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Sustainable Innovations in Natural Resources Management Using Earth Observation Data and Geospatial Technologies

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Resources and Sustainable Utilization".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 14304

Special Issue Editors


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Guest Editor
Department of Ecology and Natural Resources Management, Center for Development Research (ZEF), University of Bonn, Bonn, Germany
Interests: water and natural resources management; geoinformatics (remote sensing and GIS); climate modeling and climate change; hydrology; risk and impact assessment and sustainable development
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Guest Editor
Department of Geoinformatics, School of Natural Resources Management, Central University of Jharkhand, Ranchi, India
Interests: vegetation remote sensing; disaster and hazard risk analysis; natural resources management; climate Modelling and climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural resources are under pronounced pressure resulting from the interactions between human, nature and climate. The human footprint has been extending over the primeval natural environmental landscape, and the equilibrium of the natural system is disturbed via human activities and global warming. Sustainable innovations in natural resources management are vitally necessary and can be achieved using readily available satellite data from affordable sources along with various remote sensing and geographic information system (GIS) techniques. Earth observation (EO) can make a great contribution to both achieving the sustainable development goals (SDGs) and to assess progress toward targets by measuring and monitoring indicators. The cost-effective and readily available satellite images and geospatial technologies have the capability to produce and support various statistics covering many domains of natural resources to complement traditional sources of socioeconomic, environmental and climate data. Remote sensing techniques offer an effective way to monitor changes in land use and land cover due to anthropogenic activities, which in turn affects environment and climate. The changes in water resources and naturally occurring hydrometeorological disasters are altered due to global warming as well as altered patterns of rainfall and temperature. The environmental changes and their impacts on vegetation and water resources can be monitored using EO. The data assimilation using multi-sensor and multi-source EO can support monitoring and management of natural resources at different scales in data scarce regions. The present Special Issue aims to explore cross-disciplinary scientific knowledge on the applicability of EO and climate data for monitoring and managing natural resources and potential disasters using the geospatial technologies.

A list of possible topics of interests:

  • Impacts of land use and land cover change on sustainability of ecosystems
  • Vegetation remote sensing and climate impacts
  • Water resources and climate change
  • Hydrological modeling for water quantity and quality
  • Glacier retreat and mass balance
  • Environmental change monitoring using EO data
  • Forest and agriculture sustainability under climate change
  • Mangrove forest and coastal erosion
  • Geospatial technologies, pattern recognition, and machine learning for natural resources management
  • Geospatial technologies in disaster monitoring and management (e.g., geohazards, hydrometeorological, forest fire, and fluvial, among others)
  • Terrestrial ecosystem modeling and climate change using EO data
  • Urban resilience, microclimate, and sustainability
  • Environmental pollution and solid waste management

Dr. Navneet Kumar
Dr. Bikash Ranjan Parida
Guest Editors

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Published Papers (6 papers)

