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Song W, A Y, Wang Y, Fang Q, Tang R. Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning. J Contam Hydrol 2024; 260:104282. [PMID: 38101229 DOI: 10.1016/j.jconhyd.2023.104282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/27/2023] [Accepted: 12/06/2023] [Indexed: 12/17/2023]
Abstract
Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China's surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake's water quality. To ensure the continued health of Hulun Lake's water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.
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Affiliation(s)
- Wei Song
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Yinglan A
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; Innovation Research Center of Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Yuntao Wang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; Innovation Research Center of Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
| | - Qingqing Fang
- School of Water Conservancy and Hydropower Engineering, North China Electric Power University, Beijing 102206, China
| | - Rong Tang
- China Institute of Water Resources and Hydropower Research, Beijing 100038, China
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2
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Ibrahim GRF, Rasul A, Abdullah H. Assessing how irrigation practices and soil moisture affect crop growth through monitoring Sentinel-1 and Sentinel-2 data. Environ Monit Assess 2023; 195:1262. [PMID: 37782379 DOI: 10.1007/s10661-023-11871-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 09/11/2023] [Indexed: 10/03/2023]
Abstract
This study authorizes processes and approaches using optical and microwave data to determine the availability of water in the study area at any given moment. This will aid in identifying the optimal time and location for irrigation to enhance crop growth. For this purpose, a set of spectral vegetation parameters (from Sentinel-2), soil moisture (from Sentinel-1), evapotranspiration, and surface temperature (from Landsat-8) were used, along with field data on water content and irrigation timing. The results showed that both NDVI and NDMI are highly sensitive to moisture, making them the best indices for determining the timing and location of irrigation. This research contributes to sustainable agricultural development. It has implications for farmers, policymakers, and researchers in optimizing irrigation schedules, developing policies for sustainable agriculture, and enhancing crop productivity while conserving water resources. This approach can be particularly useful in regions facing water scarcity, where the efficient use of water resources is crucial for sustainable agricultural development.
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Affiliation(s)
- Gaylan Rasul Faqe Ibrahim
- Geography Department, Faculty of Arts, Soran University, Soran, Kurdistan Region, 44008, Iraq.
- Department of Geography, College of Human Sciences, University of Halabja, Halabja, 46006, Iraq.
| | - Azad Rasul
- Geography Department, Faculty of Arts, Soran University, Soran, Kurdistan Region, 44008, Iraq.
| | - Haidi Abdullah
- ITC Faculty Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
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3
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Vaithiyanathan D, Sudalaimuthu K. Area-to-point regression Kriging approach fusion of Landsat 8 OLI and Sentinel 2 data for assessment of soil macronutrients at Anaimalai, Coimbatore. Environ Monit Assess 2022; 194:916. [PMID: 36255534 DOI: 10.1007/s10661-022-10571-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 04/05/2022] [Indexed: 06/16/2023]
Abstract
Spectral indices-based soil prediction models derived from multispectral datasets are too intricate in terms of accuracy as well as resolution. Complications arise while incorporating multispectral datasets for regional-scale spatial assessment of soil macronutrients. Sporadically satellite image fusion techniques have been used for soil nutrient interpolation to circumvent the complications. The fusion of multispectral bands encompasses precise soil information that cannot be observed as accurate with single satellite dataset. In this study, fusion of near infrared regions of Landsat 8 Operational Land Imager and Sentinel 2 has been observed for its contribution on soil macronutrient assessments. Area-to-point regression Kriging (ATPRK) approach is followed in fusing the two satellite imagery and in situ soil spectral have used for the validation of the resultant. Comparative statistical analysis on Landsat 8 OLI band 5 (wavelength: 845-885 nm), Sentine-2 band 8,8A (wavelength: 785-900 nm) datasets and fused satellite bands provides R2 values of 0.8209, 0.8436, and 0.8763 respectively. Regression models y = (0.25006 ± 0.00754) + (0.0000313)x, y = (0.25252 ± 0.0062) + (0.0000810)x, and y = (0.23715 ± 0.0062) + (0.0001210)x for nitrogen, phosphorus, and potassium respectively aids for soil macronutrient interpolation and assessments. Computations reveals the ranges of nitrogen, phosphorus and potassium that floats from 48 to 295 kg/ha, 5.0 to 37 kg/ha, and 32 to 455 kg/ha in the study area. Fusion of satellite imagery by ATPRK approaches in soil macronutrient study at regional scale brings the novelty of the study.
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Affiliation(s)
- Dhayalan Vaithiyanathan
- Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, India
| | - Karuppasamy Sudalaimuthu
- Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
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Kulkarni R, Khare K, Khanum H. Detecting, extracting, and mapping of inland surface water using Landsat 8 Operational Land Imager: A case study of Pune district, India. F1000Res 2022; 11:774. [PMID: 36704046 PMCID: PMC9839946 DOI: 10.12688/f1000research.121740.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/04/2022] [Indexed: 02/02/2023] Open
Abstract
Background: Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. Methods: A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared. Results: The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement. Conclusions: MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.
