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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 (31 October 2023) | Viewed by 21853

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Dr. Jian Yang

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Guest Editor

Department of Forestry and Natural Resources, University of Kentucky, 730 Rose Street, Lexington, KY 40546, USA
Interests: forest landscape ecology; disturbance ecology; ecosystem modeling; land use and land cover change; ecosystem services; remote sensing and GIS; spatial statistics
Special Issues, Collections and Topics in MDPI journals

Dr. Le Yu

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Guest Editor

Department for Earth System Science, Tsinghua University, Beijing 100084, China
Interests: land system science; land remote sensing; GIS; biodiversity conservation
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Special Issue Information

Dear Colleagues,

Remote sensing (RS) and geographic information systems (GIS) often work hand in hand to map, analyze, and disseminate spatial information. As a science of obtaining information from a distance, RS extracts spatially explicit attributes about the Earth’s land and water surfaces using images acquired from aircraft or satellites. Such RS-derived geospatial attributes can be integrated into a GIS framework to (1) map spatial patterns of the characteristics of interest, (2) identify the relationships of RS-derived Earth surface attributes to GIS-derived landscape features, (3) determine how the Earth’s surface characteristics change over time, and (4) estimate new characteristics or emergent properties from the existing remote sensing products. In essence, RS provides invaluable spatial data, often in raster format, to the GIS for further geoprocessing. Vice versa, many critical analyses of remotely sensed data such as geometric registration, radiometric correction, image classification, and change detection can benefit from the use of ancillary GIS data (often in vector format) and geoprocessing procedures (e.g., masking, overlay, and proximity analysis). The integration of RS/GIS has been successfully applied in many fields related to natural resources and environmental science, including agriculture, forestry, land use, biological conversation, ecological restoration, and natural hazard management. With the recent advances in computing innovation, artificial intelligence, and big data science, the integration of remote sensing and GIS is approaching a new phase that will further enhance the analysis of spatial data from various sources.

In this Special Issue, we would like to invite you to submit original research showcasing the innovative use of integrating remote sensing and GIS to solve complex research questions closely related to natural resources and environmental sciences. Comprehensive reviews of this subject are also welcome. Potential topics include but are not limited to the following:

State-of-the-art geospatial techniques integrating remote sensing and GIS;
Original methods or tools developed to seamlessly integrate remote sensing and GIS in the applications of natural resources and environmental science;
Comprehensive use of multifaceted geoprocessing tools and GIS data to enhance remote sensing image processing operations;
Novel GIS analysis of recently developed remote sensing data to assess natural resources and environmental conditions.

You may choose our Joint Special Issue in Land.

Dr. Jian Yang
Dr. Le Yu
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

Integration of remote sensing and GIS
Geoprocessing of remote sensing data
Natural resource mapping
Remote sensing of environment
Land surface processes
Landscape approach
Ecosystem modeling
Spatial analysis
System integration

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Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science in Land

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Research

18 pages, 9956 KiB

Open AccessArticle

Simulation of Land Use Based on Multiple Models in the Western Sichuan Plateau

by Xinran Yu, Jiangtao Xiao, Ke Huang, Yuanyuan Li, Yang Lin, Gang Qi, Tao Liu and Ping Ren

Remote Sens. 2023, 15(14), 3629; https://doi.org/10.3390/rs15143629 - 21 Jul 2023

Cited by 4 | Viewed by 1490

Abstract

Many single-land-use simulation models are available to simulate and predict Land Use and Land Cover Change (LUCC). However, few studies have used multiple models to simulate LUCC in the same region. The paper utilizes the CA-Markov model, Land Change Modeler (LCM), and Patch-generating Land Use Simulation model (PLUS) with natural and social driving factors to simulate the LUCC on the Western Sichuan Plateau, using Kappa coefficient, overall accuracy (OA), and Figure of Merit (FoM) to verify the accuracy of the model, and selects a suitable model to predict the LUCC and landscape pattern in the study area from 2020 to 2070. The results are as follows: (1) The LCM has the highest simulation effect, and its Kappa coefficient, OA, and FoM are higher than the other two models. (2) The area of land types other than grassland and wetland will increase from 2020 to 2070. Among them, the grassland area will decrease, but is still most prominent land category in this region. The proportion of wetland areas remains unchanged. The fragmentation degree of forest (F), grassland (GL), shrubland (SL), water bodies (WBs), bare areas (BAs), and permanent ice and snow (PIS) decreases, and the distribution shows a trend of aggregation. The dominance of F and C decreases but still dominates in the landscape. The overall landscape aggregation increased and complexity decreased, and each landscape type’s diversity, evenness, and richness increased, presenting as a more reasonable development. Using multiple models to simulate the LUCC in the same region, and choosing the most suitable local land model is of great significance to scientifically manage and effectively allocate the land resources in the field. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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16 pages, 5377 KiB