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Research

15 pages, 3439 KiB  
Communication
An Improved Understanding of Natural Hazards and Disasters through the Use of Satellite Technologies: Contributions from UN-SPIDER
by Juan Carlos Villagran de Leon
Sustainability 2023, 15(13), 10624; https://doi.org/10.3390/su151310624 - 5 Jul 2023
Cited by 1 | Viewed by 1075
Abstract
This communication informs on how the UN-SPIDER program of the United Nations Office for Outer Space Affairs contributes to the efforts of the United Nations’ system to mobilize international cooperation to mitigate the risks related to natural hazards, to coordinate disaster relief efforts, [...] Read more.
This communication informs on how the UN-SPIDER program of the United Nations Office for Outer Space Affairs contributes to the efforts of the United Nations’ system to mobilize international cooperation to mitigate the risks related to natural hazards, to coordinate disaster relief efforts, and to facilitate the links between disaster response and recovery. This communication presents information on how UN-SPIDER shapes its advisory support to developing countries to contribute to the institutionalization of the use of space-based data, information, products, and services by government agencies in charge of disaster management. It does so by providing examples of step-by-step procedures developed using open software and open satellite imagery; information regarding some of the efforts carried out by the program to facilitate access to space-based information provided by mechanisms established by the space community to support disaster response efforts worldwide; and information on its advisory support to developing countries, including training efforts. Lastly, this paper shows that the approach implemented by UN-SPIDER is allowing national disaster management agencies and other government agencies and ministries to institutionalize the use of space-based data, information, products, and services. Full article
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21 pages, 10039 KiB  
Article
Impact Assessment of Tropical Cyclones Amphan and Nisarga in 2020 in the Northern Indian Ocean
by K. K. Basheer Ahammed, Arvind Chandra Pandey, Bikash Ranjan Parida, Wasim and Chandra Shekhar Dwivedi
Sustainability 2023, 15(5), 3992; https://doi.org/10.3390/su15053992 - 22 Feb 2023
Cited by 5 | Viewed by 2312
Abstract
The Northern Indian Ocean (NIO) is one of the most vulnerable coasts to tropical cyclones (TCs) and is frequently threatened by global climate change. In the year 2020, two severe cyclones formed in the NIO and devastated the Indian subcontinent. Super cyclone Amphan [...] Read more.
The Northern Indian Ocean (NIO) is one of the most vulnerable coasts to tropical cyclones (TCs) and is frequently threatened by global climate change. In the year 2020, two severe cyclones formed in the NIO and devastated the Indian subcontinent. Super cyclone Amphan, which formed in the Bay of Bengal (BOB) on 15 May 2020, made landfall along the West Bengal coast with a wind speed of above 85 knots (155 km/h). The severe cyclone Nisarga formed in the Arabian Sea (ARS) on 1 June 2020 and made landfall along the Maharashtra coast with a wind speed above 60 knots (115 km/h). The present study has characterized both TCs by employing past cyclonic events (1982–2020), satellite-derived sea surface temperature (SST), wind speed and direction, rainfall dataset, and regional elevation. Long-term cyclonic occurrences revealed that the Bay of Bengal encountered a higher number of cyclones each year than the ARS. Both cyclones had different intensities when making landfall; however, the regional elevation played a significant role in controlling the cyclonic wind and associated hazards. The mountain topography on the east coast weakened the wind, while the deltas on the west coast had no control over the wind. Nisarga weakened to 30 knots (56 km/h) within 6 h from making landfall, while Amphan took 24 h to weaken to 30 knots (56 km/h). We analyzed precipitation patterns during the cyclones and concluded that Amphan had much more (1563 mm) precipitation than Nisarga (684 mm). Furthermore, the impact on land use land cover (LULC) was examined in relation to the wind field. The Amphan wind field damaged 363,837 km2 of land, whereas the Nisarga wind field affected 167,230 km2 of land. This research can aid in the development of effective preparedness strategies for disaster risk reduction during cyclone impacts along the coast of India. Full article
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28 pages, 35329 KiB  
Article
Evolution of Iceberg A68 since Its Inception from the Collapse of Antarctica’s Larsen C Ice Shelf Using Sentinel-1 SAR Data
by Shivangini Singh, Shashi Kumar and Navneet Kumar
Sustainability 2023, 15(4), 3757; https://doi.org/10.3390/su15043757 - 18 Feb 2023
Cited by 2 | Viewed by 2045
Abstract
This research focuses on the evolution of the largest iceberg A68 and analyzes the trajectory using Sentinel-1 SAR data. The monitoring began when A68 calved Larsen C Ice shelf on 12 July 2017, and ended on 1 February 2021. A total of 47 [...] Read more.