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Affiliation(s)
- Rushikesh Kulkarni
- Department of Civil Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, 412115, India,
| | - Kanchan Khare
- Department of Civil Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, 412115, India,
| | - Humera Khanum
- Department of Civil Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, 412115, India
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Deutsch ES, Fortin MJ, Cardille JA. Assessing the current water clarity status of ~100,000 lakes across southern Canada: A remote sensing approach. Sci Total Environ 2022; 826:153971. [PMID: 35183627 DOI: 10.1016/j.scitotenv.2022.153971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 01/18/2022] [Accepted: 02/14/2022] [Indexed: 06/14/2023]
Abstract
Canada has more lakes than any other country, making comprehensive monitoring a huge challenge. As more and more satellite data become readily available, and as faster data processing systems make massive satellite data operations possible, new opportunities exist to use remote sensing to develop comprehensive assessments of water quality at very large spatial scales. In this study, we use a published empirical algorithm to estimate Secchi depth from Landsat 8 reflectance data in order to estimate water clarity in lakes across southern Canada. Combined with ancillary information on lake morphological, hydrological, and watershed geological and landuse characteristics, we were able to assess broad spatial patterns in water clarity for the first time. Ecological zones, underlying geological substrate, and lake depth had particularly strong influences on clarity across the whole country. Lakes in western mountain ecozones had significantly clearer waters than those in the prairies and plains, while lakes in sedimentary rock formations tended to have lower clarity than lakes in intrusive rock. Deep lakes were significantly clearer than shallow lakes over most of the country. Water clarity was also significantly influenced by human impact (urbanization, agriculture, and industry) in the watershed, with most lakes in high impact areas having low clarity or very low clarity. Finally, we used in situ measured data to help interpret the underlying optical water column constituents influencing clarity across Canada, and found that chlorophyll-a, total suspended solids, and color dissolved organic matter all had strong but varying underlying effects on water clarity across different ecozones. This research provides an important step towards further research on the relationship between water column optical properties and the health and vulnerability status of lakes across the country.
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Affiliation(s)
- Eliza S Deutsch
- Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada.
| | - Marie-Josée Fortin
- Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada.
| | - Jeffrey A Cardille
- Department of Natural Resources Sciences and Bieler School of Environment, McGill University, Macdonald-Stewart Building, Montreal, QC H9X 3V9, Canada.
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Sánchez-Hernández D, Aguirre-Salado CA, Sánchez-Díaz G, Aguirre-Salado AI, Soubervielle-Montalvo C, Reyes-Cárdenas O, Reyes-Hernández H, Santana-Juárez MV. Modeling spatial pattern of dengue in North Central Mexico using survey data and logistic regression. Int J Environ Health Res 2021; 31:872-888. [PMID: 31835907 DOI: 10.1080/09603123.2019.1700938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 11/27/2019] [Indexed: 06/10/2023]
Abstract
Dengue is a major public health concern mainly in tropical and subtropical environments worldwide. Despite several attempts to prevent this disease occurring in tropical regions of Mexico, it has not yet been controlled. This work focused on spatial modeling of confirmed dengue fever cases that occurred during the period 2010-2014 in the Huasteca Potosina region of Mexico. Multivariable Logistic Regression Modeling (MLRM) was used to determine the relationship between explanatory variables and the presence/absence of dengue. Model performance was evaluated using the area under curve (AUC) of the relative operating characteristic (ROC); AUC > 0.95. A high spatial resolution map was created to reveal the most probable patterns of dengue risk. Our results can be used for targeted control and prevention programs at local and regional levels. This methodology can be applied to other major diseases that are spatially distributed in accordance with environmental factors.
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Affiliation(s)
| | | | - Guillermo Sánchez-Díaz
- Faculty of Engineering, Universidad Autonoma de San Luis Potosí, San Luis Potosí, Mexico
| | | | | | - Oscar Reyes-Cárdenas
- Faculty of Engineering, Universidad Autonoma de San Luis Potosí, San Luis Potosí, Mexico
| | - Humberto Reyes-Hernández
- Faculty of Social Sciences and Humanities, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico
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Arias-Rodriguez LF, Duan Z, Díaz-Torres JJ, Basilio Hazas M, Huang J, Kumar BU, Tuo Y, Disse M. Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine. Sensors (Basel) 2021; 21:4118. [PMID: 34203863 DOI: 10.3390/s21124118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 11/16/2022]
Abstract
Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.
