Change Detection In Areas Research Articles

Change Detection Using Multispectral Satellite Images for Sustainable Development in Regions of Al-Najaf Al-Ashraf, Iraq

The change detection in urban and surrounding areas, such as vegetation and bare, is a significant achievement for sustainable development, especially nowadays. The research conducted a supervisor classification of two images representing two time periods (1973 and 2023) using the method of land cover and land use (LCLU) using the ArcGIS 10.8.2 program. The classified pixels were converted into digital data to compare using the land cover method and land use change (LCLUG). The results showed a significant expansion in the urban areas of Najaf and Kufa districts, 5.72 km2 to 134.95 km2, with a decrease in the area of green areas, 17.26 km2 to 15.16 km2, and the absence of water areas, in addition to the transformation of most of the barren areas to urban areas 215.64 km2 to 88.51 km2, The study concluded that there was a significant decrease in the area of green areas in the study area and the absence of water bodies in urban areas, and these two indicators - along with the increase in urban areas - harm the environment of the region and the quality of environmental influences on the lives of the people who live in it. Which negatively affects its sustainable development plans.

Crop Land Change Detection with MC&N-PSPNet

To enhance the accuracy of agricultural area classification and enable remote sensing monitoring of agricultural regions, this paper investigates classification models and their application in change detection within rural areas, proposing the MC&N-PSPNet (CBAM into MobileNetV2 and NAM into PSPNet) network model. Initially, the HRSCD (High Resolution Semantic Change Detection) dataset labels undergo binary redrawing. Subsequently, to efficiently extract image features, the original PSPNet (Pyramid Scene Parsing Network) backbone network, ResNet50 (Residual Network-50), is substituted with the MobileNetV2 (Inverted Residuals and Linear Bottlenecks) model. Furthermore, to enhance the model’s training efficiency and classification accuracy, the NAM (Normalization-Based Attention Module) attention mechanism is introduced into the improved PSPNet model to obtain the categories of land cover changes in remote sensing images before and after the designated periods. Finally, the final change detection results are obtained by performing a different operation on the classification results for different periods. Through experimental analysis, this paper demonstrates the proposed method’s superior capability in segmenting agricultural areas, which is crucial for effective agricultural area change detection. The model achieves commendable performance metrics, including overall accuracy, Kappa value, MIoU, and MPA values of 95.03%, 88.15%, 93.55%, and 88.90%, respectively, surpassing other models. Moreover, the model exhibits robust performance in final change detection, achieving an overall accuracy and Kappa value of 93.24% and 92.29%, respectively. The results of this study show that the MC&N-PSPNet model has significant advantages in the detection of changes in agricultural zones, which provides a scientific basis and technical support for agricultural resource management and policy formulation.

A Trial of Urban Area Change Detection by Open Satellite Data Platform

A Trial of Urban Area Change Detection by Open Satellite Data Platform

A TomoSAR regularization-based method for height change detection in urban areas

Over the past years, urban change detection has always been a challenging issue for researchers. Due to the two-dimensional intrinsic property of satellite imageries and the inherent less intuitive interpretability of radar images, height change detection and SAR imagery-based change detection have been even more complicated tasks. However, TomoSAR-based height change detection is a novel topic on which only a few research works have ever been realized. This study explores the viability of tomographic-based change detection in urban areas, tackling the common challenges encountered in 2D change detection with SAR images. In contrast with the standard TomoSAR methods that usually suffer from the ill-posed inverse problem, the TomoSAR regularization-based methods are considered powerful aids for reconstructing the reflectivity function of the observed scenes. The TomoSAR regularization-based method utilizes the Constrained Least Squares (CLS) and the Weighted Constrained Least Squares (WCLS) as two efficient strategies for coming up with a well-posed solution to the prevalent ill-posed TomoSAR problem. They can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. This advantage helps enhance the accuracy of the results. The experimental results are investigated in terms of completeness and correctness measures to evaluate the feasibility and reliability of the proposed method. The proposed method for height change detection has been performed on two datasets acquired by the TerraSAR-X sensor in 2013 and the Sentinel-1 sensor in 2022 over Tehran City, Iran. The final results demonstrate that the proposed algorithm can detect height changes with good feasibility and reliability in terms of completeness and correctness of better than 84% and 77%, respectively.

