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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 35903

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Special Issue Editors

Dr. Tien Yin Chou

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Guest Editor

GIS Research Center, Feng Chia University, Taichung City 40874, Taiwan
Interests: GIS; remote sensing; UAV; land management; disasters prevention and protection information
Special Issues, Collections and Topics in MDPI journals

Dr. Gianluca Groppelli

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Guest Editor

Istituto di Geologia Ambientale e Geoingegneria, Consiglio Nazionale delle Ricerche, 20131 Milano, Italy
Interests: geology of volcanic areas; field survey; volcano-tectonic; volcanic geomorphology; volcanology; neotectonics; paleoseismology; volcaniclastic deposits; volcano lateral collapses; volcanic hazard assessment; GIS; geothermal resources
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Special Issue Information

Dear Colleagues,

An increasing number of innovations on remote sensing applications have emerged in recent years thanks to ever-growing new technologies. With the global impacts of climate change, the topics of resource conservation in daily life, production activities, and ecological issues are actively discussed. Combinations of remote sensing techniques with the Internet of Things (IoT), Artificial Intelligence, Big Data, and UAV observations make resource conservation applications more dynamic. Multilateral remote sensing techniques support resource conservation data analysis, including land use and land cover analysis, natural environment monitoring, agricultural resource surveys, and 3-dimensional environment simulations.

The key objective of this Special Issue is to collect the latest studies on remote sensing in resource conservation. Submitted manuscripts should focus on innovative applications in these areas. The following topics are some examples of the issues we expect manuscripts to deal with:

Remote sensing information
Climate change
Ocean debris monitoring
Hydrology and water source
3-dimensional remote sensing and Earth surface modeling
Disaster management and emergency plans
Resource conservation
Flood prediction
Forest and agricultural management
Big data in remote sensing
Coastal mapping
AI in remote sensing
VR/AR reality
Internet of Things (IoT)

This Special Issue on “Applications of Remote Sensing for Resources Conservation” is jointly organized between the journals of Remote Sensing and Earth. Contributors are required to check the website below and follow the specific instructions for authors: https://www.mdpi.com/journal/remotesensing/instructions
https://www.mdpi.com/journal/earth/instructions

You will have the opportunity to choose to publish your papers in Earth, which will offer a lot of discounts or full waivers for your papers based on peer-review results.

Dr. Tien Yin Chou
Dr. Gianluca Groppelli
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 submissions that pass pre-check are 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 2700 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

Climate change
Resource conservation
Big data in remote sensing
VR/AR reality
Environmental protection

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Published Papers (9 papers)

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Research

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20 pages, 7783 KiB

Open AccessArticle

Pineapples’ Detection and Segmentation Based on Faster and Mask R-CNN in UAV Imagery

by Yi-Shiang Shiu, Re-Yang Lee and Yen-Ching Chang

Remote Sens. 2023, 15(3), 814; https://doi.org/10.3390/rs15030814 - 31 Jan 2023

Cited by 8 | Viewed by 2738

Abstract

Early production warnings are usually labor-intensive, even with remote sensing techniques in highly intensive but fragmented growing areas with various phenological stages. This study used high-resolution unmanned aerial vehicle (UAV) images with a ground sampling distance (GSD) of 3 cm to detect the plant body of pineapples. The detection targets were mature fruits mainly covered with two kinds of sun protection materials—round plastic covers and nets—which could be used to predict the yield in the next two to three months. For round plastic covers (hereafter referred to as wearing a hat), the Faster R-CNN was used to locate and count the number of mature fruits based on input image tiles with a size of 256 × 256 pixels. In the case of intersection-over-union (IoU) > 0.5, the F1-score of the hat wearer detection results was 0.849, the average precision (AP) was 0.739, the precision was 0.990, and the recall was 0.743. We used the Mask R-CNN model for other mature fruits to delineate the fields covered with nets based on input image tiles with a size of 2000 × 2000 pixels and a mean IoU (mIoU) of 0.613. Zonal statistics summed up the area with the number of fields wearing a hat and covered with nets. Then, the thresholding procedure was used to solve the potential issue of farmers’ harvesting in different batches. In pineapple cultivation fields, the zonal results revealed that the overall classification accuracy is 97.46%, and the kappa coefficient is 0.908. The results were expected to demonstrate the critical factors of yield estimation and provide researchers and agricultural administration with similar applications to give early warnings regarding production and adjustments to marketing. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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23 pages, 12050 KiB

