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Ma R, Zhang J, Shen X, Liu B, Lu X, Jiang M. Impacts of climate change on fractional vegetation coverage of temperate grasslands in China from 1982 to 2015. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 350:119694. [PMID: 38035505 DOI: 10.1016/j.jenvman.2023.119694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023]
Abstract
The vegetation coverage of temperate grasslands in China has changed substantially due to climate change during the past decades, which significantly affects the function of grassland ecosystems. To appropriately carry out adaptive management and protect temperate grassland vegetation, it is important to understand the variations in fractional vegetation coverage (FVC) of China's temperate grasslands and how they are responding to climate change. Using the GIMMS NDVI and climatic datasets, this study explored the dynamics of FVC and their climatic drivers across the temperate grassland region of China during 1982∼2015. The results showed that the growing season mean FVC increased by 0.12% per year during 1982∼2015. The increases in precipitation and minimum temperature in the growing-season (especially in spring) could enhance the FVC of various vegetation types. In summer, the FVC of temperate steppe and desert steppe could drastically increase with increasing precipitation. In addition, this study found that the impacts of daytime and night-time warming on the FVC of temperate grasslands were asymmetric. Daytime warming can moderately increase FVC of temperate grasslands, while night-time warming could significantly increase it. Furthermore, the increase in summer daytime and night-time temperatures leads to a weak decrease and a moderate increase in FVC, respectively. This asymmetric effect was more evident for the temperate steppe and desert steppe in the central area. In autumn, the temperatures increase had significant positive impacts on the FVC of temperate meadows and steppes. This study highlights the differences in the impacts of climate change at different time scales on the FVC of grasslands with various vegetation types, and indicates that the asymmetric influences of daytime and night-time temperatures in different seasons on FVC must be included in calculating the vegetation coverage of China's temperate grasslands. The results could provide information for maintaining grassland ecosystem functions and managing environmental systems.
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Affiliation(s)
- Rong Ma
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; College of Forestry, Northeast Forestry University, Harbin, 150040, China
| | - Jiaqi Zhang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Xiangjin Shen
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China.
| | - Binhui Liu
- College of Forestry, Northeast Forestry University, Harbin, 150040, China
| | - Xianguo Lu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Ming Jiang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
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2
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Liu X, Cui Y, Li W, Li M, Li N, Shi Z, Dong J, Xiao X. Urbanization expands the fluctuating difference in gross primary productivity between urban and rural areas from 2000 to 2018 in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:166490. [PMID: 37611713 DOI: 10.1016/j.scitotenv.2023.166490] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/16/2023] [Accepted: 08/20/2023] [Indexed: 08/25/2023]
Abstract
Urban and rural vegetation are affected by both climate change and human activities, but the role of urbanization in vegetation productivity is unclear given the dual impacts. Here, we delineated urban area (UA) and rural area (RA), quantified the relative impacts of climate change and human activities on gross primary production (GPP) in 34 major cities (MCs) in China from 2000 to 2018, and analyzed the intrinsic impacts of urbanization on GPP. First, we found that the total urban impervious surface coverage (ISC) of the 34 MCs increased by 13.25 % and the mean annual GPP increased by 211 gC m-2 during the study period. GPP increased significantly in urban core areas, but decreased significantly in urban expansion areas, which was mainly due to a large amount of vegetation loss due to land use conversion. Second, the variability of GPP in UA was generally lower than in RA. Both climate change and human activities had a positive impact on GPP in UA and RA in the 34 MCs, of which the contribution was 49 % and 51 % in UA, and 76 % and 24 % in RA, respectively. Third, under climate change and human activities, the increase in GPP offset 4.96 % and 12.35 % of the impact of land use conversion on GPP in 2000 and 2018, respectively, which indicated that the offset strengthened over time. These findings emphasize the role of human activities in promoting carbon sequestration in urban vegetation, which is crucial for better understanding the processes and mechanisms of urban carbon cycles. Decision-makers can manage urban vegetation based on vegetation carbon sequestration potential as regions urbanize, aiding comprehensive decision-making.