Integrating bi-temporal VHR optical and long-term SAR images for built-up area change detection

ABSTRACT With the rapid expansion of urbanization, it is imperative to monitor built-up areas changes to promote the sustainable development of cities, aligning with the goals of Sustainable Development Goal 11(SDG 11). Remote sensing big data is valuable for automatically mapping these changes. Bi-temporal very high-resolution (Bi-VHR) optical images have been widely utilized for fine-grained change detection (CD). However, the significant spatiotemporal inconsistency due to imaging conditions and seasonal variations poses challenges for VHR optical CD. Unlike optical images, synthetic aperture radar (SAR) images are unaffected by atmospheric interference and provide robust spatiotemporal features as a supplement. Previous CD algorithms with SAR overlooked the exploration of long-term features of time series. In this study, we propose a novel CD framework combining long-term SAR with Bi-VHR images. It incorporates a spatial-frequency learning module to enhance SAR temporal features and a multisource feature fusion module to adaptively fuse the heterogeneous features. The experiments are conducted on OS-BCD dataset, which is the first dataset specifically designed for this task. The results demonstrate that our proposal outperforms advanced CD methods with F1 score, IoU and OA of 64.99%, 48.13%, and 95.11%, validating the efficacy of our proposal in accurately detecting changes in built-up areas.

Segmentation-based VHR SAR images built-up area change detection: a coarse-to-fine approach

Segmentation-based VHR SAR images built-up area change detection: a coarse-to-fine approach

Leveraging Diffusion Modeling for Remote Sensing Change Detection in Built-Up Urban Areas

Leveraging Diffusion Modeling for Remote Sensing Change Detection in Built-Up Urban Areas

Use of LiDAR drone data for O-mapping and change detection in mountainous areas

Use of LiDAR drone data for O-mapping and change detection in mountainous areas

Monitoring Urban Growth and Change Detection in Built-up Areas with High-Resolution Satellite Imageries

Monitoring Urban Growth and Change Detection in Built-up Areas with High-Resolution Satellite Imageries

Surface area change detection of snow/ice cover over Zemu Glacier of Sikkim Himalaya

This study assessed the snow/ice surface area changes over the Zemu Glacier in the Eastern Himalayas. Zemu is considered to be the largest glacier in the Eastern Himalayas located in the Sikkim State of India. Change detection in the snow/ice surface areal extent of the Zemu Glacier was delineated from the year 1945 using US Army Map Service-Topographical Sheets and Landsat imageries available from 1987 to 2020. The results obtained solely focus on surface-change delineation using remote sensing satellite data and GIS software. Landsat imagery of the years 1987, 1997, 2009, 2018, and 2020 was used for snow and ice pixel extraction. Normalized Difference Snow Index (NDSI), Snow Cover Index (S3), and new band ratio index were used to extract the pure snow and ice pixels, fresh snow, and debris-covered snow/ice area and pixels mixed with shadow to demarcate the surface area changes. Manual delineation was required and undertaken for better results. A slope raster image was generated from Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data to delineate the slope and hill shade. The results illustrate that the snow/ice surface covering the glacier had an area of 113.5km2 in the year 1945 which was reduced to 78.31km2 in the year 2020 indicating a total snow/ice surface area loss of 31% in 75years. A loss of 11.45% was observed from 1945 to 1987 in the areal extent, while from 1987 to 2009, a decadal loss of approximately 7% was observed. The surface area loss of 8.46% from 2009 to 2018 led to infer the maximum loss of snow and ice over the glacier body at a rate of 0.94% per year. Between the years 2018 and 2020, the glacier lost 1.08% of its surface area. The Accumulation Area Ratio (AAR) considering the accumulation and ablation areas of the glacier indicated a gradual decline of the accumulation zone in the recent years. Global Land Ice Measurement from Space (GLIMS) program data with RGI version 6.0 was used as a reference to demarcate the areal extent of Zemu Glacier. The study gained above 80% of overall accuracy by generating a confusion matrix in ArcMap. The analysis of this seasonal snow/ice cover showed that there has been a drastic decline in the surface snow/ice cover area over the Zemu Glacier over the years 1987 to 2020 and NDSI; S3 analysis provided improved results to delineate snow/ice cover area over such steep terrain topography of Sikkim Himalaya.

Application of global mapping indices for changedetection in coal mining areas of Salanpur block ofPaschim Bardhaman district, West Bengal

The article is about the application of global mapping indices for detecting changes in vegeta-tion cover, water bodies, bare soil, and built-up areas using the Normalized Difference Vegeta-tion Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Soil Index (NDSI), Normalized Difference Water Index (NDWI) and the Global Environment Moni-toring Index (GEMI). In this study, Landsat remote sensing imagery was used to quantify change detection in coal mining areas of the Salanpur block. The aim was to analyze different landcover types using Landsat imagery with a twenty-year interval from 2001 to 2021. The mineral-bearing areas have been identified as one of the global sources of income but the research carried out from such resource-rich areas revealed that these areas experienced a rapid loss of vegetation cover and other environmental degradation due to the gigantic expansion of mining projects. These indices are used to extract information concerning land issues due to anthropo-genic activities. The results obtained from these indices in this study show conversions of vegetation land when the land was put to some excavation activities or other developmental work. This study shows an effort to monitor major entities like water bodies, vegetation cover, bare ground, and urban areas. It also shows the suitability of these mapping indices for detecting changes concerning the mining environment.

