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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 January 2024) | Viewed by 19889

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Special Issue Editors

Dr. Seonyoung Park

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Guest Editor

Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: drought; evapotranspiration; disaster; environmental impact assessment; soil and water conservation; remote sensing and GIS; agriculture
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jungho Im

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Guest Editor

Department of Civil, Urban, Earth, and Environmental Engineering, UNIST (Ulsan National Institute of Science and Technology), Ulsan, Republic of Korea
Interests: satellite remote sensing; aerosols; air quality; wild fire; urban heatwave; drought; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Cheolhee Yoo

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Guest Editor

School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
Interests: remote sensing; artificial intelligence; urban climate

Special Issue Information

Dear Colleagues,

For the last decade or so, there has been intense research activity regarding the exploitation of remote sensing technologies in environment monitoring (air quality, extreme temperatures, land-use/cover changes (LUCCs), disasters, etc.). It is important to monitor these environmental changes using enhanced technologies. Enhanced technologies such as new observations and analysis tools have been developed to monitor types, magnitudes, and rates of environmental changes. Remote sensing is one such technology that is suitable for effectively collecting data on a large scale with varied spatial, spectral, and temporal resolutions. A mass of satellite data has been employed to monitor environmental changes. Timely observations by remote sensing are enabling a better understanding of the environmental changes.

This Special Issue invites state-of-the-art research on monitoring environmental changes using satellite remote sensing data. In this Special Issue, we expect to introduce various studies covering remote sensing technologies that can be applied in monitoring environmental changes. Topics may cover a broad range of environmental changes as well as applications, such as ecosystem assessment and monitoring, urban environmental monitoring, and advanced methods for environmental applications. This Special Issue welcomes short letter-style manuscripts (10 pages or less) describing new observations, methods, and models for monitoring environmental changes.

Air quality;
Extreme temperatures;
Ecosystem assessment and monitoring;
Land-use/cover changes (LUCCs);
Arid environments and droughts;
Urban environmental monitoring;
Advanced methods for environmental applications;
Climate change;
Disaster monitoring;
New sensors/platforms for environmental studies.

Dr. Seonyoung Park
Prof. Dr. Jungho Im
Dr. Cheolhee Yoo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

environmental changes
disaster
climate change
remote sensing
LUCC
urban environmental monitoring

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Published Papers (8 papers)

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18 pages, 5927 KiB

Open AccessArticle

Greening the Urban Landscape: Assessing the Impact of Tree-Planting Initiatives and Climate Influences on Miami-Dade County’s Greenness

by Julius R. Dewald, Jane Southworth, Jose Szapocznik, Joanna L. Lombard and Scott C. Brown

Remote Sens. 2024, 16(1), 157; https://doi.org/10.3390/rs16010157 - 30 Dec 2023

Cited by 4 | Viewed by 1396

Abstract

In urban settings, trees and greenery play a vital role in environmental well-being and community vitality. This study explores the impact of Miami-Dade County’s tree-planting initiative on urban greenness and considers the influence of climate dynamics. Using Landsat data from 2006 to 2019, we find stable overall greenness, with 5.64% of the Census blocks exhibiting significant changes. Seasonal analysis reveals winter as prominent, with 61.47% of Census blocks showing increased greenness. Temperature and precipitation, especially post-2010, correlate with greenness changes. Despite a reported increase in tree cover from 14% to 20%, our findings show only 5–6% of Census blocks with statistically significant changes, highlighting the complexity of achieving substantial improvements in green canopy coverage. The study raises questions about the efficacy of large-scale tree-planting initiatives in densely urbanized areas when human factors are not well understood. Implications for urban planning stress the importance of preserving green spaces and informed decision-making for enhancing vegetation cover in Miami-Dade County, emphasizing the need to consider local conditions, seasonal variations, policies, and human factors in urban greening efforts. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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24 pages, 6804 KiB

Open AccessArticle

Spatiotemporal Analysis of Ecosystem Status in China’s National Key Ecological Function Zones

by Xiongyi Zhang, Quanqin Shao, Bing Wang, Xiang Niu, Jia Ning, Meiqi Chen, Tingjing Zhang, Guobo Liu, Shuchao Liu, Linan Niu and Haibo Huang

Remote Sens. 2023, 15(18), 4641; https://doi.org/10.3390/rs15184641 - 21 Sep 2023

