Special Issue "Hazards, Disasters and Land Science toward Asian and Global SDGs for Land Systems: Approaches from Remotely Sensed Geospatial Data Analyses"

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Ram Avtar
E-Mail Website
Guest Editor
Dr. Yunus P. Ali
E-Mail Website
Guest Editor
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Interests: GIS; remote sensing; geomorphology; geoscience; geography
Special Issues and Collections in MDPI journals
Prof. Dr. Teiji Watanabe
E-Mail Website
Guest Editor
Group of Environmental Geography, Section of Integrated Environmental Science, Faculty of Environmental Earth Science, Hokkaido University W-5, N-10, Sapporo 060-0810, Japan
Interests: mountain geoecology; landscape changes in high mountain areas; geodiversity; sustainable use and management in mountain protected areas/national parks; trail erosion in mountain areas
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Land is an epicenter for several natural and anthropogenic activities on the planet. Human-induced changes have consequences for the global environmental standards. The changes in agricultural patterns, shrinking forests resources, and expanding industrial townships have become a regular feature of the 21st century. These changes have taken an unprecedented route, threatening the sustainability of ecosystems, evidenced by the growing frequency of extreme weather events such as floods, droughts, cyclones, and landslides. The concepts of climate change adaptation and mitigation, and building community resilience under the template of sustainable development are inevitable options now. The role of geospatial data has been widely discussed to develop effective approaches to understand and monitor environmental transformations. The geospatial data, apart from establishing a baseline, also help with future projections and policy formulations. The recent advances in the field of space technology with hypersensitive data on time and location scales offer immense opportunities to explore land processes and their relationship with the increasing environmental challenges. This Special Issue will gather papers by interested experts worldwide and selected papers from the 3rd Global Land Programme (GLP) 2021 Asia Conference, which will be held in Sapporo, Japan on 14–17 September, 2021. The conference focuses on hazards, disasters, and Land Science for SDGs in the Asian and global contexts.

Papers selected for this Special Issue are subjected to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications in the environment.

Dr. Ram Avtar
Dr. Yunus P. Ali
Prof. Dr. Teiji Watanabe
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 papers will be 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. Land is an international peer-reviewed open access monthly 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 1800 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

  • Opportunities and challenges of land use/cover mapping in the new remote sensing era
  • Geospatial data for terrestrial ecosystem monitoring
  • Rethinking and rejuvenating urban spaces along sustainability practices in Asia and other parts of the world
  • Environmental monitoring using cloud-based processing of remote sensing datasets
  • Connectivity and feedback of land systems across multiple temporal and spatial scales
  • Monitoring and evaluation of climate- and geo-related risk management

Published Papers (2 papers)

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Research

Article
Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform
Land 2020, 9(10), 377; https://doi.org/10.3390/land9100377 - 08 Oct 2020
Cited by 3 | Viewed by 1755
Abstract
Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the [...] Read more.
Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia. Full article
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Article
Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data
Land 2020, 9(5), 133; https://doi.org/10.3390/land9050133 - 28 Apr 2020
Cited by 14 | Viewed by 2561
Abstract
The western part of Crete Island has undergone serious landslide events in the past. The intense rainfalls that took place in the September 2018 to February 2019 period provoked extensive landslide events at the northern part of Chania prefecture, along the motorway A90. [...] Read more.
The western part of Crete Island has undergone serious landslide events in the past. The intense rainfalls that took place in the September 2018 to February 2019 period provoked extensive landslide events at the northern part of Chania prefecture, along the motorway A90. Geospatial analysis methods and earth observation data were utilized to investigate the impact of the various physical and anthropogenic factors on landslides and to evaluate landslide susceptibility. The landslide inventory map was created based on literature, aerial photo analysis, satellite images, and field surveys. A very high-resolution Digital Elevation Model (DEM) and land cover map was produced from a dense point cloud and Earth Observation data (Landsat 8), accordingly. Sentinel-2 data were used for the detection of the recent landslide events and offered suitable information for two of them. Eight triggering factors were selected and manipulated in a GIS-based environment. A semi-quantitative method of Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) was applied to evaluate the landslide susceptibility index (LSI) both for Chania prefecture and the motorway A90 in Chania. The validation of the two LSI maps provided accurate results and, in addition, several susceptible points with high landslide hazards along the motorway A90 were detected. Full article
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