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Affiliation(s)
- Xiaoyan Liu
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China; School of Geography and Environmental Science, Henan University, Kaifeng 475004, China; Dabieshan National Observation and Research Field Station of Forest Ecosystem at Henan, Zhengzhou 450046, China; Xinyang Ecological Research Institute, Xinyang 464000, China
| | - Yaoping Cui
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China; School of Geography and Environmental Science, Henan University, Kaifeng 475004, China; Dabieshan National Observation and Research Field Station of Forest Ecosystem at Henan, Zhengzhou 450046, China; Xinyang Ecological Research Institute, Xinyang 464000, China.
| | - Wanlong Li
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China; School of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Mengdi Li
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China; School of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Nan Li
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China; School of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Zhifang Shi
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China; School of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Jinwei Dong
- Institute of Geographical Sciences and Resources, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiangming Xiao
- Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA.
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Deng G, Gao J, Jiang H, Li D, Wang X, Wen Y, Sheng L, He C. Response of vegetation variation to climate change and human activities in semi-arid swamps. FRONTIERS IN PLANT SCIENCE 2022; 13:990592. [PMID: 36237507 PMCID: PMC9552615 DOI: 10.3389/fpls.2022.990592] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Vegetation is a sensitive factor in marsh ecosystems, which can provide nesting sites, foraging areas, and hiding places for waterfowl and can affect their survival environment. The Jilin Momoge National Nature Reserve, which consists of large areas of marshes, is located in the semi-arid region of northeast China and is an important stopover site for the critically endangered species of the Siberian Crane (Grus leucogeranus). Global climate change, extreme droughts and floods, and large differences in evaporation and precipitation in this region can cause rapid vegetation succession. In recent years, increased grain production and river-lake connectivity projects carried out in this area to increase grain outputs and restore wetlands have caused significant changes in the hydrological and landscape patterns. Therefore, research on the response of variation trends in vegetation patterns to the main driving factors (climate change and human activities) is critical for the conservation of the Siberian Crane. Based on the Google Earth Engine (GEE) platform, we obtained and processed the Normalized difference vegetation index (NDVI) data of the study area during the peak summer vegetation period for each year from 1984 to 2020, estimated the annual vegetation cover using Maximum value composites (MVC) method and the image dichotomy method, calculated and analyzed the spatial and temporal trends of vegetation cover, explored the response of vegetation cover change in terms of climate change and human activities, and quantified the relative contribution of both. The results revealed that first, from the spatial and temporal changes, the average annual growth rate of regional vegetation was 0.002/a, and 71.14% of the study area was improved. The vegetation cover showed a trend of degradation and then recovery, in which the percentage of high vegetation cover area decreased from 51.22% (1984-2000) to 28.33% (2001-2005), and then recovered to 55.69% (2006-2020). Second, among climate change factors, precipitation was more correlated with the growth of vegetation in the study area than temperature, and the increase in precipitation during the growing season could promote the growth of marsh vegetation in the Momoge Reserve. Third, overall, human activities have contributed to the improvement of vegetation cover in the study area with the implementation of important ecological projects, such as the return of farmland to wetlands, the return of grazing to grass, and the connection of rivers and lakes. Fourth, climate change and human activities jointly drive vegetation change, but the contribution of human activities in both vegetation improvement and degradation areas (85.68% and 78.29%, respectively) is higher than that of climate change (14.32% and 21.71%, respectively), which is the main reason for vegetation improvement or degradation in the study area. The analysis of vegetation pattern change within an intensive time series in semi-arid regions can provide a reference and basis for studying the driving factors in regions with rapid changes in vegetation and hydrological conditions.
