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The Application of Remote Sensing for Environmental Planning and Management
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The Application of Remote Sensing for Environmental Planning and Management

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 25993

Special Issue Editor

Prof. Dr. Maruthi Sridhar Balaji Bhaskar

E-Mail Website1 Website2
Guest Editor
Department of Earth and Environment, Florida International University, Miami, FL 33199, USA
Interests: remote sensing/GIS; soil and water quality; evapotranspiration; agricultural sustainability; land use and land cover change analysis; soil science; agriculture; forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is efficient in monitoring landscapes vital to the conservation of natural resources, ecosystems, land use and landcover changes, environmental contamination, and degradation. Change detection using satellite, aerial and drone imagery, Geographic Information Systems, spectral reflectance, and various other techniques is a means to address environmental management effectively over large areas without creating further disturbance. This is especially important in today’s changing world given the increased pressures from urbanization, and industrialization through land use conversion, natural and anthropogenic disturbance, and global climate change.

Advances in in situ sensor systems to capture spatial measurements across time provide an important step forward to correlate with frequent satellite-based observations to develop landscape-level models. Remote sensing has the potential to provide an important source of information for quantifying and mapping dynamic terrestrial and aquatic ecosystem services and hydrological processes. Remote sensing data products are being successfully integrated with various in situ observations to monitor environmental changes by ecologists, land managers, conservation groups, and scientists for effective monitoring and conservation of various natural resources.

This Special Issue invites manuscripts on the application of remote sensing for environmental management and planning as pertinent to applications to soil, plant, water, and air. We expect each paper to incorporate the current state of knowledge, summarize existing environmental issues and limitations, and provide new insights for future research and development in the field of environmental remote sensing. Topics for this Special Issue include but are not limited to:

  • Ecosystem assessment and monitoring
  • Land use/cover changes
  • Water resources assessment
  • Wetland and coastal dynamics
  • Geohazards and land subsidence
  • Time series analysis
  • New sensor/platform applications
  • Environmental and public health applications
  • Assessment of terrestrial ecosystems
  • Biotic and abiotic plant stress
  • Precision agriculture

Dr. Maruthi Sridhar Balaji Bhaskar
Guest Editor

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

  • Change detection
  • Climate change
  • Environmental hazard
  • Environmental pollution
  • Image processing
  • LIDAR
  • Satellite imagery
  • Spatiotemporal analysis
  • Spectral reflectance
  • Synthetic aperture radar

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Published Papers (6 papers)

