Research

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22 pages, 7845 KiB

Open AccessArticle

Transboundary Basins Need More Attention: Anthropogenic Impacts on Land Cover Changes in Aras River Basin, Monitoring and Prediction

by Sajad Khoshnoodmotlagh, Jochem Verrelst, Alireza Daneshi, Mohsen Mirzaei, Hossein Azadi, Mohammad Haghighi, Masoud Hatamimanesh and Safar Marofi

Remote Sens. 2020, 12(20), 3329; https://doi.org/10.3390/rs12203329 - 13 Oct 2020

Cited by 13 | Viewed by 4072

Abstract

Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict the LC composition of a major, transboundary [...] Read more.

Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict the LC composition of a major, transboundary basin contributing to the Caspian Sea, the Aras River Basin (ARB). To this end, four LC maps of ARB corresponding to the years 1984, 2000, 2010, and 2017 were generated using Landsat satellite imagery from Armenia and the Nakhchivan Autonomous Republic. The LC gains and losses, net changes, exchanges, and the spatial trend of changes over 33 years (1984–2017) were investigated. The most important drivers of these changes and the most accurate LC transformation scenarios were identified, and a land change modeler (LCM) was applied to predict the LC change for the years 2027 and 2037. Validation results showed that LCM, with a Kappa index higher than 81%, is appropriate for predicting LC changes in the study area. The LC changes observed in the past indicate significant anthropogenic impacts on the basin, mainly by constructing new reservoir dams and expanding agriculture and urban areas, which are the major water-consuming sectors. Results show that over the past 33 years, agricultural areas have grown by more than 57% from 1984 to 2017 in the study area. Results also indicate that the given similar anthropogenic activities will keep on continuing in the ARB, and agricultural areas will increase by 2% from 2017 to 2027, and by another 1% from 2027 to 2037. Results of this study can support transboundary decision-making processes to analyze potential adverse impacts following past policies with neighboring countries that share the same water resources. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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17 pages, 4734 KiB

Open AccessFeature PaperArticle

Quantifying Land Use Land Cover Changes in the Lake Victoria Basin Using Satellite Remote Sensing: The Trends and Drivers between 1985 and 2014

by Robinson Mugo, Rose Waswa, James W. Nyaga, Antony Ndubi, Emily C. Adams and Africa I. Flores-Anderson

Remote Sens. 2020, 12(17), 2829; https://doi.org/10.3390/rs12172829 - 01 Sep 2020

Cited by 33 | Viewed by 5229

Abstract

The Lake Victoria Basin (LVB) is a significant resource for five states within East Africa, which faces major land use land cover changes that threaten ecosystem integrity and ecosystem services derived from the basin’s resources. To assess land use land cover changes between [...] Read more.

The Lake Victoria Basin (LVB) is a significant resource for five states within East Africa, which faces major land use land cover changes that threaten ecosystem integrity and ecosystem services derived from the basin’s resources. To assess land use land cover changes between 1985 and 2014, and subsequently determine the trends and drivers of these changes, we used a series of Landsat images and field data obtained from the LVB. Landsat image pre-processing and band combinations were done in ENVI 5.1. A supervised classification was applied on 118 Landsat scenes using the maximum likelihood classifier in ENVI 5.1. The overall accuracy of classified images was computed for the 2014 images using 124 reference data points collected through stratified random sampling. Computations of area under various land cover classes were calculated between the 1985 and 2014 images. We also correlated the area from natural vegetation classes to farmlands and settlements (urban areas) to explore relationships between land use land cover conversions among these classes. Based on our land cover classifications, we obtained overall accuracy of 71% and a moderate Kappa statistic of 0.56. Our results indicate that the LVB has undergone drastic changes in land use land cover, mainly driven by human activities that led to the conversion of forests, woodlands, grasslands, and wetlands to either farmlands or settlements. We conclude that information from this work is useful not only for basin-scale assessments and monitoring of land cover changes but also for targeting, prioritizing, and monitoring of small scale, community led efforts to restore degraded and fragmented areas in the basin. Such efforts could mitigate the loss of ecosystem services previously derived from large contiguous land covers which are no longer tenable to restore. We recommend adoption of a basin scale, operational, Earth observation-based, land use change monitoring framework. Such a framework can facilitate rapid and frequent assessments of gains and losses in specific land cover classes and thus focus strategic interventions in areas experiencing major losses, through mitigation and compensatory approaches. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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25 pages, 37578 KiB

Open AccessArticle

Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops

by David Alejandro Jimenez-Sierra, Hernán Darío Benítez-Restrepo, Hernán Darío Vargas-Cardona and Jocelyn Chanussot

Remote Sens. 2020, 12(17), 2683; https://doi.org/10.3390/rs12172683 - 19 Aug 2020

Cited by 29 | Viewed by 4607

Abstract

The complementary nature of different modalities and multiple bands used in remote sensing data is helpful for tasks such as change detection and the prediction of agricultural variables. Nonetheless, correctly processing a multi-modal dataset is not a simple task, owing to the presence [...] Read more.