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Affiliation(s)
- Guangyi Deng
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Key Laboratory for Vegetation Ecology Ministry of Education, Northeast Normal University, Changchun, China
| | - Jin Gao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Key Laboratory for Vegetation Ecology Ministry of Education, Northeast Normal University, Changchun, China
| | - Haibo Jiang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Key Laboratory for Vegetation Ecology Ministry of Education, Northeast Normal University, Changchun, China
| | - Dehao Li
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Key Laboratory for Vegetation Ecology Ministry of Education, Northeast Normal University, Changchun, China
| | - Xue Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Key Laboratory for Vegetation Ecology Ministry of Education, Northeast Normal University, Changchun, China
| | - Yang Wen
- Key Laboratory of Environmental Materials and Pollution Control, The Education Department of Jilin Province, School of Engineering, Jilin Normal University, Siping, China
| | - Lianxi Sheng
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Key Laboratory for Vegetation Ecology Ministry of Education, Northeast Normal University, Changchun, China
| | - Chunguang He
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Key Laboratory for Vegetation Ecology Ministry of Education, Northeast Normal University, Changchun, China
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Effects of Human Activities on Urban Vegetation: Explorative Analysis of Spatial Characteristics and Potential Impact Factors. REMOTE SENSING 2022. [DOI: 10.3390/rs14132999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Since the 21st century, large cities around the world have experienced the transition from economically destructive development to a harmonious eco-environment. Understanding the dynamic relationships between human activities and urban eco-environment in this transition is a challenging and essential topic. The normalized difference vegetation index (NDVI) can reflect the urban vegetation cover status well. Socio-economic indexes can present the intensity and spatiality of human activities quantitatively. This work aims to use traditional regression models and machine learning algorithms to analyze the impact of socio-economic factors on NDVI accurately. Random forest regression (RFR) was performed to initially assess the contributions of all factors on NDVI, which was the numerical basis for feature selection. Subsequently, detailed dynamic relationship simulations were implemented using geographically weighted regression. In the case of Wuhan in China, the results showed that the goodness-of-fit of NDVI with socio-economic factors generally exceeded 50%. The influence coefficients changed from negative to positive, and 2010 was the turning point, indicating that human activities gradually played a favorable role in protecting vegetation during this transition period. The urban–rural interface, which was located between urban centers and marginal urban suburbs, was the area where human activities contributed most to vegetation. Thus, policy makers should focus on planning and managing housing construction and vegetation planting in urban–rural interface to relieve the population burden of the central area and improve the environmental conditions of the urban eco-environment subconsciously.
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Huang C, Yang Q, Huang W. Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in the Wei and Jing River Basins. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:11863. [PMID: 34831620 PMCID: PMC8618191 DOI: 10.3390/ijerph182211863] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/08/2021] [Accepted: 11/08/2021] [Indexed: 12/02/2022]
Abstract
This study aimed to explore the long-term vegetation cover change and its driving factors in the typical watershed of the Yellow River Basin. This research was based on the Google Earth Engine (GEE), a remote sensing cloud platform, and used the Landsat surface reflectance datasets and the Pearson correlation method to analyze the vegetation conditions in the areas above Xianyang on the Wei River and above Zhangjiashan on the Jing River. Random forest and decision tree models were used to analyze the effects of various climatic factors (precipitation, temperature, soil moisture, evapotranspiration, and drought index) on NDVI (normalized difference vegetation index). Then, based on the residual analysis method, the effects of human activities on NDVI were explored. The results showed that: (1) From 1987 to 2018, the NDVI of the two watersheds showed an increasing trend; in particular, after 2008, the average increase rate of NDVI in the growing season (April to September) increased from 0.0032/a and 0.003/a in the base period (1987-2008) to 0.0172/a and 0.01/a in the measurement period (2008-2018), for the Wei and Jing basins, respectively. In addition, the NDVI significantly increased from 21.78% and 31.32% in the baseline period (1987-2008) to 83.76% and 92.40% in the measurement period (2008-2018), respectively. (2) The random forest and classification and regression tree model (CART) can assess the contribution and sensitivity of various climate factors to NDVI. Precipitation, soil moisture, and temperature were found to be the three main factors that affect the NDVI of the study area, and their contributions were 37.05%, 26.42%, and 15.72%, respectively. The changes in precipitation and soil moisture in the entire Jing River Basin and the upper and middle reaches of the Wei River above Xianyang caused significant changes in NDVI. Furthermore, changes in precipitation and temperature led to significant changes in NDVI in the lower reaches of the Wei River. (3) The impact of human activities in the Wei and Jing basins on NDVI has gradually changed from negative to positive, which is mainly due to the implementation of soil and water conservation measures. The proportions of areas with positive effects of human activities were 80.88% and 81.95%, of which the proportions of areas with significant positive effects were 11.63% and 7.76%, respectively. These are mainly distributed in the upper reaches of the Wei River and the western and eastern regions of the Jing River. These areas are the key areas where soil and water conservation measures have been implemented in recent years, and the corresponding land use has transformed from cultivated land to forest and grassland. The negative effects accounted for 1.66% and 0.10% of the area, respectively, and were mainly caused by urban expansion and coal mining.