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Research

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32 pages, 5326 KiB  
Article
Multi-Temporal SamplePair Generation for Building Change Detection Promotion in Optical Remote Sensing Domain Based on Generative Adversarial Network
by Yute Li, He Chen, Shan Dong, Yin Zhuang and Lianlin Li
Remote Sens. 2023, 15(9), 2470; https://doi.org/10.3390/rs15092470 - 8 May 2023
Cited by 1 | Viewed by 2338
Abstract
Change detection is a critical task in remote sensing Earth observation for identifying changes in the Earth’s surface in multi-temporal image pairs. However, due to the time-consuming nature of image collection, labor-intensive pixel-level labeling with the rare occurrence of building changes, and the [...] Read more.
Change detection is a critical task in remote sensing Earth observation for identifying changes in the Earth’s surface in multi-temporal image pairs. However, due to the time-consuming nature of image collection, labor-intensive pixel-level labeling with the rare occurrence of building changes, and the limitation of the observation location, it is difficult to build a large, class-balanced, and diverse building change detection dataset, which can result in insufficient changed sample pairs for training change detection models, thus degrading their performance. Thus, in this article, given that data scarcity and the class-imbalance issue lead to the insufficient training of building change detection models, a novel multi-temporal sample pair generation method, namely, Image-level Sample Pair Generation (ISPG), is proposed to improve the change detection performance through dataset expansion, which can generate more valid multi-temporal sample pairs to overcome the limitation of the small amount of change information and class-imbalance issue in existing datasets. To achieve this, a Label Translation GAN (LT-GAN) was designed to generate complete remote sensing images with diverse building changes and background pseudo-changes without any of the complex blending steps used in previous works. To obtain more detailed features in image pair generation for building change detection, especially the surrounding context of the buildings, we designed multi-scale adversarial loss (MAL) and feature matching loss (FML) to supervise and improve the quality of the generated bitemporal remote sensing image pairs. On the other hand, we also consider that the distribution of generated buildings should follow the pattern of human-built structures. The proposed approach was evaluated on two building change detection datasets (LEVIR-CD and WHU-CD), and the results proved that the proposed method can achieve state-of-the-art (SOTA) performance, even if using plain models for change detection. In addition, the proposed approach to change detection image pair generation is a plug-and-play solution that can be used to improve the performance of any change detection model. Full article
(This article belongs to the Special Issue The Application of Remote Sensing for Environmental Planning and Management)
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22 pages, 18830 KiB  
Article
Influence of Land Use and Topographic Factors on Soil Organic Carbon Stocks and Their Spatial and Vertical Distribution
by Kyle W. Blackburn, Zamir Libohova, Kabindra Adhikari, Charles Kome, Xander Maness and Miles R. Silman
Remote Sens. 2022, 14(12), 2846; https://doi.org/10.3390/rs14122846 - 14 Jun 2022
Cited by 4 | Viewed by 3374
Abstract
Soil organic carbon (SOC) plays a critical role in major ecosystem processes, agriculture, and climate mitigation. Accurate SOC predictions are challenging due to natural variation, as well as variation in data sources, sampling design, and modeling approaches. The goal of this study was [...] Read more.
Soil organic carbon (SOC) plays a critical role in major ecosystem processes, agriculture, and climate mitigation. Accurate SOC predictions are challenging due to natural variation, as well as variation in data sources, sampling design, and modeling approaches. The goal of this study was to (i) understand SOC stock distribution due to land use (restored prairie grass—PG; lawn grass—LG; and forest—F), and local topography, and (ii) assess the scalability of SOC stock predictions from the study site in North Carolina (Lat: 36°7′ N; Longitude: 80°16′ W) to the geographic extension of the Fairview soil series based on the US Soil Survey Geographic (gSSURGO) database. Overall, LG had the highest SOC stock (82 Mg ha−1) followed by PG (79 Mg ha−1) and forest (73.1 Mg ha−1). SOC stock decreased with the depth for LG and PG, which had about 60% concentrated on the surface horizon (0–23 cm), while forest had only 40%. The differences between measured SOC stocks and those estimated by gSSURGO and modeled based on land use for the Fairview series extent were comparable. However, subtracting maps of the uncertainty predictions based on the 90% confidence interval (CI) derived from the measured values and estimated gSSURGO upper and lower values (an estimated CI) resulted in a range from −17 to 41 Mg ha−1 which, when valued monetarily, varied from USD 33 million to USD 824 million for the Fairview soil series extent. In addition, the spatial differences found by subtracting the gSSURGO estimations from measured uncertainties aligned with the county administrative boundaries. The distribution of SOC stock was found to be related to land use, topography, and soil depth, while accuracy predictions were also influenced by data source. Full article
(This article belongs to the Special Issue The Application of Remote Sensing for Environmental Planning and Management)
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14 pages, 13161 KiB  
Article
Source Apportionment of Heavy Metal Contamination in Urban-Agricultural-Aquacultural Soils near the Bohai Bay Coast, Using Land-Use Classification and Google Satellite Tracing
by Ling Zeng, Shan Jiang, Linhai Jing and Yuan Xue
Remote Sens. 2022, 14(10), 2436; https://doi.org/10.3390/rs14102436 - 19 May 2022
Cited by 1 | Viewed by 2382
Abstract
Heavy metal concentrations of Cd, As, Pb, Cu, Cr, and Hg were investigated for 86 soil samples in Jinzhou near the Bohai Sea in China, in order to identify what anthropological activities influenced their distribution levels. Ordinary cokriging (OCK) was utilized to map [...] Read more.
Heavy metal concentrations of Cd, As, Pb, Cu, Cr, and Hg were investigated for 86 soil samples in Jinzhou near the Bohai Sea in China, in order to identify what anthropological activities influenced their distribution levels. Ordinary cokriging (OCK) was utilized to map six heavy-metal distributions by incorporating their main environmental influencers. The resultant p values for the six OCK mapping models of 0–2.78% indicated good statistical significance of the models, and the relative mean absolute errors of 4.82–12.53% and relative root mean square errors of 6.23–18.21% indicated allowable predication precision for their concentrations. The contamination distributions by OCK mapping were then graded based on the standards of the China National Environmental Monitoring Center and the Chinese Environmental Protection Administration, which showed that Cu and As contaminations in parts of this area were over the natural level but not polluted, Cr contamination was omnipresent over the natural level in this area and even reached the polluted level in parts of this area. The graded contamination maps that were overlapped with land-use maps and Google satellite maps, as well as the verifications reported in literatures, enabled correlations of the different contamination levels of As, Cu, and Cr with human activities. Resultantly, it indicated that As and Cu contamination over the natural level may be related to agricultural planting and aquacultural activities along the coast of Bohai Bay, with the contaminants transported via watercourses; Cr contamination over the natural level may have been due to vehicle emissions; and, Cr pollution may have been from steel manufacturing and geochemical factories Full article
(This article belongs to the Special Issue The Application of Remote Sensing for Environmental Planning and Management)
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19 pages, 8877 KiB  
Article
Spatio-Temporal Analysis of Sawa Lake’s Physical Parameters between (1985–2020) and Drought Investigations Using Landsat Imageries
by Yousif A. Mousa, Ali F. Hasan and Petra Helmholz
Remote Sens. 2022, 14(8), 1831; https://doi.org/10.3390/rs14081831 - 11 Apr 2022
Cited by 4 | Viewed by 2375
Abstract
Lake Sawa located in Southwest Iraq is a unique natural landscape and without visible inflow and outflow from its surrounding regions. Investigating the environmental and physical dynamics and the hydrological changes in the lake is crucial to understanding the impact of hydrological changes, [...] Read more.
Lake Sawa located in Southwest Iraq is a unique natural landscape and without visible inflow and outflow from its surrounding regions. Investigating the environmental and physical dynamics and the hydrological changes in the lake is crucial to understanding the impact of hydrological changes, as well as to inform planning and management in extreme weather events or drought conditions. Lake Sawa is a saltwater lake, covering about 4.9 square kilometers at its largest in the 1980s. In the last decade, the lake has dried out, shrinking to less than 75% of its average size. This contribution focuses on calculating the bank erosion and accretion of Lake Sawa utilizing remote sensing data captured by Landsat platforms (1985–2020). The methodology was validated using higher-resolution Sentinel imagery and field surveys. The outcomes indicated that the area of accretion is significantly higher than erosion, especially of the lake’s banks in the far north and the south, in which 1.31 km2 are lost from its surface area. Further analysis of especially agricultural areas around the lake have been performed to better understand possible reasons causing droughts. Investigations revealed that one possible reason behind droughts is related to the rapid increase in agriculture areas surrounding the lake. It has been found that the agriculture lands have expanded by 475% in 2020 compared to 2010. Linear regression analysis revealed that there is a high correlation (69%) between the expanding of agriculture lands and the drought of Lake Sawa. Full article
(This article belongs to the Special Issue The Application of Remote Sensing for Environmental Planning and Management)
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Review

