Insights into Spatiotemporal Variations in the NPP of Terrestrial Vegetation in Africa from 1981 to 2018
"> Figure 1
<p>Study area. The background is land cover types.</p> "> Figure 2
<p>Spatial distribution of average NPP of 1981–2018 in Africa.</p> "> Figure 3
<p>Slope trend analysis results of the annual average NPP from 1981–2018 in Africa (significantly increased: <span class="html-italic">slope</span> > 0 & F ≥ 7.396; increased: <span class="html-italic">slope</span> > 0 & 4.113 ≤ F < 7.396; no significant change: F < 4.113; reduced: <span class="html-italic">slope</span> < 0 & 4.113 ≤ F < 7.396; significantly reduced: <span class="html-italic">slope</span> < 0 & F ≥ 7.396).</p> "> Figure 4
<p>Slope trend analysis results of average NPP from 1981–2018 in the spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) (significantly increased: <span class="html-italic">slope</span> > 0 & F ≥ 7.396; increased: <span class="html-italic">slope</span> > 0 & 4.113 ≤ F < 7.396; no significant change: F < 4.113; reduced: <span class="html-italic">slope</span> < 0 & 4.113 ≤ F < 7.396; significantly reduced: <span class="html-italic">slope</span> < 0 & F ≥ 7.396).</p> "> Figure 4 Cont.
<p>Slope trend analysis results of average NPP from 1981–2018 in the spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) (significantly increased: <span class="html-italic">slope</span> > 0 & F ≥ 7.396; increased: <span class="html-italic">slope</span> > 0 & 4.113 ≤ F < 7.396; no significant change: F < 4.113; reduced: <span class="html-italic">slope</span> < 0 & 4.113 ≤ F < 7.396; significantly reduced: <span class="html-italic">slope</span> < 0 & F ≥ 7.396).</p> "> Figure 5
<p>Degree of fluctuation of the annual average NPP from 1981 to 2018 in Africa (less fluctuation: CV ≤ 0.1; low fluctuation: 0.1 < CV ≤ 0.2; moderate fluctuation: 0.2 < CV ≤ 0.3; high fluctuation: 0.3 < CV ≤ 0.4; very high fluctuation: CV > 0.4).</p> "> Figure 6
<p>Degree of fluctuation of average NPP from 1981 to 2018 in the spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) (less fluctuation: CV ≤ 0.1; low fluctuation: 0.1 < CV ≤ 0.2; moderate fluctuation: 0.2 < CV ≤ 0.3; high fluctuation: 0.3 < CV ≤ 0.4; very high fluctuation: CV > 0.4).</p> "> Figure 7
<p>Interannual changes in anomalous NPP during different seasons.</p> "> Figure 8
<p>Mutation analysis results of annual NPP from 1981 to 2018.</p> "> Figure 9
<p>Mutation analysis results of NPP from 1981 to 2018 in the spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>) and winter (<b>d</b>).</p> "> Figure 10
<p>Wavelet time series analysis of NPP in Africa, 1981–2018. The wavelet coefficient contour map (<b>left</b>) and wavelet variance graph (<b>right</b>).</p> "> Figure 11
<p>Wavelet time series analysis of NPP in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>) and winter (<b>d</b>). The wavelet coefficient contour map (<b>left</b>) and wavelet variance graph (<b>right</b>).</p> ">
Abstract
:1. Introduction
2. Study Region and Data
2.1. Study Region
2.2. Research Data
2.2.1. NPP Dataset and Preprocessing
2.2.2. Land Cover Data and Preprocessing
3. Research Methods
3.1. NPP Trend Analysis
3.2. NPP Volatility Analysis
3.3. Anomaly NPP
3.4. Mann–Kendall Mutation Test
3.5. Wavelet Analysis
4. Results and Analysis
4.1. NPP Distribution Pattern in Africa
4.2. NPP Trend Analysis in Africa
4.3. Volatility Analysis of NPP in Africa
4.4. NPP Temporal Features in Different Seasons
4.5. Mutation Analysis of NPP Time Series in Different Seasons
4.6. Wavelet Analysis of the NPP
5. Discussion
6. Conclusions
- The NPP of the tropical rainforest and the deciduous broadleaved forest and deciduous needle-leaved forest on the north and south sides of tropical rainforest showed an increasing trend and showed low fluctuations because the tropical rainforest is in the equatorial rainy climate, with sufficient rainfall throughout the year and no obvious dry or wet season. However, the Congo basin, Gabon, Cameroon, Ghana, Nigeria, and Tanzania are affected by human activities, and the NPP in those areas showed a significant reduced trend and a high degree of fluctuation. The NPP in the Sahara arid area and South Africa arid area showed a significant reduced trend and showed a high degree of fluctuation.
