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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 (20 April 2024) | Viewed by 26256

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Dr. Emanuele Mandanici

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Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
Interests: geomatics; remote sensing; change detection; thermography; radiometric calibration; environmental monitoring
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Dr. Sara Kasmaeeyazdi

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Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
Interests: remote sensing and geostatistical tools in geoscience; multispectral and hyperspectral remote sensing; geostatistical mapping; mining residues
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Dr. Christian Köhler

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Institute of Mine Surveying and Geodesy, Geoengineering and Mining, Technical University Bergakademie Freiberg, Freiberg, Germany
Interests: environmental remote sensing; radar and hyperspectral data

Special Issue Information

Dear Colleagues,

The sustainable management of the environment is one of the major challenges of the modern era, with the goal of a wise use of the resources, while preserving ecosystems integrity. A deep understanding of the status of the environment and an accurate monitoring of its dynamics, especially in response to anthropogenic actions, are crucial to develop a correct management strategy. In this context, Remote Sensing techniques can provide a major contribution. Indeed, the increasing number of satellite platforms and the enhanced performances of the imaging sensors have been making available an unprecedented amount of information about land and ocean surfaces.

In this perspective, research efforts are needed to develop methods and tools for the integration of platforms and sensors with different spectral, spatial and temporal resolutions. This integration is essential to expand the capabilities of a multi-temporal and multi-scale monitoring of the environment and enlarge the number of applications that may benefit from remote sensing data. Furthermore, the development of best practices to validate the results and predict the accuracy of the proposed approaches is another crucial aspect. This Special Issue aims to collect high quality contribution to the advancement of satellite remote sensing technology and solutions for environmental monitoring applications.

Dr. Emanuele Mandanici
Dr. Sara Kasmaeeyazdi
Dr. Christian Köhler
Guest Editors

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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.

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Keywords

environmental monitoring
sustainability and resilience
LULC mapping
change-detection
hazard assessment
image classification
multi and hyperspectral remote sensing
multi-sensor integration
optical and SAR integration
multi-scale analysis
time-series analysis
satellite imagery calibration
validation strategies
geostatistical analysis for RS

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Related Special Issue

Environmental Monitoring Using Satellite Remote Sensing (Second Edition) in Remote Sensing (1 article)

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18 pages, 26335 KiB

Open AccessArticle

Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

by Meiling Zhou, Zhenhong Li, Meiling Gao, Wu Zhu, Shuangcheng Zhang, Jingjing Ma, Liangyu Ta and Guijun Yang

Remote Sens. 2024, 16(11), 2018; https://doi.org/10.3390/rs16112018 - 4 Jun 2024

Cited by 3 | Viewed by 1104

Abstract

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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29 pages, 42875 KiB

Open AccessArticle

Analysis of the Substantial Growth of Water Bodies during the Urbanization Process Using Landsat Imagery—A Case Study of the Lixiahe Region, China

by Haoran Jiang, Luyan Ji, Kai Yu and Yongchao Zhao

Remote Sens. 2024, 16(4), 711; https://doi.org/10.3390/rs16040711 - 18 Feb 2024

Cited by 1 | Viewed by 1353

Abstract

In the process of urbanization, water bodies bear considerable anthropogenic pressure, resulting in a reduction of their surface area in most instances. Nevertheless, in contrast to many other regions, the Lixiahe region in Jiangsu Province, located in China’s eastern plain, has experienced a continuous expansion of water bodies over the past few decades amid rapid urbanization. Using Landsat images spanning from 1975 to 2023, this study analyzed changes in water resources and the growth of impervious surfaces during urbanization. The findings revealed that the area of impervious surfaces in the region increased from 227.1 km² in 1975 to 1883.1 km² in 2023. Natural wetland suffered significant losses, declining from 507.2 km² in 1975 to near disappearance by the year 2000, with no significant recovery observed thereafter. Simultaneously, the water area expanded from 459.3 km² in 1975 to 2373.1 km² in 2023, primarily propelled by the significant contribution of aquaculture ponds, accounting for 2175.0 km² or 91.7% of the total water area. Driver analysis revealed that these changes were found to be influenced by factors such as population, economy, demand, and policies. However, alongside the economic development brought by urbanization, negative impacts such as lake shrinkage, eutrophication, and increased flood risks have emerged. The Lixiahe region, as a relatively underdeveloped part of Jiangsu Province, faces the challenge of striking a balance between economic growth and environmental conservation. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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20 pages, 6184 KiB

