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Special Issue "Geospatial Technologies for Sustainable Natural Resources"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 December 2017

Special Issue Editors

Guest Editor
Dr. Ashraf Dewan

Department of Spatial Sciences, Curtin University, Kent St, Bentley WA 6102, Australia
Website | E-Mail
Phone: +08 9266 4930
Interests: climate change; disaster and natural hazards; health geography; natural resources management
Guest Editor
Dr. Todd Robinson

Department of Spatial Sciences, Curtin University, Kent St, Bentley WA 6102, Australia
Website | E-Mail
Phone: +61-8-9266-7559
Interests: geospatial analysis and modelling; remote sensing; invasive species; rangeland management; endemism; refugia; precision agriculture

Special Issue Information

Dear Colleagues,

The purpose of sustainable natural resource management is to ensure that resources are utilized in a way that does not adversely affect their on-going quality for current and future populations. Factors such as climate change, population growth, environmental pollution, agricultural intensification, and urban expansion can seriously affect the use and availability of resources for future generations. Geospatial Science, including spatial analytical techniques, earth observation satellites (active and passive), Volunteered Geographic Information (VGI), and crowdcourcing are new and existing technologies to map, monitor and sense our scarce natural resources. In this special issue, we invite original contributions on issues ranging from rangeland condition management, invasive species detection and modelling, biodiversity and conservation, urban sustainability, and land and forest management from all around the world. Papers incorporating novel and interesting Geospatial Science techniques are particularly encouraged.

Dr. Ashraf Dewan
Dr. Todd Robinson
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. Sustainability 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 1400 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

  • geospatial technologies
  • natural resources
  • sustainable development
  • global environmental change
  • sustainable ecosystem

Published Papers (3 papers)

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Research

Open AccessArticle Application of WEHY-HCM for Modeling Interactive Atmospheric-Hydrologic Processes at Watershed Scale to a Sparsely Gauged Watershed
Sustainability 2017, 9(9), 1554; doi:10.3390/su9091554
Received: 29 June 2017 / Revised: 25 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
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Abstract
A lack of observations within watersheds can make the production of streamflow data via hydrologic models a big challenge. This study evaluates the model performance of the Watershed Environmental Hydrology Hydro-Climate Model (WEHY-HCM), reproducing streamflow in a sparsely gauged watershed. The fifth generation
[...] Read more.
A lack of observations within watersheds can make the production of streamflow data via hydrologic models a big challenge. This study evaluates the model performance of the Watershed Environmental Hydrology Hydro-Climate Model (WEHY-HCM), reproducing streamflow in a sparsely gauged watershed. The fifth generation mesoscale model (MM5) is utilized within WEHY-HCM as an atmospheric module coupling with its process-based hydrologic module, WEHY. The WEHY-HCM is set up over a sparsely gauged watershed and the spatially downscaled reconstructed atmospheric data to a 3-km horizontal grid resolution with an hourly time increment, is obtained by the fifth generation mesoscale model (MM5) from NCAR/NCEP global reanalysis data (reanalysis I). Hydrologic simulations by WEHY-HCM were applied to the Upper Putah Creek watershed based on the reconstructed atmospheric data and the estimated WEHY model parameters. The simulation results of WEHY-HCM were evaluated by means of statistical tests for both calibration and validation periods. The results of statistical tests performed using observed and simulated values indicated that the model performance can be considered as exhibiting an acceptable accuracy during both calibration and validation periods. The spatial maps of the evapotranspiration rate and runoff volume showed that the WEHY-HCM can represent a sparsely gauged watershed with unique topography well. This study found that the WEHY-HCM can be a useful tool to simulate the hydrologic processes in a sparsely gauged watershed. Full article
(This article belongs to the Special Issue Geospatial Technologies for Sustainable Natural Resources)
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Open AccessArticle A Performance Evaluation of Dynamical Downscaling of Precipitation over Northern California
Sustainability 2017, 9(8), 1457; doi:10.3390/su9081457
Received: 29 June 2017 / Revised: 28 July 2017 / Accepted: 9 August 2017 / Published: 17 August 2017
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Abstract
It is important to assess the reliability of high-resolution climate variables used as input to hydrologic models. High-resolution climate data is often obtained through the downscaling of Global Climate Models and/or historical reanalysis, depending on the application. In this study, the performance of
[...] Read more.
It is important to assess the reliability of high-resolution climate variables used as input to hydrologic models. High-resolution climate data is often obtained through the downscaling of Global Climate Models and/or historical reanalysis, depending on the application. In this study, the performance of dynamically downscaled precipitation from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis data (NCEP/NCAR reanalysis I) was evaluated at point scale, watershed scale, and regional scale against corresponding in situ rain gauges and gridded observations, with a focus on Northern California. Also, the spatial characteristics of the simulated precipitation and wind fields, with respect to various grid sizes, were investigated in order to gain insight to the topographic effect on the atmospheric state variables. To this end, dynamical downscaling was performed using the mesoscale atmospheric model MM5, and the synoptic scale reanalysis data were downscaled to a 3 km grid spacing with hourly temporal resolution. The results of comparisons at point scale and watershed scale over a 50-year time period showed that the MM5-simulated precipitation generally produced the timing and magnitude of the observed precipitation in Northern California. The spatial distributions of MM5-simulated precipitation matched the corresponding observed precipitation reasonably well. Furthermore, the statistical goodness of fit tests of the MM5-simulated precipitation against the corresponding observed precipitation showed the reliability and capability of MM5 simulations for downscaling precipitation. A comparison of the spatial characteristics of the results with respect to various grid sizes indicated that precipitation and wind fields are significantly affected by the local topography. In particular, the banded structures and orographic effects on precipitation and wind fields can be well described by a mesoscale model at 3 km and 9 km grid resolutions while 27 km and 81 km grid model simulation may not be sufficient for watershed-scale or sub-watershed-scale studies. Full article
(This article belongs to the Special Issue Geospatial Technologies for Sustainable Natural Resources)
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Open AccessArticle Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series
Sustainability 2017, 9(7), 1215; doi:10.3390/su9071215
Received: 2 June 2017 / Revised: 26 June 2017 / Accepted: 6 July 2017 / Published: 13 July 2017
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Abstract
The quick and accurate extraction of information on woodland resources and distributions using remote sensing technology is a key step in the management, protection, and sustainable use of woodlands. This paper presents a low-cost and high-precision extraction method for large woodland areas based
[...] Read more.
The quick and accurate extraction of information on woodland resources and distributions using remote sensing technology is a key step in the management, protection, and sustainable use of woodlands. This paper presents a low-cost and high-precision extraction method for large woodland areas based on the fractal features of the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for Beijing, China. The blanket method was used for computing the upper and lower fractal signals of each pixel in the NDVI time series images. The fractal signals of woodlands and other land use/land cover types at corresponding scales were analyzed and compared, and the attributes of woodlands were enhanced at the fifth lower fractal signal. The spatial distributions of woodlands were extracted using the Iterative Self-Organizing Data Analysis technique (ISODATA), and an accuracy assessment of the extracted results was conducted using the China Land Use and Land Cover Data Set (CLUCDS) from the same period. The results showed that the overall accuracy, kappa coefficient, and error coefficient were 90.54%, 0.74, and 8.17%, respectively. Compared with the extracted results for woodlands using the MODIS NDVI time series only, the average error coefficient decreased from 30.2 to 7.38% because of these fractal features. The method developed in this study can rapidly and effectively extract information on woodlands from low spatial resolution remote sensing data and provide a robust operational tool for use in further research. Full article
(This article belongs to the Special Issue Geospatial Technologies for Sustainable Natural Resources)
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