- From 1981 to 2018, NPP in Africa generally showed a slow upward trend, which can be segmented into three stages. In the declining phase from 1981 to 1992, the NPP was below the average in most years. In the stable growth stage from 1993 to 2000, the NPP peaked in 2000. In the fluctuation stage from 2001 to 2018, the NPP value was above average in all years except 2015 and 2016, when the NPP value was low due to abnormally high temperatures and drought. The Mann–Kendall test further showed that the variation in NPP in Africa followed a true rising trend, rather than a random fluctuation, and the annual and seasonal changes reached a significant level. The year and season had one mutation point within the confidence interval in 1994, 1995, 1992, 1993, and 1994.
- On an annual scale, Africa obviously has short cycles of 4 to 8 years, 15 to 21 years, and 23 to 35 years and long cycles of 42 to 62 years, while the annual and seasonal NPPs have obvious oscillations on timescales of 7 years, 20 years, 29 years, and 55 years. Among them, the 55-year period had the strongest signal, which was the first primary period. The second main cycle was different from the third main cycle.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Liu, J.; Chen, J.M.; Cihlar, J.; Chen, W. Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data. J. Geophys. Res.-Atmos. 1999, 104, 27735–27754. [Google Scholar] [CrossRef]
- Field, C.B.; Behrenfeld, M.J.; Randerson, J.T.; Falkowski, P. Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components. Science 1998, 281, 237–240. [Google Scholar] [CrossRef]
- IGBP Terrestrial Carbon Working Group. The Terrestrial Carbon Cycle: Implications for the Kyoto Protocol. Science 1998, 280, 1393–1394. [Google Scholar] [CrossRef]
- Cai, X.M. The Ecology of Ecosystem; Science Press: Beijing, China, 2000. [Google Scholar]
- Cui, J.B.; Li, C.S.; Trettin, C. Analyzing the ecosystem carbon and hydrologic characteristics of forested wetland using a biogeochemical process model. Glob. Chang. Biol. 2005, 11, 278–289. [Google Scholar] [CrossRef]
- Lieth, H. Primary Production: Terrestrial Ecosystems. Hum. Ecol. 1973, 1, 303–332. [Google Scholar] [CrossRef]
- Seino, H.S.; Uchijima, Z.B. Global Distribution of Net Primary Productivity of Terrestrial Vegetation. J. Agric. Meteorol. 1992, 48, 39–48. [Google Scholar] [CrossRef]
- Parton, W.J.; Stewart, J.W.B.; Cole, C.V. Dynamics of C, N, P and S in Grassland Soils: A Model. Biogeochemistry 1988, 5, 109–131. [Google Scholar] [CrossRef]
- Running, S.W.; Hunt, E.R. 8—Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models. In Scaling Physiological Processes; Ehleringer, J.R., Field, C.B., Eds.; Academic Press: San Diego, CA, USA, 1993; pp. 141–158. [Google Scholar]
- Potter, C.S.; Randerson, J.T.; Field, C.B.; Matson, P.A.; Vitousek, P.M.; Mooney, H.A.; Klooster, S.A. Terrestrial ecosystem production: A process model based on global satellite and surface data. Glob. Biogeochem. Cycles 1993, 7, 811–841. [Google Scholar] [CrossRef]
- Prince, S.D.; Goward, S.N.; Hanan, N.P. Estimation of Global Primary Production Using NOAA/NASA Pathfinder AVHRR Land Data Set. Igarss 1995, 2, 1000–1002. [Google Scholar]
- Hong, C.Q.; Jin, X.B.; Chen, C.C.; Wang, S.M.; Yang, X.H.; Xiang, X.M. Overview on estimation models of land net primary productivity integrating remote sensing data. Prog. Geogr. 2017, 36, 924–939. [Google Scholar]
