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Remote Sensing and GIS in Environmental Monitoring

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 September 2021) | Viewed by 30063

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Dr. Lorena Parra

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Universitat Politecnica de Valencia, Valencia, Spain
Interests: environmental monitoring; image processing; precision agriculture; remote sensing; sensors
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Special Issue Information

Dear Colleagues,

The use of satellite and drone data has become more and more significant for environmental monitoring. The remote sensing allows us to surveil any region of the Earth's surface. Since the beginning of satellite deployment, the efforts of many scientists have been focused on Earth observation. Nowadays, we count with several options to obtain images of the surface, including satellites and drones. Both options have advantages and disadvantages, while drones offer a high temporal and spatial resolution, most of them only gave us information in the visible region of the electromagnetic spectrum. On the other side, satellites use to be equipped with hyperspectral cameras, but their temporal and spatial resolution might be limited. Either remote sensing sources are destined to coexist, and scientists must take profit from the particularities of each one.
Their use in different regions of the Earth and its applications in various fields is well-nkown. Nonetheless, several uses and applications of remote sensing are still being in development. They have been used in climate change monitoring, in evaluating temporal changes in ecosystems, for precision agriculture, and in some cases even to characterize the distribution of certain species in a region. In oceanography and sea monitoring, remote sensing is useful to identify changes in water quality, detect blooms of phytoplankton, or register the morphology in shallow coastal waters. Nowadays, the location, quantification, and identification of microplastics is a challenge that remote sensing can be capable to solve in the years to come.
In precision farming, the use of LIDAR technology has proved its value for determining the canopy and even the productivity of a given portion of farmland. In the lasts years, drones have become accessible for monitoring the plant and soil status of several cropping systems. The satellites are mainly used to control the vast extension of cereals, in which the drones cannot be used due to its size. The trending applications are related to identifications of pests and illnesses for minimizing the use of phytosanitary products and smart irrigation for water optimization.
The abovementioned are only some examples of multiple applications in different fields. The topics of interest for this Special Issue include but are not limited to the following:

Innovative applications of remote sensing for Earth monitoring.
Combination of remote sensing with wireless sensor networks for enhanced GIS.
Multitemporal analysis for changes detection in natural, anthropized, and urban areas.
Comparison of the feasibility of satellite sources with drone data for a particular application.
Evaluation of different image processing techniques for the identification of surfaces and objects.
Contributions of remote in smart city applications.
Visualization techniques and management for Big Data in the field of GIS.
Combination of remote sensing data with Artificial Intelligence for desition making or automatic classification.
Application of remote sensing for the monitoring of climate change, precision farming, oceanography, urban areas, and ecosystems, among others.

Dr. Lorena Parra
Guest Editor

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Keywords

Satellite
Drone
LIDAR
Remote sensing
Image processing
Hyperspectral camera
Optical sensor
Multitemporal analysis
Earth monitoring
Environmental surveillance

Published Papers (10 papers)

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Research

17 pages, 6355 KiB

Open AccessArticle

The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

by Pedro V. Mauri, Lorena Parra, David Mostaza-Colado, Laura Garcia, Jaime Lloret and Jose F. Marin

Appl. Sci. 2021, 11(24), 11769; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411769 - 10 Dec 2021

Cited by 5 | Viewed by 2124

Abstract

In gardening, particularly in golf courses, soil moisture management is critical for maximizing water efficiency. Remote sensing has been used to estimate soil moisture in recent years with relatively low accuracies. In this paper, we aim to use remote sensing and wireless sensor networks to generate soil moisture indexes for a golf course. In the golf course, we identified three types of soil, and data was gathered for three months. Mathematical models were obtained using data from Sentinel-2, bands with a resolution of 10 and 20 m, and sensed soil moisture. Models with acceptable accuracy were obtained only for one out of three soil types, the natural soil in which natural vegetation is grown. Two multiple regression models are presented with an R² of 0.46 for bands at 10 m and 0.70 for bands at 20 m. Their mean absolute error was lower than 3% in both cases. For the modified soils, the greens, and the golf course fairway, it was not feasible to obtain regression models due to the temporal uniformity of the grass and the range of variation of soil moisture. The developed moisture indexes were compared with existing options. The attained accuracies improve the current models. The verification indicates that the model generated with band 4 and band 12 is the one with better accuracy. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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28 pages, 12147 KiB

