Special Issue "Remote Sensing of Human-Environment Interactions"

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 January 2020).

Special Issue Editors

Prof. Stephen Walsh
E-Mail Website
Guest Editor
Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA
Interests: Dr. Stephen J. Walsh’s research interests involve the fusion of multi-scale remote sensing assets and information extraction methods to assess the spatial-temporal patterns of land cover/land use change (LCLUC) and the associated social-ecological drivers of change. Through extensive work in Thailand, the Ecuadorian Amazon, and the Galapagos Islands of Ecuador, remote sensing data products are integrated with multi-dimensional human-environment data to examine pattern process relationship and the feedbacks between human behavior and environmental dynamics. Scenario testing is conducted to study the impact of exogenous and endogenous factors in shaping and reshaping human-environment interactions and LCLUC patterns that are assessed through statistical methods and spatial simulation models, including, agent-based models and dynamic systems models
Prof. Conghe Song
E-Mail Website
Guest Editor
Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA
Interests: remote sensing of the environment with multiple sensors for natural resource monitoring; land surface biophysical parameter extraction; land cover/land use change (LCLUC) detection; modeling of the ecological consequence of the terrestrial ecosystem as a result of LCLUC in the context of climate change, and understanding the socioeconomic driving factors of LCLUC
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Special Issue Information

Dear Colleagues,

We are calling for papers for a Special Issue on “Remote Sensing of Human-Environment Interactions”. Due to the rapid increase in the global human population, its re-distribution through migration, and associated economic growth, the direct and indirect human footprint on the natural environment has never been larger and it is increasing in areal extent and intensity, seriously threatening the welfare of future generations. Humans are extracting increasingly more resources from the environment, including, but not limited to, unprecedented use of fertile land for urban expansion, agricultural land extensification and intensification for food production, timber harvesting, freshwater usage, and mineral, gas and oil excavation, all of which have profound environmental and social consequences. On the other hand, the waste matter coming out of the human system in solid, liquid or gaseous forms, and entering into the atmosphere and/or water system, further compromises the vital ecosystem services that the natural environment provides and the health and well-being of humans require. At the same time, tremendous efforts have been invested by national and international agencies and government organizations in conservation of the existing vital ecosystems, restoration of the degraded environments, and creative management for sustainable use of key natural resources. Remote Sensing provides an indispensable tool to monitor, visualize, analyze, and model human-environment interactions for better understanding of what has happened in the past and the consequences of the future. Linking geospatial data to remotely sensed data to characterize people, environment, and their interactions is vital to implementing and accomplishing sustainable development involving the integration of policy actors across multiple sectors and levels of government. To stimulate more research on human–environment interactions using remotely sensed data in both the continental and island settings and its international dissemination, we call for papers on a range of topics in this Special Issue, such as

(1) Urban-agricultural land use dynamics and the social-ecological consequences.
(2) Natural resource management programs or environmental policies.
(3) Deforestation and reforestation and other environmental restoration programs.
(4) Mining, fracking and other forms of extraction of underground natural resources.
(5) Social-ecological impacts of tourism and population migration.
(6) Island ecosystems and challenges to their sustainability.

Prof. Stephen J. Walsh
Prof. Conghe Song
Guest Editors

Manuscript Submission Information

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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

  • Human-Environment Interactions
  • Land-Cover/Land-Use Change
  • Ecosystem Services
  • Natural Resource Management
  • Environmental Policy Evaluation
  • Tourism and Development
  • Climate and Environmental Change
  • Urbanization
  • Population Migration
  • Land Abandonment
  • Land Degradation
  • Invasive Species
  • Agricultural intensification & Extensification

Published Papers (8 papers)

