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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 (26 March 2023) | Viewed by 11836

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Special Issue Editors

Prof. Dr. Mohamed Elhag

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Guest Editor

Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Interests: remote sensing applications in water resources management; GIS applications in natural resources management; geo-information in natural hazards

Dr. Changping Huang

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; SIF; vegetation disease and pests; remote sensing big data and smart agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Silvena Boteva Boteva

E-Mail Website
Guest Editor

Faculty of Biology, Department of Ecology and Environmental Protection, Sofia University "St. Kliment Ohridski", 1164 Sofia, Bulgaria
Interests: application of remote sensing methods in environmental studies

Dr. Georgios Eleftheriou

E-Mail Website
Guest Editor

Institute of Oceanography, Hellenic Centre for Marine Research, 19013 Anavyssos, Greece
Interests: applications of environmental radioactivity in geosciences and oceanography; soil radon monitoring for seismic hazard; submarine groundwater discharges estimation via in-situ gamma-spectrometry; study of sedimentary processes via radiotracers; monte carlo simulations of radionuclides' detectors; marine radiomodelling and radioecology

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the application of remote sensing methods for the assessment and management of environmental resources and natural hazards.

Environment resources are an important prerequisite for the development of the economy at local and global levels. The increasing world population leads to increasing demand for resources and, hence, they become scarce since most of them are not renewable. Irrational exploitation of environmental resources can lead to social, economic, and environmental disturbances. In this regard, natural hazards also represent a significant element of the environmental processes. On the other hand, they can be a threat both for humans and to environmental resource availability. Problems related to environmental resources and natural hazards will continue to increase with processes such as economic development, overexploitation of resources, urbanization, and overpopulation. In recent years, remote sensing has been widely used due to the large amount of data generated globally from an increasing number of sensors and the ability to analyze areas that are located in remote places. Due to their higher accuracies, remote sensing methods can be used for the identification and assessment of natural resources that will lead to the proper management of the latter and improving the environmental conditions. When applied to natural hazard processes, these remote methods could be appropriate for risk assessment and the subsequent development of early warning systems.

Prof. Dr. Mohamed Elhag
Prof. Dr. Changping Huang
Dr. Silvena Boteva Boteva
Dr. Georgios Eleftheriou
Guest Editors

Manuscript Submission Information

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Keywords

environmental management
hyperspectral remote sensing in natural hazards
optical and microwave remote sensing applications

Published Papers (5 papers)

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Research

26 pages, 12628 KiB

Open AccessArticle

DEM Study on Hydrological Response in Makkah City, Saudi Arabia

by Asep Hidayatulloh, Anis Chaabani, Lifu Zhang and Mohamed Elhag

Sustainability 2022, 14(20), 13369; https://doi.org/10.3390/su142013369 - 17 Oct 2022

Cited by 8 | Viewed by 2309

Abstract

The changes in catchments can be analyzed through the generation of DEM, which is important as input data in hydrologic modeling. This study aims to analyze the effect of anthropogenic activities on hydrological studies based on DEM comparison and GIUH hydrographs. The four DEM datasets (SRTM, ALOS, Copernicus, Sentinel-1) were compared to the topographic map of Makkah City and GPS data in order to assess the quality of the DEM elevation. The GIS Arc Hydro toolbox was used to extract morphometric and Horton–Strahler ratio characteristics to generate a GIUH hydrograph of the catchments of Wadi Nouman and Wadi Ibrahim inside Makkah City. Based on the DEM comparison, Copernicus and SRTM have the highest accuracy, with R² = 0.9788 and 0.9765, and the lowest RMSE, 3.89 m and 4.23 m, respectively. ALOS and Sentinel-1 have the lowest R², 0.9687 and 0.9028, and the highest RMSE, 4.27 m and 6.31 m, respectively. GIUH Copernicus DEM on Wadi Nouman has a higher

q_{p}

and lower

t_{p}

(0.21 1/h and 2.66 h) than SRTM (0.20 1/h and 2.75 h), respectively. On Wadi Ibrahim, the SRTM has a greater

q_{p}

and lower

t_{p}

than Copernicus due to the wadi having two shapes. Based on the anthropogenic effect, the stream network in the mountain area is quite similar for SRTM and Copernicus due to the dominant influence of the mountainous relief and relatively inconsequential influence of anthropogenic activities and DEM noise. In the urban area, the variation of the stream network is high due to differing DEM noise and significant anthropogenic activities such as urban redevelopment. The Copernicus DEM has the best performance of the others, with high accuracy, less RMSE, and stream flow direction following the recent condition. Full article

(This article belongs to the Special Issue Remote Sensing Applications on Environment Resources and Natural Hazards)

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10 pages, 4139 KiB

Open AccessArticle

Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images

by Jinmei Kou, Long Duan, Caixia Yin, Lulu Ma, Xiangyu Chen, Pan Gao and Xin Lv

Sustainability 2022, 14(15), 9259; https://doi.org/10.3390/su14159259 - 28 Jul 2022

Cited by 9 | Viewed by 1661

Abstract

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R² = 0.80 and RMSE = 1.67 g kg⁻¹ of the Xinluzao 45 optimal model, and R² = 0.42 and RMSE = 3.13 g kg⁻¹ of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved. Full article

(This article belongs to the Special Issue Remote Sensing Applications on Environment Resources and Natural Hazards)

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26 pages, 10605 KiB

Open AccessArticle

Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions

by Mohamed Abdekareem, Nasir Al-Arifi, Fathy Abdalla, Abbas Mansour and Farouk El-Baz