Open AccessArticle

Analysis of Sea Storm Events in the Mediterranean Sea: The Case Study of 28 December 2020 Sea Storm in the Gulf of Naples, Italy

by Alberto Fortelli, Alessandro Fedele, Giuseppe De Natale, Fabio Matano, Marco Sacchi, Claudia Troise and Renato Somma

Appl. Sci. 2021, 11(23), 11460; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311460 - 03 Dec 2021

Cited by 10 | Viewed by 2979

Abstract

The coastline of the Gulf of Naples, Italy, is characterized by a series of infrastructures of strategic importance, including touristic and commercial ports between Pozzuoli to Sorrento, main roads, railways, and urban areas. Furthermore, the Gulf of Naples hosts an intense traffic of touristic and commercial maritime routes. The risk associated with extreme marine events is hence very significant over this marine and coastal area. On 28 December 2020, the Gulf of Naples was hit by an extreme sea storm, with severe consequences. This study focuses on the waterfront area of Via Partenope, where the waves overrun the roadway, causing massive damage on coastal seawall, road edges, and touristic structures (primarily restaurants). Based on the analysis of the meteorological evolution of the sea storm and its effects on the waterfront, we suggest that reflective processes induced on the sea waves by the tuff cliffs at the base of Castel dell’Ovo had an impact in enhancing the local-scale waves magnitude. This caused in turn severe flooding of the roadway and produced widespread damage along the coast. The analysis of the event of 28 December 2020, also suggests the need of an effective mitigation policy in the management of coastal issues induced by extreme sea storm events. Wind-based analysis and prediction of the sea wave conditions are currently discussed in the literature; however, critical information on wave height is often missing or not sufficient for reliable forecasting. In order to improve our ability to forecast the effects of sea storm events on the coastline, it is necessary to analyze all the components of the coastal wave system, including wave diffraction and reflection phenomena and the tidal change. Our results suggest in fact that only an integrated approach to the analysis of all the physical and anthropic components of coastal system may provide a correct base of information for the stakeholders to address coastal zone planning and protection. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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22 pages, 7885 KiB

Open AccessArticle

Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

by Khaled Yousef Almansi, Abdul Rashid Mohamed Shariff, Ahmad Fikri Abdullah and Sharifah Norkhadijah Syed Ismail

Appl. Sci. 2021, 11(22), 11054; https://0-doi-org.brum.beds.ac.uk/10.3390/app112211054 - 22 Nov 2021

Cited by 9 | Viewed by 3180

Abstract

Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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24 pages, 242214 KiB

Open AccessArticle

Automated Coastline Extraction Using the Very High Resolution WorldView (WV) Satellite Imagery and Developed Coastline Extraction Tool (CET)

by Fran Domazetović, Ante Šiljeg, Ivan Marić, Josip Faričić, Emmanuel Vassilakis and Lovre Panđa

Appl. Sci. 2021, 11(20), 9482; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209482 - 12 Oct 2021

Cited by 10 | Viewed by 3659

Abstract

The accurate extraction of a coastline is necessary for various studies of coastal processes, as well as for the management and protection of coastal areas. Very high-resolution satellite imagery has great potential for coastline extraction; however, noises in spectral data can cause significant errors. Here, we present a newly developed Coastal Extraction Tool (CET) that overcomes such errors and allows accurate and time-efficient automated coastline extraction based on a combination of WorldView-2 (WV-2) multispectral imagery and stereo-pair-derived digital surface model (DSM). Coastline extraction is performed and tested on the Iž-Rava island group, situated within the Northern Dalmatian archipelago (Croatia). Extracted coastlines were compared to (a) coastlines extracted from state topographic map (1:25,000), and (b) coastline extracted by another available tool. The accuracy of the extracted coastline was validated with centimeter accuracy reference data acquired using a UAV system (Matrice 600 Pro + MicaSense RedEdge-MX). Within the study area, two small islets were detected that have not been mapped during the earlier coastline mapping efforts. CET proved to be a highly accurate coastline mapping technique that successfully overcomes spectral-induced errors. In future research, we are planning to integrate data obtained by UAVs infrared thermography (IRT) and in situ sensors, measuring sea and land surface temperatures (SST and LST), into the CET, given that this has shown promising results. Considering its accuracy and ease of use, we suggest that CET can be applied for automated coastline extraction in other large and indented coastal areas. Additionally, we suggest that CET could be applied in longitudinal geomorphological coastal erosion studies for the automated detection of spatio-temporal coastline displacement. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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10 pages, 2327 KiB