Open AccessArticle

Selection of Lunar South Pole Landing Site Based on Constructing and Analyzing Fuzzy Cognitive Maps

by Yutong Jia, Lei Liu, Xingchen Wang, Ningbo Guo and Gang Wan

Remote Sens. 2022, 14(19), 4863; https://doi.org/10.3390/rs14194863 - 29 Sep 2022

Cited by 12 | Viewed by 4812

Abstract

The Permanently Shadowed Regions (PSRs) of the lunar south pole have never been directly sampled. To explore and discover lunar resources, the Chinese lunar south pole exploration mission is scheduled to land in direct sunlight near the PSR, where sampling and analysis will be carried out. The selection of sites for lunar landing sampling sites is one of the key steps of the mission. The main factors affecting the site selection are the distribution of PSRs, lunar surface slopes, rock distribution, light intensity, and maximum temperature. In this paper, the main factors affecting site selection are analyzed based on lunar multi-source remote sensing data. Combined with previous engineering constraints, we then propose a comprehensive multi-factor fuzzy cognition and selection model for the lunar south site selection. An analytical model based on a fuzzy cognitive map algorithm is also established. Furthermore, to make a preliminary landing area selection, we determine the evaluation index for the candidate landing areas using fuzzy reasoning. Using the proposed model and combined scoring index, we also verify and analyze the prominent impact craters at the lunar south pole. The scores of de Gerlache (88.48°S 88.34°W), Shackleton (89.67°S 129.78°E), and Amundsen (84.5°S, 82.8°E) craters are determined using fuzzy interference as 0.816, 0.814, and 0.784, respectively. Moreover, using our proposed approach, we identify feasible landing sites around the de Gerlache crater close to the PSR to facilitate discovery of water ice exposures in future missions. The proposed method is capable of evaluating alternative landing zones subject to multiple engineering constraints on the Moon or Mars based on the existing data. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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21 pages, 3246 KiB

Open AccessArticle

Use of Unoccupied Aerial Systems to Characterize Woody Vegetation across Silvopastoral Systems in Ecuador

by Juan Pablo Iñamagua-Uyaguari, David R. Green, Nuala Fitton, Pamela Sangoluisa, Jonathan Torres and Pete Smith

Remote Sens. 2022, 14(14), 3386; https://doi.org/10.3390/rs14143386 - 14 Jul 2022

Cited by 2 | Viewed by 2376

Abstract

The trees in pastures are recognized for the benefits they provide to livestock, farmers, and the environment; nevertheless, their study has been restricted to small areas, making it difficult to upscale this information to national levels. For tropical developing countries, it is particularly important to understand the contribution of these systems to national carbon budgets. However, the costs associated with performing field measurements might limit the acquisition of this information. The use of unoccupied aerial systems (UAS) for ecological surveys has proved useful for collecting information at larger scales and with significantly lower costs. This study proposes a methodology that integrates field and UAS surveys to study trees on pasture areas across different terrain conditions. Our overall objective was to test the suitability of UAS surveys to the estimation of aboveground biomass (AGB), relying mainly on open-source software. The tree heights and crown diameters were measured on 0.1-hectare circular plots installed on pasture areas on livestock farms in the Amazon and Coastal regions in Ecuador. An UAS survey was performed on 1-hectare plots containing the circular plots. Field measurements were compared against canopy-height model values and biomass estimates using the two sources of information. Our results demonstrate that UAS surveys can be useful for identifying tree spatial arrangements and provide good estimates of tree height (RMSE values ranged from 0.01 to 3.53 m), crown diameter (RMSE values ranged from 0.04 to 4.47 m), and tree density (density differences ranging from 21.5 to 64.3%), which have a direct impact on biomass estimates. The differences in biomass estimates between the UAS and the field-measured values ranged from 25 to 75%, depending on site characteristics, such as slope and tree coverage. The results suggest that UASs are reliable and feasible tools with which to study tree characteristics on pastures, covering larger areas than field methods only. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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15 pages, 2477 KiB

Open AccessArticle

A Universal Multi-Frequency Micro-Resistivity Array Imaging Method for Subsurface Sensing

by Haining Yang, Yuting Liu, Tingjun Li, Shijia Yi and Na Li

Remote Sens. 2022, 14(13), 3116; https://doi.org/10.3390/rs14133116 - 28 Jun 2022

