1
|
Lai L, Liu Y, Zhang Y, Cao Z, Yin Y, Chen X, Jin J, Wu S. Long-term spatiotemporal mapping in lacustrine environment by remote sensing:Review with case study, challenges, and future directions. WATER RESEARCH 2024; 267:122457. [PMID: 39312829 DOI: 10.1016/j.watres.2024.122457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 09/13/2024] [Accepted: 09/14/2024] [Indexed: 09/25/2024]
Abstract
Satellite remote sensing, unlike traditional ship-based sampling, possess the advantage of revisit capabilities and provides over 40 years of data support for observing lake environments at local, regional, and global scales. In recent years, global freshwater and coastal waters have faced adverse environmental issues, including harmful phytoplankton blooms, eutrophication, and extreme temperatures. To comprehensively address the goal of 'reviewing the past, assessing the present, and predicting the future', research increasingly focuses on developing and producing algorithms and products for long-term and large-scale mapping. This paper provides a comprehensive review of related research, evaluating the current status, shortcomings, and future trends of remote sensing datasets, monitoring targets, technical methods, and data processing platforms. The analysis demonstrated that the long-term spatiotemporal dynamic lake monitoring transition is thriving: (i) evolving from single data sources to satellite collaborative observations to keep a trade-off between temporal and spatial resolutions, (ii) shifting from single research targets to diversified and multidimensional objectives, (iii) progressing from empirical/mechanism models to machine/deep/transfer learning algorithms, (iv) moving from local processing to cloud-based platforms and parallel computing. Future directions include, but are not limited to: (i) establishing a global sampling data-sharing platform, (ii) developing precise atmospheric correction algorithms, (iii) building next-generation ocean color sensors and virtual constellation networks, (iv) introducing Interpretable Machine Learning (IML) and Explainable Artificial Intelligence (XAI) models, (v) integrating cloud computing, big data/model/computer, and Internet of Things (IoT) technologies, (vi) crossing disciplines with earth sciences, hydrology, computer science, and human geography, etc. In summary, this work offers valuable references and insights for academic research and government decision-making, which are crucial for enhancing the long-term tracking of aquatic ecological environment and achieving the Sustainable Development Goals (SDGs).
Collapse
Affiliation(s)
- Lai Lai
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuchen Liu
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China
| | - Yuchao Zhang
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China.
| | - Zhen Cao
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuepeng Yin
- University of Chinese Academy of Sciences, Beijing 100049, China; State Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Xi Chen
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jiale Jin
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Shuimu Wu
- Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Nanjing University of Information Science and Technology, Nanjing 210044, China
| |
Collapse
|
2
|
Lai L, Zhang Y, Han T, Zhang M, Cao Z, Liu Z, Yang Q, Chen X. Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121134. [PMID: 38749137 DOI: 10.1016/j.jenvman.2024.121134] [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: 05/19/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 06/05/2024]
Abstract
Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.
Collapse
Affiliation(s)
- Lai Lai
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuchao Zhang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Tao Han
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Min Zhang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhen Cao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhaomin Liu
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qiduo Yang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xi Chen
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; Nanjing University of Information Science and Technology, Nanjing, 210044, China
| |
Collapse
|
3
|
Hu M, Ma R, Xue K, Cao Z, Xiong J, Loiselle SA, Shen M, Hou X. Eutrophication evolution of lakes in China: Four decades of observations from space. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134225. [PMID: 38583204 DOI: 10.1016/j.jhazmat.2024.134225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/24/2024] [Accepted: 04/04/2024] [Indexed: 04/09/2024]
Abstract
The lake eutrophication is highly variable in both time and location, and greatly restricts the sustainable development of water resources. The lack of national eutrophication evaluation for multi-scale lakes limits the pertinent governance and sustainable management of water quality. In this study, a remote sensing approach was developed to capture 40-year dynamics of trophic state index (TSI) for nationwide lakes in China. 32% of lakes (N = 1925) in China were eutrophic and 26% were oligotrophic, and a longitudinal pattern was discovered, with the 40-year average TSI of 62.26 in the eastern plain compared to 23.72 in the Tibetan Plateau. A decreasing trend was further observed in the past four decades with a correlation of -0.16, which was mainly discovered in the Tibetan Plateau lakes (r > -0.90, p < 0.01). The contribution of climate change and human activities was quantified and varied between lake zones, with anthropogenic factors playing a dominant role in the east plain lakes (88%, N = 473) and large lakes are subject to a more complex driving mechanism (≥ 3 driving factors). The study expands the spatiotemporal scale for eutrophication monitoring and provides an important base for strengthening lake management and ecological services.
