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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 June 2022) | Viewed by 20717

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Dr. Rudiger Gens

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Guest Editor

Alaska Satellite Facility, Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Interests: microwave remote sensing; digital image processing; geospatial data analysis; change detection

Dr. Jordi Cristóbal Rosselló

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Guest Editor

Department of Geography, Autonomous University of Barcelona, 08193 Cerdanyola del Vallès, Spain
Interests: surface energy balance; thermal infrared; time series analysis; hydrological modeling; radiometric correction; field spectroscopy; land cover and land use analysis; snow cover; water resources
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Dear Colleagues,

Our dynamic earth system has taught us that change is the only constant. Both natural causes and human interventions are changing the earth’s environment. Decades of multi-source geospatial data allow us to map, monitor, analyze, and visualize these changes at local to global scales. Advances in sensors and platforms have provided the flexibility to tailor the data collection to meet the application needs of the users. Easier access to geospatial data through open-source resources and crowd sourcing has contributed to rapid advances in our ability to study change and in turn guide decisions and policies to slow down or potentially reverse some of these changes.

This Special Issue is dedicated to capturing advances in the rapidly growing area of geospatial data analysis for change detection. Changes in land, water, air, or the human dimension of the Earth system, occurring at centimeter to global scales, within minutes or decadal time scales, are all important as they contribute to the understanding of our dynamic planet. The Special Issue also welcomes contributions that focus on the techniques of integrated data analysis and effective visualization of complex changes that help to communicate the trends and impacts of change.

You may choose our Joint Special Issue in Geomatics.

Dr. Rudiger Gens
Dr. Jordi Cristóbal Rosselló
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

remote sensing
GIS
geospatial data
change detection
time series analysis
digital data processing
data visualization

Published Papers (9 papers)

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30 pages, 27637 KiB

Open AccessArticle

An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data

by Mojtaba Saboori, Yousef Mousivand, Jordi Cristóbal, Reza Shah-Hosseini and Ali Mokhtari

Remote Sens. 2022, 14(24), 6253; https://doi.org/10.3390/rs14246253 - 9 Dec 2022

Viewed by 1270

Abstract

The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day⁻¹, coefficient of determination (R²) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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20 pages, 8028 KiB

Open AccessArticle

A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection

by Qiuxia Li, Tingkui Mu, Hang Gong, Haishan Dai, Chunlai Li, Zhiping He, Wenjing Wang, Feng Han, Abudusalamu Tuniyazi, Haoyang Li, Xuechan Lang, Zhiyuan Li and Bin Wang

Remote Sens. 2022, 14(12), 2838; https://doi.org/10.3390/rs14122838 - 13 Jun 2022

Cited by 5 | Viewed by 2130

Abstract

Hyperspectral image change detection (HSI-CD) is an interesting task in the Earth’s remote sensing community. However, current HSI-CD methods are feeble at detecting subtle changes from bitemporal HSIs, because the decision boundary is partially stretched by strong changes so that subtle changes are ignored. In this paper, we propose a superpixel-by-superpixel clustering framework (SSCF), which avoids the confusion of different changes and thus reduces the impact on decision boundaries. Wherein the simple linear iterative clustering (SLIC) is employed to spatially segment the different images (DI) of the bitemporal HSIs into superpixels. Meanwhile, the Gaussian mixture model (GMM) extracts uncertain pixels from the DI as a rough threshold for clustering. The final CD results are obtained by passing the determined superpixels and uncertain pixels through K-means. The experimental results of two spaceborne bitemporal HSIs datasets demonstrate competitive efficiency and accuracy in the proposed SSCF. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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29 pages, 10069 KiB

Open AccessArticle

Remote Sensing Analysis of Ecological Maintenance in Subtropical Coastal Mountain Area, China

by Run Han, Jinming Sha, Xiaomei Li, Shuhui Lai, Zejing Lin, Qixin Lin and Jinliang Wang