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28 pages, 2936 KiB  
Review
Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
by Xiaoning Zhang, Ziti Jiao, Changsen Zhao, Ying Qu, Qiang Liu, Hu Zhang, Yidong Tong, Chenxia Wang, Sijie Li, Jing Guo, Zidong Zhu, Siyang Yin and Lei Cui
Remote Sens. 2022, 14(6), 1382; https://doi.org/10.3390/rs14061382 - 12 Mar 2022
Cited by 26 | Viewed by 11457
Abstract
Surface albedo plays a controlling role in the surface energy budget, and albedo-induced radiative forcing has a significant impact on climate and environmental change (e.g., global warming, snow and ice melt, soil and vegetation degradation, and urban heat islands (UHIs)). Several existing review [...] Read more.
Surface albedo plays a controlling role in the surface energy budget, and albedo-induced radiative forcing has a significant impact on climate and environmental change (e.g., global warming, snow and ice melt, soil and vegetation degradation, and urban heat islands (UHIs)). Several existing review papers have summarized the algorithms and products of surface albedo as well as climate feedback at certain surfaces, while an overall understanding of various land types remains insufficient, especially with increasing studies on albedo management methods regarding mitigating global warming in recent years. In this paper, we present a comprehensive literature review on the variance pattern of surface albedo, the subsequent climate impact, and albedo management strategies. The results show that using the more specific term “surface albedo” is recommended instead of “albedo” to avoid confusion with similar terms (e.g., planetary albedo), and spatiotemporal changes in surface albedo can indicate subtle changes in the energy budget, land cover, and even the specific surface structure. In addition, the close relationships between surface albedo change and climate feedback emphasize the important role of albedo in climate simulation and forecasting, and many albedo management strategies (e.g., the use of retroreflective materials (RRMs)) have been demonstrated to be effective for climate mitigation by offsetting CO2 emissions. In future work, climate effects and management strategies regarding surface albedo at a multitude of spatiotemporal resolutions need to be systematically evaluated to promote its application in climate mitigation, where a life cycle assessment (LCA) method considering both climate benefits and side effects (e.g., thermal comfort) should be followed. Full article
(This article belongs to the Special Issue The Application of Remote Sensing for Environmental Planning and Management)
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Other