Open AccessArticle

Evolution and Driving Forces of Ecological Service Value in Anhui Based on Landsat Land Use and Land Cover Change

by Li’ao Quan, Shuanggen Jin, Junyun Chen and Tuwang Li

Remote Sens. 2024, 16(2), 269; https://doi.org/10.3390/rs16020269 - 10 Jan 2024

Cited by 3 | Viewed by 1423

Abstract

The main challenge in protecting ecosystems and improving the supply of ecosystem services is to quantify the ecological services value (ESV). However, the detailed spatiotemporal changes, sensitivity, spatial autocorrelation, and driving mechanisms of ESV are not clear in rapidly developing regions, particularly subsidence, floods, landslides, and the rapid urban development of Anhui province, China. In this paper, the ecological service value of Anhui Province in the past 30 years was calculated using the improved equivalent factor assessment method from satellite remote sensing such as Landsat. The spatiotemporal evolution characteristics of ESV were analyzed and the driving mechanism of ESV changes was studied using Geodetector. Finally, The GeoSOS-FLUS model was selected to predict the ecosystem service value until 2030 with three scenarios: business as usual (BAU), ecological protection (EP), and cultivated land protection (CLP). The main results were obtained: (1) the ESV in Anhui Province continued to decrease by 2.045 billion yuan (−6.03%) from 1990 to 2020. The top two contributors were the forest land, followed by water area. (2) The global Moran’s I of ESV at the landform subdivision, county, town, and grid scales in Anhui Province were −0.157, 0.321, 0.357 and 0.759, respectively. (3) The order of influence degree of driving factors was: precipitation (F4), distance to intercity road (F9), net primary productivity, NPP (F6), distance to urban road (F8), population (F13), temperature (F5), aspect (F3), distance to settlement (F11), slope (F2), elevation (F1), GDP (F14), distance to water (F12), distance to railway (F10), and soil erosion (F7). (4) In 2030, the simulated ESV under the three scenarios will decrease to varying degrees. Compared with 2020, the ESV of the three scenarios will decrease successively as follows: BAU (−1.358 billion yuan), EP (−0.248 billion yuan), and CLP (−1.139 billion yuan). Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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21 pages, 9368 KiB

Open AccessArticle

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

by Oscar Rojas-Munoz, Jean-Christophe Calvet, Bertrand Bonan, Nicolas Baghdadi, Catherine Meurey, Adrien Napoly, Jean-Pierre Wigneron and Mehrez Zribi

Remote Sens. 2023, 15(17), 4329; https://doi.org/10.3390/rs15174329 - 2 Sep 2023

Cited by 2 | Viewed by 1386

Abstract

Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017–2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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22 pages, 8526 KiB

Open AccessArticle

Remote Sensing-Based Approach for the Assessing of Ecological Environmental Quality Variations Using Google Earth Engine: A Case Study in the Qilian Mountains, Northwest China

by Hong Wang, Chenli Liu, Fei Zang, Youyan Liu, Yapeng Chang, Guozhu Huang, Guiquan Fu, Chuanyan Zhao and Xiaohuang Liu