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8
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Cao H, Han L, Li W, Liu Z, Li L. Inversion and distribution of total suspended matter in water based on remote sensing images-A case study on Yuqiao Reservoir, China. Water Environ Res 2021; 93:582-595. [PMID: 32954623 DOI: 10.1002/wer.1460] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 08/10/2020] [Accepted: 08/28/2020] [Indexed: 06/11/2023]
Abstract
In this paper, Yuqiao Reservoir is taken as the research object. The total suspended matter (TSM) produced by the economic development in the upper reaches of the reservoir and its surrounding areas has brought great ecological harm to the safe operation of the reservoir. Satellite remote sensing technology provides a good way to obtain the temporal and spatial variation of TSM in the study area. Two field surveys were carried out in the Yuqiao Reservoir, a total of 44 sampling points collected in the two tests. The spectral data and concentration of TSM were obtained. We developed and validated a robust empirical model to estimate the concentration of TSM in the water of the Yuqiao Reservoir for the first time. The TSM distribution map of the Yuqiao Reservoir in 2013-2018 is retrieved based on Landsat 8 OLI images. This paper analyzes the spatial distribution characteristics of TSM concentration in the Yuqiao Reservoir for several years, as well as the interannual, seasonal, and monthly variation laws and development trends. The results show that the spatial distribution of TSM in Yuqiao Reservoir shows a decreasing trend from the periphery to the center; the interannual changes are mainly as follows: The annual change trend of TSM in Yuqiao Reservoir is not obvious; the seasonal changes are significant: the highest in summer (higher than 40 mg/L), the second in autumn, and the lowest in spring and winter (lower than 15 mg/L); and the monthly changes show regular fluctuations: In a year cycle, the concentration of TSM generally shows an inverted V-shaped trend; that is, TSM increases gradually from January to August and decreases gradually from August to December. The research results of this paper can be applied to other similar types of land water bodies, which will promote the wide application of Landsat 8 OLI images in the monitoring of TSM in lakes, rivers, and reservoirs in different regions across China, and provide data support for the scientific management of the safe operation of research areas. PRACTITIONER POINTS: The monitoring model of TSM in Yuqiao Reservoir was built for the first time. Temporal and spatial analysis of TSM concentration in Yuqiao Reservoir for the first time. The concentration of TSM is in Yuqiao Reservoir greatly affected by wind speed and precipitation.
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Affiliation(s)
- Hongye Cao
- Geological Engineering and Geomatics, Chang'an University, Xi'an, China
| | - Ling Han
- Geological Engineering and Geomatics, Chang'an University, Xi'an, China
| | - Wei Li
- School of Environmental Science and Engineering, Tiangong University, Tianjin, China
| | - Zhiheng Liu
- Geological Engineering and Geomatics, Chang'an University, Xi'an, China
| | - Liangzhi Li
- Geological Engineering and Geomatics, Chang'an University, Xi'an, China
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9
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Chen Q, Huang M, Tang X. Eutrophication assessment of seasonal urban lakes in China Yangtze River Basin using Landsat 8-derived Forel-Ule index: A six-year (2013-2018) observation. Sci Total Environ 2020; 745:135392. [PMID: 31892484 DOI: 10.1016/j.scitotenv.2019.135392] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 11/04/2019] [Accepted: 11/04/2019] [Indexed: 06/10/2023]
Abstract
Lakes eutrophication have been a complex and serious problem for China's Yangtze River Basin. A series of algorithms based on different remote sensing dataset have been proposed to simulate the lakes trophic state. However, these algorithms are often targeted at a particular lake and cannot be applied to a watershed management. In this study, a Forel-Ule index (FUI) method based on Landsat 8 OLI image is proposed to simulate trophic state index (TSI) in three typical urban lakes (Dianchi, Donghu, and Chaohu) from 2013 to 2018. The results show that the Landsat 8 derived FUI can well represent the lake TSI with an accuracy of R2 = 0.6464 for the in situ experimental TSI dataset (N = 115) and R2 = 0.8065 for the lake average TSI dataset (N = 315). In the study period 2013-2018, the order of the simulated TSI is Dianchi > Chaohu > Donghu. Seasonal dynamics show differences where the percentage of eutrophic area in summer is significantly lower than the other seasons for Lake Dianchi and Chaohu. However, the percentage of eutrophic area for Lake Donghu is highest in summer and lowest in winter. To further detect the driving factors of eutrophication in study lakes, the Pearson correlation and multiple linear regression analyses were conducted. The results show that sunshine and temperature are, respectively, the most and the second most significant factors for Lake Dianchi with explanations of 14.8% and 22.0%; temperature and pollution are the main influencing factors for Lake Donghu (39.2% and 10.9% explanation, respectively) and Chaohu (57.2% and 60.7% explanations, respectively). In addition, the wind is another negatively significant factor for Lake Chaohu with an explanation of 31.3%. Our results serve as an example for other lakes in the Yangtze River Basin and support the formulation of effective strategies to reduce seasonal eutrophication.
