Special Issue "Innovative Remote Sensing for Monitoring and Assessment of Natural Resources"

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

Deadline for manuscript submissions: closed (30 November 2019).

Special Issue Editors

Prof. Francesco Pirotti
E-Mail Website
Guest Editor
University of Padova - Via dell'Università, 16, 35020 - Legnaro (PD), Italy
Interests: LiDAR; Geomatics; Natural Resources; Natural Hazard; Risk
Special Issues and Collections in MDPI journals
Dr. Mitsunori Yoshimura
E-Mail Website
Guest Editor
Center for Spatial Information Science, University of Tokyo,Tokyo 113-8654, Japan
Interests: remote sensing, natural resources, Ecological Monitoring, hyperspectral
Prof. Dr. Baoxin Hu
E-Mail Website
Guest Editor
Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto, ON M3J1P3, Canada
Interests: remote sensing of vegetation; hyperspectral; LiDAR; SAR/InSAR/PolSAR; 3D scene characterization

Special Issue Information

Dear Colleagues,

It is well known that natural resources in our planet are under constant pressure, mainly from anthropic-related threats. Causes are varied and numerous: over-exploitation, non-sustainable land-use, climate change, soil and water salinity, just to name a few. Earth observation (EO) via remote sensing is a well-established technique for monitoring and quantifying these phenomena, and for this reason has been the focus of intense efforts for research and development. In the last years innovative technologies in the realm of EO opened new possibilities for investigators. New platforms have been, and still are, sent into orbit, enriching the availability and diversity of data, which are provided with commercial or open channels. New sensors are being engineered and sent to market, providing new challenges related to data that are provided e.g. structure, size and format. Single photon-counting technology in laser scanning is a representative of many examples. New solutions also for data processing and analysis have opened new frontiers; e.g. simultaneous localization and mapping (SLAM) for unstructured point cloud data, and machine/deep learning for data interpretation

Adequate monitoring and assessment of the condition of natural resources remains a prerequisite for supporting environmental decisions and for tracking effects over time. This special issue aims to summarize the latest progress in techniques and algorithms developed for monitoring and assessment of natural resources. Authors are invited to contribute to this special Issue of Remote Sensing by submitting an original manuscript. Contributions may focus on, but are not limited to:

  1. new and improved algorithms for data processing and information extraction related to natural resources;
  2. application of multi-sensor and multi-scale approaches;
  3. multi-temporal analysis of imagery to define trends over time;
  4. effects of land-cover changes on natural resources - e.g. urbanization, desertification;
  5. close-range sensing applications, e.g. from remotely piloted aircraft systems;
  6. quantification of risk and hazard related to natural resources;
  7. new possibilities available from new sensors and platforms;

Prof. Francesco Pirotti
Dr. Mitsunori Yoshimura
Dr. Baoxin Hu
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 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

  • Natural resources
  • SAR/InSAR/PolSAR
  • Laser scanning / LiDAR
  • Hyperspectral Imagery Analysis
  • Multi-scale and multi-source remote sensing
  • Spatiotemporal analysis

Published Papers (7 papers)