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Affiliation(s)
- Chenlu Huang
- College of Tourist (Institute of Human Geography), Xi’an International Studies University, Xi’an 710127, China;
| | - Qinke Yang
- College of Urban and Environment Sciences, Northwest University, Xi’an 710127, China
| | - Weidong Huang
- Hydrology and Water Resources Bureau of Gansu Province, Lanzhou 730000, China;
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Feng R, Wang F, Wang K. Spatial-temporal patterns and influencing factors of ecological land degradation-restoration in Guangdong-Hong Kong-Macao Greater Bay Area. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 794:148671. [PMID: 34323775 DOI: 10.1016/j.scitotenv.2021.148671] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/19/2021] [Accepted: 06/21/2021] [Indexed: 06/13/2023]
Abstract
Despite the fact that urban agglomerations have undergone extensive ecological land coverage modifications, exploration of the patterns and driving mechanisms associated with ecological land degradation (ELD) and ecological land restoration (ELR) in urban agglomerations is still limited. This study combined remote sensing technology, as well as landscape index and geographical detector to characterize the spatiotemporal patterns of ELD (isolating, adjacent, and enclosing degradation) and ELR (outlying, edge-expansion, and infilling restoration) in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1990 to 2019. Subsequently, the contributions, interactions, and driver changes were quantified. The results showed an ecological land shift from over-exploitation to balanced co-existence, which was facilitated by a spatiotemporal pattern transition from adjacent degradation-led (1990-2010) to edge-expansion restoration-led (2010-2019). Land urbanization rate and population density showed a stronger promoting effect on ELD than natural factors, while tertiary industry, topography, and soil conditions were more significant in ELR. The factors' nonlinear interaction enhanced the degradation-restoration pattern evolution and continued to increase over time-particularly the interaction between construction land expansion and other drivers. Additionally, from 2010 to 2019, 80% of the ELR socio-economic factors turned from negative to positive and gradually became to play a significant role. This study is expected to help ecological protection and restoration planners/managers recognize the factors' interactions and variations, and ultimately improve the ecological network structure that is designed to integrate the city with the ecosystem.
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Affiliation(s)
- Rundong Feng
- Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Fuyuan Wang
- Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China.
| | - Kaiyong Wang
- Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China.