- Liu, J.; Chen, J.M.; Cihlar, J.; Chen, W.; Park, W.M. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sens. Environ. 1997, 62, 158–175. [Google Scholar] [CrossRef]
- Matsushita, B.; Tamura, M. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sens. Environ. 2002, 81, 58–66. [Google Scholar] [CrossRef]
- Qiu, S.Y.; Liang, L.; Wang, Q.J.; Geng, D.; Wu, J.J.; Wang, S.G.; Chen, B.Q. Estimation of European Terrestrial Ecosystem NEP Based on an Improved CASA Model. IEEE J.-Stars. 2023, 16, 1244–1255. [Google Scholar] [CrossRef]
- Mao, J.F.; Wang, B.; Dai, Y.J.; Woodward, F.I.; Hanson, P.J.; Lomas, M.R. Improvements of a dynamic global vegetation model and simulations of carbon and water at an upland-oak forest. Adv. Atmos. Sci. 2007, 24, 311–322. [Google Scholar] [CrossRef]
- Gulbeyaz, O.; Bond-Lamberty, B.; Akyurek, Z.; West, T.O. A new approach to evaluate the MODIS annual NPP product (MOD17A3) using forest field data from Turkey. Int. J. Remote Sens. 2018, 39, 2560–2578. [Google Scholar] [CrossRef]
- Ghaderpour, E.; Mazzanti, P.; Mugnozza, G.S.; Bozzano, F. Coherency and phase delay analyses between land cover and climate across Italy via the least-squares wavelet software. Int. J. Appl. Earth Obs. 2023, 118, 103241. [Google Scholar] [CrossRef]
- Aalijahan, M.; Karatas, A.; Lupo, A.R.; Efe, B.; Khosravichenar, A. Analyzing and Modeling the Spatial-Temporal Changes and the Impact of GLOTI Index on Precipitation in the Marmara Region of Turkiye. Atmosphere 2023, 14, 489. [Google Scholar] [CrossRef]
- Liang, L.; Qiu, S.Y.; Yan, J.; Shi, Y.Y.; Geng, D. VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China. Int. J. Environ. Res. Public Health 2021, 18, 7967. [Google Scholar] [CrossRef]
- Castañeda-Moya, E.; Twilley, R.R.; Rivera-Monroy, V.H. Allocation of biomass and net primary productivity of mangrove forests along environmental gradients in the Florida Coastal Everglades, USA. For. Ecol. Manag. 2013, 307, 226–241. [Google Scholar] [CrossRef]
- Eisfelder, C.; Klein, I.; Niklaus, M.; Kuenzer, C. Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables. J. Arid Environ. 2014, 103, 17–30. [Google Scholar] [CrossRef]
- Liang, L.; Di, L.P.; Zhang, L.P.; Deng, M.X.; Qin, Z.H.; Zhang, S.H.; Lin, H. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sens. Environ. 2015, 165, 123–134. [Google Scholar] [CrossRef]
- Chen, F.J.; Shen, Y.J.; Li, Q.; Guo, Y.; Xu, L.M. Spatio-temporal Variation Analysis of Ecological Systems NPP in China in Past 30 years. Sci. Geogr. Sin. 2011, 31, 1409–1414. [Google Scholar]
- Liang, L.; Di, L.P.; Huang, T.; Wang, J.H.; Li, L.; Wang, L.J.; Yang, M.H. Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm. Remote Sens. 2018, 10, 1940. [Google Scholar] [CrossRef]
- Lu, G.; Han, M.; Xu, Z.H.; Zhu, J.Q.; Niu, X.R. Spatiotemporal variations of net primary productivity in new wetlands of the Yellow River Delta. Chin. J. Ecol. 2019, 38, 1113–1122. [Google Scholar]
- Jin, Y.; Lin, Q.N.; Mao, S.P. Challenges and Countermeasures for Deepening China-Africa Agricultural Investment Cooperation. Intertrade 2022, 10, 19–26. [Google Scholar]
- Zhan, S.M. To Meet Climate Changes: The African Stance and Concerns. West Asia Afr. 2009, 10, 42–49+80. [Google Scholar]
- Statistics Division, United Nations. Available online: https://unstats.un.org/unsd/methodology/m49/ (accessed on 15 October 2022).
