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Remote Sensing Application for Environmental Sustainability

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 31814

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Prof. Dr. Szilárd Szabó

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Department of Physical Geography, Faculty of Science and Technology, University of Debrecen, 4032 Debrecen, Hungary
Interests: data mining; machine learning; remote sensing; satellites; aerial photographs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few decades, remote sensing became a very effective tool in surveying the environment. The advantage of getting data from the surface from different dates makes this technology essential in environmental research. Satellite imagery and aerial photography, including unmanned aerial systems (UAS), provide a wide range of data to analyze an area or for environmental monitoring, which can lead us to conclusions that reveal the driving forces of changes or to understand different processes.

Agricultural production, industrial activities, or even the growing tourism sector can result in rapid land use change and urban sprawl, which should be monitored and evaluated from the perspective of sustainability. If a landscape’s carrying capacity reached its limits, changes can accelerate and, consequently, will lead to a detrimental state. This Special Issue aims to reveal these crucial or hazardous changes with the help of remote sensing in different scales, identifying the issues from the local to the regional level.

Prof. Dr. Szilárd Szabó
Guest Editor

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Keywords

Landscape level analysis
Species protection and conservation issues
Urban issues of sustainability
Social concerns of development
Carrying capacity of regions
Watershed management
Flood risk and land use change

Published Papers (9 papers)

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Research

20 pages, 3899 KiB

Open AccessArticle

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

by Eunbeen Park, Jiwon Kim, Cholho Song, Hyun-Woo Jo, Sujong Lee, Sea Jin Kim, Sugyeong Park, Chul-Hee Lim and Woo-Kyun Lee

Sustainability 2020, 12(18), 7256; https://0-doi-org.brum.beds.ac.uk/10.3390/su12187256 - 4 Sep 2020

Cited by 5 | Viewed by 4166

Abstract

Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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25 pages, 5090 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Land Cover Change Detection in Gozamin District, Ethiopia: From Remote Sensing and DPSIR Perspectives

by Abebaw Andarge Gedefaw, Clement Atzberger, Thomas Bauer, Sayeh Kassaw Agegnehu and Reinfried Mansberger

Sustainability 2020, 12(11), 4534; https://0-doi-org.brum.beds.ac.uk/10.3390/su12114534 - 2 Jun 2020

Cited by 14 | Viewed by 3930

Abstract

Land cover patterns in sub-Saharan Africa are rapidly changing. This study aims to quantify the land cover change and to identify its major determinants by using the Drivers, Pressures, State, Impact, Responses (DPSIR) framework in the Ethiopian Gozamin District over a period of 32 years (1986 to 2018). Satellite images of Landsat 5 (1986), Landsat 7 (2003), and Sentinel-2 (2018) and a supervised image classification methodology were used to assess the dynamics of land cover change. Land cover maps of the three dates, focus group discussions (FGDs), interviews, and farmers’ lived experiences through a household survey were applied to identify the factors for changes based on the DPSIR framework. Results of the investigations revealed that during the last three decades the study area has undergone an extensive land cover change, primarily a shift from cropland and grassland into forests and built-up areas. Thus, quantitative land cover change detection between 1986 and 2018 revealed that cropland, grassland, and bare areas declined by 10.53%, 5.7%, and 2.49%. Forest, built-up, shrub/scattered vegetation, and water bodies expanded by 13.47%, 4.02%, 0.98%, and 0.25%. Household surveys and focus group discussions (FGDs) identified the population growth, the rural land tenure system, the overuse of land, the climate change, and the scarcity of grazing land as drivers of these land cover changes. Major impacts were rural to urban migration, population size change, scarcity of land, and decline in land productivity. The outputs from this study could be used to assure sustainability in resource utilization, proper land use planning, and proper decision-making by the concerned government authorities. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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16 pages, 3827 KiB

Open AccessArticle

Spatiotemporal Variation of Land Surface Temperature and Vegetation in Response to Climate Change Based on NOAA-AVHRR Data over China

by Zhaoqi Wang, Zhiyuan Lu and Guolong Cui

Sustainability 2020, 12(9), 3601; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093601 - 29 Apr 2020