Collapse
Affiliation(s)
- Minqi Hu
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Ronghua Ma
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Nanjing 211135, China.
| | - Kun Xue
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Zhigang Cao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Junfeng Xiong
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | | | - Ming Shen
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Xuan Hou
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| |
Collapse
|
4
|
Lai L, Liu Y, Zhang Y, Cao Z, Yang Q, Chen X. MODIS Terra and Aqua images bring non-negligible effects to phytoplankton blooms derived from satellites in eutrophic lakes. WATER RESEARCH 2023; 246:120685. [PMID: 37804806 DOI: 10.1016/j.watres.2023.120685] [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: 08/19/2023] [Revised: 09/18/2023] [Accepted: 09/29/2023] [Indexed: 10/09/2023]
Abstract
Phytoplankton-induced lake eutrophication has drawn ongoing interest on a global scale. One of the most popular remote sensing satellite data for observing long-term dynamic changes in phytoplankton is Moderate-resolution Imaging Spectroradiometer (MODIS). However, it is worth noting that MODIS provides two images with different transit times: Terra (local time, about 10:30 am) and Aqua (local time, about 1:30 pm), which may result in a considerable bias in monitoring phytoplankton bloom areas due to the rapid migration of phytoplankton under wind or hydrodynamic conditions. To analyze this quantitatively, we selected MODIS Terra and Aqua images to generate datasets of phytoplankton bloom areas in Lake Taihu from 2003 to 2022. The results showed that Terra more frequently detected larger ranges of phytoplankton blooms than Aqua, whether on daily, monthly, or annual scales. In addition, long-term trend changes, seasonal characteristics, and abrupt years also varied with different transit times. Terra detected mutation years earlier, while Aqua displayed more pronounced seasonal characteristics. There were also differences in sensitivity to climate factors, with Terra being more responsive to temperature and wind speed on monthly and annual scales, while Aqua was more sensitive to nutrient and meteorological factors. These conclusions have also been further confirmed in Lake Chaohu, Lake Dianchi, and Lake Hulun. In conclusion, our findings strongly advocate for a linear relationship to fit Terra to Aqua results to mitigate long-term monitoring errors of phytoplankton blooms in inland lakes (R2 = 0.70, RMSE = 101.56). It is advised to utilize satellite data with transit times between 10 am and 1 pm to track phytoplankton bloom changes and to consider the diverse applications resulting from the transit times of Terra and Aqua.
Collapse
Affiliation(s)
- Lai Lai
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing ,100049, China
| | - Yuchen Liu
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China; Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing, 210093, China
| | - Yuchao Zhang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing ,100049, China.
| | - Zhen Cao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing ,100049, China
| | - Qiduo Yang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing ,100049, China
| | - Xi Chen
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; Nanjing University of Information Science and Technology, Nanjing, 210044, China
| |
Collapse
|
5
|
Li N, Zhang Y, Zhang Y, Shi K, Qian H, Yang H, Niu Y, Qin B, Zhu G, Woolway RI, Jeppesen E. The unprecedented 2022 extreme summer heatwaves increased harmful cyanobacteria blooms. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165312. [PMID: 37414191 DOI: 10.1016/j.scitotenv.2023.165312] [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: 03/07/2023] [Revised: 06/27/2023] [Accepted: 07/02/2023] [Indexed: 07/08/2023]
Abstract
Heatwaves are increasing and expected to intensify in coming decades with global warming. However, direct evidence and knowledge of the mechanisms of the effects of heatwaves on harmful cyanobacteria blooms are limited and unclear. In 2022, we measured chlorophyll-a (Chla) at 20-s intervals based on a novel ground-based proximal sensing system (GBPSs) in the shallow eutrophic Lake Taihu and combined in situ Chla measurements with meteorological data to explore the impacts of heatwaves on cyanobacterial blooms and the potential relevant mechanisms. We found that three unprecedented summer heatwaves (July 4-15, July 22-August 16, and August 18-23) lasting a total of 44 days were observed with average maximum air temperatures (MATs) of 38.1 ± 1.9 °C, 38.7 ± 1.9 °C, and 40.2 ± 2.1 °C, respectively, and that these heatwaves were characterized by high air temperature, strong PAR, low wind speed and rainfall. The daily Chla significantly increased with increasing MAT and photosynthetically active radiation (PAR) and decreasing wind speed, revealing a clear promotion effect on harmful cyanobacteria blooms from the heatwaves. Moreover, the combined effects of high temperature, strong PAR and low wind, enhanced the stability of the water column, the light availability and the phosphorus release from the sediment which ultimately boosted cyanobacteria blooms. The projected increase in heatwave occurrence under future climate change underscores the urgency of reducing nutrient input to eutrophic lakes to combat cyanobacteria growth and of improving early warning systems to ensure secure water management.