- Statistics Division, United Nations. Standard Country or Area Codes for Statistical Use; United Nations: New York, NY, USA, 1999. [Google Scholar]
- ONU. World Statistics Pocketbook 2019; United Nations: New York, NY, USA, 2019. [Google Scholar]
- Liu, L.Y.; Bai, Y.; Sun, R.; Niu, Z.G. Stereo Observation and Inversion of the Key Parameters of Global Carbon Cycle: Project Overview and Mid-Term Progressess. Remote Sens. Technol. Appl. 2021, 36, 11–24. [Google Scholar]
- Yu, T.; Sun, R.; Xiao, Z.Q.; Zhang, Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global Land Surface Satellite Data. Remote Sens. 2018, 10, 327. [Google Scholar] [CrossRef]
- National Earth System Science Data Center, National Science & Technology Infrastructure of China. Available online: http://www.geodata.cn.
- Congalton, R.G.; Gu, J.Y.; Yadav, K.; Thenkabail, P.; Ozdogan, M. Global Land Cover Mapping: A Review and Uncertainty Analysis. Remote Sens. 2015, 6, 12070–12093. [Google Scholar] [CrossRef]
- Yang, R.F.; Yin, S.Y. Analysis of the variation of NPP and its responses with climate changes of global tropical forests. Acta Ecol. Sin. 2019, 39, 8504–8515. [Google Scholar]
- Liang, L.; Geng, D.; Yan, J.; Qiu, S.Y.; Shi, Y.Y.; Wang, S.G.; Wang, L.J.; Zhang, L.P.; Kang, J.R. Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China. Remote Sens. 2022, 14, 1902. [Google Scholar] [CrossRef]
- Li, Y.; Wang, Y.F.; Sun, Y.J.; Lei, Y.C.; Shao, W.C.; Li, J. Temporal-spatial characteristics of NPP and its response to climate change of Larix forests in Jilin Province. Acta Ecol. Sin. 2022, 42, 947–959. [Google Scholar]
- Wang, F.; Wang, Z.; Zhang, Y. Spatio-temporal Variations in Vegetation Net Primary Productivity and Their driving Factors in Anhui Province from 2000 to 2015. Acta Ecol. Sin. 2018, 38, 2754–2767. [Google Scholar]
- Guo, Z.X.; Wang, Z.M.; Liu, D.W.; Song, K.S.; Song, C.C. Analysis of temporal and spatial features of farmland productivity in the Sanjiang Plain. Trans. CSA 2009, 25, 249–254. [Google Scholar]
- Wei, F.Y. Statistical Diagnosis and Prediction Technology of Modern Climate; China Meteorological Press: Beijing, China, 1999. [Google Scholar]
- Mallick, J.; Talukdar, S.; Alsubih, M.; Salam, R.; Ahmed, M.; Kahla, N.B.; Shamimuzzaman, M. Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theor. Appl. Climatol. 2020, 143, 823–841. [Google Scholar] [CrossRef]
- Sneyers, R. On the Statistical Analysis of Series of Observations. Technical. Note 1990, 143. Available online: https://www.researchgate.net/publication/309122890_On_the_statistical_analysis_of_series_of_observations (accessed on 21 May 2023).