Cited by 1 | Viewed by 1302

Abstract

The National Key Ecological Function Zones (NKEFZ) serve as crucial ecological security barriers in China, playing a vital role in enhancing ecosystem services. This study employed the theoretical framework of ecological benefits assessment in major ecological engineering projects. The primary focus was on the ecosystem macrostructure, ecosystem quality, and key ecosystem services, enabling quantitative analysis of the spatiotemporal changes in the ecosystem status of the NKEFZ from 2000 to 2019. To achieve this, remote sensing data, meteorological data, and model simulations were employed to investigate five indicators, including land use types, vegetation coverage, net primary productivity of vegetation, soil conservation services, water conservation services, and windbreak and sand fixation services. The analysis incorporated the Theil–Sen Median method to construct an evaluation system for assessing the restoration status of ecosystems, effectively integrating ecosystem quality and ecosystem services indicators. The research findings indicated that land use changes in NKEFZ were primarily characterized by the expansion of unused land and the in of grassland. The overall ecosystem quality of these zones improved, showing a stable and increasing trend. However, there were disparities in the changes related to ecosystem services. Water conservation services exhibited a decreasing trend, while soil conservation and windbreak and sand fixation services showed a steady improvement. The ecosystem of the NKEFZ, in general, displayed a stable and recovering trend. However, significant spatial heterogeneity existed, particularly in the southern region of the Qinghai–Tibet Plateau and at the border areas between western Sichuan and northern Yunnan, where some areas still experienced deteriorating ecosystem conditions. Compared to other functional zones, the trend in the ecosystem of the NKEFZ might not have been the most favorable. Nonetheless, this could be attributed to the fact that most of these areas were situated in environmentally fragile regions, and conservation measures may not have been as effective as in other functional zones. These findings highlighted the considerable challenges ahead in the construction and preservation of the NKEFZ. In future development, the NKEFZ should leverage their unique natural resources to explore distinctive ecological advantages and promote the development of eco-friendly economic industries, such as ecological industry, ecological agriculture, and eco-tourism, transitioning from being reliant on external support to self-sustainability. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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18 pages, 8222 KiB

Open AccessArticle

A Study on Spatial and Temporal Dynamic Changes of Desertification in Northern China from 2000 to 2020

by Zhaolin Jiang, Xiliang Ni and Minfeng Xing

Remote Sens. 2023, 15(5), 1368; https://doi.org/10.3390/rs15051368 - 28 Feb 2023

Cited by 11 | Viewed by 2533

Abstract

Desertification is of significant concern as one of the world’s most serious ecological and environmental problems. China has made great achievements in afforestation and desertification control in recent years. The climate varies greatly across northern China. Using a long-time series of remote sensing data to study the effects of desertification will further the understanding of China’s desertification control engineering and climate change mechanisms. The moist index was employed in this research to determine the climate type and delineate the potential occurrence range of desertification in China. Then, based on the Google Earth Engine platform, MODIS data were used to construct various desertification monitoring indicators and applied to four machine learning models. By comparing different combinations of indicators and machine learning models, it was concluded that the random forest model with four indicator combinations had the highest accuracy of 86.94% and a Kappa coefficient of 0.84. Therefore, the random forest model with four indicator combinations was used to monitor desertification in the study area from 2000 to 2020. According to our studies, the area of desertification decreased by more than 237,844 km² between 2000 and 2020 due to the impact of human activities and in addition to climatic factors such as the important role of precipitation. This research gives a database for the cause and control of desertification as well as a reference for national-scale desertification monitoring. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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18 pages, 5818 KiB

Open AccessArticle

Stability Analysis of Unmixing-Based Spatiotemporal Fusion Model: A Case of Land Surface Temperature Product Downscaling

by Min Li, Shanxin Guo, Jinsong Chen, Yuguang Chang, Luyi Sun, Longlong Zhao, Xiaoli Li and Hongming Yao