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Affiliation(s)
- Qi Chen
- College of Hydropower and Information Engineering, Huazhong University of Science and Technology, NO. 1037, Luoyu Road, Wuhan 430074, China
| | - Mutao Huang
- College of Hydropower and Information Engineering, Huazhong University of Science and Technology, NO. 1037, Luoyu Road, Wuhan 430074, China.
| | - Xiaodong Tang
- College of Hydropower and Information Engineering, Huazhong University of Science and Technology, NO. 1037, Luoyu Road, Wuhan 430074, China
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10
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Chen N, Wang S, Zhang X, Yang S. A risk assessment method for remote sensing of cyanobacterial blooms in inland waters. Sci Total Environ 2020; 740:140012. [PMID: 32569911 DOI: 10.1016/j.scitotenv.2020.140012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/27/2020] [Accepted: 06/04/2020] [Indexed: 06/11/2023]
Abstract
The widespread occurrence of Cyanobacterial blooms (CABs) in inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatially continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, the dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial, and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA was executed and validated in three different lakes of Wuhan urban agglomeration (WUA) with different trophic states. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in the east part of Lake LongGan was higher than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification data and results of the existing study, including trophic level, ecology risk, and algal extent, the MIRA method was valuable for accurate and spatially continuous identifying the risk of CABs in inland waters with potential eutrophication trends.
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Affiliation(s)
- Nengcheng Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.; Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
| | - Siqi Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
| | - Xiang Zhang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China..
| | - Shangbo Yang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
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11
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Ferdous J, Rahman MTU. Developing an empirical model from Landsat data series for monitoring water salinity in coastal Bangladesh. J Environ Manage 2020; 255:109861. [PMID: 31786436 DOI: 10.1016/j.jenvman.2019.109861] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 10/28/2019] [Accepted: 11/11/2019] [Indexed: 06/10/2023]
Abstract
This study aims to develop an empirical model from Landsat data series to monitor the water salinity of coastal Bangladesh efficiently. Such a model can substitute expensive conventional techniques for assessing remote water quality. A set of equations connecting sensors 5 TM and 8 OLI were generated using multiple regression analysis. Radiometric and atmospheric corrections were carried out to enhance the quality of satellite images. Total 13 compositions of different bands including blue, green and red were considered to find the Coefficient of Determination (r2) with the field level EC (electrical conductivity) values collected from 74 sampling locations. Salinity data mainly EC values of coastal water were collected from primary and secondary sources. Considering the r2 values, significant band compositions were identified and then employed to generate linear equations. Such equation for Landsat 5 TM could detect water salinity (i.e. EC) accurately of around 82%. Similarly, the r2 value for Landsat 8 OLI was found as 0.76 that can confirm the applicability of Landsat data series to detect the change of salinity level of coastal water for a long period. The availability of coastal water was delineated by NDWI whereas salinity level was assessed using the developed equations for the year 2001 and 2019. Interestingly, it was observed that coastal areas having lower level of EC almost vanished whereas those of having higher level of EC were increased significantly between 2001 and 2019. Such increase in coastal water salinity is the result of combined effects of climatic and anthropogenic factors, which can pose a considerable risk to the coastal inhabitants including freshwater scarcity, food insecurity, and health hazard.
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Affiliation(s)
- Jannatul Ferdous
- Climate Change Lab, Department of Civil Engineering, Military Institute of Science and Technology, Mirpur, Dhaka-1216, Bangladesh.
| | - M Tauhid Ur Rahman
- Department of Civil Engineering, Military Institute of Science and Technology, Mirpur, Dhaka-1216, Bangladesh
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12
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Herndon K, Muench R, Cherrington E, Griffin R. An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel. Sensors (Basel) 2020; 20:s20020431. [PMID: 31940917 PMCID: PMC7014253 DOI: 10.3390/s20020431] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/09/2020] [Accepted: 01/10/2020] [Indexed: 11/28/2022]
Abstract
Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.
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Affiliation(s)
- Kelsey Herndon
- NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA
- Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35899, USA
- Correspondence:
| | - Rebekke Muench
- NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA
- Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35899, USA
| | - Emil Cherrington
- NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA
- Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35899, USA
| | - Robert Griffin
- NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA
- Department of Atmospheric and Earth Science, The University of Alabama in Huntsville, Huntsville, AL 35899, USA
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Abdi O. Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis. Sensors (Basel) 2019; 19:s19183965. [PMID: 31540009 PMCID: PMC6767512 DOI: 10.3390/s19183965] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 09/09/2019] [Accepted: 09/12/2019] [Indexed: 02/05/2023]
Abstract
Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.
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Affiliation(s)
- Omid Abdi
- Institute for Cartography, Department of Geosciences, Faculty of Environmental Sciences, TU Dresden, 01069 Dresden, Germany.