Collapse
Affiliation(s)
- Na Li
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Science, Beijing 100049, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China
| | - Yunlin Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China.
| | - Yibo Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China; Nanjing Zhongke Deep Insight Technology Research Institute Co., Ltd., Nanjing 211899, China
| | - Kun Shi
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China; Nanjing Zhongke Deep Insight Technology Research Institute Co., Ltd., Nanjing 211899, China
| | - Haiming Qian
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China; School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, China
| | - Huayin Yang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Science, Beijing 100049, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China
| | - Yongkang Niu
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China
| | - Boqiang Qin
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China; Nanjing Zhongke Deep Insight Technology Research Institute Co., Ltd., Nanjing 211899, China
| | - Guangwei Zhu
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China; Nanjing Zhongke Deep Insight Technology Research Institute Co., Ltd., Nanjing 211899, China
| | - R Iestyn Woolway
- School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey, Wales, United Kingdom
| | - Erik Jeppesen
- Department of Ecoscience and WATEC, Aarhus University, 6000 Aarhus, Denmark; Sino-Danish Centre for Education and Research, Beijing 100049, China; Limnology Laboratory, Department of Biological Sciences, Centre for Ecosystem Research and Implementation (EKOSAM), Middle East Technical University, 06800 Ankara, Turkey; Institute of Marine Sciences, Middle East Technical University, 33731 Mersin, Turkey
| |
Collapse
|
6
|
Lai L, Zhang Y, Cao Z, Liu Z, Yang Q. Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163357. [PMID: 37028659 DOI: 10.1016/j.scitotenv.2023.163357] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 05/27/2023]
Abstract
Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R2 = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R2 = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R2 = 0.85, MAPE = 22.68 %; XGBoost: R2 = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R2 = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R2 = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.
Collapse
Affiliation(s)
- Lai Lai
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuchao Zhang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhen Cao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaomin Liu
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiduo Yang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
7
|
Zeng S, Qin Z, Ruan B, Lei S, Yang J, Song W, Sun Q. Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China. ENVIRONMENTAL RESEARCH 2023; 221:115219. [PMID: 36608765 DOI: 10.1016/j.envres.2023.115219] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/01/2023] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (CPP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating CPP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the CPP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in CPP. This study provides the long-term datasets of CPP in inland waters and new insights for future water eutrophication control and restoration efforts.
Collapse
Affiliation(s)
- Shuai Zeng
- South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou, 510535, PR China
| | - Zihong Qin
- South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou, 510535, PR China
| | - Baozhen Ruan
- School of Geography and Remote Sensing, Guangzhou University, Guangzhou, 510006, PR China
| | - Shaohua Lei
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, PR China
| | - Jian Yang
- South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou, 510535, PR China
| | - Weiwei Song
- South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou, 510535, PR China
| | - Qiang Sun
- South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou, 510535, PR China.
| |
Collapse
|
8
|
Rolim SBA, Veettil BK, Vieiro AP, Kessler AB, Gonzatti C. Remote sensing for mapping algal blooms in freshwater lakes: a review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:19602-19616. [PMID: 36642774 DOI: 10.1007/s11356-023-25230-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.
Collapse
Affiliation(s)
- Silvia Beatriz Alves Rolim
- Programa de Pós-Graduação Em Sensoriamento Remoto, Universidade Federal do Rio Grande Do Sul (UFRGS), Rio Grande do Sul, Porto Alegre, Brazil
| | - Bijeesh Kozhikkodan Veettil
- Laboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City, Vietnam.
- Faculty of Applied Technology, School of Engineering and Technology, Van Lang University, Ho Chi Minh City, Vietnam.