The complementary nature of different modalities and multiple bands used in remote sensing data is helpful for tasks such as change detection and the prediction of agricultural variables. Nonetheless, correctly processing a multi-modal dataset is not a simple task, owing to the presence of different data resolutions and formats. In the past few years, graph-based methods have proven to be a useful tool in capturing inherent data similarity, in spite of different data formats, and preserving relevant topological and geometric information. In this paper, we propose a graph-based data fusion algorithm for remotely sensed images applied to (i) data-driven semi-unsupervised change detection and (ii) biomass estimation in rice crops. In order to detect the change, we evaluated the performance of four competing algorithms on fourteen datasets. To estimate biomass in rice crops, we compared our proposal in terms of root mean squared error (RMSE) concerning a recent approach based on vegetation indices as features. The results confirm that the proposed graph-based data fusion algorithm outperforms state-of-the-art methods for change detection and biomass estimation in rice crops. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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22 pages, 7542 KiB

Open AccessArticle

A Method to Automatically Detect Changes in Multitemporal Spectral Indices: Application to Natural Disaster Damage Assessment

by Luca Pulvirenti, Giuseppe Squicciarino and Elisabetta Fiori

Remote Sens. 2020, 12(17), 2681; https://doi.org/10.3390/rs12172681 - 19 Aug 2020

Cited by 9 | Viewed by 2866

Abstract

This paper presents a new method, based on clustering and thresholding, to automatically perform binary change detection in multitemporal spectral indices. The method is denoted as Buffer-From-Cluster Approach (BFCA). To estimate the distributions of changed and unchanged pixels, as needed for the purpose [...] Read more.

This paper presents a new method, based on clustering and thresholding, to automatically perform binary change detection in multitemporal spectral indices. The method is denoted as Buffer-From-Cluster Approach (BFCA). To estimate the distributions of changed and unchanged pixels, as needed for the purpose of a reliable thresholding of a spectral index, a clustering algorithm is preliminarily applied to identify image objects possibly corresponding to areas where significant changes occurred. Then, a buffer zone is created around the selected cluster to identify unchanged areas surrounding changed ones. The cluster and the buffer zone are jointly analyzed to estimate the distributions of changed and unchanged pixels and to verify that they can be distinguished from each other. Finally, the results of thresholding and clustering are combined to generate the binary change map. The BFCA has been conceived to map the extent of the areas affected by a natural disaster like wildfire. To validate the proposed method, burned area maps produced by applying the BFCA to spectral indices derived from Sentinel-2 data have been compared to maps produced by the Copernicus Emergency Management Service. For testing the multi-hazard detection capability, the same kind of exercise has been carried out for a flooding test case too. The positive results of the comparison have confirmed the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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19 pages, 5088 KiB

Open AccessArticle

TCDNet: Trilateral Change Detection Network for Google Earth Image

by Junhao Qian, Min Xia, Yonghong Zhang, Jia Liu and Yiqing Xu

Remote Sens. 2020, 12(17), 2669; https://doi.org/10.3390/rs12172669 - 19 Aug 2020

Cited by 16 | Viewed by 3720

Abstract

Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use [...] Read more.

Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has three branches (a main module and two auxiliary modules, all of them are composed of convolutional neural networks (CNNs)), which focus on the overall information of bitemporal Google Earth image pairs, the changed areas and the unchanged areas, respectively. The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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19 pages, 3797 KiB

Open AccessArticle

Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data

by Minmin Huang and Shuanggen Jin

Remote Sens. 2020, 12(13), 2073; https://doi.org/10.3390/rs12132073 - 27 Jun 2020

Cited by 56 | Viewed by 6786

Abstract

Rapid flood mapping is crucial in hazard evaluation and forecasting, especially in the early stage of hazards. Synthetic aperture radar (SAR) images are able to penetrate clouds and heavy rainfall, which is of special importance for flood mapping. However, change detection is a [...] Read more.

Rapid flood mapping is crucial in hazard evaluation and forecasting, especially in the early stage of hazards. Synthetic aperture radar (SAR) images are able to penetrate clouds and heavy rainfall, which is of special importance for flood mapping. However, change detection is a key part and the threshold selection is very complex in flood mapping with SAR. In this paper, a novel approach is proposed to rapidly map flood regions and estimate the flood degree, avoiding the critical step of thresholding. It converts the change detection of thresholds to land cover backscatter classifications. Sentinel-1 SAR images are used to get the land cover backscatter classifications with the help of Sentinel-2 optical images using a supervised classifier. A pixel-based change detection is used for change detection. Backscatter characteristics and variation rules of different ground objects are essential prior knowledge for flood analysis. SAR image classifications of pre-flood and flooding periods both take the same input to make sense of the change detection between them. This method avoids the inaccuracy caused by a single threshold. A case study in Shouguang is tested by this new method, which is compared with the flood map extracted by Otsu thresholding and normalized difference water index (NDWI) methods. The results show that our approach can identify the flood beneath vegetation well. Moreover, all required data and data processing are simple, so it can be popularized in rapid flooding mapping in early disaster relief. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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21 pages, 4678 KiB