Remote Sens. 2022, 14(12), 2734; https://doi.org/10.3390/rs14122734 - 7 Jun 2022

Cited by 1 | Viewed by 1633

Abstract

Mountain areas in China account for 69% of the total land area; however, it is still an urgent that we grasp the special ecological structure of mountain areas and maximize the resource advantages of mountain areas under the principle of maintaining a certain ecological level. In this paper, Landsat 5, Landsat 8 and Sentinel-2A images were used as data sources to monitor and analyze land development and ecological change in Gui ’an in 2010, 2013, 2016, 2019 and 2021, so as to explore ecological maintenance mechanisms. Firstly, random forest classification based on multi-source remote sensing data was used to classify land, and the five phases of land-use change were assessed using quantitative analysis, in order to analyze the mountain region’s land-use-change characteristics at different stages of development. The results show that Gui ’an has the “two-stage”, rapid-development, rapid-recovery mode. Each stage includes a development-and-expansion period and a construction-and-protection period. In the construction period, ecological recovery construction will be emphasized, and the change intensity and rate of the second stage are lower than that of the first stage. Secondly, using a remote sensing ecological index, vegetation coverage, and a landscape index, an ecological evaluation model of the study area was constructed to analyze changes in the ecological environment and its protection in the process of land development. The ecological maintenance status of the five stages was quantitatively monitored using the analysis methods of difference change and coefficient of variation. The results showed that in the first stage of land development and expansion, the landscape pattern and ecological quality fluctuated greatly, and the proportion of ecological quality of an excellent grade decreased by 28.46%. However, in the second stage, the change slowed down and remained unchanged, and gradually moved to the middle and upper level. The results show that there is a close relationship between ecological maintenance and the land development mode, and new mountain towns can maintain ecological quality and achieve sustainable development through a reasonable land development mode. At the end of this paper, the factors affecting the ecological maintenance capacity of Gui ’an are discussed, providing effective reference material and development models for the development of mountainous areas. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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22 pages, 5417 KiB

Open AccessArticle

The Applicability of LandTrendr to Surface Water Dynamics: A Case Study of Minnesota from 1984 to 2019 Using Google Earth Engine

by Audrey C. Lothspeich and Joseph F. Knight

Remote Sens. 2022, 14(11), 2662; https://doi.org/10.3390/rs14112662 - 2 Jun 2022

Cited by 5 | Viewed by 2175

Abstract

The means to accurately monitor wetland change over time are crucial to wetland management. This paper explores the applicability of LandTrendr, a temporal segmentation algorithm designed to identify significant interannual trends, to monitor wetlands by modeling surface water presence in Minnesota from 1984 to 2019. A time series of harmonized Landsat and Sentinel-2 data in the spring is developed in Google Earth Engine, and calculated to sub-pixel water fraction. The optimal parameters for modeling this time series with LandTrendr are identified by minimizing omission of known surface water locations, and the result of this optimal model of sub-pixel water fraction is evaluated against reference images and qualitatively. Accuracy of this method is high: overall accuracy is 98% and producer’s and user’s accuracies for inundation are 82% and 88% respectively. Maps summarizing the trendlines of multiple pixels, such as frequency of inundation over the past 35 years, also show LandTrendr as applied here can accurately model long-term trends in surface water presence across wetland types. However, the tendency of omission for more variable prairie pothole wetlands and the under-prediction of inundation for small or emergent wetlands suggests the algorithm will require careful development of the segmented time series to capture inundated conditions more accurately. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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25 pages, 4304 KiB