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Kou P, Xu Q, Jin Z, Yunus AP, Luo X, Liu M. Complex anthropogenic interaction on vegetation greening in the Chinese Loess Plateau. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 778:146065. [PMID: 33721649 DOI: 10.1016/j.scitotenv.2021.146065] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/16/2021] [Accepted: 02/20/2021] [Indexed: 06/12/2023]
Abstract
Vegetation greening steered by land use management in the Chinese Loess Plateau has been widely reported, however studies that quantitatively assessing and explicitly linking the anthropogenic forcing on vegetation greening and browning are scarce. Here in this study, we calculate the increment and rate of change of fractional vegetation cover (FVC) from 1998 to 2018 in the Loess Plateau, and compare the results with changing rainfall, soil types, and Gross Domestic Product (GDP), to detail a systematic assessment of the role of the climate-vegetation-human nexus. We have observed that nearly 80% of the study area has undergone greening, and noticed that rainfall was not the main driver of rapid vegetation change, instead of human land use management such as, irrigation along the Yellow River, snowmelt-runoff irrigation, and irrigation from reservoirs formed by check dams contributed the most for the increased FVC in the Chinese Loess Plateau. Concurrently, rapid vegetation browning is almost fully driven by urban expansion. Our findings show that GDP growth promotes both browning and greening, indicative of sustainable development in the Loess plateau region. These contrasting trends reveal that the relationship between human activities and greening is very complex.
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Affiliation(s)
- Pinglang Kou
- The Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Qiang Xu
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China.
| | - Zhao Jin
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China; CAS Center for Excellence in Quaternary Science and Global Change, Xian 710061, China
| | - Ali P Yunus
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China
| | - Xiaobo Luo
- The Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Minghao Liu
- The Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Lamchin M, Wang SW, Lim CH, Ochir A, Pavel U, Gebru BM, Choi Y, Jeon SW, Lee WK. Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014. Glob Ecol Conserv 2020. [DOI: 10.1016/j.gecco.2020.e01299] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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9
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A Semi-Parametric Geographically Weighted Regression Approach to Exploring Driving Factors of Fractional Vegetation Cover: A Case Study of Guangdong. SUSTAINABILITY 2020. [DOI: 10.3390/su12187512] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R2 higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.
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A basin-level analysis of flood risk in urban and periurban areas: A case study in the metropolitan region of Buenos Aires, Argentina. Heliyon 2020; 6:e04517. [PMID: 32802974 PMCID: PMC7417906 DOI: 10.1016/j.heliyon.2020.e04517] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 01/15/2020] [Accepted: 07/17/2020] [Indexed: 11/24/2022] Open
Abstract
Flooding in urban and periurban areas is a complex phenomenon that results from the interplay between urban expansion and the dynamics of the hydrological system. Understanding both processes is essential to manage flood risk. This study aimed to analyze the flood risk in urban and periurban areas of the upper and middle basin of the Luján River (Metropolitan Region of Buenos Aires, Argentina) between 1985 and 2015. We assessed the factors that affect flood frequency by analyzing the precipitation variations obtained from meteorological data and applying hydrological models. We also used supervised classification of remote sensing imagery to detect increases in impervious surface areas that could enhance flooding. Furthermore, we combined both analyses to identify flood risk situations in the region. Our results indicated that maximum precipitation and hydrometric values remained stable during the study period, with a marked interannual variability due to the presence of dry and wet phases. During the dry phase (2011–2015), when flooding events were infrequent, there was a steady urban sprawl in the floodplain area and, as a result, more people would have subsequently become exposed to flood risk. Our results evidence the lack of regional policies to regulate the urban sprawl in flood risk regions.
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11
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Gao L, Wang X, Johnson BA, Tian Q, Wang Y, Verrelst J, Mu X, Gu X. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING : OFFICIAL PUBLICATION OF THE INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING (ISPRS) 2020; 159:364-377. [PMID: 36082112 PMCID: PMC7613353 DOI: 10.1016/j.isprsjprs.2019.11.018] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Green fractional vegetation cover (fc ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of fc via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIS, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of fc using RA algorithms are discussed to guide future research and directions.