Sustainability 2022, 14(13), 7871; https://doi.org/10.3390/su14137871 - 28 Jun 2022

Cited by 17 | Viewed by 2214

Abstract

Remote sensing and GIS approaches have provided valuable information on modeling water resources, particularly in arid regions. The Sahara of North Africa, which is one of the driest regions on Earth, experienced several pluvial conditions in the past that could have stored significant amounts of groundwater. Thus, harvesting the stored water by revealing the groundwater prospective zones (GWPZs) is highly important to water security and the management of water resources which are necessary for sustainable development in such regions. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Tropical Rainfall Measuring Mission (TRMM), and Landsat-8 OLI data have all successfully revealed the geologic, geomorphic, climatic, and hydrologic features of Wadi El-Tarfa east of Egypt’s Nile River. The fusion of eleven predictive GIS maps including lithology, radar intensity, lineament density, altitude, slope, depressions, curvature, topographic wetness index (TWI), drainage density, runoff, and rainfall data, after being ranked and normalized through the GIS-based analytic hierarchy process (AHP) and weighted overlay methods, allowed the GWPZs to be demarcated. The resulting GWPZs map was divided into five classes: very high, high, moderate, low, and very low potentiality, which cover about 10.32, 24.98, 30.47, 24.02, and 10.20% of the entire basin area, respectively. Landsat-8 and its derived NDVI that was acquired on 15 March 2014, after the storm of 8–9 March 2014, along with existing well locations validated the GWPZs map. The overall results showed that an integrated approach of multi-criteria through a GIS-based AHP has the capability of modeling groundwater resources in arid regions. Additionally, probing areas of GWPZs is helpful to planners and decision-makers dealing with the development of arid regions. Full article

(This article belongs to the Special Issue Remote Sensing Applications on Environment Resources and Natural Hazards)

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16 pages, 3775 KiB

Open AccessArticle

Spatiotemporal Dynamics of Vegetation Net Primary Productivity and Its Response to Climate Change in Inner Mongolia from 2002 to 2019

by Lei Hao, Shan Wang, Xiuping Cui and Yongguang Zhai

Sustainability 2021, 13(23), 13310; https://doi.org/10.3390/su132313310 - 01 Dec 2021

Cited by 19 | Viewed by 2239

Abstract

Understanding vegetation dynamics and their responses to climate change are essential to enhance the carbon sequestration of the terrestrial ecosystem under global warming. Although some studies have identified that there is a close relationship between vegetation net primary productivity and climate change, it is unclear whether this response exists in ecologically fragile areas, especially in Inner Mongolia, in which multiple ecological ecotones are related to vegetation types. This study uses the Carnegie–Ames–Stanford Approach (CASA) model to estimate vegetation NPP in Inner Mongolia from 2002 to 2019 and focuses on the spatial and temporal changes of NPP of different vegetation types and their responses to three typical climate factors: precipitation, temperature, and solar radiation. The results show that the NPP estimated by the CASA model agrees well with the observed NPP (R² = 0.66, p < 0.001). The vegetation NPP in Inner Mongolia decreases gradually from northeast to southwest, and the average NPP is 223.50 gC ∙ m⁻². From 2002 to 2019, the NPP of all vegetation types trended upward, but exhibiting different rates. The vegetation types, ranked in order of decreasing NPP, are forest, cropland, grassland, and desert. The NPP response of different vegetation types to climate factors possesses significant differences. The cropland NPP and grassland NPP are mainly affected by precipitation, the desert NPP is controlled by both precipitation and solar radiation, and the forest NPP is determined by all three climate factors. Full article

(This article belongs to the Special Issue Remote Sensing Applications on Environment Resources and Natural Hazards)

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14 pages, 8775 KiB

Open AccessArticle

Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale in Arid Environments, Saudi Arabia

by Jaka Budiman, Jarbou Bahrawi, Asep Hidayatulloh, Mansour Almazroui and Mohamed Elhag

Sustainability 2021, 13(8), 4115; https://doi.org/10.3390/su13084115 - 07 Apr 2021

Cited by 8 | Viewed by 2342

Abstract

Actual flood mapping and quantification in an area provide valuable information for the stakeholder to prevent future losses. This study presents the actual flash flood quantification in Al-Lith Watershed, Saudi Arabia. The study is divided into two steps: first is actual flood mapping using remote sensing data, and the second is the flood volume calculation. Two Sentinel-1 images are processed to map the actual flood, i.e., image from 25 May 2018 (dry condition), and 24 November 2018 (peak flood condition). SNAP software is used for the flood mapping step. During SNAP processing, selecting the backscatter data representing the actual flood in an arid region is challenging. The dB range value from 7.23–14.22 is believed to represent the flood. In GIS software, the flood map result is converted into polygon to define the flood boundary. The flood boundary that is overlaid with Digital Elevation Map (DEM) is filled with the same elevation value. The Focal Statistics neighborhood method with three iterations is used to generate the flood surface elevation inside the flood boundary. The raster contains depth information is derived by subtraction of the flood surface elevation with DEM. Several steps are carried out to minimize the overcalculation outside the flood boundary. The flood volume can be derived by the multiplication of flood depth points with each cell size area. The flash flood volume in Al-Lith Watershed on 24 November 2018 is 155,507,439 m³. Validity checks are performed by comparing it with other studies, and the result shows that the number is reliable. Full article

(This article belongs to the Special Issue Remote Sensing Applications on Environment Resources and Natural Hazards)

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