Remote Sens. 2023, 15(4), 901; https://doi.org/10.3390/rs15040901 - 6 Feb 2023

Cited by 5 | Viewed by 1986

Abstract

The unmixing-based spatiotemporal fusion model is one of the effective ways to solve limitations in temporal and spatial resolution tradeoffs in a single satellite sensor. By using fusion data from different satellite platforms, high resolution in both temporal and spatial domains can be produced. However, due to the ill-posed characteristic of the unmixing function, the model performance may vary due to the different model setups. The key factors affecting the model stability most and how to set up the unmixing strategy for data downscaling remain unknown. In this study, we use the multisource land surface temperature as the case and focus on the three major factors to analyze the stability of the unmixing-based fusion model: (1) the definition of the homogeneous change regions (HCRs), (2) the unmixing levels, and (3) the number of HCRs. The spatiotemporal data fusion model U-STFM was used as the baseline model. The results show: (1) The clustering-based algorithm is more suitable for detecting HCRs for unmixing. Compared with the multi-resolution segmentation algorithm and k-means algorithm, the ISODATA clustering algorithm can more accurately describe LST’s temporal and spatial changes on HCRs. (2) For the U-STFM model, applying the unmixing processing at the change ratio level can significantly reduce the additive and multiplicative noise of the prediction. (3) There is a tradeoff effect between the number of HCRs and the solvability of the linear unmixing function. The larger the number of HCRs (less than the available MODIS pixels), the more stable the model is. (4) For the fusion of the daily 30 m scale LST product, compared with STARFM and ESTARFM, the modified U-STFM (iso_USTFM) achieved higher prediction accuracy and a lower error (

R^{2}

: 0.87 and RMSE:1.09 k). With the findings of this study, daily fine-scale LST products can be predicted based on the unmixing-based spatial–temporal model with lower uncertainty and stable prediction. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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22 pages, 5552 KiB

Open AccessArticle

Impacts of Climate Change on European Grassland Phenology: A 20-Year Analysis of MODIS Satellite Data

by Edoardo Bellini, Marco Moriondo, Camilla Dibari, Luisa Leolini, Nicolina Staglianò, Laura Stendardi, Gianluca Filippa, Marta Galvagno and Giovanni Argenti

Remote Sens. 2023, 15(1), 218; https://doi.org/10.3390/rs15010218 - 30 Dec 2022

Cited by 9 | Viewed by 3086

Abstract

The use of very long spatial datasets from satellites has opened up numerous opportunities, including the monitoring of vegetation phenology over the course of time. Considering the importance of grassland systems and the influence of climate change on their phenology, the specific objectives of this study are: (a) to identify a methodology for a reliable estimation of grassland phenological dates from a satellite vegetation index (i.e., kernel normalized difference vegetation index, kNDVI) and (b) to quantify the changes that have occurred over the period 2001–2021 in a representative dataset of European grasslands and assess the extent of climate change impacts. In order to identify the best methodological approach for estimating the start (SOS), peak (POS) and end (EOS) of the growing season from the satellite, we compared dates extracted from the MODIS-kNDVI annual trajectories with different combinations of fitting models (FMs) and extraction methods (EM), with those extracted from the gross primary productivity (GPP) measured from eddy covariance flux towers in specific grasslands. SOS and POS were effectively identified with various FM×EM approaches, whereas satellite-EOS did not obtain sufficiently reliable estimates and was excluded from the trend analysis. The methodological indications (i.e., FM×EM selection) were then used to calculate the SOS and POS for 31 grassland sites in Europe from MODIS-kNDVI during the period 2001–2021. SOS tended towards an anticipation at the majority of sites (83.9%), with an average advance at significant sites of 0.76 days year⁻¹. For POS, the trend was also towards advancement, although the results are less homogeneous (67.7% of sites with advancement), and with a less marked advance at significant sites (0.56 days year⁻¹). From the analyses carried out, the SOS and POS of several sites were influenced by the winter and spring temperatures, which recorded rises during the period 2001–2021. Contrasting results were recorded for the SOS-POS duration, which did not show a clear trend towards lengthening or shortening. Considering latitude and altitude, the results highlighted that the greatest changes in terms of SOS and POS anticipation were recorded for sites at higher latitudes and lower altitudes. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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19 pages, 6755 KiB

Open AccessArticle

Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing

by Junwoo Kim, Hwisong Kim, Duk-jin Kim, Juyoung Song and Chenglei Li

Remote Sens. 2022, 14(24), 6373; https://doi.org/10.3390/rs14246373 - 16 Dec 2022