Spatio-temporal Change Detection of Built-Up Areas with Sentinel-1 SAR Data Using Random Forest Classification Arnavutköy Istanbul

As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BA of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BA footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. The changes experienced with the spatial-temporal data were determined, and causes and effects were investigated. In order to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

AbstractTerraces are an important cultivated land resource. Terrace abandonment affects the soil quality, soil and water conservation benefits, and biodiversity of terraces. Therefore, it is important to quantify the number and spatial distribution of abandoned terraces to protect cultivated land and food security. However, the traditional remote sensing method cannot identify small plots and make accurate assessment of abandoned farmland quickly in mountainous areas. To accurately identifying abandoned terraces, this study used semantic segmentation based on deep learning and change detection to identify abandoned terraces and their spatial distribution in a small watershed on the Loess Plateau in 2021. A comparative analysis of the accuracy of three deep learning models revealed that RefineNet is superior to DeepLabv3+ and DeepLabv3 for identifying abandoned terraces. The user's accuracy, producer's accuracy, overall accuracy, and appa values for RefineNet were 0.817, 0.894, 0.800, and 0.539, respectively. For change detection, the corresponding values were 0.821, 0.753, 0.731, and 0.426, respectively. In addition, semantic segmentation produced better recognition results than change detection in complex terrain and geomorphological areas. The abandoned terraces in the study area were mainly distributed in mountainous areas far from residential areas and more likely at high elevations with large slopes. This study provides a new method for recognizing abandoned terraces and spatial distribution information for managing and utilizing abandoned terraces.

STCD-EffV2T Unet: Semi Transfer Learning EfficientNetV2 T-Unet Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images

Change detection in urban areas can be helpful for urban resource management and smart city planning. The effects of human activities on the environment and ground have gained momentum over the past decades, causing remote sensing data sources analysis (such as satellite images) to become an option for swift change detection in the environment and urban areas. We proposed a semi-transfer learning method of EfficientNetV2 T-Unet (EffV2 T-Unet) that combines the effectiveness of composite scaled EfficientNetV2 T as the first path or encoder for feature extraction and convolutional layers of Unet as the second path or decoder for reconstructing the binary change map. In the encoder path, we use EfficientNetV2 T, which was trained by the ImageNet dataset. In this research, we employ two datasets to evaluate the performance of our proposed method for binary change detection. The first dataset is Sentinel-2 satellite images which were captured in 2017 and 2021 in urban areas of northern Iran. The second one is the Onera Satellite Change Detection dataset (OSCD). The performance of the proposed method is compared with YoloX-Unet families, ResNest-Unet families, and other well-known methods. The results demonstrated our proposed method’s effectiveness compared to other methods. The final change map reached an overall accuracy of 97.66%.

Rapid online plant leaf area change detection with high-throughput plant image data

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

Mapping Area Changes of Glacial Lakes Using Stacks of Optical Satellite Images

Glacial lakes are an important and dynamic component of terrestrial meltwater storage, responding to climate change and glacier retreat. Although there is evidence of rapid worldwide growth of glacial lakes, changes in frequency and magnitude of glacier lake outbursts under climatic changes are not yet understood. This study proposes and discusses a method framework for regional-scale mapping of glacial lakes and area change detection using large time-series of optical satellite images and the cloud processing tool Google Earth Engine in a semi-automatic way. The methods are presented for two temporal scales, from the 2-week Landsat revisit period to annual resolution. The proposed methods show how constructing an annual composite of pixel values such as minimum or maximum values can help to overcome typical problems associated with water mapping from optical satellite data such as clouds, or terrain and cloud shadows. For annual-resolution glacial lake mapping, our method set only involves two different band ratios based on multispectral satellite images. The study demonstrates how the proposed method framework can be applied to detect rapid lake area changes and to produce a complete regional-scale glacial lake inventory, using the Greater Caucasus as example.