Open AccessArticle

Integrating GIS, Remote Sensing, and Citizen Science to Map Oak Decline Risk across the Daniel Boone National Forest

by Ellen Crocker, Kumari Gurung, Jared Calvert, C. Dana Nelson and Jian Yang

Remote Sens. 2023, 15(9), 2250; https://doi.org/10.3390/rs15092250 - 24 Apr 2023

Cited by 5 | Viewed by 2212

Abstract

Oak decline is a general term used for the progressive dieback and eventual mortality of oak trees due to many compounding stressors, typically a combination of predisposing, inciting, and contributing factors. While pinpointing individual causes of decline in oak trees is a challenge, past studies have identified site and stand characteristics associated with oak decline. In this study, we developed a risk map of oak decline for the Daniel Boone National Forest (DBNF), combining GIS, remote sensing (RS), and public reporting (citizen science, CS). Starting with ground reports of decline (CS), we developed a site-scale model (GIS and RS) for oak decline based on four previously identified predisposing factors: elevation, slope, solar radiation, and topographic wetness. We found that areas identified in the model as having a high oak decline risk also reflected areas of observed oak decline (CS). We then optimized and expanded this risk model to the entire range of the DBNF, based on both site characteristics (as piloted for the case study site) and stand inventory data. The stand inventory data (including species composition and age) further improved the model, resulting in a risk map at the landscape level. This case study can serve as a planning tool and highlights the potential usefulness of integrating GIS, remote sensing, and citizen science. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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24 pages, 5835 KiB

Open AccessArticle

Open Source Data-Based Solutions for Identifying Patterns of Urban Earthquake Systemic Vulnerability in High-Seismicity Areas

by Andra-Cosmina Albulescu

Remote Sens. 2023, 15(5), 1453; https://doi.org/10.3390/rs15051453 - 5 Mar 2023

Cited by 4 | Viewed by 2187

Abstract

Urban settlements located in high-seismicity areas should benefit from comprehensive vulnerability analyses, which are essential for the proper implementation of vulnerability modelling actions. Alas, many developing countries face a shortage of knowledge on seismic vulnerability, particularly concerning its systemic component, as a consequence of a combination of data scarcity and a lack of interest from authorities. This paper aims to identify primary time-independent spatial patterns of earthquake systemic vulnerability based on the accessibility of key emergency management facilities (e.g., medical units, fire stations), focusing on the urban settlements located in the high-seismicity area nearby the Vrancea Seismogenic Zone in Romania. The proposed methodological framework relies on open source data extracted from OpenStreetMap, which are processed via GIS techniques and tools (i.e., Network Analyst, Weighted Overlay Analysis), to compute the service areas of emergency management centres, and to map earthquake systemic vulnerability levels. The analysis shows that accessibility and systemic vulnerability patterns are significantly impacted by a synergy of factors deeply rooted in the urban spatial layout. Although the overall accessibility was estimated to be medium-high, and the overall systemic vulnerability to be low-medium, higher systemic vulnerability levels in certain cities (e.g., Bacău, Onești, Tecuci, Urziceni). The presented findings have multi-scalar utility: they aid in the development of improved, locally tailored seismic vulnerability reduction plans, as well as the allocation of financial and human resources required to manage earthquake-induced crises at regional scale. Further to that, the paper provides a transparent methodological framework that can be replicated to put cities in high-seismicity areas on the map of systemic vulnerability assessments, laying the groundwork for positive change in countries where the challenges associated with high-level seismic risk are often overlooked. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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17 pages, 13094 KiB

Open AccessArticle

Estimation of Gully Growth Rate and Erosion Amount Using UAV and Worldview-3 Images in Yimeng Mountain Area, China

