOI: 10.3390/rs14143477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We examine Land Use Land Cover Change (LULCC) in the Dedza and Ntcheu districts of Central Malawi and model anthropogenic and environmental drivers. We present an integrative approach to understanding heterogenous landscape interactions and short- to long-term shocks and how they inform future land management and policy in Malawi. Landsat 30-m satellite imagery for 2001, 2009, and 2019 was used to identify and quantify LULCC outcomes based on eight input classes: agriculture, built-up areas, barren, water, wetlands, forest-mixed vegetation, shrub-woodland, and other. A Multilayer Perceptron (MLP) neural network was developed to examine land-cover transitions based on the drivers; elevation, slope, soil texture, population density and distance from roads and rivers. Agriculture is projected to dominate the landscape by 2050. Dedza has a higher probability of future land conversion to agriculture (0.45 to 0.70) than Ntcheu (0.30 to 0.45). These findings suggest that future land management initiatives should focus on spatiotemporal patterns in land cover and develop multidimensional policies that promote land conservation in the local context.
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12
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Spatial Simulation of Land-Use Development of Feixi County, China, Based on Optimized Productive–Living–Ecological Functions. SUSTAINABILITY 2022. [DOI: 10.3390/su14106195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Rural revitalization places higher demands on the productive–living–ecological (P-L-E) spaces of towns and cities. It is necessary, therefore, to identify, evaluate, and optimize P-L-E spaces to better guide spatial planning. Existing studies typically evaluate a single space, lacking a comprehensive consideration of whole-area integration. This study, therefore, developed a coupled spatial/developmental suitability evaluation system for Feixi County, Anhui Province, China, combining spatial quality evaluation, a coupled coordination model, and future land-use simulation (FLUS) model. The spatial quality of Feixi County in 2010, 2015, and 2020 was obtained by applying the evaluation system to the spatial development pattern. The results were analyzed and verified using the landscape pattern index and development suitability evaluation. The results showed the following: (1) The coupling coordination degree of the region increased from 0.131 to 0.372, changing from low to moderate coordination. (2) Based on the FLUS model to better capture the uncertainty and stochastic basis of the development in the study area. The kappa coefficient and Figure of Merit (FoM) index of the land-use simulation accuracy verification index were 0.7647 and 0.0508, respectively, and the logistic regression ROC values were above 0.75, thus meeting accuracy requirements. This demonstrated that the simulation model—based on a factor library of the evaluation of resource and environmental carrying capacity and suitability for development and construction—could better reflect future land-use changes. (3) The simulation showed that under the baseline development scenario, the area’s spatial layout is too concentrated in terms of construction land, ignoring P-L-E coordination. Under the ecological optimization scenario, high-quality ecological space is ensured, but other types of spaces are lacking. Under the comprehensive guidance scenario, lagging ecological space is optimized and P-L-E spatial development is enhanced through aggregation, clustering, concentration and integration. This way, the spatial quantity structure and distribution form can meet P-L-E spatial development needs in Feixi County. In this study, on the basis of scientific assessment of the current P-L-E space, the FLUS model was applied to carry out a scenario simulation according to different objectives. Moreover, based on the construction of the coupling system of human–nature system, the driving factors were improved to enhance the prediction accuracy of the FLUS model. This study’s findings can help improve the scientificity, flexibility and management efficiency of Feixi County’s P-L-E spatial layout, thereby supporting its sustainable development.
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