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Xu X, Chen D. Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity. ENVIRONMENTAL RESEARCH 2024; 252:119063. [PMID: 38740292 DOI: 10.1016/j.envres.2024.119063] [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: 08/14/2023] [Revised: 04/22/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024]
Abstract
The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001 to 2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20 to 19.29% for forest types and 20.49-35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001-2018 were 132.6 Pg C yr-1, 0.25 Pg C yr-2, and 1.57 Pg C yr-1, respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP.
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Affiliation(s)
- Xiaojun Xu
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China; College of Environment and Resources, College of Carbon Neutrality, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China.
| | - Danna Chen
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China; College of Environment and Resources, College of Carbon Neutrality, Zhejiang A&F University, Hangzhou, 311300, Zhejiang, China
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Banerjee A, Kang S, Meadows ME, Xia Z, Sengupta D, Kumar V. Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India. ENVIRONMENTAL RESEARCH 2023; 234:116541. [PMID: 37419198 DOI: 10.1016/j.envres.2023.116541] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/22/2023] [Accepted: 07/01/2023] [Indexed: 07/09/2023]
Abstract
To explore the spatio-temporal dynamics and mechanisms underlying vegetation cover in Haryana State, India, and implications thereof, we obtained MODIS EVI imagery together with CHIRPS rainfall and MODIS LST at annual, seasonal and monthly scales for the period spanning 2000 to 2022. Additionally, MODIS Potential Evapotranspiration (PET), Ground Water Storage (GWS), Soil Moisture (SM) and nighttime light datasets were compiled to explore their spatial relationships with vegetation and other selected environmental parameters. Non-parametric statistics were applied to estimate the magnitude of trends, along with correlation and residual trend analysis to quantify the relative influence of Climate Change (CC) and Human Activities (HA) on vegetation dynamics using Google Earth Engine algorithms. The study reveals regional contrasts in trends that are evidently related to elevation. An annual increasing trend in rainfall (21.3 mm/decade, p < 0.05), together with augmented vegetation cover and slightly cooler (-0.07 °C/decade) LST is revealed in the high-elevation areas. Meanwhile, LST in the plain regions exhibit a warming trend (0.02 °C/decade) and decreased in vegetation and rainfall, accompanied by substantial reductions in GWS and SM related to increased PET. Linear regression demonstrates a strongly significant relationship between rainfall and EVI (R2 = 0.92), although a negative relationship is apparent between LST and vegetation (R2 = -0.83). Additionally, increased LST in the low-elevation parts of the study area impacted PET (R2 = 0.87), which triggered EVI loss (R2 = 0.93). Moreover, increased HA resulted in losses of 25.5 mm GSW and 1.5 mm SM annually. The relative contributions of CC and HA are shown to vary with elevation. At higher elevations, CC and HA contribute respectively 85% and 15% to the increase in EVI. However, at lower elevations, reduced EVI is largely (79%) due to human activities. This needs to be considered in managing the future of vulnerable socio-ecological systems in the state of Haryana.
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Affiliation(s)
- Abhishek Banerjee
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China; Haryana Forest Department (HFD), Government of Haryana, Panchkula, 134109, India.
| | - Shichang Kang
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Michael E Meadows
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China; Department of Environmental and Geographical Science, University of Cape Town, Cape Town, 7701, South Africa
| | - Zilong Xia
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Dhritiraj Sengupta
- School of Geography and Environmental Sciences, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
| | - Vinod Kumar
- Haryana Forest Department (HFD), Government of Haryana, Panchkula, 134109, India
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Abstract
The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.
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Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis. REMOTE SENSING 2022. [DOI: 10.3390/rs14061331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).
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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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Populus euphratica Phenology and Its Response to Climate Change in the Upper Tarim River Basin, NW China. FORESTS 2021. [DOI: 10.3390/f12101315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Quantifying the phenological variations of Populus euphratica Olivier (P. euphratica) resulting from climate change is vital for desert ecosystems. There has previously been great progress in the influence of climate change on vegetation phenology, but knowledge of the variations in P. euphratica phenology is lacking in extremely arid areas. In this study, a modified method was proposed to explore P. euphratica phenology and its response to climate change using 18-year Global Land Surface Satellite (GLASS) leaf area index (LAI) time series data (2000–2017) in the upper Tarim River basin. The start of the growing season (SOS), length of the growing season (LOS), and end of the growing season (EOS) were obtained with the dynamic threshold method from the reconstructed growth time series curve by using the Savitzky–Golay filtering method. The grey relational analysis (GRA) method was utilized to analyze the influence between the phenology and the key climatic periods and factors. Importantly, we also revealed the positive and negative effects between interannual climate factors and P. euphratica phenology using the canonical correlation analysis (CCA) method, and the interaction between the SOS in spring and EOS in autumn. The results revealed that trends of P. euphratica phenology (i.e., SOS, EOS, and LOS) were not significant during the period from 2000–2017. The spring temperature and sunshine duration (SD) controlled the SOS, and the EOS was mainly affected by the temperature and SD from June–November, although the impacts of average relative humidity (RH) and precipitation (PR) on the SOS and EOS cannot be overlooked. Global warming may lead to SOS advance and EOS delay, and the increase in SD and PR may lead to earlier SOS and later EOS. Runoff was found to be a more key factor for controlling P. euphratica phenology than PR in this region.
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Tang Y, Xu X, Zhou Z, Qu Y, Sun Y. Estimating global maximum gross primary productivity of vegetation based on the combination of MODIS greenness and temperature data. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101307] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques. REMOTE SENSING 2021. [DOI: 10.3390/rs13122352] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.
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