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Affiliation(s)
- Lin Gao
- International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
| | - Xiaofei Wang
- International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
- Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China
| | - Brian Alan Johnson
- Institute for Global Environmental Strategies, Hayama, Kanagawa 240-0115, Japan
| | - Qingjiu Tian
- International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
- Corresponding authors at: International Institute for Earth System Science, Nanjing University, Nanjing 210023, China. (L. Gao), (X. Wang), (B.A. Johnson), (Q. Tian), (Y. Wang), (J. Verrelst), (X. Mu), (X. Gu)
| | - Yu Wang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Científc, Universitat de València, Paterna, València 46980, Spain
| | - Xihan Mu
- State Key Laboratory of Remote Sensing Science, College of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Xingfa Gu
- Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
- Corresponding authors at: International Institute for Earth System Science, Nanjing University, Nanjing 210023, China. (L. Gao), (X. Wang), (B.A. Johnson), (Q. Tian), (Y. Wang), (J. Verrelst), (X. Mu), (X. Gu)
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12
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Qi X, Zhang C, Wang K. Comparing Remote Sensing Methods for Monitoring Karst Rocky Desertification at Sub-pixel Scales in a Highly Heterogeneous Karst Region. Sci Rep 2019; 9:13368. [PMID: 31527677 PMCID: PMC6746695 DOI: 10.1038/s41598-019-49730-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 08/28/2019] [Indexed: 11/10/2022] Open
Abstract
Rugged karst terrain relief that creates shadows in satellite imagery, combined with high karst landscape heterogeneity stand in the way of fractional cover retrieval on karst rocky desertification (KRD) monitoring. In this study, we explored the feasibility of applying multispectral high spatial resolution Advanced Land Observing Satellite (ALOS) imagery for the fractional cover extraction of rocky outcrops. Dimidiate pixel model (DPM) and spectral mixture analysis (SMA) approaches (including simple endmember spectral mixture analysis and multiple endmember spectral mixture analysis) were selected to explore their feasibility for KRD monitoring through accuracy improvement for fraction estimation. Results showed fractional cover retrievals at the sub-pixel scale is essential in highly heterogeneous karst landscapes. Indeed, mixed pixels accounted for 93.7% of the study area in southwest China. Multiple endmember spectral mixture analysis achieved high overall accuracy (80.5%) in monitoring the percentage of rocky outcrop land cover. Furthermore, the predicted exposed bedrock coverage via spectral mixture analysis were similar in sunlit and shadow areas for the same surface types. This reflected that SMA methods could effectively reduce topographic effects of satellite imagery to improve the accuracy of fractional cover extraction at sub-pixel level in heterogeneous and rugged landscapes.
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Affiliation(s)
- Xiangkun Qi
- Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China.,Huanjiang Observation and Research Station for Karst Ecosystem, Chinese Academy of Sciences, Huanjiang, Hechi, 547100, China
| | - Chunhua Zhang
- Department of Geography and Geology, Algoma University, Sault Ste. Marie, ON, P6A2G4, Canada
| | - Kelin Wang
- Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China. .,Huanjiang Observation and Research Station for Karst Ecosystem, Chinese Academy of Sciences, Huanjiang, Hechi, 547100, China.
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Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016. REMOTE SENSING 2019. [DOI: 10.3390/rs11172044] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year−1 and 0.0020 year−1, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.
