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Human–Environment Interactions Research Using Remote Sensing

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 43078

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Special Issue Editors

Prof. Dr. Nina Lam

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Guest Editor

Department of Environmental Sciences, College of the Coast and Environment, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: geographic information science; remote sensing; spatial analysis; environmental health; disaster resilience; sustainability
Special Issues, Collections and Topics in MDPI journals

Dr. Heng Cai

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Guest Editor

Department of Geography, Texas A&M University, 3147 TAMU, College Station, TX 77843, USA
Interests: spatial modeling; geographic information science and technology (GIST); disaster resilience; coupled nature–human system modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Zou

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Guest Editor

Department of Geography, Texas A&M University, College Station, TX 3147, USA
Interests: geographic information science (GIScience); spatial big data analytics; social sensing; remote sensing; human–environment interactions; disaster resilience; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are calling for papers for a Special Issue on “Human–Environment Interaction Research Using Remote Sensing”. Human communities are increasingly vulnerable to the threats from global environmental changes, including global warming, sea-level rise, and increasingly frequent natural hazards. Meanwhile, human activities such as urbanization and industrialization are also altering the landscape and biodiversity of the natural environment. Interdisciplinary research on integrated socioenvironmental dynamics at various spatial–temporal scales is crucial to building long-term sustainability. Remote sensing technology has evolved from satellite remote sensing from space to local sensing of human mobility. The rapidly growing body of large remote sensing and other geospatial data sets has brought forth new opportunities, as well as significant challenges, to explore the complex interactions between humans and the environment.

This forthcoming Special Issue invites manuscripts integrating remote sensing technologies and other cutting-edge geospatial technologies, such as crowdsourcing, spatial modeling, geostatistics, and machine learning to answer questions on how human systems respond to a dynamic environment, and the best pathways to achieve sustainability. Potential topics include but are not limited to the following:

- State-of-the-art technologies and applications on human–environmental interactions using remote sensing;
- Interaction effects of socioenvironmental dynamics on food, water, and energy securities, disease spread, and public health;
- Impacts of human activities on environmental changes, such as coastal land loss, flooding, deforestation, wildfires, and decreasing biodiversity and natural resources;
- Human–environment interactions under concurrent disasters;
- Short-term and long-term disaster resilience assessment and modeling;
- Integration of remote sensing with geospatial big data (e.g., social media, volunteered geographic information, distributed sensors, portable sensing, phones, drones, and other sensor networks) on human dynamics modeling;
- Applications of Artificial Intelligence algorithms on socioenvironmental dynamics simulation and modeling.
- Scale effects on coupled human–environmental system modeling;
- Geospatial data fusion challenges and considerations in human and natural interactions research.

Dr. Nina Lam
Dr. Heng Cai
Dr. Lei Zou
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

Integrated socioenvironmental dynamics
Human–environment interactions
Climate change and natural hazards
Disaster resilience and sustainability
Spatial modeling and simulation
Spatial data fusion
Human dynamics sensor technology
Remote sensing image processing

Published Papers (8 papers)

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Editorial

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5 pages, 201 KiB

Open AccessEditorial

Editorial for the Special Issue: “Human-Environment Interactions Research Using Remote Sensing”

by Nina S.-N. Lam, Heng Cai and Lei Zou

Remote Sens. 2022, 14(11), 2720; https://doi.org/10.3390/rs14112720 - 6 Jun 2022

Cited by 2 | Viewed by 2092

Abstract

In the wake of increasingly frequent extreme weather events and population growth in hazard-prone areas worldwide, human communities are faced with growing threats from natural hazards [...] Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

Research

Jump to: Editorial

20 pages, 5200 KiB

Open AccessArticle

Spatial–Temporal Land Loss Modeling and Simulation in a Vulnerable Coast: A Case Study in Coastal Louisiana

by Mingzheng Yang, Lei Zou, Heng Cai, Yi Qiang, Binbin Lin, Bing Zhou, Joynal Abedin and Debayan Mandal

