Detecting and Monitoring the Vegetal Cover of Karbala Province (Iraq) Using Change Detection Methods

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060367 ◽

2021 ◽

Vol 10 (6) ◽

pp. 367

Author(s):

Simoni Alexiou ◽

Georgios Deligiannakis ◽

Aggelos Pallikarakis ◽

Ioannis Papanikolaou ◽

Emmanouil Psomiadis ◽

...

Keyword(s):

Soil Erosion ◽

Change Detection ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Mean Value ◽

Detection Methods ◽

Mountainous Catchments ◽

Aerial Vehicle ◽

Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

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Navigational Data Change Detection Based on Rasterizing Features & Attributes

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.256-259.2279 ◽

2012 ◽

Vol 256-259 ◽

pp. 2279-2284

Author(s):

Lian Ying Li ◽

Zhang Huang ◽

Xiao Lan Xu

Keyword(s):

Change Detection ◽

Detection Methods ◽

Vehicle Navigation ◽

Digital Map ◽

Navigation Data ◽

Data Production ◽

History Of ◽

Map Data ◽

Data Updating

A necessary updating degree is vital for the digital map data in a vehicle navigation system. Only when the digital map data are well updated, can the quality of the navigation be assured. Today the companies devoting to the production of digital map data for vehicle navigation have to cost much labor, material and capital to collect and update data in order to maintain a necessary updating degree. Throughout the history of electronic navigation data updating, they have made considerable progress both on the methods and processes of data production, and the way of map management. Updating from the CD to the network, from the wired to the wireless, from the replacing to the incremental way, each of the technical changes is a power source to enhance the data updating rate. As we all know, the change detection is a prerequisite and base for the electronic navigation data updating. By rapidly developing the area with changes and using the appropriate updating method, we can scientifically maintain the original database of navigation data and terminal physical data. In view of this, starting from application needs for dynamic data updating, this paper analyses change detection methods of navigation data in different versions used for generating incremental data, and focuses on that of rasterizing features and attributes, exploring a new approach to quickly get the incremental data between versions.

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Persistent Homology for Land Cover Change Detection

Oxford Research Encyclopedia of Natural Hazard Science ◽

10.1093/acrefore/9780199389407.013.366 ◽

2021 ◽

Author(s):

Djamel Bouchaffra ◽

Faycal Ykhlef

Keyword(s):

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Vegetation Index ◽

Homology Theory ◽

Urban Morphology ◽

Detection Methods ◽

Landsat Images ◽

Data Repositories ◽

Land Cover Change Detection

The need for environmental protection, monitoring, and security is increasing, and land cover change detection (LCCD) can aid in the valuation of burned areas, the study of shifting cultivation, the monitoring of pollution, the assessment of deforestation, and the analysis of desertification, urban growth, and climate change. Because of the imminent need and the availability of data repositories, numerous mathematical models have been devised for change detection. Given a sample of remotely sensed images from the same region acquired at different dates, the models investigate if a region has undergone change. Even if there is no substantial advantage to using pixel-based classification over object-based classification, a pixel-based change detection approach is often adopted. A pixel can encompass a large region, and it is imperative to determine whether this pixel (input) has changed or not. A changed image is compared to the available ground truth image for pixel-based performance evaluation. Some existing change detection systems do not take into account reversible changes due to seasonal weather effects. In other words, when snow falls in a region, the land cover is not considered as a change because it is seasonal (reversible). Some approaches exploit time series of Landsat images, which are based on the Normalized Difference Vegetation Index technique. Others evaluate built-up expansion to assess urban morphology changes using an unsupervised approach that relies on labels clustering. Change detection methods have also been applied to the field of disaster management using object-oriented image classification. Some methodologies are based on spectral mixture analysis. Other techniques invoke a similarity measure based on the evolution of the local statistics of the image between two dates for vegetation LCCD. Probabilistic approaches based on maximum entropy have been applied to vegetation and forest areas, such as Hustai National Park in Mongolia. Researchers in this field have proposed an LCCD scheme based on a feed-forward neural network using backpropagation for training. This paper invokes the new concept of homology theory, a subfield of algebraic topology. Homology theory is incorporated within a Structural Hidden Markov Model.

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Finding Underground Targets by Means of Change-Detection Methods in Huynen Spaces

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.2010.5545197 ◽

2010 ◽

Vol 46 (3) ◽

pp. 1404-1421 ◽

Cited By ~ 6

Author(s):

Firooz A. Sadjadi ◽

Anders Sullivan ◽

Guillermo C. Gaunaurd

Keyword(s):

Change Detection ◽

Detection Methods

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Review ArticleDigital change detection methods in ecosystem monitoring: a review

International Journal of Remote Sensing ◽

10.1080/0143116031000101675 ◽

2004 ◽

Vol 25 (9) ◽

pp. 1565-1596 ◽

Cited By ~ 1205

Author(s):

P. Coppin ◽

I. Jonckheere ◽

K. Nackaerts ◽

B. Muys ◽

E. Lambin

Keyword(s):

Change Detection ◽

Detection Methods ◽

Ecosystem Monitoring

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Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

Remote Sensing ◽

10.3390/rs11020142 ◽

2019 ◽

Vol 11 (2) ◽

pp. 142 ◽

Cited By ~ 10

Author(s):

Wenping Ma ◽

Hui Yang ◽

Yue Wu ◽

Yunta Xiong ◽

Tao Hu ◽

...

Keyword(s):

Change Detection ◽

Representation Learning ◽

Local Information ◽

Learning Ability ◽

Detection Methods ◽

Detection Accuracy ◽

Image Block ◽

Sar Images ◽

Difference Image ◽

Multi Scale

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.

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TCDNet: Trilateral Change Detection Network for Google Earth Image

Remote Sensing ◽

10.3390/rs12172669 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2669

Author(s):

Junhao Qian ◽

Min Xia ◽

Yonghong Zhang ◽

Jia Liu ◽

Yiqing Xu

Keyword(s):

Deep Learning ◽

Change Detection ◽

Remote Sensing Data ◽

Semantic Segmentation ◽

Google Earth ◽

Detection Methods ◽

Global Information ◽

Supervised Methods ◽

Image Pairs ◽

Main Module

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.

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Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

Remote Sensing ◽

10.3390/rs10121987 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1987 ◽

Cited By ~ 5

Author(s):

Rocío Ramos-Bernal ◽

René Vázquez-Jiménez ◽

Raúl Romero-Calcerrada ◽

Patricia Arrogante-Funes ◽

Carlos Novillo

Keyword(s):

Change Detection ◽

Vegetation Index ◽

Thermal Emission ◽

Principal Component ◽

Detection Methods ◽

Detection Accuracy ◽

Data Types ◽

Statistical Parameters ◽

Wide Range ◽

The World

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

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