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14
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Nguyen UNT, Pham LTH, Dang TD. An automatic water detection approach using Landsat 8 OLI and Google Earth Engine cloud computing to map lakes and reservoirs in New Zealand. Environ Monit Assess 2019; 191:235. [PMID: 30900016 DOI: 10.1007/s10661-019-7355-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 03/01/2019] [Indexed: 06/09/2023]
Abstract
Monitoring water surface dynamics is essential for the management of lakes and reservoirs, especially those are intensively impacted by human exploitation and climatic variation. Although modern satellites have provided a superior solution over traditional methods in monitoring water surfaces, manually downloading and processing imagery associated with large study areas or long-time scales are time-consuming. The Google Earth Engine (GEE) platform provides a promising solution for this type of "big data" problems when it is combined with the automatic water extraction index (AWEI) to delineate multi-temporal water pixels from other forms of land use/land cover. The aim of this study is to assess the performance of a completely automatic water extraction framework by combining AWEI, GEE, and Landsat 8 OLI data over the period 2014-2018 in the case study of New Zealand. The overall accuracy (OA) of 0.85 proved the good performance of this combination. Therefore, the framework developed in this research can be used for lake and reservoir monitoring and assessment in the future. We also found that despite the temporal variability of climate during the period 2014-2018, the spatial areas of most of the lakes (3840) in the country remained the same at around 3742 km2. Image fusion or aerial photos can be employed to check the areal variation of the lakes at a finer scale.
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Affiliation(s)
- Uyen N T Nguyen
- Environmental Research Institute, The University of Waikato, Hamilton, New Zealand.
| | - Lien T H Pham
- HCMC University of Science, Vietnam National University, Ho Chi Minh City, Vietnam
| | - Thanh Duc Dang
- Institute for Water and Environment Research, Thuy Loi University, Ho Chi Minh City, Vietnam
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15
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Wang J, Sui L, Yang X, Wang Z, Liu Y, Kang J, Lu C, Yang F, Liu B. Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery. Sensors (Basel) 2019; 19:s19051221. [PMID: 30862001 PMCID: PMC6427152 DOI: 10.3390/s19051221] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 02/20/2019] [Accepted: 03/07/2019] [Indexed: 11/16/2022]
Abstract
Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.
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Affiliation(s)
- Jun Wang
- Geological Engineering and Institute of Surveying and Mapping, Chang'an University, Xi'an 710054, China.
| | - Lichun Sui
- Geological Engineering and Institute of Surveying and Mapping, Chang'an University, Xi'an 710054, China.
| | - Xiaomei Yang
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhihua Wang
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Yueming Liu
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Junmei Kang
- Geological Engineering and Institute of Surveying and Mapping, Chang'an University, Xi'an 710054, China.
| | - Chen Lu
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Fengshuo Yang
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Bin Liu
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
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Ren J, Zheng Z, Li Y, Lv G, Wang Q, Lyu H, Huang C, Liu G, Du C, Mu M, Lei S, Bi S. Remote observation of water clarity patterns in Three Gorges Reservoir and Dongting Lake of China and their probable linkage to the Three Gorges Dam based on Landsat 8 imagery. Sci Total Environ 2018; 625:1554-1566. [PMID: 29996452 DOI: 10.1016/j.scitotenv.2018.01.036] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 12/27/2017] [Accepted: 01/05/2018] [Indexed: 06/08/2023]
Abstract
The Secchi disk depth (ZSD) plays a critical role in describing water clarity. Several studies have shown linkages between Three Gorges Dam (TGD) and the downstream lacustrine ecosystem in the middle and lower Yangtze River basin. However, the potential influence on the ZSD fluctuation in the entire anthropogenic reservoirs of Three Gorges (ER) and Dongting Lake (DTL) has not been reported, possibly due to technical obstacles in obtaining statistically significant spatial and temporal results. We addressed this challenge by using remote sensing technology: the Landsat 8 Operational Land Imager (OLI). We proposed a new, robust remote-sensing algorithm to estimate ZSD from OLI imagery using red and green band-ratio, leading to MAPE of 21.68% and RMSE of 0.076m for ZSD ranging from 0.1m to 1.05m. After satisfactory image-based validation, the algorithm was implemented on OLI data to derive ZSD patterns over ER and DTL from 2013 to 2017. Several crucial findings can be drawn: 1) Spatial-temporal patterns of ZSD exhibited notable fluctuations over both ER and DTL, and they also demonstrated a significant correlation with each other because of the opposite temporal cycle of ZSD fluctuations between ER and DTL; 2) Temporally, monthly fluctuations of ZSD between ER and DTL had opposite temporal cycles, which was mainly attributed to the surface runoff and sediment discharge driven by the outbound runoff variations of TGD. Spatially, the heterogeneity of the ZSD pattern in ER might have resulted from the different geographical regions being divided by large anthropologic hydrological facilities, such as TGD; 3) The relationship between ZSD and total suspended matter (TSM) showed a significant negative correlation, as did the relationship between ZSD and Kd(490). These findings demonstrate that TSM often plays a principal role in light attenuation of extremely turbid inland waters; 4) An inversed phenomenon of water clarity was observed at the intersection of DTL and the Yangtze River around Chenglingji site (YRAC), which was due to the opposite temporal cycle of ZSD fluctuations between DTL and ER after the impoundment of TGD; and 5) Owing to the analysis of noise-equivalent ZSD, OLI data can be used to derive ZSD, since the imagery uncertainty is 0.07m by means of our band-ratio algorithm, which demonstrates similar results to MODIS. The proposed ZSD-derived algorithm in this study could be suitable for other turbid lakes or reservoirs to formulate related strategies of water quality management in the middle and lower Yangtze River basin, and the unveiled findings here improve our understanding of ZSD spatiotemporal fluctuations in large river-connected lakes, such as Poyang Lake.