Cited by 12 | Viewed by 2307

Abstract

The dynamics of land surface temperature (LST) and its correlation with vegetation are crucial to understanding the effects of global climate change. This study intended to retrieve the LST of China, based on the NOAA-AVHRR images, by using a split-window algorithm. The spatiotemporal variation of LST, Normalized difference vegetation index (NDVI), and the correlation between the two was investigated in China from 1982–2016. Moreover, eight scenarios were established to explore the driving forces in vegetation variation. Results indicated that the LST increased by 0.06 °C/year in nearly 81.1% of the study areas. The NDVI with an increasing rate of 0.1%/year and occupied 58.6% of the study areas. By contrast, 41.4% of the study areas with a decreasing rate of 0.7 × 10⁻³/year, was mainly observed in northern China. The correlation coefficients between NDVI and LST were higher than that between NDVI and precipitation, and the increase in LST could stimulate vegetation growth. Most regions of China have experienced significant warming over the past decades, specifically, desertification happens in northern China, because it is getting drier. The synergy of LST and precipitation is the primary cause of vegetation dynamics. Therefore, long-term monitoring of LST and NDVI is necessary to better understand the adaptation of the terrestrial ecosystem to global climate change. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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27 pages, 5326 KiB

Open AccessArticle

A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

by Anjar Dimara Sakti and Wataru Takeuchi

Sustainability 2020, 12(8), 3227; https://0-doi-org.brum.beds.ac.uk/10.3390/su12083227 - 16 Apr 2020

Cited by 15 | Viewed by 4097

Abstract

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km², which increased to 8.3 million km² in 2006–2010, and then to approximately 8.6 million km² in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km², respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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16 pages, 1957 KiB

Open AccessArticle

Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

by Chia-An Ku

Sustainability 2020, 12(7), 2964; https://0-doi-org.brum.beds.ac.uk/10.3390/su12072964 - 8 Apr 2020

Cited by 17 | Viewed by 2687

Abstract

The deterioration of air quality in urban areas is often closely related to urbanization, as this has led to a significant increase in energy consumption and the massive emission of air pollutants, thereby exacerbating the current state of air pollution. However, the relationship between urban development and air quality is complex, thus making it difficult to be analyzed using traditional methods. In this paper, a framework integrating spatial analysis and statistical methods (based on 170 regression models) is developed to explore the spatial and temporal relationship between urban land use patterns and air quality, aiming to provide solid information for mitigation planning. The thresholds for the influence of urban patterns are examined using different buffer zones. In addition, the differences in the effects of various types of land use pattern on air quality were also explored. The results show that there were significant differences between 1999 and 2013 with regards to the correlations between land use patterns and air pollutant concentrations. Among all land uses, forest, water and built-up areas were proved to influence concentrations the most. It is suggested that the developed framework should be applied further in the real-world mitigation planning decision-making process Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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21 pages, 19127 KiB

Open AccessArticle

Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management

by Boudewijn van Leeuwen, Zalán Tobak and Ferenc Kovács

Sustainability 2020, 12(7), 2854; https://0-doi-org.brum.beds.ac.uk/10.3390/su12072854 - 3 Apr 2020

Cited by 15 | Viewed by 3427

Abstract

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m². The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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18 pages, 3572 KiB

Open AccessArticle

Quantifying Changes in Plant Species Diversity in a Savanna Ecosystem Through Observed and Remotely Sensed Data

by Lazarus Chapungu, Luxon Nhamo, Roberto Cazzolla Gatti and Munyaradzi Chitakira

Sustainability 2020, 12(6), 2345; https://0-doi-org.brum.beds.ac.uk/10.3390/su12062345 - 17 Mar 2020

Cited by 15 | Viewed by 3224

Abstract

This study examined the impact of climate change on plant species diversity of a savanna ecosystem, through an assessment of climatic trends over a period of forty years (1974–2014) using Masvingo Province, Zimbabwe, as a case study. The normalised difference vegetation index (NDVI) was used as a proxy for plant species diversity to cover for the absence of long-term historical plant diversity data. Observed precipitation and temperature data collected over the review period were compared with the trends in NDVI to understand the impact of climate change on plant species diversity over time. The nonaligned block sampling design was used as the sampling framework, from which 198 sampling plots were identified. Data sources included satellite images, field measurements, and direct observations. Temperature and precipitation had significant (p < 0.05) trends over the period under study. However, the trend for seasonal total precipitation was not significant but declining. Significant correlations (p < 0.001) were identified between various climate variables and the Shannon index of diversity. NDVI was also significantly correlated to the Shannon index of diversity. The declining trend of plant species in savanna ecosystems is directly linked to the decreasing precipitation and increasing temperatures. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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18 pages, 10605 KiB