Nonlocal feature learning based on a variational graph auto-encoder network for small area change detection using SAR imagery

Synthetic aperture radar (SAR) image change detection is a challenging task due to inherent speckle noise, imbalanced class occurrence and the requirement for discriminative feature learning. The traditional handcrafted feature extraction and current convolution-based deep learning techniques have some advantages, but suffer from being limited to neighborhood-based spatial information. The nonlocally observable imbalance phenomenon that exists naturally in small area change detection has presented a huge challenge to methods that focus on local features only. In this paper, an unsupervised method based on a variational graph auto-encoder (VGAE) network was developed for object-based small area change detection using SAR images, with the advantages of alleviating the negative impact of class imbalance and suppressing speckle noise. The main steps include: 1) Three types of difference image (DI) are combined to establish a three-channel fused DI (TCFDI), which lays the data-level foundation for subsequent analysis. 2) Simple linear iterative clustering (SLIC) is used to divide the TCFDI into superpixels regarded as nodes. Two functions are proposed and developed to measure the similarity between nodes to build a weighted undirected graph. 3) A VGAE network is designed and trained using the graph and nodes, and high-level nonlocal feature representations of each node are extracted. The network, with a Gaussian Radial Basis Function constrained by geospatial distances, establishes the connection among nonlocal, but similar superpixels in the process of feature learning, which leads to speckle noise suppression and distinguishable features learned in latent space. The nodes are then identified as changed or unchanged classes via k-means clustering. Five real SAR datasets were used in comparative experiments. Up to 99.72% accuracy was achieved, which is superior to state-of-the-art methods that pay attention only to local information, thus, demonstrating the effectiveness and robustness of the proposed approach.

Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

Coconut area mapping and change detection analysis of coconut growing areas of Coimbatore and Tirupur istricts of Tamil Nadu, India

Coconut, an important perennial tree largely grown in Tamil Nadu to the extent of 4.61 lakh hectares. Inthis study, attempt has been made to create a spatial data base on spatial changes of coconut acreage inCoimbatore and Tiruppur districts of Tamil Nadu. The methodology involves digital classification of satelliteimage and identifying coconut plantations and creation of spatial database of coconut area in Coimbatoreand Tiruppur districts for the period 2019 and compare the same with 2013 data. Total area of coconutunder Coimbatore and Tiruppur was found to be 80,065 and 58,357 ha respectively. A comparison betweenarea obtained from this study and the area statistics released by Coconut Development Board data wasdone using paired t-test and the p-value was 0.005, which showed that there was no significant differencebetween the two data and the RMSE value was also found to be low, which indicated that this methodologycan be adopted for mapping the coconut area. Change detection analysis showed that the area decreased in Coimbatore to the tune of 5.4 per cent, whereas there was not much change in Tiruppur district and reduction in area was only 0.2 per cent. The accuracy was worked out by comparing the assessment based on the Remote sensing data and the government estimates which showed a difference of 9.54 per cent indicating the accuracy of 90.46 per cent.

The spectralrao-monitoring Python package: A RAO's Q diversity index-based application for land-cover/land-use change detection in multifunctional agricultural areas

• The Rao's Q diversity index is used to detect LCLU changes in multifunctional agricultural areas. • We developed and provide a Rao’s Q diversity index-based open-source Python application: spectralrao-monitoring . • We tested this application with Landsat 8 satellite data. • Overall accuracies of our three tested methodological approaches range from 0.88 to 0.92. • The study area is part of the largest Portuguese multifunctional farmstead. Monitoring multifunctional agricultural areas is paramount to ensure their cost-effective management. The remote sensing-based detection of land-cover/land-use (LCLU) changes and analysis of vegetation dynamics constitute a relevant indicator to support robust monitoring schemes, allowing the control of agri-environmental conditions and enforcing related measures and policies. The Rao's Q diversity index (RaoQ) is frequently used to measure functional diversity in ecology, thanks to the textural analysis of the environment. This paper aims to develop and provide an open-source Python application whose workflow may constitute a RaoQ-based LCLU change monitoring tool for multifunctional agricultural areas. Here, a use case is presented for detecting and mapping LCLU changes leveraging the free and open access Landsat 8 (L8) satellite data. The workflow is organized in four main stages: (1) data processing; (2) Normalized Difference Vegetation Index (NDVI) calculation; (3) RaoQ calculation; and (4) detection and mapping of LCLU changes through thresholding of RaoQ. Three methodological approaches were developed (RaoC – “classic” RaoQ; RaoMD – “multidimensional” RaoQ, and “classic + multidimensional” RaoQ) with overall accuracies ranging from 0.88 to 0.92. An example of an agri-environmental monitoring decision-support framework based on spectralrao-monitoring is presented. The application is easily reproducible, and the code is fully available and utilizable with other sensors at different resolutions to support monitoring other types of agricultural areas.