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Li H, Duan F, Ma Y, He K, Zhu L, Ma T, Ye S, Yang S, Huang T, Kimoto T. Case study of spring haze in Beijing: Characteristics, formation processes, secondary transition, and regional transportation. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 242:544-554. [PMID: 30007265 DOI: 10.1016/j.envpol.2018.07.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 04/28/2018] [Accepted: 07/01/2018] [Indexed: 05/13/2023]
Abstract
Continuous haze monitoring was conducted from 12:00 3 April to 12:00 8 April 2016 in Beijing, China to develop a more detailed understanding of spring haze characteristics. The PM2.5 concentration ranged from 6.30 to 165 μg m-3 with an average of 63.8 μg m-3. Nitrate was the most abundant species, accounting for 36.4% of PM2.5, followed by organic carbon (21.5%), NH4+ (19.3%), SO42- (18.8%), and elemental carbon (4.10%), indicating the key role of nitrate in this haze event. Species contribution varied based on the phase of the haze event. For example, sulfate concentration was high during the haze formation phase, nitrate was high during the haze, and secondary organic carbon (SOC) had the highest contribution during the scavenging phase. The secondary transition of sulfate was influenced by SO2, followed by relative humidity (RH) and Ox (O3+NO2). Nitrate formation occurred in two stages: through NO2 oxidation, which was vulnerable to Ox; and by the partitioning of N (+5) which was susceptible to RH and temperature. SOC tended to form when Ox and RH were balanced. According to hourly species behavior, sulfate and nitrate were enriched during haze formation when the mixed layer height decreased. However, SOC accumulated prior to the haze event and during formation, which demonstrated the strong contribution of secondary inorganic aerosols, and the limiting contribution of SOC to this haze case. Investigating backward trajectories showed that high speed northwestern air masses following a straight path corresponded to the clear periods, while southwesterly air masses which traversed heavily polluted regions brought abundant pollutants to Beijing and stimulated the occurrence of haze pollution. Results indicate that the control of NO2 needs to be addressed to reduce spring haze. Finally, the correlation between air mass trajectories and pollution conditions in Beijing reinforce the necessity of inter-regional cooperation and control.
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Affiliation(s)
- Hui Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Yongliang Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Lidan Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Siqi Ye
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Shuo Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Tao Huang
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka 543-0024, Japan
| | - Takashi Kimoto
- Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku Osaka 543-0024, Japan
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Li T, Lü Y, Fu B, Comber AJ, Harris P, Wu L. Gauging policy-driven large-scale vegetation restoration programmes under a changing environment: Their effectiveness and socio-economic relationships. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 607-608:911-919. [PMID: 28711852 DOI: 10.1016/j.scitotenv.2017.07.044] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 07/04/2017] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
Large-scale ecological restoration has been widely accepted globally as an effective strategy for combating environmental crises and to facilitate sustainability. Assessing the effectiveness of ecological restoration is vital for researchers, practitioners, and policy-makers. However, few practical tools are available to perform such tasks, particularly for large-scale restoration programmes in complex socio-ecological systems. By taking a "before and after" design, this paper formulates a composite index (Ej) based on comparing the trends of vegetation cover and vegetation productivity to assess ecological restoration effectiveness. The index reveals the dynamic and spatially heterogenic process of vegetation restoration across different time periods, which can be informative for ecological restoration management at regional scales. Effectiveness together with its relationship to socio-economic factors is explored via structural equation modeling for three time periods. The results indicate that the temporal scale is a crucial factor in representing restoration effectiveness, and that the effects of socio-economic factors can also vary with time providing insight for improving restoration effectiveness. A dual-track strategy, which promotes the development of tertiary industry in absorbing the rural labor force together with improvements in agricultural practices, is proposed as a promising strategy for enhancing restoration effectiveness. In this process, timely and long-term ecological restoration monitoring is advocated, so that the success and sustainability of such programmes is ensured, together with more informative decision making where socio-ecological interactions at differing temporal scales are key concerns.
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Affiliation(s)
- Ting Li
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yihe Lü
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Joint Center for Global Change Studies, Beijing 100875, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Bojie Fu
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Joint Center for Global Change Studies, Beijing 100875, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Alexis J Comber
- Leeds Institute for Data Analytics (LIDA) and School of Geography, University of Leeds, Leeds LS2 9JT, UK
| | - Paul Harris
- Sustainable Soil and Grassland Systems, Rothamsted Research, Okehampton, Devon, UK
| | - Lianhai Wu
- Sustainable Soil and Grassland Systems, Rothamsted Research, Okehampton, Devon, UK
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Spatial Heterogeneity of Typical Ecosystem Services and Their Relationships in Different Ecological–Functional Zones in Beijing–Tianjin–Hebei Region, China. SUSTAINABILITY 2017. [DOI: 10.3390/su10010006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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17
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18
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Observational Quantification of Climatic and Human Influences on Vegetation Greening in China. REMOTE SENSING 2017. [DOI: 10.3390/rs9050425] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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