Remote Sens. 2023, 15(4), 960; https://doi.org/10.3390/rs15040960 - 9 Feb 2023

Cited by 15 | Viewed by 2946

Abstract

Due to climate change and human activities, the eco-environment quality (EEQ) of eco-fragile regions has undergone massive change, especially in the Tibet Plateau. The Qilian Mountains (QLM) region is an essential ecological function zone in the northeastern Tibet Plateau, which plays a vital role in northwestern China’s eco-environmental balance. However, EEQ changes in the QLM during the 21st century remain poorly understood. In this study, the spatiotemporal variations of the EEQ in the QLM were analyzed from 2000 to 2020 using a remote sensing ecological index (RSEI). The EEQ driving factors are identified by the geographic detector, and the spatial influence of critical factors is represented by a geographically weighted regression model. The results show low EEQ in the QLM. From 2000 to 2020, the EEQ initially slightly improved, then deteriorated, and finally gradually recovered. Spatially, the EEQ shows an increasing trend from northwest to southeast. Moran’s I of EEQ remains at around 0.95, representing high spatial aggregation. “High–High” and “Low–Low” clustering features dominate in the local spatial autocorrelation, indicating the EEQ of the QLM is polarized. Precipitation is the dominant positive factor in the EEQ, with a q statistics exceeding 0.644. Furthermore, the key factors (precipitation, distance to towns, distance to roads) affecting EEQ in different periods vary significantly in space. From results we can draw the conclusion that the natural factors mainly control the spatial patterns of EEQ, while the human factors mainly impact the temporal trend of EEQ, the EEQ in the QLM has been significantly improved since 2015. Our findings can provide theoretical support for future eco-environmental protection and restoration in the QLM. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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16 pages, 3891 KiB

Open AccessArticle

Extinction Effect of Foliar Dust Retention on Urban Vegetation as Estimated by Atmospheric PM10 Concentration in Shenzhen, China

by Tianfang Yu, Junjian Wang, Yiwen Chao and Hui Zeng

Remote Sens. 2022, 14(20), 5103; https://doi.org/10.3390/rs14205103 - 12 Oct 2022

Cited by 3 | Viewed by 1886

Abstract

Foliar dust retention is a crucial source of uncertainty when monitoring the vegetation index using satellite remote sensing. As ground sampling conditions are limited by vegetation dust retention, separating the extinction effect of foliar dust retention from the normalized difference vegetation index (NDVI) poses a significant challenge. In this study, we conducted a correlation test between the relative change in NDVI (δNDVI, an indicator of extinction effect) retrieved by the Gaofen-4 satellite and the atmospheric PM10 concentration in different meteorological periods (before, during, and after rainfall) across 14 stations in Shenzhen City, China. The results showed a significant correlation between δNDVI and atmospheric PM10 concentration during the before-rainfall period and weaker correlations for the other periods (R = 0.680, p < 0.001, n = 63 when excluding the during- and after-rainfall data). The correlation was more significant for the stations with low NDVI values, and a coastal station had a distinct regression slope of δNDVI versus PM10 from the other stations, indicating that the extinction effect of foliar dust retention in high-NDVI and coastal areas may not be well predicted by the general δNDVI–PM10 relationship. This provides a new quantitative basis for estimating the extinction effect of foliar dust retention using PM10 data for future improvement of the accuracy of vegetation monitoring by remote sensing. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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32 pages, 10402 KiB

Open AccessEditor’s ChoiceArticle

A Spatial Long-Term Trend Analysis of Estimated Chlorophyll-a Concentrations in Utah Lake Using Earth Observation Data

by Kaylee Brook Tanner, Anna Catherine Cardall and Gustavious Paul Williams

Remote Sens. 2022, 14(15), 3664; https://doi.org/10.3390/rs14153664 - 30 Jul 2022