Open AccessArticle

The Impact of Spatiotemporal Changes in Land Development (1984–2019) on the Increase in the Runoff Coefficient in Erbil, Kurdistan Region of Iraq

by Andam Mustafa and Michał Szydłowski

Remote Sens. 2020, 12(8), 1302; https://doi.org/10.3390/rs12081302 - 20 Apr 2020

Cited by 30 | Viewed by 5531

Abstract

Nowadays, geospatial techniques are a popular approach for estimating urban flash floods by considering spatiotemporal changes in urban development. In this study, we investigated the impact of Land Use/Land Cover (LULC) changes on the hydrological response of the Erbil basin in the Kurdistan [...] Read more.

Nowadays, geospatial techniques are a popular approach for estimating urban flash floods by considering spatiotemporal changes in urban development. In this study, we investigated the impact of Land Use/Land Cover (LULC) changes on the hydrological response of the Erbil basin in the Kurdistan Region of Iraq (KRI). In the studied area, the LULC changes were calculated for 1984, 1994, 2004, 2014 and 2019 using the Digital Elevation Model (DEM) and satellite images. The analysis of LULC changes showed that the change between 1984 and 2004 was slower than that between 2004 and 2019. The LULC analysis revealed a 444.4% growth in built-up areas, with a 60.4% decrease in agricultural land between 1984 and 2019. The influence of LULC on urban floods caused by different urbanization scenarios was ascertained using the HEC-GeoHMS and HEC-HMS models. Over 35 years, there was a 15% increase in the peak discharge of outflow, from 392.2 m³/s in 1984 to 450 m³/s in 2014, as well as the runoff volume for a precipitation probability distribution of 10%, which increased from 27.4 mm in 1984 to 30.9 mm in 2014. Overall, the probability of flash floods increased in the center of the city due to the large expansion of built-up areas. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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19 pages, 3957 KiB

Open AccessArticle

Surface Water Evolution (2001–2017) at the Cambodia/Vietnam Border in the Upper Mekong Delta Using Satellite MODIS Observations

by Filipe Aires, Jean-Philippe Venot, Sylvain Massuel, Nicolas Gratiot, Binh Pham-Duc and Catherine Prigent

Remote Sens. 2020, 12(5), 800; https://doi.org/10.3390/rs12050800 - 02 Mar 2020

Cited by 13 | Viewed by 3714

Abstract

Studying the spatial and temporal distribution of surface water resources is critical, especially in highly populated areas and in regions under climate change pressure. There is an increasing number of satellite Earth observations that can provide information to monitor surface water at global [...] Read more.

Studying the spatial and temporal distribution of surface water resources is critical, especially in highly populated areas and in regions under climate change pressure. There is an increasing number of satellite Earth observations that can provide information to monitor surface water at global scale. However, mapping surface waters at local and regional scales is still a challenge for numerous reasons (insufficient spatial resolution, vegetation or cloud opacity, limited time-frequency or time-record, information content of the instrument, lack in global retrieval method, interpretability of results, etc.). In this paper, we use 17 years of the MODIS (MODerate-resolution Imaging Spectro-radiometer) observations at a 8-day resolution. This satellite dataset is combined with ground expertise to analyse the evolution of surface waters at the Cambodia/Vietnam border in the Upper Mekong Delta. The trends and evolution of surface waters are very significant and contrasted, illustrating the impact of agriculture practices and dykes construction. In most of the study area in Cambodia. surface water areas show a decreasing trend but with a strong inter-annual variability. In specific areas, an increase of the wet surfaces is even observed. Ground expertise and historical knowledge of the development of the territory enable to link the decrease to ongoing excavation of drainage canals and the increase of deforestation and land reclamation, exposing flooded surfaces previously hidden by vegetation cover. By contrast, in Vietnam, the decreasing trend in wet surfaces is very clear and can be explained by the development of dykes dating back to the 1990s with an acceleration in the late 2000s as part of a national strategy of agriculture intensification. This study shows that coupling satellite data with ground-expertise allows to monitor surface waters at mesoscale (<100 × 100 km²), demonstrating the potential of interdisciplinary approaches for water ressource management and planning. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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Review

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35 pages, 4708 KiB

Open AccessReview

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

by Wenzhong Shi, Min Zhang, Rui Zhang, Shanxiong Chen and Zhao Zhan

Remote Sens. 2020, 12(10), 1688; https://doi.org/10.3390/rs12101688 - 25 May 2020

Cited by 306 | Viewed by 27524

Abstract

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial [...] Read more.

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field. Full article

(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)

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