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Research

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Open AccessArticle
Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques
Remote Sens. 2020, 12(3), 584; https://doi.org/10.3390/rs12030584 - 10 Feb 2020
Abstract
Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of [...] Read more.
Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of the urban transformation, these rapid changes need to be mapped with high precision and frequency. Urban detection in rural areas in optical remote sensing is problematic when urban structures are built using the same materials as their surroundings. To overcome this limitation, we propose a multi-temporal classification approach based on satellite data fusion and artificial neural networks. We applied the proposed methodology to data of the Egyptian regions of El-Minya and part of Asyut governorates collected from 1998 until 2015. The produced multi-temporal land cover maps capture the evolution of the area and improve the urban detection of the European Space Agency (ESA) Climate Change Initiative Sentinel-2 Prototype Land Cover 20 m map of Africa and the Global Human Settlements Layer from the Joint Research Center (JRC). The extension of urban and agricultural areas increased over 65 km2 and 200 km2, respectively, during the entire period, with an accelerated increase analysed during the last period (2010–2015). Finally, we identified the trends in urban population density as well as the relationship between farmed and built-up land. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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Open AccessArticle
Land Cover Classification of Complex Agroecosystems in the Non-Protected Highlands of the Galapagos Islands
Remote Sens. 2020, 12(1), 65; https://doi.org/10.3390/rs12010065 - 23 Dec 2019
Abstract
The humid highlands of the Galapagos are the islands’ most biologically productive regions and a key habitat for endemic animal and plant species. These areas are crucial for the region’s food security and for the control of invasive plants, but little is known [...] Read more.
The humid highlands of the Galapagos are the islands’ most biologically productive regions and a key habitat for endemic animal and plant species. These areas are crucial for the region’s food security and for the control of invasive plants, but little is known about the spatial distribution of its land cover. We generated a baseline high-resolution land cover map of the agricultural zones and their surrounding protected areas. We combined the high spatial resolution of PlanetScope images with the high spectral resolution of Sentinel-2 images in an object-based classification using a RandomForest algorithm. We used images collected with an unmanned aerial vehicle (UAV) to verify and validate our classified map. Despite the astounding diversity and heterogeneity of the highland landscape, our classification yielded useful results (overall Kappa: 0.7, R2: 0.69) and revealed that across all four inhabited islands, invasive plants cover the largest fraction (28.5%) of the agricultural area, followed by pastures (22.3%), native vegetation (18.6%), food crops (18.3%), and mixed forest and pioneer plants (11.6%). Our results are consistent with historical trajectories of colonization and abandonment of the highlands. The produced dataset is designed to suit the needs of practitioners of both conservation and agriculture and aims to foster collaboration between the two areas. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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Open AccessArticle
Simulating Land Cover Change Impacts on Groundwater Recharge under Selected Climate Projections, Maui, Hawaiʻi
Remote Sens. 2019, 11(24), 3048; https://doi.org/10.3390/rs11243048 - 17 Dec 2019
Abstract
This project developed an integrated land cover/hydrological modeling framework using remote sensing and geographic information systems (GIS) data, stakeholder input, climate information and projections, and empirical data to estimate future groundwater recharge on the Island of Maui, Hawaiʻi, USA. End-of-century mean annual groundwater [...] Read more.
This project developed an integrated land cover/hydrological modeling framework using remote sensing and geographic information systems (GIS) data, stakeholder input, climate information and projections, and empirical data to estimate future groundwater recharge on the Island of Maui, Hawaiʻi, USA. End-of-century mean annual groundwater recharge was estimated under four future land cover scenarios: Future 1 (conservation-focused), Future 2 (status-quo), Future 3 (development-focused), and Future 4 (balanced conservation and development), and two downscaled climate projections: a coupled model intercomparison project (CMIP) phase 5 (CMIP5) representative concentration pathway (RCP) 8.5 “dry climate” future and a CMIP3 A1B “wet climate” future. Results were compared to recharge estimated using the 2017 baseline land cover to understand how changing land management and climate could influence groundwater recharge. Estimated recharge