Cited by 3 | Viewed by 2550

Abstract

Satellite-based flood monitoring for providing visual information on the targeted areas is crucial in responding to and recovering from river floods. However, such monitoring for practical purposes has been constrained mainly by obtaining and analyzing satellite data, and linking and optimizing the required processes. For these purposes, we present a deep learning-based flood area extraction model for a fully automated flood monitoring system, which is designed to continuously operate on a cloud-based computing platform for regularly extracting flooded area from Sentinel-1 data, and providing visual information on flood situations with better image segmentation accuracy. To develop the new flood area extraction model using deep learning, initial model tests were performed more than 500 times to determine optimal hyperparameters, water ratio, and best band combination. The results of this research showed that at ‘waterbody ratio 30%’, which yielded higher segmentation accuracies and lower loss, precision, overall accuracy, IOU, recall, and F1 score of ‘VV, aspect, topographic wetness index, and buffer input bands’ were 0.976, 0.956, 0.894, 0.964, and 0.970, respectively, and averaged inference time was 744.3941 s, which demonstrate improved image segmentation accuracy and reduced processing time. The operation and robustness of the fully automated flood monitoring system were demonstrated by automatically segmenting 12 Sentinel-1 images for the two major flood events in Republic of Korea during 2020 and 2022 in accordance with the hyperparameters, waterbody ratio, and band combinations determined through the intensive tests. Visual inspection of the outputs showed that misclassification of constructed facilities and mountain shadows were extremely reduced. It is anticipated that the fully automated flood monitoring system and the deep leaning-based waterbody extraction model presented in this research could be a valuable reference and benchmark for other countries trying to build a cloud-based flood monitoring system for rapid flood monitoring using deep learning. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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21 pages, 4523 KiB

Open AccessArticle

Empirical Examinations of Whether Rural Population Decline Improves the Rural Eco-Environmental Quality in a Chinese Context

by Zhen Liu

Remote Sens. 2022, 14(20), 5217; https://doi.org/10.3390/rs14205217 - 18 Oct 2022

Cited by 3 | Viewed by 2314

Abstract

Rural population has continually declined in response to the rapid urbanization process occurring in China, and the related negative socioeconomic impacts on rural development have attracted considerable attention from scholars. Currently, few studies have investigated the eco-environmental impact of rural population decline. By employing remote-sensing data, including land-use and normalized difference vegetation index (NDVI) data, this study proposed a method based on the eco-environmental quality index (EQI) to measure the changes in the rural eco-environmental quality (REQ) at the prefectural level from 2000 to 2020. Then, we examined the impacts of rural population decline on REQ variations. We found that (1) most of the research units experienced continuous rural population decline during the research period, with the rural population density declining more than 25% from 2010 to 2020 in approximately half of the research units; (2) the REQ improved in most of the units, especially in the western region, but there were still many units that experienced a decline in the REQ, which were primarily concentrated in the coastal and central regions; (3) rural population decline improved the REQ, but its impacts varied regionally; and (4) rural population density, natural factors, and eco-environmental protection programs had significant influences on REQ variations. These findings may provide a reference for sustainable-development policies in rural China and other developing countries. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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11 pages, 1529 KiB

Open AccessTechnical Note

Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural Systems

by Seonyoung Park, Jaese Lee, Jongmin Yeom, Eunkyo Seo and Jungho Im

Remote Sens. 2022, 14(23), 6161; https://doi.org/10.3390/rs14236161 - 5 Dec 2022

Cited by 3 | Viewed by 2757

Abstract

Drought affects a region’s economy intensively and its severity is based on the level of infrastructure present in the affected region. Therefore, it is important not only to reflect on the conventional environmental properties of drought, but also on the infrastructure of the target region for adequate assessment and mitigation. Various drought indices are available to interpret the distinctive meteorological, agricultural, and hydrological characteristics of droughts. However, these drought indices do not consider the effective assessment of damage of drought impact. In this study, we evaluated the applicability of satellite-based drought indices over North Korea and South Korea, which have substantially different agricultural infrastructure systems to understand their characteristics. We compared satellite-based drought indices to in situ-based drought indices, standardized precipitation index (SPI), and rice yield over the Korean Peninsula. Moderate resolution imaging spectroradiometer (MODIS), tropical rainfall measuring mission (TRMM), and global land data assimilation system (GLDAS) data from 2001 to 2018 were used to calculate drought indices. The correlations of the indices in terms of monitoring meteorological and agricultural droughts in rice showed opposite correlation patterns between the two countries. The difference in the prevailing agricultural systems including irrigation resulted in different impacts of drought. Vegetation condition index (VCI) and evaporative stress index (ESI) are best suited to assess agricultural drought under well-irrigated regions as in South Korea. In contrast, most of the drought indices except for temperature condition index (TCI) are suitable for regions with poor agricultural infrastructure as in North Korea. Full article

(This article belongs to the Special Issue Monitoring Environmental Changes by Remote Sensing)

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