Cover Change Detection Research Articles

Self-supervision assisted multimodal remote sensing image classification with coupled self-looping convolution networks

Recently, remote sensing community has seen a surge in the use of multimodal data for different tasks such as land cover classification, change detection and many more. However, handling multimodal data requires synergistically using the information from different sources. Currently, deep learning (DL) techniques are being religiously used in multimodal data fusion owing to their superior feature extraction capabilities. But, DL techniques have their share of challenges. Firstly, DL models are mostly constructed in the forward fashion limiting their feature extraction capability. Secondly, multimodal learning is generally addressed in a supervised setting, which leads to high labelled data requirement. Thirdly, the models generally handle each modality separately, thus preventing any cross-modal interaction. Hence, we propose a novel self-supervision oriented method of multimodal remote sensing data fusion. For effective cross-modal learning, our model solves a self-supervised auxiliary task to reconstruct input features of one modality from the extracted features of another modality, thus enabling more representative pre-fusion features. To counter the forward architecture, our model is composed of convolutions both in backward and forward directions, thus creating self-looping connections, leading to a self-correcting framework. To facilitate cross-modal communication, we have incorporated coupling across modality-specific extractors using shared parameters. We evaluate our approach on three remote sensing datasets, namely Houston 2013 and Houston 2018, which are HSI-LiDAR datasets and TU Berlin, which is an HSI-SAR dataset, where we achieve the respective accuracy of 93.08%, 84.59% and 73.21%, thus beating the state of the art by a minimum of 3.02%, 2.23% and 2.84%.

Land Use Land Cover Change Detection of Oil Exploration and Production in Melut County, Upper Nile State, South Sudan

This study explores the Land Use Land Cover Change (LULC) of oil industry on environment and local communities in Melut County located in Upper Nile State in South Sudan; as well as engaging the stakeholders in oil sectors to adopt the best techniques of sustainable oil exploration and production in the area. This research is based on remote sensing imageries collected for 2000 and 2018. The satellite imageries data were collected from Landsat-5 TM and Landsat-8 ETM+ for the two points. These satellite imageries were used to reach inaccessible areas in the Area. ENVI 5.1 software was used to analyze the satellite imageries. The results reveal tremendously significant environmental degradation as a result of land use changes over time that leads to deterioration of the livelihoods of the oil producing communities in Melut County. The main contributions to LULC were from exploration and production activities such as drilling, and oil processing. Produced water and oil well pads were the major catastrophes that lead to destruction of croplands, damage of vegetation cover, and contamination of surface and ground water in the area. There are inadequate social services in the areas around oil fields; hence, the areas are characterized by poverty, lack of access to resources, unimproved social amenities and vulnerability to the poor unhealthy conditions in the oil environment. Furthermore, the government should regulate the oil industry by scrutinizing any environmental violation, and heavily fine the non-complying individuals and companies.

Automatic forest change detection through a bi-annual time series of satellite imagery: Toward production of an integrated land cover map

Land cover mapping is fundamental for national and international agencies to monitor forest resources. However, monitoring forest disturbances by direct comparison of these maps poses several difficulties and challenges. As a result, different methodologies have been explored to detect forest disturbances. However, most of them cannot be fully integrated with land cover map production since they require additional input data, while others are not suitable for monitoring small land parcels. This study presents a methodology that fulfils the need to integrate land cover mapping with land cover change detection. Specifically, this methodology was designed to complement the Sentinel-2-based land cover mapping used in Galicia, northwest Spain, a region characterized by small land parceling. First, two previously obtained land cover maps from 2019 and 2020 were compared to identify all the pixels with potential land cover changes using QGIS. The behavior of spectral indexes in a time series were then analyzed to identify which of the previously identified pixels correspond to forest disturbances. This step was implemented in the software R. Using the Normalized Difference Vegetation Index (NDVI) to detect different land cover changes it was obtained an overall accuracy of 82%, considering the existence of varying phenologies, diverse topographic conditions, and areas with a high level of stand fragmentation. This study could help agencies that have already developed their own land cover maps to easily advance the integration of their maps with land cover change detection, since this technique can be applied with any land cover mapping methodology based on multitemporal analysis of satellite images, without the need for additional input data.