- Liang, L.; Sun, Q.; Luo, X.; Wang, J.H.; Zhang, L.P.; Deng, M.X.; Di, L.P.; Liu, Z.X. Long-term spatial and temporal variations of vegetative drought based on vegetation condition index in China. Ecosphere 2017, 8, e01919. [Google Scholar] [CrossRef]
- Fu, C.B.; Wang, Q. Definition and detection methods of abrupt climate change. Sci. Atmos. Sinica 1992, 16, 482–493. [Google Scholar]
- Torrence, C.; Compo, G.P. A practical guide to wavelet analysis. B Am. Meteorol. Soc. 1998, 79, 61–78. [Google Scholar] [CrossRef]
- Liu, Y.; Xu, G.; Yin, Z.; Hu, C.; Wang, Y.; Liao, F. Spatio-temporal change of surface air temperature in Anhui province in the context of global warming from 1960 to 2014. J. Nat. Resour. 2017, 32, 680–691. [Google Scholar]
- Lara, C.; Saldías, G.S.; Paredes, A.L.; Cazelles, B.; Broitman, B.R. Temporal Variability of MODIS Phenological Indices in the Temperate Rainforest of Northern Patagonia. Remote Sens. 2018, 10, 956. [Google Scholar] [CrossRef]
- Wanders, N.; Bachas, A.; He, X.G.; Huang, H.; Koppa, A.; Mekonnen, Z.T.; Pagán, B.R.; Peng, L.Q.; Vergopolan, N.; Wang, K.J.; et al. Forecasting the hydroclimatic signature of the 2015-16 El Niño event on the western U.S. J. Hydrometeorol. 2017, 18, 177–186. [Google Scholar] [CrossRef]
- Zhao, X.J.; Wu, W.J.; Liu, G.L. Analysis of Global NPP Variation Pattern Using Data Driven Method. Remote Sens. Inf. 2019, 34, 26–33. [Google Scholar]
- Yao, G.M. The Roots of Agricultural Crisis in Africa. West Asia Afr. 2002, 3, 22–25+79. [Google Scholar]
- Shi, Y.J. Study on Innovation of Agricultural Cooperation Model Between China and African Countries. Ph.D. Thesis, Shihezi University, Shihezi, China, 2009. [Google Scholar]
- Zuo, B. Development of a New Detection Method for Climate Trend Turning and the Spatial-Temporal Characteristics of Global Surface Air Temperature Trend Turnings. Ph.D. Thesis, Lanzhou University, Lanzhou, China, 2019. [Google Scholar]
- Li, Y. Study on Global Semi-Arid Climate Change. Ph.D. Thesis, Lanzhou University, Lanzhou, China, 2015. [Google Scholar]
- Yang, Z.F.; Qi, Y.L.; Wu, Z.X. Variation characteristics of global dryland temperature and precipitation in the past 60 years. Gansu Sci. Technol. 2018, 34, 23–27. [Google Scholar]
- Chen, S.Y. Study on the Response Mechanism of Main Vegetation Types Growth Variation to Climate Change in Africa Based on GLASS-LAI. Master’s Thesis, Nanjing University, Nanjing, China, 2016. [Google Scholar]
- Wang, T.; Wang, M. The Current Situation, Opportunities and Challenges of Low-Carbon Development in Africa. J. Southwest Pet. Univ. (Soc. Sci. Ed.) 2015, 17, 1–9. [Google Scholar]
- Kankeu, R.S.; Sonwa, D.J.; Atyi, I.E.; Nkal, N.M.M. Quantifying post logging biomass loss using satellite images and ground measurements in Southeast Cameroon. J. For. Res. 2016, 27, 1415–1426. [Google Scholar] [CrossRef]