Cited by 2 | Viewed by 1834

Abstract

In this paper, a universal multi–frequency micro-resistivity array imaging (UMMAI) system for subsurface sensing is developed and verified. Different from conventional micro-resistivity imaging equipments, UMMAI is capable to provide high-resolution fullbore formation images in multiple logging environments including an oil-based mud scene, water-based mud scene and water-oil mixed mud scene, owning to the large dynamic range and good linearity of transceivers. With the advantage of diversity in excitation signal frequency, UMMAI presents abundant amplitude–frequency characteristics response images and phase–frequency characteristics response images of subsurface formations at the same time, which is beneficial to multi–frequency image fusion in the future. The fullbore imaging ability of UMMAI is evaluated in three different field tests, and the results show that UMMAI can give satisfactory credible formation images with high resolution, which is suitable for subsurface formation discrimination and useful for reservoir identification. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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14 pages, 9321 KiB

Open AccessArticle

Robust Extraction of Soil Characteristics Using Landsat 8 OLI/TIRS

by Thanh-Van Hoang, Tien-Yin Chou, Yao-Min Fang, Chun-Tse Wang, Ching-Yun Mu, Nguyen Quang Tuan, Do Thi Viet Huong, Ha Van Hanh and Doan Ngoc Nguyen Phong

Remote Sens. 2022, 14(10), 2490; https://doi.org/10.3390/rs14102490 - 23 May 2022

Viewed by 3093

Abstract

This research utilized various methods for extracting soil characteristics from Landsat 8 OLI/TIRS imagery in the Thua Thien Hue province, Vietnam. In this study, the Object-Based Oriented Classification (OBOC) method was used to extract information about land cover (focusing on rock outcrops) on the basis of the TGSI, NDVI, and NDBI indicators. The soil moisture information was determined by examining the correlation between the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). The findings indicated that 40 locations in the study area were covered with rock outcrops, with a Kappa index of 85.10%. In addition, soil moisture varied markedly from the sandy coastal regions, urban areas, and hilly and mountainous areas on the study area’s surface. The extracted soil information can serve as a foundation for local socio-economic development planning. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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20 pages, 10565 KiB

Open AccessArticle

The Evaluation of Color Spaces for Large Woody Debris Detection in Rivers Using XGBoost Algorithm

by Min-Chih Liang, Samkele S. Tfwala and Su-Chin Chen

Remote Sens. 2022, 14(4), 998; https://doi.org/10.3390/rs14040998 - 18 Feb 2022

Cited by 2 | Viewed by 2469

Abstract

Large woody debris (LWD) strongly influences river systems, especially in forested and mountainous catchments. In Taiwan, LWD are mainly from typhoons and extreme torrential events. To effectively manage the LWD, it is necessary to conduct regular surveys on river systems. Simple, low cost, and accurate tools are therefore necessary. The proposed methodology applies image processing and machine learning (XGBoost classifier) to quantify LWD distribution, location, and volume in river channels. XGBoost algorithm was selected due to its scalability and faster execution speeds. Nishueibei River, located in Taitung County, was used as the area of investigation. Unmanned aerial vehicles (UAVs) were used to capture the terrain and LWD. Structure from Motion (SfM) was used to build high-resolution orthophotos and digital elevation models (DEM), after which machine learning and different color spaces were used to recognize LWD. Finally, the volume of LWD in the river was estimated. The findings show that RGB color space as LWD recognition factor suffers serious collinearity problems, and it is easy to lose some LWD information; thus, it is not suitable for LWD recognition. On the contrary, the combination of different factors in different color spaces enhances the results, and most of the factors are related to the YCbCr color space. The CbCr factor in the YCbCr color space was best for identifying LWD. LWD volume was then estimated from the identified LWD using manual, field, and automatic measurements. The results indicate that the manual measurement method was the best (R² = 0.88) to identify field LWD volume. Moreover, automatic measurement (R² = 0.72) can also obtain LWD volume to save time and workforce. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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15 pages, 6306 KiB

Open AccessArticle

Gradient Boosting Machine and Object-Based CNN for Land Cover Classification

by Quang-Thanh Bui, Tien-Yin Chou, Thanh-Van Hoang, Yao-Min Fang, Ching-Yun Mu, Pi-Hui Huang, Vu-Dong Pham, Quoc-Huy Nguyen, Do Thi Ngoc Anh, Van-Manh Pham and Michael E. Meadows