Land Use / Land Cover Change Detection and Its Driving Factors in <i>Suluh</i> River Basin, Northern Highland Part of Ethiopia

The <i>Suluh</i> river basin is subjected to land use and land cover change due to population pressure, improper farming practices, lack of alternative non-farm activities, and rugged topography. Yet, land use/land cover change detection and its driving factors have not been applied in the study area. Thus, the current study detected land use and land use change and identified the drivers for it in the <i>Suluh</i> river basin, the northern highland part of Ethiopia. Landsat image data and Ancillary data sources were used to achieve the objectives. With the aid software’s of <i>eCognition</i> Developer 9.2 and IDRISI <i>Selva</i> 17.3, images were classified and changes were detected. Both qualitative and quantitative data were analyzed. According to the study's findings, between 1990 and 2018, cultivated land expanded by 7.98%, plantation land by 43.7%, built up land by 135.5%, and bar land by 9.8%. A decline trend was found to exist for water bodies by 79.6%, pasture land by 48.6%, shrub and bush land by 61.7%, and forest land by 576.7%. Thus, in order to implement sustainable land management practices in the <i>Suluh</i> river basin, land use planners should take into account information about land use and land cover change, as well as the corresponding drivers.

V-BANet: Land cover change detection using effective deep learning technique

Urban development alters landscapes, frequently degrading environmental services and quality of life. High-resolution remote sensing images provide a chance to detect subtle changes in land cover and can capture the features of a ground object. However, traditional approaches usually experience difficulties when processing large and quickly expanding datasets, low levels of automation, limited computational efficiency, and inconsistent identification accuracies and standards brought on by inconsistent operators. Conducting change detection in a more accurate, automated, and standardized manner has become crucial and increasingly difficult due to the quick collection of remote sensing data. Therefore, in this paper, V-Net and Bilateral Attention Network (V-BANet) based deep learning is implemented to segment the landscapes and extract the features from the images. Initially, the bi-temporal images are segmented using V-Net to independently identify the objects in each image. Then spatial and channel attention blocks are employed in Bilateral Attention Network to learn more discriminative features from the images. Finally, the features' relationships are discovered by contrasting the original feature map in one image with the updated feature map in the other. Objective and subjective experiments are performed on a public bi-temporal high-resolution ONERA Satellite Change Detection (OSCD) dataset and the LEVIR-CD dataset. Moreover, the proposed approach reached an accuracy of 99.29% and IoU of 98.31% with the OSCD Dataset and 99.42% accuracy and 98.83% IoU with the LEVIR-CD Dataset. The experimental outcomes with each specified dataset demonstrated that the suggested methodology outperformed several state-of-the-art techniques and produced superior results.

Spatiotemporal change analysis of land use/land cover in NCT of Delhi, India using geospatial technology

Increasing anthropological and economic activity has transformed human–environment connections. Using remote sensing and geographic information system (GIS), severe issues connected to rapid growth, such as supplemental infrastructure, informal residents, demolition of ecological construction, shortage of natural resources, and environmental contamination, have been studied for a fast-growing metropolis like Delhi. Industrialization accelerates urbanization, resulting in land transformations, which are one of the major uses of natural resources. In a fast-growing city like Delhi, the development has been rapid, and it is important to investigate the factors behind these shifts. For the present study, remote sensing and GIS models are used for land use and land cover (LULC) change detection. The applied model in the study uses the satellite datasets of two different satellites, i.e., Landsat 5 (TM) of 2010 and Landsat 8 (OLI) of 2021, from November with little or no cloud cover, which could block the LULC features. The study compares the temporal LULC map and analyzes the change and increase in urbanization for two periods, 2010 and 2021. In the study, it is revealed that urbanization causes constant shifts in vegetation, built-up area ratio, and land-use patterns. To confirm the same, indices like the normalized difference vegetation index and the normalized difference building index are also studied. Inequitable land use is a major contributor to environmental degradation. The spatial datasets from two time periods used in the study and the database results are helpful in extensive LULC investigations, land use planning, spatial growth, and urbanization patterns in the NCT of Delhi. Further, the change detection model used in the study was supported by the standard accuracy assessment of the Kappa coefficient. Overall accuracy in 2010 was 89.52% with a Kappa statistic of 0.863 and 89.92% with a Kappa statistic of 0.868 in 2021. Such studies using remote sensing and GIS are extremely helpful in understanding and monitoring urban sprawl patterns.