by Guanghe Zhang, Weijun Zhao, Tingting Yan, Wei Qin and Xiaojing Miao

Remote Sens. 2023, 15(1), 233; https://doi.org/10.3390/rs15010233 - 31 Dec 2022

Cited by 9 | Viewed by 1992

Abstract

Non-homogeneous soil’s high gravel content (also known as the “soil-rock dual structure”) may render it more prone to erosion and the significant development of gullies. In order to reveal the morphological characteristics and erosion rate of gullies in “soil-rock dual structure” areas, this study focused on the Shagou Reservoir basin in the Yimeng mountain area as the study area. Based on a complete digital orthophoto map (DOM, 0.03 m) and a digital elevation model (DEM, 0.03 m) acquired by an unmanned aerial vehicle (UAV), the researchers calculated the length (L), top width (TW), depth (D), area (A) and volume (V) of 19 gullies and built and optimized the volume estimation model. The DOM and the DEM were used to modify the morphological parameters of 43 gullies extracted from high-resolution remote sensing (RS) stereopair images (Worldview, 0.5 m), and the development and evolution of gully erosion were evaluated in large scale. The results showed that: (1) after correction, the average relative errors of parameters L, TW, D and A computed from the UAV data and the high-resolution RS stereopair image data fell below 0.005%; (2) the mean of TW/D was 5.20, i.e., the lateral erosion development of gullies far outweighed the downcutting erosion. The retrogressive erosion, lateral erosion and downcutting erosion rates of gullies were 0.01~0.83 m/a (averaged at 0.23 m/a), 0.01~0.68 m/a (averaged at 0.25 m/a) and 0.01~0.19 m/a (averaged at 0.09 m/a), respectively, between 2014 and 2021; (3) the volume-area (V-A) model for gullies is the optimal one (p < 0.01, R² = 0.944).A total of 90.7% of the gully volume was growing at an erosion rate of 0.42~399.39 m³/a and the total erosion rate of the gullies was 3181.56 m³/a from 2014 to 2021. These research findings can serve as a basis for the quantitative modeling of gully erosion in water-eroded locations with a large-dimension “soil-rock dual structure”. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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31 pages, 5284 KiB

Open AccessArticle

A Possible Land Cover EAGLE Approach to Overcome Remote Sensing Limitations in the Alps Based on Sentinel-1 and Sentinel-2: The Case of Aosta Valley (NW Italy)

by Tommaso Orusa, Duke Cammareri and Enrico Borgogno Mondino

Remote Sens. 2023, 15(1), 178; https://doi.org/10.3390/rs15010178 - 28 Dec 2022

Cited by 22 | Viewed by 3226

Abstract

Land cover (LC) maps are crucial to environmental modeling and define sustainable management and planning policies. The development of a land cover mapping continuous service according to the new EAGLE legend criteria has become of great interest to the public sector. In this work, a tentative approach to map land cover overcoming remote sensing (RS) limitations in the mountains according to the newest EAGLE guidelines was proposed. In order to reach this goal, the methodology has been developed in Aosta Valley, NW of Italy, due to its higher degree of geomorphological complexity. Copernicus Sentinel-1 and 2 data were adopted, exploiting the maximum potentialities and limits of both, and processed in Google Earth Engine and SNAP. Due to SAR geometrical distortions, these data were used only to refine the mapping of urban and water surfaces, while for other classes, composite and timeseries filtered and regularized stack from Sentinel-2 were used. GNSS ground truth data were adopted, with training and validation sets. Results showed that K-Nearest-Neighbor and Minimum Distance classification permit maximizing the accuracy and reducing errors. Therefore, a mixed hierarchical approach seems to be the best solution to create LC in mountain areas and strengthen local environmental modeling concerning land cover mapping. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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16 pages, 2599 KiB

Open AccessArticle

Deciphering the Drivers of Net Primary Productivity of Vegetation in Mining Areas

by Huiwen Tian, Shu Liu, Wenbo Zhu, Junhua Zhang, Yaping Zheng, Jiaqi Shi and Rutian Bi

Remote Sens. 2022, 14(17), 4177; https://doi.org/10.3390/rs14174177 - 25 Aug 2022

Cited by 9 | Viewed by 2156

Abstract

Spatial differentiation of the net primary productivity (NPP) of vegetation is an important factor in the ecological protection and restoration of mining areas. However, most studies have focused on climatic productivity constraints and rarely considered the effects of soil properties and mining activities. Thus, the impact of the forces driving NPP in mining areas on spatial location remains unclear. Taking the Changhe Basin mining area as an example, we used the Carnegie–Ames–Stanford approach (CASA) model to estimate NPP and quantified the impact of climate, soil properties, and mining activities based on factorial experiments. Our results indicate that the average NPP in the Changhe Basin mining area was 290.13 gC/(m²·yr), and the NPP in the western Changhe Basin, an intensive coal mining area, was significantly lower than that in the east. The correlations between each driver and NPP varied by location, with mean annual temperature and precipitation, soil organic carbon, total nitrogen, and land degradation showing strong correlations. The relative importance of climate, soil properties, and mining activities on the spatial variability of NPP was 38.97%, 31.50%, and 29.53%, respectively. Furthermore, 70.72% of the NPP variability in mining areas was controlled by the coupled effects of climate and soil properties (CS + SC) or climate and mining activities (CM + MC). Meanwhile, The NPP in the western Changhe Basin mining area was mainly controlled by mining activities (M) or climate and mining activities (CM), while that in the east was mainly controlled by soil properties and climate (CS). Overall, our study extends the knowledge regarding the impacts of driving forces on spatial variation of NPP in mining areas and provides a reference point for forming strategies and practices of ecological restoration and land reclamation in different spatial locations in mining areas. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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16 pages, 3029 KiB