Cited by 5 | Viewed by 4633

Abstract

We analyzed chlorophyll-a (chl-a) concentrations in shallow, turbid Utah Lake using Landsat data from 1984 to 2021. Utah Lake is ~40 km by 21 km, has a surface area of ~390 km², an average depth of ~3 m, and loses ~50% of inflow to evaporation. This limits spatial mixing, allowing us to evaluate impacts on smaller lake regions. We evaluated long-term trends at the pixel level and for areas related to boundary conditions. We created 17 study areas based on differences in shoreline development and nutrient inflows. We expected impacted areas to exhibit increasing chl-a trends, as population growth and development in the Utah Lake watershed have been significant. We used the non-parametric Mann–Kendall test to evaluate trends. The majority of the lake exhibited decreasing trends, with a few pixels in Provo and Goshen Bays exhibiting slight increasing or no trends. We estimated trend magnitudes using Sen’s slope and fitted linear regression models. Trend magnitudes in all pixels (and regions), both decreasing and increasing, were small; with the largest decreasing and increasing trends being about −0.05 and −0.005 µg/L/year, and about 0.1 and 0.005 µg/L/year for the Sen’s slope and linear regression slope, respectively. Over the ~40 year-period, this would result in average decreases of 2 to 0.2 µg/L or increases of 4 and 0.2 µg/L. All the areas exhibited decreasing trends, but the monthly trends in some areas exhibited no trends rather than decreasing trends. Monthly trends for some areas showed some indications that algal blooms are occurring earlier, though evidence is inconclusive. We found essentially no change in algal concentrations in Utah Lake at either the pixel level or for the analysis regions since the 1980′s; despite significant population expansion; increased nutrient inflows; and land-use changes. This result matches prior research and supports the hypothesis that algal growth in Utah Lake is not limited by direct nutrient inflows but limited by other factors. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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22 pages, 4063 KiB

Open AccessArticle

Assessing Climate Influence on Spatiotemporal Dynamics of Macrophytes in Eutrophicated Reservoirs by Remotely Sensed Time Series

by Leandro Fernandes Coladello, Maria de Lourdes Bueno Trindade Galo, Milton Hirokazu Shimabukuro, Ivana Ivánová and Joseph Awange

Remote Sens. 2022, 14(14), 3282; https://doi.org/10.3390/rs14143282 - 8 Jul 2022

Viewed by 1606

Abstract

The overgrowth of macrophytes is a recurrent problem within reservoirs of urbanized and industrialized areas, a condition triggered by the damming of rivers and other human activities. Although the occurrence of aquatic plants in waterbodies has been widely monitored using remote sensing, the influence of climate variables on macrophyte spatiotemporal dynamics is rarely considered in studies developed for medium scales to long periods of time. We hypothesize that the spatial dispersion of macrophytes has its natural rhythms influenced by climate fluctuations, and, as such, its effects on the heterogeneous spatial distribution of this vegetation should be considered in the monitoring of water bodies. A eutrophic reservoir is selected for study, which uses the Normalized Difference Vegetation Index (NDVI) as a proxy for macrophytes. Landsat’s NDVI long-term time series are constructed and matched with the Climate Variable (CV) from the National Oceanic and Atmospheric Administration (NOAA) to assess the spatiotemporal dynamics of aquatic plants and their associated climate triggers. The NDVI and CV time series and their seasonal and trend components are correlated for the entire reservoir, compartments, and segmented areas of the water body. Granger-causality of these climate variables show that they contribute to describe and predict the spatial dispersion of macrophytes. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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21 pages, 7234 KiB

Open AccessArticle

Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China

by Kun Feng, Tao Wang, Shulin Liu, Wenping Kang, Xiang Chen, Zichen Guo and Ying Zhi