This research focuses on the evolution of the largest iceberg A68 and analyzes the trajectory using Sentinel-1 SAR data. The monitoring began when A68 calved Larsen C Ice shelf on 12 July 2017, and ended on 1 February 2021. A total of 47 images were analyzed and studied to ascertain the changes in the area, trajectory and the factors that might have influenced said changes. The big size of the iceberg caught the scientific community’s attention when it started moving towards South Georgia Island, a habitat of penguins and seals. The pattern of decrease and increase in the iceberg’s size was analyzed and compared with the surrounding sea ice extent to account for longitudinal stretching and shrinkage. Iceberg’s trajectory was also studied to take into account the underlying seabed and ice rises, and their implication on A68’s maneuverability, giving rise to unique motions in the coastal regime. Two subsequent calving events in the iceberg were distinctly observed in March 2019 and April 2020. Since its inception up to December 2019, its drift was fairly gradual, with the pick up in pace observed upon its entry into open waters and departure from the peninsular region. The decrease in size was also fairly gradual with only two main calving events, as mentioned above. The cold water and sea ice surrounding the iceberg potentially helped maintain a steady state. Post its sojourn into the Southern Ocean, major calving began in December 2020 and continued through January 2021. This study explores the potential of SAR remote sensing in iceberg monitoring and tracking. Full article
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16 pages, 3889 KiB  
Article
Multi-Hazard Risk Assessment and Landslide Susceptibility Mapping: A Case Study from Bensekrane in Algeria
by Faïla Benzenine, Mohamed Amine Allal, Chérifa Abdelbaki, Navneet Kumar, Mattheus Goosen and John Mwangi Gathenya
Sustainability 2023, 15(3), 2812; https://doi.org/10.3390/su15032812 - 3 Feb 2023
Cited by 1 | Viewed by 1573
Abstract
Landslides and their disastrous consequences on the environment and human life have emphasized the need for a better understanding of the dangers associated with slope movement. The objective of this research was to assess and utilize mapping methods for predicting the hazards of [...] Read more.
Landslides and their disastrous consequences on the environment and human life have emphasized the need for a better understanding of the dangers associated with slope movement. The objective of this research was to assess and utilize mapping methods for predicting the hazards of landslides and thus to limit the damage of these phenomena more effectively. In the current investigation, multi-hazard mapping was employed in evaluating the risk of slope movements for the municipality of Bensekrane in Tlemcen in Algeria. There has been no hazard assessment made for the study area although it has factors responsible for triggering landslides. The standard Farès method (arithmetic and probabilistic) was employed, and the results were compared with those obtained from the modified Farès technique (arithmetic and probabilistic), which was developed based on a synthesis or combination of previous approaches. In the modified Farès technique, dynamic factors were also included, such as seismic activity, vegetation cover and groundwater level, and, thus, it was considered more reliable. However, the choice of method depended mainly on the availability of data from the study area. The maps obtained showed that the study area is susceptible to slope movements and will be employed for land use planning. The maps obtained by the arithmetic modified Farès method were different from those obtained by the arithmetic Farès method. The former presented a large part of the surface (88%) with an average hazard, unlike the latter, which presented the largest surface (66%) and a low hazard. The maps generated by the probabilistic modified Farès method showed a surface with a high hazard, unlike that obtained by the probabilistic Farès method, where a high hazard did not exist. These differences between the maps were due to the addition of dynamic factors. It is better to choose the modified Farès method, which takes into account all the factors that exist in reality. In this study, enhanced spatial, natural hazard maps were created using the modified Farès method to better aid decision makers and builders in making correct choices for increased safety and town planning. It is crucial to be able to utilize reliable maps based on multi-hazard risk assessment for land development purposes to lessen the possibility of destructive landslides. The modified Farès method can be applied to any other comparable areas around the world. Full article
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19 pages, 8103 KiB  
Article
Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India
by Arvind Chandra Pandey, Tirthankar Ghosh, Bikash Ranjan Parida, Chandra Shekhar Dwivedi and Reet Kamal Tiwari
Sustainability 2022, 14(23), 15731; https://doi.org/10.3390/su142315731 - 25 Nov 2022
Cited by 4 | Viewed by 2863
Abstract
The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in [...] Read more.
The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions. Full article
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13 pages, 2702 KiB  
Article
Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas
by Vishakha Sood, Reet Kamal Tiwari, Sartajvir Singh, Ravneet Kaur and Bikash Ranjan Parida
Sustainability 2022, 14(20), 13485; https://doi.org/10.3390/su142013485 - 19 Oct 2022
Cited by 13 | Viewed by 2353
Abstract
Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies [...] Read more.
Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen’s kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis. Full article
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