increased island-wide under all future land cover and climate combinations and was dominated by specific land cover transitions. For the dry future climate, recharge for land cover Futures 1 to 4 increased by 12%, 0.7%, 0.01%, and 11% relative to 2017 land cover conditions, respectively. Corresponding increases under the wet future climate were 10%, 0.9%, 0.6%, and 9.3%. Conversion from fallow/grassland to diversified agriculture increased irrigation, and therefore recharge. Above the cloud zone (610 m), conversion from grassland to native or alien forest led to increased fog interception, which increased recharge. The greatest changes to recharge occurred in Futures 1 and 4 in areas where irrigation increased, and where forest expanded within the cloud zone. Furthermore, new future urban expansion is currently slated for coastal areas that are already water-stressed and had low recharge projections. This study demonstrated that a spatially-explicit scenario planning process and modeling framework can communicate the possible consequences and tradeoffs of land cover change under a changing climate, and the outputs from this study serve as relevant tools for landscape-level management and interventions. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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Open AccessArticle
Impact of Urbanization and Climate on Vegetation Coverage in the Beijing–Tianjin–Hebei Region of China
Remote Sens. 2019, 11(20), 2452; https://doi.org/10.3390/rs11202452 - 22 Oct 2019
Cited by 1
Abstract
Worldwide urbanization leads to ecological changes around urban areas. However, few studies have quantitatively investigated the impacts of urbanization on vegetation coverage so far. As an important indicator measuring regional environment change, fractional vegetation cover (FVC) is widely used to analyze changes in [...] Read more.
Worldwide urbanization leads to ecological changes around urban areas. However, few studies have quantitatively investigated the impacts of urbanization on vegetation coverage so far. As an important indicator measuring regional environment change, fractional vegetation cover (FVC) is widely used to analyze changes in vegetation in urban areas. In this study, on the basis of a partial derivative model, we quantified the effect of temperature, precipitation, radiation, and urbanization represented as nighttime light on vegetation coverage changes in the Beijing–Tianjin–Hebei (BTH) region during its period of rapid resident population growth from 2001 to 2011. The results showed that (1) the FVC of the BTH region varied from 0.20 to 0.26, with significant spatial heterogeneity. The FVC increased in small cities such as Cangzhou and in the Taihang Mountains, while it decreased in megacities with populations greater than 1 million, such as Beijing and Zhangjiakou Bashang. (2) The BTH region experienced rapid urbanization, with the area of artificial surface increasing by 18.42%. From the urban core area to the fringe area, the urbanization intensity decreased, but the urbanization rate increased. (3) Urbanization and precipitation had the greatest effect on FVC changes. Urbanization dominated the FVC changes in the expanded area, while precipitation had the greatest impacts on the FVC changes in the core area. For future studies on the major influencing factors of FVC changes, quantitative analysis of the contribution of urbanization to FVC changes in urban regions is crucial and will provide scientific perspectives for sustainable urban planning. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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Open AccessEditor’s ChoiceArticle
Impacts of Land-Use Changes on Soil Erosion in Water–Wind Crisscross Erosion Region of China
Remote Sens. 2019, 11(14), 1732; https://doi.org/10.3390/rs11141732 - 23 Jul 2019
Abstract
Soil erosion affects food production, biodiversity, biogeochemical cycles, hydrology, and climate. Land-use changes accelerated by intensive human activities are a dominant anthropogenic factor inducing soil erosion globally. However, the impacts of land-use-type changes on soil erosion dynamics over a continuous period for constructing [...] Read more.
Soil erosion affects food production, biodiversity, biogeochemical cycles, hydrology, and climate. Land-use changes accelerated by intensive human activities are a dominant anthropogenic factor inducing soil erosion globally. However, the impacts of land-use-type changes on soil erosion dynamics over a continuous period for constructing a sustainable ecological environment has not been systematically quantified. This study investigates the spatial–temporal dynamics of land-use change and soil erosion across a specific area in China with water–wind crisscross erosion during three periods: 1995–1999, 2000–2005, and 2005–2010. We analyzed the impacts of each land-use-type conversion on the intensity changes of soil erosion caused by water and wind, respectively. The major findings include: (1) land-use change in the water–wind crisscross erosion region of China was characterized as cultivated land expansion at the main cost of grassland during 1995–2010; (2) the strongest land-use change moved westward in space from the central Loess Plateau area in 1995–2005 to the western piedmont alluvial area in 2005–2010; (3) soil erosion area is continuously increasing, but the trend is declining from the late 1990s to the late 2000s; (4) the soil conservation capability of land-use types in water–wind crisscross erosion regions could be compiled from high to low as high coverage grasslands, medium coverage grasslands, paddy, drylands, low coverage grasslands, built-up lands, unused land of sandy lands, the Gobi Desert, and bare soil. These findings could provide some insights for executing reasonable land-use approaches to balance human demands and environment sustainability. