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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 22979

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Special Issue Editors

Dr. Hamdi A. Zurqani

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Guest Editor

Arkansas Agricultural Experiment Station, Arkansas Forest Resources Center, University of Arkansas, Monticello, AR 71655, USA
Interests: GIS and remote sensing; environmental information science; land evaluation; pedology; sustainability
Special Issues, Collections and Topics in MDPI journals

Dr. Christopher Post

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Guest Editor

Professor of Environmental Information Science, Forestry and Environmental Conservation Department, Clemson University, Clemson, SC 29634, USA
Interests: My research interests focus on the use of innovative technologies to answer important scientific questions relating to our natural and built world. Tools range from satellite and UAV based remote sensing to water and terrestrial sensor networks. Specific focus areas include water quality and quantity, urban and production forestry and agriculture production.

Special Issue Information

Dear Colleagues,

Remote sensing and its applications have long been used for mapping and monitoring change across the Earth’s surface. Moreover, remotely sensed data holds an advantage in monitoring detection on the surface of the Earth because of its large spatial coverage, high time resolution, and wide availability. The availability of the historical record of remotely sensed data and the modern geospatial technology such as the Google Earth Engine (GEE) has enabled the scientific community to investigate and identify environmental disturbances to study the relationship between the human influence on Earth’s surface and its consequences on the environment over time.

This Special Issue focuses on the latest research advances in remote sensing technologies and their applications that are particularly tied with various mapping and monitoring changes across the Earth’s surface.

We invite authors to submit their work on applications that use remotely sensed data for earth observation and geo-information science. We encourage the submission of works related to the use of methods and applications for natural resource and environmental monitoring with a wide range of optical and radar remote sensing materials. Topics considered for this Special Issue should emphasize practical applications and reach beyond theoretical and model-based studies. Suggested topics include, but are not limited to, the following:

Cloud Computing and Big Data Analysis (i.e., Google Earth Engine)
Machine and Deep Learning for Earth Observation Analysis
Multi-Sensor and Multi-Resolution Data Analysis
Environmental Change Detection of a Global and Regional Scale
Land Use and Land Cover Change Monitoring and Assessments
Monitoring of Deforestation and Forest Degradation
Monitoring and Assessment of Urban Growth Patterns
Water Resources Modeling and Monitoring
Natural Hazards Mapping and Monitoring
Climate Change Impact Assessment

Dr. Hamdi A. Zurqani
Dr. Christopher Post
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

Photogrammetry
Remote sensing
Light detection and ranging (LiDAR)
Unmanned aerial vehicle (UAV)
Machine and deep learning
Big data analysis (i.e., Google Earth Engine)
Multispectral, hyperspectral, and thermal image analysis
Multi-sensor and multi-resolution data analysis
Environmental monitoring (soil, water, vegetation, biomass, forest, and etc.)
Change detection monitoring and assessment

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Published Papers (7 papers)

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Research

24 pages, 15733 KiB

Open AccessArticle

Evolution Patterns and Dominant Factors of Soil Salinization in the Yellow River Delta Based on Long-Time-Series and Similar Phenological-Fusion Images

by Bing Guo, Mei Xu and Rui Zhang

Remote Sens. 2024, 16(17), 3332; https://doi.org/10.3390/rs16173332 - 8 Sep 2024

Viewed by 897

Abstract

Previous studies were mostly conducted based on sparse time series and different phenological images, which often ignored the dramatic changes in salinization evolution throughout the year. Based on Landsat and moderate-resolution-imaging spectroradiometer (MODIS) images from 2000 to 2020, this study applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm to obtain similar phenological images for the month of April for the past 20 years. Based on the random forest algorithm, the surface parameters of the salinization were optimized, and the feature space index models were constructed. Combined with the measured ground data, the optimal monitoring index model of salinization was determined, and then the spatiotemporal evolution patterns of salinization and its driving mechanisms in the Yellow River Delta were revealed. The main conclusions were as follows: (1) The derived long-time-series and similar phenological-fusion images enable us to reveal the patterns of change in the dramatic salinization in the year that we examined using the ESTARFM algorithm. (2) The NDSI-TGDVI feature space salinization monitoring index model based on point-to-point mode had the highest accuracy of 0.92. (3) From 2000 to 2020, the soil salinization in the Yellow River Delta showed an aggravating trend. The average value of salinization during the past 20 years was 0.65, which is categorized as severe salinization. The degree of salinization gradually decreased from the northeastern coastal area to the southwestern inland area. (4) The dominant factors affecting soil salinization in different historical periods varied. The research results could provide support for decision-making regarding the precise prevention and control of salinization in the Yellow River Delta. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Earth Observation and Geo-Information Science)

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25 pages, 21764 KiB

Open AccessArticle

Spatial Simulation and Prediction of Land Use/Land Cover in the Transnational Ili-Balkhash Basin

by Jing Kou, Jinjie Wang, Jianli Ding and Xiangyu Ge

Remote Sens. 2023, 15(12), 3059; https://doi.org/10.3390/rs15123059 - 11 Jun 2023