Assessment of land use/land cover change detection of Mettupalayam Taluk, Coimbatore district, Tamil Nadu, India, using RS and GIS

Understanding changes in land use land cover is essential managing and monitoring natural resources and development, particularly where urbanization is expanding. In present study, describes land use/land cover (LU/LC) mapping and change detection analysis of Mettupalayam taluk of Coimbatore district in Tamil Nadu. Estimation in ArcGIS for LU/LC classification from LANSAT images, is the best method for classifier for the different features were used to classify the satellite images are used to classify the land use/land cover change s for the given period. The results indicated that land cover changes have occurred in slight decrease in forests, vegetation and mountain and a corresponding tremendous increase in settlement. The comparison of LU/LC in 2000 and 2019 derived from topo-sheet and satellite imagery interpretation indicates that the settlement area shows variations. Settlement is an area of human habitation, which has a cover of buildings and a network of transport and other civic amenities, due to increasing population and the land is converted into habitation. The (Urbanization) settlement/ built-up areas have increased by 7.29% in 2000 while in 2019 it has increased to 16.43 %. It is found urban area increased due to population growth cum rapid economic progress.

Assessment of land use/land cover change detection of Mettupalayam Taluk, Coimbatore district, Tamil Nadu, India, using RS and GIS

Assessment of land use/land cover change detection of Mettupalayam Taluk, Coimbatore district, Tamil Nadu, India, using RS and GIS

Monitoring Land Cover Change by Leveraging a Dynamic Service-Oriented Computing Model

Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users’ requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection.

Land use / land cover change detection analysis through geospatial technology in Jhansi district of Bundelkhand region

Land use / land cover change detection analysis through geospatial technology in Jhansi district of Bundelkhand region

Self-structured pyramid network with parallel spatial-channel attention for change detection in VHR remote sensed imagery

Land cover change detection (CD) in very-high-resolution (VHR) images is still impeded by weak pattern separability and land cover complexity. To address these challenges, a self-structured pyramid network (S2PNet) with a parallel spatial-channel attention mechanism (PSAM) and a self-structured feature pyramid (SFP) is proposed for a finer annotation of changed land cover. The proposed PSAM refines the features of different levels in dual-branch coordinated by running parallel without mutual influence for a better recognition of varied objects, which can lead to less incorrectly detected land cover. And the SFP integrates the embedded multi-scale features to acquire an improved cognition over multi-scale objects, which can contribute to a more complete annotation over diverse objects. All-round experiments over several widely used open large-scale VHR CD data sets are carried out, which indicate the efficiency and effectiveness of the proposed method. Related comparisons suggest that the proposed method can achieve higher performance over several existing state-of-the-art CD methods. The source codes will be released at https://github.com/HaiXing-1998/S2PNet-CD.

Tunicate Swarm Algorithm with Deep Learning Based Land Use and Cover Change Detection in Nallamalla Forest India

Every biological system on the planet is severely impacted by environmental change, and its primary driver is deforestation. Meanwhile, quantitative analysis of changes in Land Use and Land Cover (LULC) is one of the prominent ways to manage and understand land transformation; thus, it is essential to inspect the performance of various techniques for LULC mapping to recognize the better classifier to more applications of earth observation. This article develops a Tunicate Swarm Algorithm with Deep Learning Enabled Land Use and Land Cover Change Detection (TSADL-LULCCD) technique in Nallamalla Forest, India. The presented TSADL-LULCCD technique mainly focuses on the identification and classification of land use in the Nallamalla forest using LANDSAT images. To accomplish this, the presented TSADL-LULCCD technique employs a dense EfficientNet model for feature extraction. In addition, the Adam optimizer is applied for the optimal hyper parameter tuning of the dense EfficientNet approach. For land cover classification, the TSADL-LULCCD technique exploits the Deep Belief Network (DBN) approach. To tune the hyper parameters related to the DBN system, the TSA is used. The experimental validation of the TSADL-LULCCD algorithm is tested on LANDSAT-7-based Nallamalla region images. The experimental results stated that the TSADL-LULCCD technique exhibits better performance over other existing models in terms of different evaluation measures.