Remote Sens. 2021, 13(14), 2709; https://doi.org/10.3390/rs13142709 - 9 Jul 2021

Cited by 32 | Viewed by 5156

Abstract

In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient boosting algorithms. In this regard, this study investigates three robust classifiers, namely XGBoost, LightGBM, and Catboost, in combination with a CNN for a land cover study in Hanoi, Vietnam. The experiments were implemented using SPOT7 imagery through (1) image segmentation and extraction of features, including spectral information and spatial metrics, (2) normalization of attribute values and generation of graphs, and (3) using graphs as the input dataset to the investigated models for classifying six land cover classes, namely House, Bare land, Vegetation, Water, Impervious Surface, and Shadow. The results show that CNN-based XGBoost (Overall accuracy = 0.8905), LightGBM (0.8956), and CatBoost (0.8956) outperform the other methods used for comparison. It can be seen that the combination of object-based image analysis and CNN-based gradient boosting algorithms significantly improves classification accuracies and can be considered as alternative methods for land cover analysis. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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35 pages, 2681 KiB

Open AccessReview

Surface Water Storage in Rivers and Wetlands Derived from Satellite Observations: A Review of Current Advances and Future Opportunities for Hydrological Sciences

by Fabrice Papa and Frédéric Frappart

Remote Sens. 2021, 13(20), 4162; https://doi.org/10.3390/rs13204162 - 18 Oct 2021

Cited by 37 | Viewed by 7215

Abstract

Surface water storage (SWS), the amount of freshwater stored in rivers/wetlands/floodplains/lakes, and its variations are key components of the water cycle and land surface hydrology, with strong feedback and linkages with climate variability. They are also very important for water resources management. However, it is still very challenging to measure and to obtain accurate estimates of SWS variations for large river basins at adequate time/space sampling. Satellite observations offer great opportunities to measure SWS changes, and several methods have been developed combining multisource observations for different environments worldwide. With the upcoming launch in 2022 of the Surface Water and Ocean Topography (SWOT) satellite mission, which will provide, for the first time, direct estimates of SWS variations with an unprecedented spatial resolution (~100 m), it is timely to summarize the recent advances in the estimates of SWS from satellite observations and how they contribute to a better understanding of large-scale hydrological processes. Here, we review the scientific literature and present major results regarding the dynamic of surface freshwater in large rivers, floodplains, and wetlands. We show how recent efforts have helped to characterize the variations in SWS change across large river basins, including during extreme climatic events, leading to an overall better understanding of the continental water cycle. In the context of SWOT and forthcoming SWS estimates at the global scale, we further discuss new opportunities for hydrological and multidisciplinary sciences. We recommend that, in the near future, SWS should be considered as an essential water variable to ensure its long-term monitoring. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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Other

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15 pages, 4299 KiB

Open AccessTechnical Note

Improved Fusion of Spatial Information into Hyperspectral Classification through the Aggregation of Constrained Segment Trees: Segment Forest

by Jianmei Ling, Lu Li and Haiyan Wang

Remote Sens. 2021, 13(23), 4816; https://doi.org/10.3390/rs13234816 - 27 Nov 2021

Cited by 3 | Viewed by 1764

Abstract

Compared with traditional optical and multispectral remote sensing images, hyperspectral images have hundreds of bands that can provide the possibility of fine classification of the earth’s surface. At the same time, a hyperspectral image is an image that coexists with the spatial and spectral. It has become a hot research topic to combine the spatial spectrum information of the image to classify hyperspectral features. Based on the idea of spatial–spectral classification, this paper proposes a novel hyperspectral image classification method based on a segment forest (SF). Firstly, the first principal component of the image was extracted by the process of principal component analysis (PCA) data dimension reduction, and the data constructed the segment forest after dimension reduction to extract the non-local prior spatial information of the image. Secondly, the images’ initial classification results and probability distribution were obtained using support vector machine (SVM), and the spectral information of the images was extracted. Finally, the segment forest constructed above is used to optimize the initial classification results and obtain the final classification results. In this paper, three domestic and foreign public data sets were selected to verify the segment forest classification. SF effectively improved the classification accuracy of SVM, and the overall accuracy of Salinas was enhanced by 11.16%, WHU-Hi-HongHu by 15.89%, and XiongAn by 19.56%. Then, it was compared with six decision-level improved space spectrum classification methods, including guided filtering (GF), Markov random field (MRF), random walk (RW), minimum spanning tree (MST), MST+, and segment tree (ST). The results show that the segment forest-based hyperspectral image classification improves accuracy and efficiency compared with other algorithms, proving the algorithm’s effectiveness. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Resources Conservation)

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