Remote Sens. 2022, 14(11), 2663; https://doi.org/10.3390/rs14112663 - 2 Jun 2022

Cited by 24 | Viewed by 4584

Abstract

Mu Us Sandy Land is a typical semi-arid vulnerable ecological zone, characterized by vegetation degradation and severe desertification. Effectively identifying desertification changes has been a topical environmental issue in China. However, most previous studies have used a single method or remote sensing index to monitor desertification, and lacked an efficient and high-precision monitoring system. In this study, an optimal monitoring scheme that considers multiple indicators combination and different machine learning methods (Classification and Regression Tree-Decision Tree, CART-DT; Random Forest, RF; Convolutional Neural Networks, CNN) was developed and used to analyze the spatial–temporal patterns of desertification from 2000 to 2018 in Mu Us Sandy Land. The results showed that: (a) The random forest model performed best for monitoring desertification based on medium and low-resolution remote sensing images, and the four-index combination (Albedo, NDVI, LST and TGSI) obtained the highest classification accuracy (OA = 87.67%) in Mu Us Sandy Land. Surprisingly, the model accuracy of the three-index combination (NDVI, LST and TGSI) (OA = 85.74%) is comparable to the four-index combination. (b) The TGSI index used to characterize soil information performs well, while the LST is not conducive to the extraction of desertified land in several desertification monitoring indicators. (c) Since 2000, the area of extremely severe desertified land has shown a reversal trend; however, there is significant interannual fluctuation in the total and light desertification land area affected by extreme climate. This research provides a novel approach and a valuable reference for monitoring the evolution of desertification in regional studies, and the results improve the research system of desertification and provide a data basis for desertification cause analysis and prevention. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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18 pages, 6323 KiB

Open AccessTechnical Note

Landsat 9 Geometric Characteristics Using Underfly Data

by Michael J. Choate, Rajagopalan Rengarajan, James C. Storey and Mark Lubke

Remote Sens. 2022, 14(15), 3781; https://doi.org/10.3390/rs14153781 - 6 Aug 2022

Cited by 9 | Viewed by 3065

Abstract

The Landsat program has a long history of providing remotely sensed data to the user community. This history is being extended with the addition of the Landsat 9 satellite, which closely mimics the Landsat 8 satellite and its instruments. These satellites contain two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI is a push-broom sensor that collects visible and near-infrared (VNIR) and short-wave infrared (SWIR) wavelengths at 30 m ground sample distance, along with a panchromatic 15 m band. The TIRS sensor contains two long-wave thermal spectral channels centered at 10.9 and 12 µm. The data from these two instruments, on both satellites, are combined into a single Landsat product. The Landsat 5–9 satellites follow a 16 day repeat cycle designated as the Worldwide Reference System (WRS-2), which provides a global notional gridded mapping for identifying individual Landsat scenes. The Landsat 8 and 9 satellites are flown such that their orbital tracks are separated by 8 days in this 16 day cycle. During the commissioning period of Landsat 9, and during its ascent to its operational WRS-2 orbit, the Landsat 9 satellite’s orbital track went under and crossed over the orbital track of the Landsat 8 satellite. This produced a unique situation where nearly time-coincident imagery could be obtained from the instruments of the two spacecrafts. From a radiometric standpoint, this allowed for near-time cross-calibration between the instruments to be performed. From a geometry perspective, calibration is achieved through high-resolution reference imagery over specific ground locations, thus ensuring calibration of the instruments and for the instruments to be well cross-calibrated geometrically. Although these underfly data do not provide calibration of the instruments between the platforms from a geometric perspective, they allow for the verification of the calibration steps involving the instruments and spacecraft. This paper discusses the co-registration of this unique set of data while also discussing other geometric aspects of these data by looking at and comparing the differences in sensor viewing and sun angles associated with the collections from the two platforms for imagery obtained over common geographic locations. The image-to-image comparisons between Landsat 8 and 9 coincident pairs, where both datasets are precision terrain products, are registered to within 2.2 m with respect to their root-mean-squared radial error (RMSEr). The 2.2 m represents less than 0.1 of a 30 m multispectral pixel in misregistration between the L9 and L8 underfly products that will be available to the user community. This unique dataset will provide well-registered, near-coincident image acquisitions between the two platforms that can be a key to any calibration or application comparisons. The paper also presents that, for images for which one of the image pairs failed precision corrections and became a terrain-corrected only product type, a range of 8–14 m RMSEr could be expected in co-registration, while, in cases where both image pairs failed the precision correction step and both images became a terrain-corrected only product type, a 14 m RMSEr could be expected for co-registration. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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