Special Issue "Socio-Economic and Environmental Aspects of Water Rational Management and Land Use/Cover as Main Drivers of a Change in Climate Patterns"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 15 June 2022.

Special Issue Editors

Dr. Tomasz Noszczyk
E-Mail Website
Guest Editor
Faculty of Environmental Engineering and Land Surveying, Department of Land Management and Landscape Architecture, University of Agriculture in Krakow, Balicka 253c, 30-149 Kraków, Poland
Interests: land change science; land use/cover change; land use modelling; statistical approaches; cadastre; environmental analysis; urban analysis
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Dr. Wiktor Halecki
E-Mail Website
Guest Editor
Department of Hydrology, Meteorology and Water Management, Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland
Interests: blue-green infrastructure; ecological resilience; hydrological modeling; nature-based solutions; urban ecology; water-sensitive urban design (WSUD); xeriscaping
Special Issues and Collections in MDPI journals
Dr. Abreham Berta Aneseyee
E-Mail Website
Guest Editor
Addis Ababa University, Center of Environmental Science, College of Natural and Computational Sciences, Addis Ababa, P.O. Box 1176, Ethiopia
Interests: land use/land cover change analysis; ecosystem service modeling and valuation; land resource conservation option; land ecology; ecosystem function, rehabilitation and restoration
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Land use/cover change stems from the multifaceted interaction of social, ecological, and geophysical processes. It is the leading cause of global environmental change. The technological, human, and biophysical factors (urbanization, agricultural expansion, population growth, deforestation), and other driving forces have been reported as the causes of the rapid land use/cover changes over the past few decades. It is essential to understand the land use/cover change and describe its drivers to provide effective support for land planning and water management regulation.

Authors are requested to submit papers on applying socioeconomic and environmental aspects to water rational management and land use/cover around the world. The integration of remote sensing, GIS, and modeling can provide valuable support for management and decision-making. Extreme weather and climate events that cause threats such as floods and droughts will become more frequent and intense in many regions. Adaptation and mitigation strategies need to be implemented at the local, regional, and national levels to anticipate the adverse effects of climate change and to prevent or minimize damage. Therefore, measures to adapt to the effects of climate change are of great importance and should be tailored to specific conditions using remote sensing and GIS techniques.

We will welcome contributions where various socioeconomic, environmental, and political aspects are combined with other factors and applied in various disciplines such as land/water management, land use/cover change, land surveying, environmental engineering, or landscape architecture.

The following list provides some examples of topics of interest to ensure the consistency of the papers in this Special Issue:

  • Integration of socioeconomic and environmental aspects for water rational management and land use/cover;
  • Best practices of rational land/water management—case studies from around the world;
  • Impact of political decisions on land/water management and land use/cover;
  • Effects of climate change in urban areas;
  • Main problems that hinder rational land/water management in the world and propositions of relevant solutions;
  • Remote sensing, GIS, or UAV data for mapping of land use/cover processes;
  • Modeling and visualization of spatial land use/cover changes.

Papers incorporating novel and interesting techniques for studying these aspects and some interesting applications will be considered. Well-prepared review papers are also welcomed.

We invite all prospective authors to share their research.

Dr. Tomasz Noszczyk
Dr. Wiktor Halecki
Dr. Abreham Berta Aneseyee
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. 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 2400 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

  • Adaptation to climate change
  • Spatiotemporal analysis
  • Land change science
  • Urban analysis
  • Spatial modeling
  • Spatial policy
  • Land management
  • Spatial analysis
  • Water management
  • Spatial planning
  • Natural resources

Published Papers (2 papers)

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Research

Article
PlanetScope Imageries and LiDAR Point Clouds Processing for Automation Land Cover Mapping and Vegetation Assessment of a Reclaimed Sulfur Mine
Remote Sens. 2021, 13(14), 2717; https://doi.org/10.3390/rs13142717 - 10 Jul 2021
Viewed by 477
Abstract
The present research investigated the possibility of using PlanetScope imageries and LiDAR point clouds for land cover assessment, especially vegetation mapping, in degraded and reclaimed areas. Studies were carried out on the former sulfur mine of Jeziórko located in Southeast Poland. In total, [...] Read more.
The present research investigated the possibility of using PlanetScope imageries and LiDAR point clouds for land cover assessment, especially vegetation mapping, in degraded and reclaimed areas. Studies were carried out on the former sulfur mine of Jeziórko located in Southeast Poland. In total, more than ca. 2000 ha of this mine area were reclaimed after borehole exploitation and afforestation. We investigated a total area of 216.72 ha. Integration of PlanetScope imageries and LiDAR point clouds processing offers the ability to derive information about the LULC classes and vegetation growth in the analyzed area and indicate the forest succession progress as an effect of the reclamation treatments. In the Jeziórko area, we identified coniferous forest (90.84 ha, 41.91% of the research area), broad-leaved forest (44.02 ha, 20.31%), and transitional woodland shrub areas with herbaceous communities (77.96 ha, 35.97%). The analyses focused on the detection and monitoring of the forest succession processes and obtaining the tree canopy profiles and characteristics of vegetation, i.e., the height and cover density. Full article
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Article
Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China
Remote Sens. 2021, 13(9), 1682; https://doi.org/10.3390/rs13091682 - 27 Apr 2021
Viewed by 714
Abstract
Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor [...] Read more.
Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions. Full article
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