Open AccessArticle

Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

by Peng Xue, Huiyu Liu, Mingyang Zhang, Haibo Gong and Li Cao

Remote Sens. 2022, 14(1), 15; https://doi.org/10.3390/rs14010015 - 21 Dec 2021

Cited by 21 | Viewed by 2842

Abstract

Monitoring vegetation net primary productivity (NPP) is very important for evaluating ecosystem health. However, the nonlinear characteristics of the vegetation NPP remain unclear in the six provinces along the Maritime Silk Road in China. In this study, using NDVI and meteorological data from 1982 to 2015, NPP was estimated with the Carnegie-Ames-Stanford Approach (CASA) model based on vegetation type dynamics, and its nonlinear characteristics were explored through the ensemble empirical mode decomposition (EEMD) method. The results showed that: (1) The total NPP in the changed vegetation types caused by ecological engineering and urbanization increased but decreased in those caused by agricultural reclamation and vegetation destruction, (2) the vegetation NPP was dominated by interannual variations, mainly in the middle of the study area, while by long-term trends, mainly in the southwest and northeast, (3) for most of the vegetation types, NPP was dominated by the monotonically increasing trend. Although vegetation NPP in the urban land mainly showed a decreasing trend (monotonic decrease and decrease from increase), there were large areas in which NPP increased from decreasing. Although vegetation NPP in the farmland mainly showed increasing trends, there were large areas that faced the risk of NPP decreasing; (4) dynamical changes of vegetation type by agricultural reclamation and vegetation destruction made the NPP trend monotonically decrease in large areas, leading to ecosystem degradation, while those caused by urbanization and ecological engineering mainly made the NPP increase from decreasing, leading to later recovery from early degradation. Our results highlighted the importance of vegetation type dynamics for accurately estimating vegetation NPP, as well as for assessing their impacts, and the importance of nonlinear analysis for deepening our understanding of vegetation NPP changes. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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21 pages, 4873 KiB

Open AccessArticle

Spatio-Temporal Analysis of Impervious Surface Expansion in the Qinhuai River Basin, China, 1988–2017

by Shanshan Wang, Yingxia Pu, Shengfeng Li, Runjie Li and Maohua Li

Remote Sens. 2021, 13(22), 4494; https://doi.org/10.3390/rs13224494 - 9 Nov 2021

Cited by 3 | Viewed by 1743

Abstract

Impervious surfaces are key indicators for urbanization monitoring and watershed degradation assessment over space and time. However, most empirical studies only extracted impervious surface from spatial, temporal or spectral perspectives, paying less attention to integrating multiple dimensions in acquiring continuous changes in impervious surfaces. In this study, we proposed a neighborhood-based spatio-temporal filter (NSTF) to obtain the continuous change information of impervious surfaces from multi-temporal Landsat images in the Qinhuai River Basin (QRB), Jiangsu, China from 1988–2017, based on the results from semi-automatic decision tree classification. Moreover, we used the expansion intensity index (EII) and the landscape extension index (LEI) to further characterize the spatio-temporal characteristics of impervious surfaces on different spatial scales. The preliminary results showed that the overall accuracies of the final classification were about 95%, with the kappa coefficients ranging between 0.9 and 0.96. The QRB underwent rapid urbanization with the percentage of the impervious surfaces increasing from 2.72% in 1988 to 25.6% in 2017. Since 2006, the center of urbanization expansion was shaped from the urban built-up areas of Nanjing and Jiangning to non-urban built-up areas of the Jiangning, Lishui, and Jurong districts. The edge expansion occupied 73% on average among the different landscape expansion types, greatly beyond outlying (12%) and infilling (15%). The window size in the NSTF has a direct impact on the subsequent analysis. Our research could provide decision-making references for future urban planning and development in the similar basins. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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20 pages, 23771 KiB

Open AccessArticle

A Refined Method of High-Resolution Remote Sensing Change Detection Based on Machine Learning for Newly Constructed Building Areas