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Affiliation(s)
- Jingli Ren
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; Gannan Normal University, Ganzhou 341000, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Zhubin Zheng
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; Gannan Normal University, Ganzhou 341000, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Yunmei Li
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Guonian Lv
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Qiao Wang
- Satellite Environment Application Center, Ministry of Environmental Protection, Beijing 100029, China.
| | - Heng Lyu
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Changchun Huang
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Ge Liu
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China.
| | - Chenggong Du
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China.
| | - Meng Mu
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China.
| | - Shaohua Lei
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China.
| | - Shun Bi
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China.
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17
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Yu H, Liu M, Du B, Wang Z, Hu L, Zhang B. Mapping Soil Salinity/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China. Sensors (Basel) 2018; 18:s18041048. [PMID: 29614727 PMCID: PMC5948890 DOI: 10.3390/s18041048] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 03/14/2018] [Accepted: 03/29/2018] [Indexed: 11/16/2022]
Abstract
Soil salinity and sodicity can significantly reduce the value and the productivity of affected lands, posing degradation, and threats to sustainable development of natural resources on earth. This research attempted to map soil salinity/sodicity via disentangling the relationships between Landsat 8 Operational Land Imager (OLI) imagery and in-situ measurements (EC, pH) over the west Jilin of China. We established the retrieval models for soil salinity and sodicity using Partial Least Square Regression (PLSR). Spatial distribution of the soils that were subjected to hybridized salinity and sodicity (HSS) was obtained by overlay analysis using maps of soil salinity and sodicity in geographical information system (GIS) environment. We analyzed the severity and occurring sizes of soil salinity, sodicity, and HSS with regard to specified soil types and land cover. Results indicated that the models' accuracy was improved by combining the reflectance bands and spectral indices that were mathematically transformed. Therefore, our results stipulated that the OLI imagery and PLSR method applied to mapping soil salinity and sodicity in the region. The mapping results revealed that the areas of soil salinity, sodicity, and HSS were 1.61 × 10⁶ hm², 1.46 × 10⁶ hm², and 1.36 × 10⁶ hm², respectively. Also, the occurring area of moderate and intensive sodicity was larger than that of salinity. This research may underpin efficiently mapping regional salinity/sodicity occurrences, understanding the linkages between spectral reflectance and ground measurements of soil salinity and sodicity, and provide tools for soil salinity monitoring and the sustainable utilization of land resources.
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Affiliation(s)
- Hao Yu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Key Laboratory of Wetland Ecology and Environment, Changchun 130102, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Mingyue Liu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Key Laboratory of Wetland Ecology and Environment, Changchun 130102, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Baojia Du
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Key Laboratory of Wetland Ecology and Environment, Changchun 130102, China.
| | - Zongming Wang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Key Laboratory of Wetland Ecology and Environment, Changchun 130102, China.
| | - Liangjun Hu
- Northeast Normal University, Key Laboratory for Vegetation Ecology Science of Ministry of Education, Changchun 130021, China.
| | - Bai Zhang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Key Laboratory of Wetland Ecology and Environment, Changchun 130102, China.
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18
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Li Y, Zhang Y, Shi K, Zhou Y, Zhang Y, Liu X, Guo Y. Spatiotemporal dynamics of chlorophyll-a in a large reservoir as derived from Landsat 8 OLI data: understanding its driving and restrictive factors. Environ Sci Pollut Res Int 2018; 25:1359-1374. [PMID: 29090433 DOI: 10.1007/s11356-017-0536-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 10/19/2017] [Indexed: 05/17/2023]
Abstract
Chlorophyll-a (Chla) is an important indicator of water quality and eutrophication status. Monitoring Chla concentration (C Chla ) and understanding the interactions between C Chla and related environmental factors (hydrological and meteorological conditions, nutrients enrichment, etc.) are necessary for assessing and managing water quality and eutrophication. An acceptable Landsat 8 OLI-based empirical algorithm for C Chla has been developed and validated, with a mean absolute percentage error of 14.05% and a root mean square error of 1.10 μg L-1. A time series of remotely estimated C Chla was developed from 2013 to 2015 and examined the relationship of C Chla to inflow rate, rainfall, temperature, and sunshine duration. Spatially, C Chla values in the riverine zone were higher than in the transition and lacustrine zones. Temporally, mean C Chla value were ranked as spring > summer > autumn > winter. A significant positive correlation [Pearson correlation coefficient (r) = 0.88, p < 0.001] was observed between the inflow rate and mean C Chla in the northwest segment of the Xin'anjiang Reservoir. However, no significant relation was observed between mean C Chla and meteorological conditions. Mean (± standard deviation) value for the ratio of total nitrogen concentration to total phosphorus concentration in our in situ dataset is 75.75 ± 55.72. This result supports that phosphorus is the restrictive factor to algal growth in Xin'anjiang Reservoir. In addition, the response of nutrients to Chla has spatial variabilities. Current results show the potential of Landsat 8 OLI data for estimating Chla in slight turbid reservoir and indicate that external pollution loading is an important driving force for the Chla spatiotemporal variability.
