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Remote Sensing Applications in Environmental Pollution Monitoring for Sustainable Development

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Special Issue Editors

Dr. Ling Yao

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
Interests: remote sensing, public health, environmental quality

Dr. Fan Zhang

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Guest Editor

Senseable City Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
Interests: GIScience; urban informatics; social sensing; GeoAI; urban visual intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Changchun Huang

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Guest Editor

School of Geography Science, Nanjing Normal University, Nanjing 210023, China
Interests: remote sensing; water optical properties; lake carbon cycle; organic carbon burial in sediment; greenhouse gas emission; dissolution and particulate organic matter

Special Issue Information

Dear Colleagues,

Environmental-pollution-associated population growth, urbanization, and industrialization have resulted in great pressure on the Earth’s sustainable development. Pollution in the atmosphere, water, soil, and so on poses a significant health threat to global citizens. Although scientists and policymakers have made great efforts to provide regular environmental reports to the public, timely or advanced pollution alerts and health advice remain an unsolved challenge. The lack of fine-grained observations of environmental pollutants, and synchronized personal health data, is the main obstacle. Recent advances in sensor resolution and data acquisition time, and the availability of big data make it possible to overcome the challenges of sparse and granular data.

Remote sensing allows for the measurement, integration, and presentation of multiscale spatiotemporal information. It has played a key role in sustainable development and covers a variety of subtopics in the fields of land resource surveying, environment change monitoring, water quality assessment, as well as near real-time disaster prevention and mitigation. Scientists and researchers recommend the use of remote sensing to integrate multidisciplinary knowledge for environmental assessment. It is believed that remote sensing applications in environment monitoring will have an increasing value in sustainable development, especially when combing data analysis with other techniques (e.g., geographic information system and deep learning).

This Special Issue seeks sustainable solutions to build a new environmental pollution monitoring system through remote sensing. We invite contributions to share their remote sensing applications in environment monitoring from the perspective of sustainable development. Any study that explores how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision making aimed at addressing sustainable environment challenges is encouraged. The topics can be but are not limited to:

Environmental assessment based on remote sensing big data;
Development of satellite-derived indices for water or soil diagnosis;
Evaluation of environment quality by integrating satellite data;
Remote-sensing-based solutions to sustainable environment;
Deep learning algorithms to retrieve pollution information from satellite observations.

Dr. Ling Yao
Dr. Fan Zhang
Dr. Changchun Huang
Guest Editors

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Keywords

remote sensing
big data
environment pollution
sustainable development
deep learning
public health
ecological service value
geospatial analysis

Published Papers (2 papers)

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Research

14 pages, 4491 KiB

Open AccessArticle

Application of Remote-Sensing-Based Hydraulic Model and Hydrological Model in Flood Simulation

by Chaowei Xu, Jiashuai Yang and Lingyue Wang

Sustainability 2022, 14(14), 8576; https://0-doi-org.brum.beds.ac.uk/10.3390/su14148576 - 13 Jul 2022

Cited by 4 | Viewed by 1429

Abstract

Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Environmental Pollution Monitoring for Sustainable Development)

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14 pages, 2129 KiB

Open AccessArticle

Retrieval of Chlorophyll a Concentration in Water Considering High-Concentration Samples and Spectral Absorption Characteristics

by Yun Xue, Yi-Min Wen, Zhong-Man Duan, Wei Zhang and Fen-Liang Liu

Sustainability 2021, 13(21), 12144; https://0-doi-org.brum.beds.ac.uk/10.3390/su132112144 - 03 Nov 2021

Cited by 1 | Viewed by 1415

Abstract

The envelope removal method has the advantage of suppressing the background spectrum and expanding the weak absorption characteristic information. However, for second-class water bodies with a relatively complex water quality, there are few studies on the inversion of chlorophyll a ( [...] Read more.

C h l - a

) concentration in water bodies that consider the spectral absorption characteristics. In addition, the current research on the inversion of the

C h l - a

concentration was carried out under the condition of sample concentration equilibrium. For areas with a highly variable

C h l - a

concentration, it is still challenging to establish a highly applicable and accurate

C h l - a

concentration inversion model. Taking Dongting Lake in China as an example, this study used high-concentration samples and spectral absorption characteristics to invert the

C h l - a

concentration. The decap method was used to preprocess the high-concentration samples with large deviations, and the envelope removal method was used to extract the spectral absorption characteristic parameters of the water body. On the basis of the correlation analysis between the water

C h l - a

concentration and the spectral absorption characteristics, the water

C h l - a

concentration was inverted. The results showed the following: (1) The bands that were significantly related to the

C h l - a

concentration and had a large correlation coefficient were mainly located in the three absorption valleys (400–580, 580–650, and 650–710 nm) of the envelope removal curve. Moreover, the correlation between the

C h l - a

concentration and the absorption characteristic parameters at 650–710 nm was better than that at 400–580 nm and 580–650 nm. (2) Compared with the conventional inversion model, the uncapped inversion model had a higher R_P² and a lower RMSE_P, and was closer to the predicted value of the 1:1 line. Moreover, the performance of the uncapped inversion model was better than that of the conventional inversion model, indicating that the uncapped method is an effective preprocessing method for high-concentration samples with large deviations. (3) The predictive capabilities of the ER_New model were significantly better than those of the R_New model. This shows that the envelope removal method can significantly amplify the absorption characteristics of the original spectrum, which can significantly improve the performance of the prediction model. (4) From the inversion models for the absorption characteristic parameters, the prediction models of A_{650–710 nm}_New and D_{650–710 nm}_New exhibited the best performance. The three combined models (A_{650–710 nm}&D_{650–710 nm}_New, A_{650–710 nm}&NI_New, A_{650–710 nm}&DI_New) also demonstrated good predictive capabilities. This demonstrates the feasibility of using the spectral absorption feature to retrieve the chlorophyll concentration. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Environmental Pollution Monitoring for Sustainable Development)

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