A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing.

Hydroelectric power dam-induced land use land cover change in Ethiopia, the case of AMerti-Nashe dams Horo Guduru Wollega Zone

ABSTRACT We examined dam-induced land use and land cover change (LULCC) detection in the Amerti-Nashe hydroelectric power reservoirs. These dams were constructed for a variety of purposes, primarily for irrigation in the lower reaches and hydropower generation in the upper reaches. The main objective of the study was to investigate dam-induced LULCC in the study area over the last 41 years. Satellite images were downloaded from the United States Geological Survey (USGS). The data from remote sensing satellite images were used to reflect the spatial variation in LULCC, and land surface temperature (LST) in the study areas near hydropower plants in the years 1985, 2003, and 2021. The supervised classification method is adopted, and the maximum likelihood rule is used as the parameter rule for image classification. The land use classification results show that grazing land, woody grassland, and cultivated land gradually decreased, which could be associated with the establishment of dams. But commercial farming, waterbodies, and built-up areas increased during these study periods. LST variations based on its relationship with Land Use Land Cover (LULC) in the area decreased after the dam was constructed, particularly in the irrigated lowland and places inundated by water from the reservoir in the upper stream. Generally, the construction of the Amerti-Nashe hydroelectric dam projects in the study area has changed land use type and LST both at a lower and upper stream. Therefore, hydropower development in the area needs strategic planning on how to minimize land use land cover change impacts on the environment and the local community.

The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan

Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation.

Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery

Today's enormous amounts of freely available high-resolution satellite imagery provide the demand for effective preprocessing methods. One such preprocessing method needed in many applications utilizing optical satellite imagery from the Landsat and Sentinel-2 archives is mosaicking. Merging hundreds of single scenes into a single satellite data mosaic before conducting analysis such as land cover classification, change detection, or modelling is often a prerequisite. Maintaining the original data structure and preserving metadata for further modelling or classification would be advantageous for many applications. Furthermore, in other applications, e.g., connected to land cover classification creating the mosaic for a specific period matching the phenological state of the phenomena in nature would be beneficial. In addition, supporting in-house and computing centers not directly connected to a specific cloud provider could be a requirement for some institutions or companies. In the current work, we present a method called Geomosaic that meets these criteria and produces analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery.•The method described produces analysis-ready satellite data mosaics.•The satellite data mosaics contain pixel metadata usable for further analysis.•The algorithm is available as an open-source tool coded in Python and can be used on multiple platforms.

Multiresolution-Based Rough Fuzzy Possibilistic C-Means Clustering Method for Land Cover Change Detection

Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.

Multiscale Attention Network Guided With Change Gradient Image for Land Cover Change Detection Using Remote Sensing Images

Learning performance is unsatisfactory when training deep-learning networks without prior-knowledge guidance. In this paper, a multi-scale change detection neural network guided by a change gradient image (CGI) was proposed. First, a multi-scale information attentional module was embedded in the backbone of UNet to achieve a multi-scale information fusion task of bi-temporal images. Second, the position channel attention module was promoted to make the neural network pay more attention to the spectral and spatial information in the multi-scale fused feature map. Finally, a change gradient guide module was proposed to optimize backpropagation and overcome the negative effects of pseudo-change. Compared with seven state-of-the-art methods using three pairs of real remote sensing images, the proposed approach could smoothen the salt-and-pepper noise from the detection maps and improve the detection accuracy. The quantitative improvements are about 1.67% and 3.00% in terms of overall accuracy and Kappa coefficient, respectively, thus confirming the feasibility and superiority of the proposed approach for detecting land cover change with remotely sensed images. Code: https://github.com/ImgSciGroup/MACGGNet.git.

A Novel Framework for Urban Land Cover Change Detection With NASA’s Black Marble Nighttime Lights Product

A Novel Framework for Urban Land Cover Change Detection With NASA’s Black Marble Nighttime Lights Product

Hierarchical Attention Feature Fusion-Based Network for Land Cover Change Detection With Homogeneous and Heterogeneous Remote Sensing Images

Hierarchical Attention Feature Fusion-Based Network for Land Cover Change Detection With Homogeneous and Heterogeneous Remote Sensing Images