Open AccessArticle

Declining Effect of Precipitation on the Normalized Difference Vegetation Index of Grasslands in the Inner Mongolian Plateau, 1982–2010

by Yanan Li, Dan Wu, Liangyan Yang and Tiancai Zhou

Appl. Sci. 2021, 11(18), 8766; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188766 - 21 Sep 2021

Cited by 8 | Viewed by 1904

Abstract

Grasslands play an irreplaceable role in maintaining carbon balance and stabilizing the entire Earth’s ecosystem. Although the grasslands in Inner Mongolia are sensitive and vulnerable to climate change, a generalized effect of climate change on the grasslands is still unavailable. In this study, we analyzed the effects of annual mean precipitation and annual mean temperature on the normalized difference vegetation index from 1982 to 2010 on the Inner Mongolia Plateau. Our results indicated that the normalized difference vegetation index was mostly affected by precipitation, followed by temperature. Spatially, temperature and precipitation had greater effects on normalized difference vegetation index in dry regions than in wet ones. In time series, the effect of precipitation on normalized difference vegetation index had significantly decreased from 1982 to 2010 (R² = 0.11, p > 0.05). However, the effect of temperature on normalized difference vegetation index remained stable. The high variation effect of precipitation on normalized difference vegetation index was due to the significant decrease in precipitation from 1980 to 2010. Thus, 35.47% and 0.56% of the dynamic of normalized difference vegetation index from 1982 to 2010 was accounted for by the precipitation and temperature, respectively. Our findings highlighted that grasslands are adaptable to the significant increase in temperature, but are sensitive to the decrease in precipitation on the Inner Mongolia Plateau. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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19 pages, 3860 KiB

Open AccessArticle

Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model

by Huanmei Yao, Yi Huang, Yiming Wei, Weiping Zhong and Ke Wen

Appl. Sci. 2021, 11(17), 7855; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177855 - 26 Aug 2021

Cited by 7 | Viewed by 1838

Abstract

Remote sensing for the monitoring of chlorophyll-a (Chl-a) is essential to compensate for the shortcomings of traditional water quality monitoring, strengthen red tide disaster monitoring and early warnings, and reduce marine environmental risks. In this study, a machine learning approach called the Gradient-Boosting Decision Tree (GBDT) was employed to develop an algorithm for estimating the Chl-a concentrations of the coastal waters of the Beibu Gulf in Guangxi, using Landsat 8 OLI image data as the image source in combination with field measurements of Chl-a concentrations. The GBDT model with B4, B3 + B4, B3, B1 − B4, B2 + B4, B1 + B4, and B2 − B4 as input features exhibited higher accuracy (MAE = 0.998 μg/L, MAPE = 19.413%, and RMSE = 1.626 μg/L) compared with different physics models, providing a new method for remote sensing inversion of water quality parameters. The GBDT model was used to study the spatial distribution and temporal variation of Chl-a concentrations in the coastal sea surface of the Beibu Gulf of Guangxi from 2013 to 2020. The results showed a spatial distribution with high concentrations in nearshore waters and low concentrations in offshore waters. The Chl-a concentration exhibited seasonal changes (concentration in summer > autumn > spring ≈ winter). Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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24 pages, 7528 KiB

Open AccessArticle

A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)

by Francisco Carreño-Conde, Ana Elizabeth Sipols, Clara Simón de Blas and David Mostaza-Colado

Appl. Sci. 2021, 11(4), 1859; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041859 - 20 Feb 2021

Cited by 10 | Viewed by 2262

Abstract

Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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16 pages, 7552 KiB

Open AccessArticle

Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine

by Lili Lin, Zhenbang Hao, Christopher J. Post, Elena A. Mikhailova, Kunyong Yu, Liuqing Yang and Jian Liu