Remote Sens. 2022, 14(4), 896; https://doi.org/10.3390/rs14040896 - 13 Feb 2022

Cited by 4 | Viewed by 3999

Abstract

Coastal areas serve as a vital interface between the land and sea or ocean and host about 40% of the world’s population, providing significant social, economic, and ecological functions. Meanwhile, the sea-level rise caused by climate change, along with coastal erosion and accretion, alters coastal landscapes profoundly, threatening coastal sustainability. For instance, the Mississippi River Delta in Louisiana is one of the most vulnerable coastal areas. It faces severe long-term land loss that has disrupted the regional ecosystem balance during the past few decades. There is an urgent need to understand the land loss mechanism in coastal Louisiana and identify areas prone to land loss in the future. This study modeled the current and predicted the future land loss and identified natural–human variables in the Louisiana Coastal Zone (LCZ) using remote sensing and machine-learning approaches. First, we analyzed the temporal and spatial land loss patterns from 2001 to 2016 in the study area. Second, logistic regression, extreme gradient boosting (XGBoost), and random forest models with 15 human and natural variables were carried out during each five-year and the fifteen-year period to delineate the short- and long-term land loss mechanisms. Finally, we simulated the land-loss probability in 2031 using the optimal model. The results indicate that land loss patterns in different parts change through time at an overall decelerating speed. The oil and gas well density and subsidence rate were the most significant land loss drivers during 2001–2016. The simulation shows that a total area of 180 km² of land has over a 50% probability of turning to water from 2016 to 2031. This research offers valuable information for decision-makers and local communities to prepare for future land cover changes, reduce potential risks, and efficiently manage the land restoration in coastal Louisiana. Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

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20 pages, 6163 KiB

Open AccessArticle

Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

by Jinwen Xu and Yi Qiang

Remote Sens. 2021, 13(20), 4128; https://doi.org/10.3390/rs13204128 - 15 Oct 2021

Cited by 10 | Viewed by 15982

Abstract

Quantitative assessment of community resilience is a challenge due to the lack of empirical data about human dynamics in disasters. To fill the data gap, this study explores the utility of nighttime lights (NTL) remote sensing images in assessing community recovery and resilience in natural disasters. Specifically, this study utilized the newly-released NASA moonlight-adjusted SNPP-VIIRS daily images to analyze spatiotemporal changes of NTL radiance in Hurricane Sandy (2012). Based on the conceptual framework of recovery trajectory, NTL disturbance and recovery during the hurricane were calculated at different spatial units and analyzed using spatial analysis tools. Regression analysis was applied to explore relations between the observed NTL changes and explanatory variables, such as wind speed, housing damage, land cover, and Twitter keywords. The result indicates potential factors of NTL changes and urban-rural disparities of disaster impacts and recovery. This study shows that NTL remote sensing images are a low-cost instrument to collect near-real-time, large-scale, and high-resolution human dynamics data in disasters, which provide a novel insight into community recovery and resilience. The uncovered spatial disparities of community recovery help improve disaster awareness and preparation of local communities and promote resilience against future disasters. The systematical documentation of the analysis workflow provides a reference for future research in the application of SNPP-VIIRS daily images. Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

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Graphical abstract

18 pages, 4186 KiB

Open AccessArticle

Mountain Landscape Dynamics after Large Wind and Bark Beetle Disasters and Subsequent Logging—Case Studies from the Carpathians

by Vladimír Falťan, František Petrovič, Marián Gábor, Vladimír Šagát and Matej Hruška

Remote Sens. 2021, 13(19), 3873; https://doi.org/10.3390/rs13193873 - 27 Sep 2021