Jump to: Research, Review

15 pages, 3656 KiB  
Technical Note
Development of the Statistical Errors Raster Toolbox with Six Automated Models for Raster Analysis in GIS Environments
by Stavroula Dimitriadou and Konstantinos G. Nikolakopoulos
Remote Sens. 2022, 14(21), 5446; https://doi.org/10.3390/rs14215446 - 29 Oct 2022
Cited by 3 | Viewed by 2918
Abstract
The Statistical Errors Raster Toolbox includes models of the most popular error metrics in the interdisciplinary literature, namely, root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), normalized mean bias error (NMBE), mean absolute error (MAE) and [...] Read more.
The Statistical Errors Raster Toolbox includes models of the most popular error metrics in the interdisciplinary literature, namely, root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), normalized mean bias error (NMBE), mean absolute error (MAE) and normalized mean absolute error (NMAE), for computing the areal errors of any raster file in .tiff format as compared with a reference raster file. The models are applicable to any size of raster files, no matter if no-data pixels are included. The only prerequisites are that the two raster files share the same units, cell size, and projection system. The novelty lies in the fact that, to date, there is no such application in ArcGIS Pro 3/ArcMap 10.8. Therefore, users who work with raster files require external software, plus the relevant expertise. An application on the reference evapotranspiration (ETo) of Peloponnese peninsula (Greece) is presented. MODIS ET products and ETo raster files for empirical methods are employed. The results of the models (for 20,440 valid values) are compared to the results of external software (for 1000 random points). Considering that the different sample sizes can lead to different accuracies and the inhomogeneity of the area, it is obvious that the results are almost identical. Full article
(This article belongs to the Special Issue The Application of Remote Sensing for Environmental Planning and Management)
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