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Research

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Open AccessFeature PaperArticle
Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
Remote Sens. 2019, 11(23), 2873; https://doi.org/10.3390/rs11232873 - 03 Dec 2019
Abstract
Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) [...] Read more.
Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6). Full article
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Open AccessArticle
Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products
Remote Sens. 2019, 11(16), 1939; https://doi.org/10.3390/rs11161939 - 19 Aug 2019
Cited by 1
Abstract
As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due [...] Read more.
As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial–temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of “high in winter and low in summer”, while the spatial distribution forms a “high in East and low in west” pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a “delayed effect” of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention. Full article
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Open AccessArticle
The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands
Remote Sens. 2019, 11(13), 1537; https://doi.org/10.3390/rs11131537 - 28 Jun 2019
Cited by 2
Abstract
Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands [...] Read more.
Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality. Full article
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Open AccessArticle
An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images
Remote Sens. 2019, 11(8), 987; https://doi.org/10.3390/rs11080987 - 25 Apr 2019
Cited by 1
Abstract
An improved GrabCut method based on a visual attention model is proposed to extract rare-earth ore mining area information using high-resolution remote sensing images. The proposed method makes use of advantages of both the visual attention model and GrabCut method, and the visual [...] Read more.
An improved GrabCut method based on a visual attention model is proposed to extract rare-earth ore mining area information using high-resolution remote sensing images. The proposed method makes use of advantages of both the visual attention model and GrabCut method, and the visual attention model was referenced to generate a saliency map as the initial of the GrabCut method instead of manual initialization. Normalized Difference Vegetation Index (NDVI) was designed as a bound term added into the Energy Function of GrabCut to further improve the accuracy of the segmentation result. The proposed approach was employed to extract rare-earth ore mining areas in Dingnan County and Xunwu County, China, using GF-1 (GaoFen No.1 satellite launched by China) and ALOS (Advanced Land Observation Satellite) high-resolution remotely-sensed satellite data, and experimental results showed that FPR (False Positive Rate) and FNR (False Negative Rate) were, respectively, lower than 12.5% and 6.5%, and PA (Pixel Accuracy), MPA (Mean Pixel Accuracy), MIoU (Mean Intersection over Union), and FWIoU (frequency weighted intersection over union) all reached up to 90% in four experiments. Comparison results with traditional classification methods (such as Object-oriented CART (Classification and Regression Tree) and Object-oriented SVM (Support Vector Machine)) indicated the proposed method performed better for object boundary identification. The proposed method could be useful for accurate and automatic information extraction for rare-earth ore mining areas. Full article
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Open AccessArticle
Estimation of Global and Diffuse Photosynthetic Photon Flux Density under Various Sky Conditions Using Ground-Based Whole-Sky Images
Remote Sens. 2019, 11(8), 932; https://doi.org/10.3390/rs11080932 - 17 Apr 2019
Cited by 1
Abstract
A knowledge of photosynthetic photon flux density (PPFD: μmol m−2 s−1) is crucial for understanding plant physiological processes in photosynthesis. The diffuse component of the global PPFD on a short timescale is required for the accurate modeling of photosynthesis. However, [...] Read more.
A knowledge of photosynthetic photon flux density (PPFD: μmol m−2 s−1) is crucial for understanding plant physiological processes in photosynthesis. The diffuse component of the global PPFD on a short timescale is required for the accurate modeling of photosynthesis. However, because the PPFD is difficult to determine, it is generally estimated from incident solar radiation (SR: W m−2), which is routinely observed worldwide. To estimate the PPFD from the SR, photosynthetically active radiation (PAR: W m−2) is separated from the SR using the PAR fraction (PF; PAR/SR: unitless), and the PAR is then converted into the PPFD using the quanta-to-energy ratio (Q/E: μmol J−1). In this procedure, PF and Q/E are considered constant values; however, it was reported recently that PF and Q/E vary under different sky conditions. Moreover, the diffuse ratio (DR) is needed to distinguish the diffuse component in the global PAR, and it is known that the DR varies depending on sky conditions. Ground-based whole-sky images can be used for sky-condition monitoring, instead of human-eye interpretation. This study developed a methodology for estimating the global and diffuse PPFD using whole-sky images. Sky-condition factors were derived through whole-sky image processing, and the effects of these factors on the PF, the Q/E of global and diffuse PAR, and the DR were examined. We estimated the global and diffuse PPFD with instantaneous values using the sky-condition factors under various sky conditions, based on which the detailed effects of the sky-condition factors on PF, Q/E, and DR were clarified. The results of the PPFD estimations had small bias errors of approximately +0.3% and +3.8% and relative root mean square errors of approximately 27% and 20% for the global and diffuse PPFD, respectively. Full article
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Review

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Open AccessReview
Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives
Remote Sens. 2018, 10(9), 1356; https://doi.org/10.3390/rs10091356 - 26 Aug 2018
Cited by 5
Abstract
LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the [...] Read more.
LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future. Full article
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Other

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Open AccessTechnical Note
A PolSAR Change Detection Index Based on Neighborhood Information for Flood Mapping
Remote Sens. 2019, 11(16), 1854; https://doi.org/10.3390/rs11161854 - 09 Aug 2019
Abstract
Change detection using Remote Sensing (RS) techniques is valuable in numerous applications, including environmental management and hazard monitoring. Synthetic Aperture Radar (SAR) images have proven to be even more effective in this regard because of their all-weather, day and night acquisition capabilities. In [...] Read more.
Change detection using Remote Sensing (RS) techniques is valuable in numerous applications, including environmental management and hazard monitoring. Synthetic Aperture Radar (SAR) images have proven to be even more effective in this regard because of their all-weather, day and night acquisition capabilities. In this study, a polarimetric index based on the ratio of span (total power) values was introduced, in which neighbourhood information was considered. The role of the central pixel and its neighbourhood was adjusted using a weight parameter. The proposed index was applied to detect flooded areas in Dongting Lake, Hunan, China, and was then compared with the Wishart Maximum Likelihood Ratio (MLR) test. Results demonstrated that although the proposed index and the Wishart MLR test yielded similar accuracies (accuracy of 94% and 93%, and Kappa Coefficients of 0.82 and 0.86, respectively), inclusion of neighbourhood information in the proposed index not only increased the connectedness and decreased the noise associated with the objects within the produced map, but also increased the consistency and confidence of the results. Full article
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