Cited by 9 | Viewed by 2174

Abstract

High winds and the subsequent infestation of subcortical insect are considered to be the most extensive types of large natural disturbances in the Central European forests. In this paper, we focus on the landscape dynamics of two representative mountain areas of Slovakia, which have been affected by aforementioned natural disturbances during last two decades. For example, on 19 November 2004, the bora caused significant damage to more than 126 km² of spruce forests in the Tatra National Park (TANAP). Several wind-related events also affected sites in the National Park Low Tatras (NAPALT). Monitoring of related land cover changes during years 2000–2019 was based on CORINE Land Cover data and methodology set up on satellite and aerial images interpretation, on detailed land cover interpretation (1:10,000) for the local case studies, as well as on the results of field research and forestry databases. The dynamics of forest recovery are different in the clear-cuts (usually with subsequent tree planting) and in the naturally developing forest. The area in the vicinity of Tatranská Lonmnica encroaching on the Studená dolina National Nature Reserve in TANAP represents a trend of the gradual return of young forest. The area of Čertovica on the border between NAPALT and its buffer zone are characterized by an increase in clear-cut sites with potentially increasing soil erosion risk, due to repeated wind disasters and widening of bark beetle. Proposed detailed, large-scale approach is being barely used, when considering recent studies dealing with the natural disturbances. Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

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24 pages, 6437 KiB

Open AccessArticle

Effects of Beach Nourishment Project on Coastal Geomorphology and Mangrove Dynamics in Southern Louisiana, USA

by Marcelo Cancela Lisboa Cohen, Adriana Vivan de Souza, Kam-Biu Liu, Erika Rodrigues, Qiang Yao, Luiz Carlos Ruiz Pessenda, Dilce Rossetti, Junghyung Ryu and Marianne Dietz

Remote Sens. 2021, 13(14), 2688; https://doi.org/10.3390/rs13142688 - 8 Jul 2021

Cited by 20 | Viewed by 3287

Abstract

Relative sea-level (RSL) rise associated with decreased fluvial sediment discharge and increased hurricane activity have contributed to the high rate of shoreline retreat and threatened coastal ecosystems in Port Fourchon, Louisiana, USA. This study, based on QuickBird/drone images (2004–2019) and LIDAR data (1998–2013), analyzed the impacts of shoreline dynamics on mangroves (Avicennia germinans) and marshes before and after the initiation of a beach nourishment project in 2013. The coastal barrier and dune crest migrated landward between 1998 and 2013. Meanwhile, the dune crest height increased between 1998 and 2001, then decreased in 2013, probably due to hurricane impacts. The total sediment volume along this sandy coastal barrier presented an overall trend of decline in the 1998–2013 period, resulting in a wetlands loss of ~15.6 ha along 4 km of coastline. This has led to a landward sand migration onto muddy tidal flats occupied by Avicennia germinans (1.08 ha) and Spartina (14.52 ha). However, the beach nourishment project resulted in the advancement of the beach barrier from Nov/2012 to Jan/2015, followed by a relatively stable period between Jan/2015 and Mar/2019. Additionally, both the dune crest height and sediment volume increased between 2013 and 2019. This set of factors favored the establishment and expansion of mangroves (3.2 ha) and saltmarshes (25.4 ha) along the backbarrier environments after 2013, allowing the tidal flats to keep pace with the RSL rise. However, waves and currents caused shoreline erosion following the beach nourishment project between Oct/2017 and Nov/2019, threatening wetlands by resuming the long-term process of shoreline retreat. Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

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Graphical abstract

32 pages, 7465 KiB

Open AccessEditor’s ChoiceArticle

GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan

by Muhammad Tayyab, Jiquan Zhang, Muhammad Hussain, Safi Ullah, Xingpeng Liu, Shah Nawaz Khan, Muhammad Aslam Baig, Waqas Hassan and Bazel Al-Shaibah

Remote Sens. 2021, 13(10), 1864; https://doi.org/10.3390/rs13101864 - 11 May 2021

Cited by 47 | Viewed by 8377

Abstract

Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—I_H, I_E, and I_S—that represent sensitivity, while the second part represents coping capacity (I_Cc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning. Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