Open AccessArticle

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

by Edith Olmos-Trujillo, Julián González-Trinidad, Hugo Júnez-Ferreira, Anuard Pacheco-Guerrero, Carlos Bautista-Capetillo, Claudia Avila-Sandoval and Eric Galván-Tejada

Sustainability 2020, 12(5), 1939; https://0-doi-org.brum.beds.ac.uk/10.3390/su12051939 - 3 Mar 2020

Cited by 29 | Viewed by 4006

Abstract

In this research, vegetation indices (VIs) were analyzed as indicators of the spatio-temporal variation of vegetation in a semi-arid region. For a better understanding of this dynamic, interactions between vegetation and climate should be studied more widely. To this end, the following methodology was proposed: (1) acquire the NDVI, EVI, SAVI, MSAVI, and NDMI by classification of vegetation and land cover categories in a monthly period from 2014 to 2018; (2) perform a geostatistical analysis of rainfall and temperature; and (3) assess the application of ordinary and uncertainty least squares linear regression models to experimental data from the response of vegetation indices to climatic variables through the BiDASys (bivariate data analysis system) program. The proposed methodology was tested in a semi-arid region of Zacatecas, Mexico. It was found that besides the high values in the indices that indicate good health, the climatic variables that have an impact on the study area should be considered given the close relationship with the vegetation. A better correlation of the NDMI and EVI with rainfall and temperature was found, and similarly, the relationship between VIs and climatic factors showed a general time lag effect. This methodology can be considered in management and conservation plans of natural ecosystems, in the context of climate change and sustainable development policies. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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16 pages, 12112 KiB

Open AccessArticle

The Temporal Analysis of Regional Cultivated Land Productivity with GPP Based on 2000–2018 MODIS Data

by Jiani Ma, Chao Zhang, Wenju Yun, Yahui Lv, Wanling Chen and Dehai Zhu

Sustainability 2020, 12(1), 411; https://0-doi-org.brum.beds.ac.uk/10.3390/su12010411 - 5 Jan 2020

Cited by 19 | Viewed by 3117

Abstract

The spatiotemporal change characteristics of Cultivated Land Productivity (CLP) are imperative for ensuring regional food security, especially given recent global warming, social development and population growth. Based on the hypothesis that the Gross Primary Productivity (GPP) is a proxy of land productivity, the Moderate Resolution Imaging Spectroradiometer (MODIS) data with 500-m spatial resolution and 8-day temporal resolution was employed by the Vegetation Photosynthesis Model (VPM) to calculate GPP in Jilin Province, China. We explored the level of CLP using the GPP mean from 2000 to 2018, and analyzed the changing trend and amplitude of CLP in the whole study period using both Theil–Sen median trend analysis and the Mann–Kendall (MK) test, and forecasted the sustainability of CLP with the Hurst exponent. The trend result and the Hurst exponent were integrated to acquire the future direction of change. The results revealed that: (1) The CLP level was generally high in the southeast and low in the northwest in cultivated land in Jilin, China. The area with the lowest productivity, located in the northwest of Jilin, accounted for 15.56%. (2) The majority (84.77%) of the area showed an increasing trend in 2000–2018, which was larger than the area that was decreasing, which accounted for 3.97%. (3) The overall change amplitude was dominated by a slightly increasing trend, which accounted for 51.48%. (4) The area with sustainability accounted for 33.45% and was mainly distributed in the northwest of Jilin. The area with anti-sustainability accounted for 26.78% and was mainly distributed in the northwest and central Jilin. (5) The Hurst exponent result showed that uncertain variation of CLP is likely to occur in the future over the entire region, and the central region is prone to display degeneration. Therefore, the results of this study indicated that quality improvement policy could be implemented for the middle-to-low yield fields in northwest Jilin, and dynamic monitoring and protection measures could be implemented for the areas with uncertain future changes and decreasing sustainability. Full article

(This article belongs to the Special Issue Remote Sensing Application for Environmental Sustainability)

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