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Affiliation(s)
- Yuan 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, 73 East Beijing Road, Nanjing, 210008, People's Republic of China
- School of Tourism and City Management, Zhejiang Gongshang University, Hangzhou, 310018, 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, 73 East Beijing Road, Nanjing, 210008, People's Republic of 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, 73 East Beijing Road, Nanjing, 210008, People's Republic of China
| | - Yongqiang Zhou
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing, 210008, People's Republic of China
- University of Chinese Academy of Sciences, Beijing, 100049, 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, 73 East Beijing Road, Nanjing, 210008, People's Republic of China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaohan Liu
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing, 210008, People's Republic of China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yulong Guo
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450002, China
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Li Y, Zhang Y, Shi K, Zhu G, Zhou Y, Zhang Y, Guo Y. Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery. Sci Total Environ 2017; 599-600:1705-1717. [PMID: 28535599 DOI: 10.1016/j.scitotenv.2017.05.075] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 05/06/2017] [Accepted: 05/08/2017] [Indexed: 06/07/2023]
Abstract
Nutrient enrichment is a major cause of water eutrophication, and variations in nutrient enrichment are influenced by environmental changes and anthropogenic activities. Accurately estimating nutrient concentrations and understanding their relationships with environmental factors are vital to develop nutrient management strategies to mitigate eutrophication. Landsat 8 Operational Land Imager (OLI) data is used to estimate nutrient concentrations and analyze their responses to hydrological and meteorological conditions. Two well-accepted empirical models are developed and validated to estimate the total nitrogen (TN) and total phosphorus (TP) concentrations (CTN and CTP) in the Xin'anjiang Reservoir using Landsat 8 OLI data from 2013 to 2016. Spatially, CTN decreased from the transition zone to the riverine zone and the lacustrine zone. On the other hand, CTP decreased from the riverine zone to the transition zone and the lacustrine zone. Temporally, CTN displayed elevated values during the late fall and winter and had lower values during the summer and early fall, whereas CTP was higher during the spring and lower during the winter. Among the environmental factors, the rainfall and the inflow rate have strong positive correlations with the nutrient concentrations. TN is more sensitive to meteorological factors (wind speed, temperature, sunshine duration), and the spatial driving forces vary among the different sections of the reservoir. However, TP is more easily influenced by human activities, such as fishery and agricultural activities. Current results would improve our understanding of the drivers of nutrients spatiotemporal variability and the approach in this study can be applicable to other similar reservoir to develop related strategies to mitigate eutrophication.
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Affiliation(s)
- Yuan 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; School of Tourism and City Management, Zhejiang Gongshang University, Hangzhou 310018, 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.
| | - 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
| | - 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
| | - Yongqiang Zhou
- 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 Sciences, Beijing 100049, 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; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yulong Guo
- College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China
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Egberth M, Nyberg G, Næsset E, Gobakken T, Mauya E, Malimbwi R, Katani J, Chamuya N, Bulenga G, Olsson H. Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands. Carbon Balance Manag 2017; 12:8. [PMID: 28413852 PMCID: PMC5392451 DOI: 10.1186/s13021-017-0076-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 03/27/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Soil carbon and biomass depletion can be used to identify and quantify degraded soils, and by using remote sensing, there is potential to map soil conditions over large areas. Landsat 8 Operational Land Imager satellite data and airborne laser scanning data were evaluated separately and in combination for modeling soil organic carbon, above ground tree biomass and below ground tree biomass. The test site is situated in the Liwale district in southeastern Tanzania and is dominated by Miombo woodlands. Tree data from 15 m radius field-surveyed plots and samples of soil carbon down to a depth of 30 cm were used as reference data for tree biomass and soil carbon estimations. RESULTS Cross-validated plot level error (RMSE) for predicting soil organic carbon was 28% using only Landsat 8, 26% using laser only, and 23% for the combination of the two. The plot level error for above ground tree biomass was 66% when using only Landsat 8, 50% for laser and 49% for the combination of Landsat 8 and laser data. Results for below ground tree biomass were similar to above ground biomass. Additionally it was found that an early dry season satellite image was preferable for modelling biomass while images from later in the dry season were better for modelling soil carbon. CONCLUSION The results show that laser data is superior to Landsat 8 when predicting both soil carbon and biomass above and below ground in landscapes dominated by Miombo woodlands. Furthermore, the combination of laser data and Landsat data were marginally better than using laser data only.