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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Open AccessArticle
Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification
Remote Sens. 2019, 11(14), 1719; https://doi.org/10.3390/rs11141719 - 20 Jul 2019
Cited by 2
Abstract
Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional [...] Read more.
Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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Open AccessArticle
Integrating Spatial Continuous Wavelet Transform and Normalized Difference Vegetation Index to Map the Agro-Pastoral Transitional Zone in Northern China
Remote Sens. 2018, 10(12), 1928; https://doi.org/10.3390/rs10121928 - 30 Nov 2018
Cited by 1
Abstract
The agro-pastoral transitional zone (APTZ) in Northern China is one of the most important ecological barriers of the world. The commonly-used method to identify the spatial distribution of ATPZ is to apply a threshold rule on climatic or land use indicators. This approach [...] Read more.
The agro-pastoral transitional zone (APTZ) in Northern China is one of the most important ecological barriers of the world. The commonly-used method to identify the spatial distribution of ATPZ is to apply a threshold rule on climatic or land use indicators. This approach is highly subjective, and the quantity standards vary among the studies. In this study, we adopted the spatial continuous wavelet transform (SCWT) technique to detect the spatial fluctuation in normalized difference vegetation index (NDVI) sequences, and as such identify the APTZ. To carry out this analysis, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI 1-month data (MODND1M) covering the period 2006–2015 were used. Based on the spatial variation in NDVI, we identified two sub-regions within the APTZ. The temporal change of APTZ showed that although vegetation spatial pattern changed annually, certain areas appeared to be stable, while others showed higher sensitivity to environmental variance. Through correlation analysis between the dynamics of APTZ and precipitation, we found that the mean center of the APTZ moved toward the southeast during dry years and toward the northwest during humid years. By comparing the APTZ spatial pattern obtained in the present study with the outcome following the traditional approach based on mean annual precipitation data, it can be concluded that our study provides a reliable basis to advance the methodological framework to identify accurately transitional zones. The identification framework is of high importance to support decision-making in land use management in Northern China as well as other similar regions around the world. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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Review

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Open AccessReview
Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions
Remote Sens. 2019, 11(23), 2783; https://doi.org/10.3390/rs11232783 - 26 Nov 2019
Abstract
The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding [...] Read more.
The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integrating remote sensing into HEI research have been and discuss emerging themes, challenges, and opportunities. Despite advances in applying remote sensing to understanding ever more complex HEI fields such as land use/land cover change and landscape degradation, agricultural dynamics, urban geography and ecology, natural hazards, water resources, epidemiology, or paleo HEIs, challenges remain in acquiring and leveraging accurately georeferenced social data and establishing transferable protocols for data integration. However, recent advances in micro-satellite, unmanned aerial systems (UASs), and sensor technology are opening new avenues of integration of remotely sensed data into HEI research at scales relevant for decision-making purposes that simultaneously catalyze developments in HDGC research. Emerging or underutilized methodologies and technologies such as thermal sensing, digital soil mapping, citizen science, UASs, cloud computing, mobile mapping, or the use of “humans as sensors” will continue to enhance the relevance of HEI research in achieving sustainable development goals and driving the science of HDGC further. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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