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Graphical abstract

21 pages, 8036 KiB

Open AccessArticle

Spatial Correlation between Ecosystem Services and Human Disturbances: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area, China

by Yeyu He, Yaoqiu Kuang, Yalan Zhao and Zhu Ruan

Remote Sens. 2021, 13(6), 1174; https://doi.org/10.3390/rs13061174 - 19 Mar 2021

Cited by 21 | Viewed by 3575

Abstract

Exploring the spatial relationship between ecosystem services (ES) and human disturbance intensity (HDI) is vital for maintaining regional ecological security. This study aims to explore the spatial correlation between ES and HDI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and provide meaningful implications for coastal ecological planning. Multi-source remote sensing data, remote sensing software, and geographic information system provided initial data and technical support for this research. We integrated four human pressures (population, land-use, traffic, and energy) to map the HDI in the GBA for 2018. Coastal ES were comprehensively considered and spatially visualized by extracting the ES sources. The geographically weighted Pearson correlation coefficient and bivariate local Moran were used to quantitatively reflect and spatially visualize the detailed relationship between ES and HDI. Our study presents several key findings. First, the central and southern parts of the GBA are under strong HDI, dominated by a dense population and intense land utilization. Second, the kernel density of ES sources can better manifest the spatial distribution of ES objectively in comparison to the traditional model calculation. Provisioning services mainly originate from the periphery of the central cities; cultural services are highly concentrated in the heartland of the GBA; and regulating and maintenance services have high density in the outermost regions. Third, ES and HDI have a significant correlation, and the geographically weighted Pearson correlation coefficient and local indicator of spatial association cluster maps illustrate that unlike the global findings, the local correlation is spatially nonstationary as the local scale is affected by specific human activities, natural conditions, regional development, and other local factors. Four, high-capacity regions of ES provision are mainly under high HDI. Areas with high provisioning service values are mainly affected by population and traffic pressure, whereas regulating and maintenance services and cultural services are mainly dominated by high-density populations. Regulating and maintenance services are also affected by land-use pressure. We determine that human disturbance has negative spillover effects on ES, which should be the focus in regional ecological planning. Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

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17 pages, 6445 KiB

Open AccessArticle

Anthropogenic Heat Flux Estimation Based on Luojia 1-01 New Nighttime Light Data: A Case Study of Jiangsu Province, China

by Zhongli Lin and Hanqiu Xu

Remote Sens. 2020, 12(22), 3707; https://doi.org/10.3390/rs12223707 - 12 Nov 2020

Cited by 13 | Viewed by 2071

Abstract

With the rapid process of urbanization, anthropogenic heat generated by human activities has become an important factor that drives the changes in urban climate and regional environmental quality. The nighttime light (NTL) data can aptly reflect the spatial distribution of social-economic activities and energy consumption, and quantitatively estimate the anthropogenic heat flux (AHF) distribution. However, the commonly used DMSP/OLS and Suomi-NPP/VIIRS NTL data are restricted by their coarse spatial resolution and, therefore, cannot exhibit the spatial details of AHF at city scale. The 130 m high-resolution NTL data obtained by Luojia 1-01 satellite launched in June 2018 shows a promise to solve this problem. In this paper, the gridded AHF spatial estimation is achieved with a resolution of 130 m using Luojia 1-01 NTL data based on three indexes, NTL_nor (Normalized Nighttime Light Data), HSI (Human Settlement Index), and VANUI (Vegetation Adjusted NTL Urban Index). We chose Jiangsu, a fast-developing province in China, as an example to determine the best AHF estimation model among the three indexes. The AHF of 96 county-level cities of the province was first calculated using energy-consumption statistics data and then correlated with the corresponding data of three indexes. The results show that based on a 5-fold cross-validation approach, the VANUI power estimation model achieves the highest R² of 0.8444 along with the smallest RMSE of 4.8277 W·m⁻² and therefore has the highest accuracy among the three indexes. According to the VANUI power estimation model, the annual mean AHF of Jiangsu in 2018 was 2.91 W·m⁻². Of the 96 cities, Suzhou has the highest annual mean AHF of 7.41 W·m⁻², followed by Wuxi, Nanjing, Changzhou and Zhenjiang, with the annual mean of 3.80–5.97 W·m⁻², while the figures of Suqian, Yancheng, Lianyungang, and Huaian, the cities in northern Jiangsu, are relatively low, ranging from 1.41 to 1.59 W·m⁻². This study has shown that the AHF estimation model developed by Luojia 1-01 NTL data can achieve higher accuracy at city-scale and discriminate the spatial detail of AHF effectively. Full article

(This article belongs to the Special Issue Human–Environment Interactions Research Using Remote Sensing)

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