- Perez, M.R.; de Blas, D.E.; Nasi, R.; Sayer, J.A.; Sassen, M.; Angoue, C.; Gami, N.; Ndoye, O.; Ngono, G.; Nguinguiri, J.C.; et al. Logging in the Congo Basin: A multi-country characterization of timber companies. For. Ecol. Manag. 2005, 214, 221–236. [Google Scholar] [CrossRef]
- Antropov, O.; Rauste, Y.; Praks, J.; Seifert, F.; Hame, T. Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series. Remote Sens. 2021, 13, 740. [Google Scholar] [CrossRef]
- Sona, M.F.M. Classification and Net Primary Productivity of Potential Natural Vegetation in Africa Continent from 1911 to 2013. Ph.D. Thesis, Northwest Normal University, Lanzhou, China, 2016. [Google Scholar]
- Chen, J.; Zhang, Y.C.; Cihlar, J.; Chen, W. Regional differences of land extreme precipitation and temperature changes under climate change condition. Plateau Meteor. 2016, 35, 955–968. [Google Scholar]
Slope of Average NPP | F Test Value | Variation Grade of NPP |
---|---|---|
slope > 0 | F ≥ 7.396 | Significantly increased |
4.113 ≤ F < 7.396 | Increased | |
F < 4.113 | No significant change | |
slope < 0 | F < 4.113 | |
4.113 ≤ F < 7.396 | Reduced | |
F ≥ 7.396 | Significantly reduced |
CV Value | Fluctuation Degree of NPP |
---|---|
CV ≤ 0.1 | Less fluctuation |
0.1 < CV ≤ 0.2 | Low fluctuation |
0.2 < CV ≤ 0.3 | Moderate fluctuation |
0.3 < CV ≤ 0.4 | High fluctuation |
CV > 0.4 | Very high fluctuation |
Change Level of NPP | Spring | Summer | Autumn | Winter |
---|---|---|---|---|
Significantly reduced | 23% | 18% | 18% | 21% |
Reduced | 5% | 6% | 7% | 5% |
No significant change | 53% | 59% | 56% | 51% |
Increased | 6% | 5% | 5% | 6% |
Significantly increased | 14% | 11% | 14% | 16% |
Fluctuation Degree of NPP | Spring | Summer | Autumn | Winter |
---|---|---|---|---|
Less fluctuation | 26% | 17% | 20% | 25% |
Low fluctuation | 19% | 29% | 21% | 20% |
Moderate fluctuation | 13% | 23% | 20% | 14% |
High fluctuation | 9% | 12% | 15 % | 13% |
Very high fluctuation | 32% | 20% | 24% | 29% |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wang, Q.; Liang, L.; Wang, S.; Wang, S.; Zhang, L.; Qiu, S.; Shi, Y.; Shi, J.; Sun, C. Insights into Spatiotemporal Variations in the NPP of Terrestrial Vegetation in Africa from 1981 to 2018. Remote Sens. 2023, 15, 2748. https://doi.org/10.3390/rs15112748
Wang Q, Liang L, Wang S, Wang S, Zhang L, Qiu S, Shi Y, Shi J, Sun C. Insights into Spatiotemporal Variations in the NPP of Terrestrial Vegetation in Africa from 1981 to 2018. Remote Sensing. 2023; 15(11):2748. https://doi.org/10.3390/rs15112748
Chicago/Turabian StyleWang, Qianjie, Liang Liang, Shuguo Wang, Sisi Wang, Lianpeng Zhang, Siyi Qiu, Yanyan Shi, Jin Shi, and Chen Sun. 2023. "Insights into Spatiotemporal Variations in the NPP of Terrestrial Vegetation in Africa from 1981 to 2018" Remote Sensing 15, no. 11: 2748. https://doi.org/10.3390/rs15112748