Open AccessArticle

Ventilation Capacities of Chinese Industrial Cities and Their Influence on the Concentration of NO₂

by Sicheng Mao, Yi Zhou, Wanjing Gao, Yuling Jin, Haile Zhao, Yuchao Luo, Siyu Chen, Xin Chen, Guoliang Zhang, Fei Lun, Zhihua Pan and Pingli An

Remote Sens. 2022, 14(14), 3348; https://doi.org/10.3390/rs14143348 - 12 Jul 2022

Cited by 2 | Viewed by 2206

Abstract

Most cities in China, especially industrial cities, are facing severe air pollution, which affects the health of the residents and the development of cities. One of the most effective ways to alleviate air pollution is to improve the urban ventilation environment; however, few studies have focused on the relationship between them. The Frontal Area Index (FAI) can reflect the obstructive effect of buildings on wind. It is influenced by urban architectural form and is an attribute of the city itself that can be used to accurately measure the ventilation capacity or ventilation potential of the city. Here, the FAIs of 45 industrial cities of different sizes in different climatic zones in China were computed, and the relationship between the FAI and the concentration of typical pollutants, i.e., NO₂, were analyzed. It was found that (1) the FAIs of most of the industrial cities in China were less than 0.45, indicating that most of the industrial cities in China have excellent and good ventilation capacities; (2) there were significant differences in the ventilation capacities of different cities, and the ventilation capacity decreased from the temperate to the tropical climate zone and increased from large to small cities; (3) there was a significant difference in the ventilation capacity in winter and summer, indicating that that with the exception of building height and building density, wind direction was also the main influencing factor of FAI; (4) the concentration of NO₂ was significantly correlated with the FAI, and the relative contribution of the FAI to the NO₂ concentration was stable at approximately 9% and was generally higher than other socioeconomic factors. There was a turning point in the influence of the FAI on the NO₂ concentration (0.18 < FAI < 0.49), below which the FAI had a strong influence on the NO₂ concentration, and above which the influence of the FAI became weaker. The results of this study can provide guidance for suppressing urban air pollution through urban planning. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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24 pages, 8083 KiB

Open AccessArticle

Estimating the Carbon Emissions of Remotely Sensed Energy-Intensive Industries Using VIIRS Thermal Anomaly-Derived Industrial Heat Sources and Auxiliary Data

by Xiaoyang Kong, Xianfeng Wang, Man Jia and Qi Li

Remote Sens. 2022, 14(12), 2901; https://doi.org/10.3390/rs14122901 - 17 Jun 2022

Cited by 5 | Viewed by 3491

Abstract

The energy-intensive industrial sector (EIIS) occupies a majority of global CO₂ emissions, but spatially monitoring the spatiotemporal dynamics of these emissions remains challenging. In this study, we used the Chinese province with the largest carbon emissions, Shandong Province, as an example to investigate the capacity of remotely sensed thermal anomaly products to identify annual industrial heat source (IHS) patterns at a 1 km resolution and estimated the carbon emissions of these sources using auxiliary datasets and the boosting regression tree (BRT) model. The IHS identification accuracy was evaluated based on two IHS references and further attributed according to corporate inventory data. We followed a bottom-up approach to estimate carbon emissions for each IHS object and conducted model fitting using the explanatory strength of the annual population density, nighttime light (NTL), and relevant thermal characteristic information derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). We generated a time series of IHS distributions from 2012 to 2020 containing a total of over 3700 IHS pixels exhibiting better alignment with the reference data than that obtained in previous work. The results indicated that the identified IHSs mostly belonged to the EIIS, such as energy-related industries (e.g., thermal power plants) and heavy manufacturing industries (e.g., chemistry and cement plants), that primarily use coal and coke as fuel sources. The BRT model exhibited a good performance, explaining 61.9% of the variance in the inventory-based carbon emissions and possessing an index of agreement (IOA) of 0.83, suggesting a feasible goodness of fit of the model when simulating carbon emissions. Explanatory variables such as the population density, thermal power radiation, NTL, and remotely sensed thermal anomaly durations were found to be important factors for improving carbon emissions modeling. The method proposed in this study is useful to aid management agencies and policymakers in tracking the carbon footprint of the EIIS and regulating high-emission corporations to achieve carbon neutrality. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science)

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