| | - Antonio Pedro Vieiro
- Departamento de Mineralogia e Petrologia, Instituto de Geociências, Universidade Federal do Rio Grande Do Sul (UFRGS), Rio Grande do Sul, Porto Alegre, Brazil
| | - Anita Baldissera Kessler
- Departamento de Geodésia, Instituto de Geociências, Universidade Federal do Rio Grande Do Sul (UFRGS), Rio Grande do Sul, Porto Alegre, Brazil
| | - Clóvis Gonzatti
- Departamento de Mineralogia e Petrologia, Instituto de Geociências, Universidade Federal do Rio Grande Do Sul (UFRGS), Rio Grande do Sul, Porto Alegre, Brazil
| |
Collapse
|
9
|
Wang S, Zhang X, Wang C, Chen N. Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159480. [PMID: 36265631 DOI: 10.1016/j.scitotenv.2022.159480] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/09/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Cyanobacterial blooms in most lakes exhibit extraordinary changes in time and space. Herein, a cyanobacterial prediction model was designed for Lake Taihu based on a machine learning method. This method can generate temporally continuous (24 moments throughout the day) cyanobacterial data at a fine spatial scale of 9 km. The hourly meteorological data for 24 moments of the day were obtained from ERA5-Land data. Areal coverage of cyanobacterial blooms was derived from the hourly Geostationary Ocean Color Imager reflectance data observed only eight times a day (from ~8:00 to ~15:00, UTC+8). The cyanobacterial and meteorological data of eight moments in Lake Taihu from 2011 to 2020 were used to design the prediction model. The results were compared and validated employing nine training strategies to determine the best cyanobacterial prediction model for Lake Taihu (R = 0.42; root mean square error = 0.10). With the best-fitted model utilizing meteorological data (2011-2020), the area coverage of cyanobacterial blooms at the other 16 moments during a day were estimated. Based on this, the regional and temporal characteristics of diurnal bloom variation were evaluated at an hourly scale. The results indicated that the hourly variations in the areal coverage of cyanobacterial blooms at 24 moments of the day had similar patterns in each subregion of Lake Taihu with minor seasonal variations. The six meteorological variables adopted to construct the model had similar diurnal changes but with diverse value ranges among the seasons. Further analysis revealed that three meteorological variables (temperature, surface pressure, and evaporation) were positively related to diurnal bloom variations at an hourly scale. Overall, these results illustrate that meteorological conditions can affect the occurrence of cyanobacterial blooms at multiple time scales (e.g., hourly, daily, or monthly). The developed cyanobacterial prediction model can provide cyanobacterial data when cyanobacterial data is unavailable for the target waterbody.
Collapse
Affiliation(s)
- Siqi Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China.
| | - Xiang Zhang
- Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China; National Engineering Research Centre of Geographic Information System, China University of Geosciences, Wuhan 430074, China
| | - Chao Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China
| | - Nengcheng Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China; National Engineering Research Centre of Geographic Information System, China University of Geosciences, Wuhan 430074, China.
| |
Collapse
|
10
|
Wang S, Zhang X, Chen N, Tian L, Zhang Y, Nam WH. A systematic review and quantitative meta-analysis of the relationships between driving forces and cyanobacterial blooms at global scale. ENVIRONMENTAL RESEARCH 2023; 216:114670. [PMID: 36341794 DOI: 10.1016/j.envres.2022.114670] [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: 06/20/2022] [Revised: 10/05/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
The global expansion of cyanobacterial blooms poses a major risk to the safety of freshwater resources. As a result, many explorations have been performed at a regional scale to determine the underlying impact mechanism of cyanobacterial blooms for one or several waterbodies. However, two questions still need to be answered quantitatively at a global scale to assist the water management. One is to specify which factors were often selected as the driving forces of cyanobacterial blooms, and the other is to estimate their quantitative relationships. For that, this paper applied a systematic literature review for 41 peer-reviewed studies published before May 2021 and a statistical meta-analysis based on the Pearson's or Spearman's correlation coefficients from 27 studies. These results showed that the water quality, hydraulic conditions, meteorological conditions and nutrient levels were often considered the driving forces of cyanobacterial blooms in global freshwater systems. Among these, meteorological conditions and nutrient level had the highest probability of being chosen as the driving force. In addition, knowledge of the quantitative relationships between these driving forces and cyanobacterial blooms was newly synthesized based on the correlation coefficients. The results indicated that, at a global scale, meteorological conditions were negatively related to cyanobacterial blooms, and other driving forces, such as water quality, hydraulic conditions and nutrient levels, were positively related to cyanobacterial blooms. In addition, the measurement indicators of these driving forces had diverse forms. For example, the nutrient level can be measured by the concentration of different forms of nitrogen or phosphorus, which may lead to different results in correlation analysis. Thus, a subgroup meta-analysis was necessary for the subdivided driving forces and cyanobacterial blooms, which had a better accuracy. Overall, the synthesized knowledge can help guide advanced cyanobacteria-centered water management, especially when the necessary cyanobacterial data of targeting waterbodies are inaccessible.
Collapse
Affiliation(s)
- Siqi Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China; Hubei Luojia Laboratory, Wuhan, 430079, China.
| | - Xiang Zhang
- National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China; Hubei Luojia Laboratory, Wuhan, 430079, China.
| | - Nengcheng Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China; National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China; Hubei Luojia Laboratory, Wuhan, 430079, China
| | - Liqiao Tian
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China
| | - Yan Zhang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China
| | - Won-Ho Nam
- School of Social Safety and Systems Engineering, Institute of Agricultural Environmental Science, National Agricultural Water Research Center, Hankyong National University, Anseong, Republic of Korea
| |
Collapse
|