Appl. Sci. 2020, 10(20), 7336; https://0-doi-org.brum.beds.ac.uk/10.3390/app10207336 - 20 Oct 2020

Cited by 29 | Viewed by 3558

Abstract

Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990–2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a ‘Grain for Green Project’ and ‘Comprehensive Pilot Zone’ policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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23 pages, 4507 KiB

Open AccessArticle

DronAway: A Proposal on the Use of Remote Sensing Drones as Mobile Gateway for WSN in Precision Agriculture

by Laura García, Lorena Parra, Jose M. Jimenez, Jaime Lloret, Pedro V. Mauri and Pascal Lorenz

Appl. Sci. 2020, 10(19), 6668; https://0-doi-org.brum.beds.ac.uk/10.3390/app10196668 - 24 Sep 2020

Cited by 20 | Viewed by 3218

Abstract

The increase in the world population has led to new needs for food. Precision Agriculture (PA) is one of the focuses of these policies to optimize the crops and facilitate crop management using technology. Drones have been gaining popularity in PA to perform remote sensing activities such as photo and video capture as well as other activities such as fertilization or scaring animals. These drones could be used as a mobile gateway as well, benefiting from its already designed flight plan. In this paper, we evaluate the adequacy of remote sensing drones to perform gateway functionalities, providing a guide for choosing the best drone parameters for successful WiFi data transmission between sensor nodes and the gateway in PA systems for crop monitoring and management. The novelty of this paper compared with existing mobile gateway proposals is that we are going to test the performance of the drone that is acting as a remote sensing tool to carry a low-cost gateway node to gather the data from the nodes deployed on the field. Taking this in mind, simulations of different scenarios were performed to determine if the data can be transmitted correctly or not considering different flying parameters such as speed (from 1 to 20 m/s) and flying height (from 4 to 104 m) and wireless sensor network parameters such as node density (1 node each 60 m² to 1 node each 5000 m²) and antenna coverage (25 to 200 m). We have calculated the time that each node remains with connectivity and the time required to send the data to estimate if the connection will be bad, good, or optimal. Results point out that for the maximum node density, there is only one combination that offers good connectivity (lowest velocity, the flying height of 24 m, and antenna with 25 m of coverage). For the other node densities, several combinations of flying height and antenna coverage allows good and optimal connectivity. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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17 pages, 7663 KiB

Open AccessArticle

Estimating Proportion of Vegetation Cover at the Vicinity of Archaeological Sites Using Sentinel-1 and -2 Data, Supplemented by Crowdsourced OpenStreetMap Geodata

by Athos Agapiou

Appl. Sci. 2020, 10(14), 4764; https://0-doi-org.brum.beds.ac.uk/10.3390/app10144764 - 10 Jul 2020

Cited by 19 | Viewed by 3290

Abstract

Monitoring vegetation cover is an essential parameter for assessing various natural and anthropogenic hazards that occur at the vicinity of archaeological sites and landscapes. In this study, we used free and open access to Copernicus Earth Observation datasets. In particular, the proportion of vegetation cover is estimated from the analysis of Sentinel-1 radar and Sentinel-2 optical images, upon their radiometric and geometric corrections. Here, the proportion of vegetation based on the Radar Vegetation Index and the Normalized Difference Vegetation Index is estimated. Due to the medium resolution of these datasets (10 m resolution), the crowdsourced OpenStreetMap service was used to identify fully and non-vegetated pixels. The case study is focused on the western part of Cyprus, whereas various open-air archaeological sites exist, such as the archaeological site of “Nea Paphos” and the “Tombs of the Kings”. A cross-comparison of the results between the optical and the radar images is presented, as well as a comparison with ready products derived from the Sentinel Hub service such as the Sentinel-1 Synthetic Aperture Radar Urban and Sentinel-2 Scene classification data. Moreover, the proportion of vegetation cover was evaluated with Google Earth red-green-blue free high-resolution optical images, indicating that a good correlation between the RVI and NDVI can be generated only over vegetated areas. The overall findings indicate that Sentinel-1 and -2 indices can provide a similar pattern only over vegetated areas, which can be further elaborated to estimate temporal changes using integrated optical and radar Sentinel data. This study can support future investigations related to hazard analysis based on the combined use of optical and radar sensors, especially in areas with high cloud-coverage. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)

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