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Affiliation(s)
- Mikael Egberth
- Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Gert Nyberg
- Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
- Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden
| | - Erik Næsset
- Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Terje Gobakken
- Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Ernest Mauya
- Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Rogers Malimbwi
- Department of Forest Mensuration and Management, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania
| | - Josiah Katani
- Department of Forest Mensuration and Management, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania
| | - Nurudin Chamuya
- Tanzania Forest Services Agency, Ministry of Natural Resources and Tourism, Morogoro, United Republic of Tanzania
| | - George Bulenga
- Department of Forest Mensuration and Management, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania
| | - Håkan Olsson
- Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden
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Zheng Z, Ren J, Li Y, Huang C, Liu G, Du C, Lyu H. Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake. Sci Total Environ 2016; 573:39-54. [PMID: 27552729 DOI: 10.1016/j.scitotenv.2016.08.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 08/01/2016] [Accepted: 08/03/2016] [Indexed: 06/06/2023]
Abstract
The diffuse attenuation coefficient, Kd(λ), is an important optical property. Environmental change and anthropogenic activities, however, have made it challenging to accurately assess Kd(λ) patterns in the extremely turbid inland waters. We addressed this challenge by using new Landsat 8 Operational Land Imager (OLI) imagery. For the bio-optical complexity of water, we proposed an empirical band-ratio algorithm for estimating Kd(490) using our in situ measurements. Based on the acceptable performance of an OLI image-based atmospheric correction and Kd(490) validation, the algorithm was then applied to OLI images to estimate Kd(490) patterns from April 2013 to April 2016, leading to several key findings: (1) Spatial-temporal patterns of Kd(490) varied significantly in Dongting Lake. The temporal heterogeneity of Kd(490) could be explained primarily by surface-runoff changes driven by regional precipitation. The spatial heterogeneity was due to sediment resuspension, resulting from sand dredging and shipping activities; (2) Kd(490) values that were inversed at the intersection of Dongting Lake and Yangtze River were observed for the first time near the Chengliji site and resulted from the opposing temporal cycle of Kd(490) variations between Dongting Lake and the Yangtze River; (3) There was a significant positive correlation between Kd(490) and total suspended matter (TSM). This confirms that TSM often plays a principal role in the attenuation of light in extremely turbid water bodies; (4) The empirical band-ratio algorithm worked well, not only for the broader Landsat archives, but also for the narrower Sentinel-2/3 for Kd(490) estimation, which demonstrates that the algorithm could be used to quantitatively monitor multi-decade records of Landsat observations and future applications of inland water quality in turbid inland waters, such as Dongting Lake and Poyang Lake.
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Affiliation(s)
- Zhubin Zheng
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; School of Geography and Planning, Gannan Normal University, Ganzhou 341000, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Jingli Ren
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; School of Geography and Planning, Gannan Normal University, Ganzhou 341000, China
| | - Yunmei Li
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Chuangchun Huang
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Ge Liu
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China
| | - Chenggong Du
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China
| | - Heng Lyu
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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Shao Z, Zhang L. Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China. Sensors (Basel) 2016; 16:s16060834. [PMID: 27338378 PMCID: PMC4934260 DOI: 10.3390/s16060834] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 05/17/2016] [Accepted: 05/27/2016] [Indexed: 11/16/2022]
Abstract
Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass.
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Affiliation(s)
- Zhenfeng Shao
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
| | - Linjing Zhang
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
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Torbick N, Corbiere M. A Multiscale Mapping Assessment of Lake Champlain Cyanobacterial Harmful Algal Blooms. Int J Environ Res Public Health 2015; 12:11560-78. [PMID: 26389930 DOI: 10.3390/ijerph120911560] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Revised: 09/03/2015] [Accepted: 09/06/2015] [Indexed: 11/17/2022]
Abstract
Lake Champlain has bays undergoing chronic cyanobacterial harmful algal blooms that pose a public health threat. Monitoring and assessment tools need to be developed to support risk decision making and to gain a thorough understanding of bloom scales and intensities. In this research application, Landsat 8 Operational Land Imager (OLI), Rapid Eye, and Proba Compact High Resolution Imaging Spectrometer (CHRIS) images were obtained while a corresponding field campaign collected in situ measurements of water quality. Models including empirical band ratio regressions were applied to map chlorophyll-a and phycocyanin concentrations; all sensors performed well with R2 and root-mean-square error (RMSE) ranging from 0.76 to 0.88 and 0.42 to 1.51, respectively. The outcomes showed spatial patterns across the lake with problematic bays having phycocyanin concentrations >25 µg/L. An alert status metric tuned to the current monitoring protocol was generated using modeled water quality to illustrate how the remote sensing tools can inform a public health monitoring system. Among the sensors utilized in this study, Landsat 8 OLI holds the most promise for providing exposure information across a wide area given the resolutions, systematic observation strategy and free cost.
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