by Haibo Wang, Jianchao Qi, Yufei Lei, Jun Wu, Bo Li and Yilin Jia

Remote Sens. 2021, 13(8), 1507; https://doi.org/10.3390/rs13081507 - 14 Apr 2021

Cited by 6 | Viewed by 2142

Abstract

Automatic detection of newly constructed building areas (NCBAs) plays an important role in addressing issues of ecological environment monitoring, urban management, and urban planning. Compared with low-and-middle resolution remote sensing images, high-resolution remote sensing images are superior in spatial resolution and display of refined spatial details. Yet its problems of spectral heterogeneity and complexity have impeded research of change detection for high-resolution remote sensing images. As generalized machine learning (including deep learning) technologies proceed, the efficiency and accuracy of recognition for ground-object in remote sensing have been substantially improved, providing a new solution for change detection of high-resolution remote sensing images. To this end, this study proposes a refined NCBAs detection method consisting of four parts based on generalized machine learning: (1) pre-processing; (2) candidate NCBAs are obtained by means of bi-temporal building masks acquired by deep learning semantic segmentation, and then registered one by one; (3) rules and support vector machine (SVM) are jointly adopted for classification of NCBAs with high, medium and low confidence; and (4) the final vectors of NCBAs are obtained by post-processing. In addition, area-based and pixel-based methods are adopted for accuracy assessment. Firstly, the proposed method is applied to three groups of GF1 images covering the urban fringe areas of Jinan, whose experimental results are divided into three categories: high, high-medium, and high-medium-low confidence. The results show that NCBAs of high confidence share the highest F1 score and the best overall effect. Therefore, only NCBAs of high confidence are considered to be the final detection result by this method. Specifically, in NCBAs detection for three groups GF1 images in Jinan, the mean Recall of area-based and pixel-based assessment methods reach around 77% and 91%, respectively, the mean Pixel Accuracy (PA) 88% and 92%, and the mean F1 82% and 91%, confirming the effectiveness of this method on GF1. Similarly, the proposed method is applied to two groups of ZY302 images in Xi’an and Kunming. The scores of F1 for two groups of ZY302 images are also above 90% respectively, confirming the effectiveness of this method on ZY302. It can be concluded that adoption of area registration improves registration efficiency, and the joint use of prior rules and SVM classifier with probability features could avoid over and missing detection for NCBAs. In practical applications, this method is contributive to automatic NCBAs detection from high-resolution remote sensing images. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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21 pages, 7414 KiB

Open AccessArticle

Assessment of Landsat Based Deep-Learning Membership Analysis for Development of from–to Change Time Series in the Prairie Region of Canada from 1984 to 2018

by Darren Pouliot, Niloofar Alavi, Scott Wilson, Jason Duffe, Jon Pasher, Andrew Davidson, Bahram Daneshfar and Emily Lindsay

Remote Sens. 2021, 13(4), 634; https://doi.org/10.3390/rs13040634 - 10 Feb 2021

Cited by 5 | Viewed by 2755

Abstract

The prairie region of Canada is a dynamically changing landscape in relation to past and present anthropogenic activities and recent climate change. Improving our understanding of the rate, timing, and distribution of landscape change is needed to determine the impact on wildlife populations and biodiversity, ultimately leading to better-informed management regarding requirements for habitat amount and its connectedness. In this research, we assessed the viability of an approach to detect from–to class changes designed to be scalable to the prairie region with the capacity for local refinement. It employed a deep-learning convolutional neural network to model general land covers and examined class memberships to identify land-cover conversions. For this implementation, eight land-cover categories were derived from the Agriculture and Agri-Food Canada Annual Space-Based Crop Inventory. Change was assessed in three study areas that contained different mixes of grassland, pasture, and forest cover. Results showed that the deep-learning method produced the highest accuracy across all classes relative to an implementation of random forest that included some first-order texture measures. Overall accuracy was 4% greater with the deep-learning classifier and class accuracies were more balanced. Evaluation of change accuracy suggested good performance for many conversions such as grassland to crop, forest to crop, water to dryland covers, and most bare/developed-related changes. Changes involving pasture with grassland or cropland were more difficult to detect due to spectral confusion among classes. Similarly, conversion to forests in some cases was poorly detected due to gradual and subtle change characteristics combined with confusion between forest, shrub, and croplands. The proposed framework involved several processing steps that can be explored to enhance the thematic content and accuracy for large regional implementation. Evaluation for understanding connectivity in natural land covers and related declines in species at risk is planned for future research. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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15 pages, 17335 KiB

Open AccessTechnical Note

Assessing the Ability to Quantify Bathymetric Change over Time Using Solely Satellite-Based Measurements

by Joan Herrmann, Lori A. Magruder, Jonathan Markel and Christopher E. Parrish

Remote Sens. 2022, 14(5), 1232; https://doi.org/10.3390/rs14051232 - 2 Mar 2022

Cited by 11 | Viewed by 2665

Abstract

Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique. Full article

(This article belongs to the Special Issue Advances in Geospatial Data Analysis for Change Detection)

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