Cited by 11 | Viewed by 2251

Abstract

Exploring the future trends of land use/land cover (LULC) changes is significant for the sustainable development of a region. The simulation and prediction of LULC in a large-scale basin in an arid zone can help the future land management planning and rational allocation of resources in this ecologically fragile region. Using the whole Ili-Balkhash Basin as the study area, the patch-generating land use simulation (PLUS) model and a combination of PLUS and Markov predictions (PLUS–Markov) were used to simulate and predict land use in 2020 based on the assessment of the accuracy of LULC classification in the global dataset. The accuracy of simulations and predictions using the model were measured for LULC data covering different time periods. Model settings with better simulation results were selected for simulating and predicting possible future land use conditions in the basin. The future predictions for 2025 and 2030, which are based on historical land change characteristics, indicate that the overall future spatial pattern of LULC in the basin remains relatively stable in general without the influence of other external factors. Over the time scale of the future five years, the expansion of croplands and barren areas in the basin primarily stems from the loss of grasslands. Approximately 48% of the converted grassland areas are transformed into croplands, while around 40% are converted into barren areas. In the longer time scale of the future decade, the conversion of grasslands to croplands in the basin is also evident. However, the expansion phenomenon of urban and built-up lands at the expense of croplands is more significant, with approximately 774.2 km² of croplands developing into urban and built-up lands. This work provides an effective new approach for simulating and predicting LULC in data-deficient basins at a large scale in arid regions, thereby establishing a foundation for future research on the impact of human activities on basin hydrology and related studies. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Earth Observation and Geo-Information Science)

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19 pages, 13629 KiB

Open AccessArticle

Evaluating the Sand-Trapping Efficiency of Sand Fences Using a Combination of Wind-Blown Sand Measurements and UAV Photogrammetry at Tottori Sand Dunes, Japan

by Jiaqi Liu, Jing Wu and Reiji Kimura

Remote Sens. 2023, 15(4), 1098; https://doi.org/10.3390/rs15041098 - 17 Feb 2023

Cited by 6 | Viewed by 2368

Abstract

Fences are commonly used in coastal regions to control wind-blown sand. Sand-trapping fences and sand-stabilizing fences have been installed at the Tottori Sand Dunes, Tottori Prefecture, Japan, to prevent damage by wind-blown sand; however, the effectiveness of these fences has not previously been quantitatively evaluated. This study analyzed the effects of sand fences on sand trapping using field observations of blown-sand flux and unmanned aerial vehicle (UAV) photogrammetry. The estimated total blown-sand flux in the near-ground surface observed inside and outside the sand fences indicated that wind-blown sand was effectively trapped by the sand fences at wind speeds lower than 17 m s⁻¹, reducing sand flux by more than 80%. The UAV photogrammetry results demonstrated that large amounts of sand were transported from the dune to the fenced area during March and April, and sand initially accumulated on the lee side of the sand-trapping fences, forming a new foredune. Sand accumulated on the existing foredune during April and May, and the vertical accretion around the foredune was two to four times the sand deposition within the sand-stabilizing fences. This indicated the effectiveness of sand-trapping fences for controlling wind-blown sand; however, their efficiency was reduced as they were gradually buried, with sand being trapped by the sand-stabilizing fences. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Earth Observation and Geo-Information Science)

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21 pages, 140129 KiB

Open AccessArticle

An Epipolar HS-NCC Flow Algorithm for DSM Generation Using GaoFen-3 Stereo SAR Images

by Jian Wang, Xiaolei Lv, Zenghui Huang and Xikai Fu

Remote Sens. 2023, 15(1), 129; https://doi.org/10.3390/rs15010129 - 26 Dec 2022

Cited by 4 | Viewed by 1774

Abstract

Radargrammetry is a widely used methodology to generate the large-scale Digital Surface Model (DSM). Stereo matching is the most challenging step in radargrammetry due to the significant geometric differences and the inherent speckle noise. The speckle noise results in significant grayscale differences of the same feature points, which makes the traditional Horn–Schunck (HS) flow or multi-window zero-mean normalized cross-correlation (ZNCC) methods degrade. Therefore, this paper proposes an algorithm named Epipolar HS-NCC Flow (EHNF) for dense stereo matching, which is an improved HS flow method with normalized cross-correction constraint based on epipolar stereo images. First, the epipolar geometry is applied to resample the image to realize the coarse stereo matching. Subsequently, the EHNF method forms a global energy function to achieve fine stereo matching. The EHNF method constructs a local normalized cross-correlation constraint term to compensate for the grayscale invariance constraint, especially for the SAR stereo images. Additionally, two assessment methods are proposed to calculate the optimal cross-correlation parameter and smoothness parameter according to the refined matched point pairs. Two GaoFen-3 (GF-3) image pairs from ascending and descending orbits and the open Light Detection and Ranging (LiDAR) data are utilized to fully evaluate the proposed method. The results demonstrate that the EHNF algorithm improves the DSM elevation accuracy by 9.6% and 27.0% compared with the HS flow and multi-window ZNCC methods, respectively. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Earth Observation and Geo-Information Science)

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20 pages, 6832 KiB

Open AccessArticle

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

by Yahao Zhang, Yuanyong Dian, Jingjing Zhou, Shoulian Peng, Yue Hu, Lei Hu, Zemin Han, Xinwei Fang and Hongxia Cui

Remote Sens. 2021, 13(22), 4682; https://doi.org/10.3390/rs13224682 - 19 Nov 2021

Cited by 16 | Viewed by 2754

Abstract

Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Earth Observation and Geo-Information Science)

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18 pages, 7313 KiB

Open AccessArticle

Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades

by Jie Xue, Yanyu Wang, Hongfen Teng, Nan Wang, Danlu Li, Jie Peng, Asim Biswas and Zhou Shi

Remote Sens. 2021, 13(20), 4063; https://doi.org/10.3390/rs13204063 - 11 Oct 2021

Cited by 30 | Viewed by 3581

Abstract

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R² = 0.48) was greater than that of temperature to NDVI (R² = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Earth Observation and Geo-Information Science)

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17 pages, 4155 KiB

Open AccessArticle

Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

by Fugen Jiang, Feng Zhao, Kaisen Ma, Dongsheng Li and Hua Sun

Remote Sens. 2021, 13(8), 1535; https://doi.org/10.3390/rs13081535 - 15 Apr 2021

Cited by 44 | Viewed by 5672

Abstract

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R² of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Earth Observation and Geo-Information Science)

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