Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Change detection (CD) methods have been crucial in exploring geo-environmental science. With the advancement of remote sensing (RS) technology, multimodal images acquired from different platforms and sensors are widely used for CD tasks. As an emerging task, multimodal CD (MCD) aims to achieve more comprehensive and precise detection of land cover changes through complementary information in multimodal images. However, there are significant differences between modalities, particularly in heterogeneous images. How to deal with modal differences while effectively integrating change information remains a challenge in MCD. In this article, we propose a novel adversarial feature equilibrium network (AFENet), which establishes an additional adversarial optimization to solve the equilibrium problem between modal differences and land cover changes. Our AFENet aligns the features and reduces the modal gap through a multiscale adversarial domain adaptation (MADA) approach. Meanwhile, a divergence-aware contrastive module (DCM) is designed as a regularization term for adversarial optimization. DCM affects the sensitivity of feature extractors by constraining the mutual information between changed and unchanged pixels. In this case, AFENet can maintain the consistency of feature representation while maximizing the discriminability of change targets. The features extracted from AFENet will then be integrated by our multistream feature fusion (MFF) module and utilized to generate change maps. The effectiveness of our approach is demonstrated on two scene-level multimodal RS datasets. Compared with existing methods, our AFENet achieves state-of-the-art (SOTA) performance on both datasets and outperforms the second-best <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score by 4.64% and 1.1%, respectively.

Land use planners require up-to-date and spatially accurate time series land resources information and changing pattern for future management. As a result, assessing the status of land cover change due to population growth and arable expansion, land degradation and poor resource management, partial implementation of policy strategies, and poorly planned infrastructural development is essential. Thus, the objective of the study was to quantify the spatiotemporal dynamics of land use land cover change between 1995 and 2014 using 5 multi-temporal cloud-free Landsat Thematic Mapper images. The maximum likelihood (ML)-supervised classification technique was applied to create signature classes for significant land cover categories using means and variances of the training data to estimate the probability that a pixel is a member of a class. The final Bayesian ML classification resulted in 12 major land cover units, and the spatiotemporal change was quantified using post-classification and statistical change detection techniques. For a period of 20 years, there was a continuously increasing demand for arable areas, which can be represented by an exponential growth model. Excepting the year 2009, the built-up area has shown a steady increase due to population growth and its need for infrastructure development. There was nearly a constant trend for water bodies with a change in slope significantly less than +0.01%. The 2014 land cover change statistics revealed that the area was mainly covered by cultivated, wood, bush, shrub, grass, and forest land mapping units accounting nearly 63%, 12%, 8%, 6%, 4%, and 2% of the total, respectively. Land cover change with agro-climatic zones, soil types, and slope classes was common in most part of the area and the conversion of grazing land into plantation trees and closure area development were major changes in the past 20 years.

Land cover change is a significant issue for environmental managers for sustainable management. Remote sensing techniques have been shown to have a high probability of recognizing land cover patterns and change detection due to periodic coverage, data integrity, and provision of data in a broad range of the electromagnetic spectrum. We evaluate the applicability of remote sensing techniques for land cover pattern recognition, as well as land cover change detection of the Hatiya Island, Bangladesh, and quantify land cover changes from 1977 to 1999. A supervised classification approach was used to classify Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM), and Multispectral Scanner (MSS) images into eight major land cover categories. We detected major land cover changes over the 22-year study period. During this period, marshy land, mud, mud with small grass, and bare soil had decreased by 85%, 46%, 44%, and 24%, respectively, while agricultural land, medium forest, forest, and settlement had positive changes of 26%, 45%, 363%, and 59%, respectively. The primary drivers of such landscape change were erosion and accretion processes, human pressure, and the reforestation and land reclamation programs of the Bangladesh Government.

Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available.

Understanding land use and land cover changes has become a necessity in managing and monitoring natural resources and development especially urban planning. Remote sensing and geographical information systems are proven tools for assessing land use and land cover changes that help planners to advance sustainability. Our study used remote sensing and geographical information system to detect and predict land use and land cover changes in one of the world’s most vulnerable and rapidly growing city of Kathmandu in Nepal. We found that over a period of 20 years (from 1990 to 2010), the Kathmandu district has lost 9.28% of its forests, 9.80% of its agricultural land and 77% of its water bodies. Significant amounts of these losses have been absorbed by the expanding urbanized areas, which has gained 52.47% of land. Predictions of land use and land cover change trends for 2030 show worsening trends with forest, agriculture and water bodies to decrease by an additional 14.43%, 16.67% and 25.83%, respectively. The highest gain in 2030 is predicted for urbanized areas at 18.55%. Rapid urbanization—coupled with lack of proper planning and high rural-urban migration—is the key driver of these changes. These changes are associated with loss of ecosystem services which will negatively impact human wellbeing in the city. We recommend city planners to mainstream ecosystem-based adaptation and mitigation into urban plans supported by strong policy and funds.

Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.

Remote sensing image change detection is the detection process of determining the surface change area and change feature type for the same image area from multiple time series remote sensing data. It is the core technical means of land use change detection and land cover change detection, and it is also the key research field of remote sensing applied science. In view of the problems of time-consuming, labor-intensive, and low detection accuracy in the past image change detection methods, researchers in related fields have proposed more and more cutting-edge remote sensing image change detection methods. This article first describes the development of remote sensing image change detection methods, then explains the conventional processing flow of change detection and the conventional methods of change monitoring, and finally discusses the research progress of more mainstream change detection methods.

The development of improved satellite technology generates a huge amount of remote sensing data, these data play the crucial role in natural resource management. The land use and land cover (LULC) change intensely affects local environment, as well as the global environment. Therefore, the quantifiable knowledge about LULC changes occur in global scale is important to make effective planning for conservation and precise use of natural resources, that has motivated the scientists to develop the various land cover change detection techniques. In this paper, we have proposed neural network-based approach, i.e., focused time delay neural network (FTDNN)-based approach for land cover change detection, which is a time series prediction-based approach and detect the sudden change in the enhanced vegetation index (EVI) time series. The performance of the proposed method has been addressed by using quantitative and qualitative analysis techniques. For the quantitative evaluation, the proposed algorithm is applied to the standard synthetic data set, which are analogous to EVI time series data set. The performance result of the proposed method compares with the four previously existing data mining-based benchmark techniques. The analysis was shown that the FTDNN-based method significantly outperforms than other techniques. For qualitative analysis, the San Francisco Bay Area data set has been used, which comprises real EVI time series. The proposed FTDNN-based method is applied to the San Francisco Bay Area data set and observe the interesting land cover changes. These outcomes indicate the effectiveness of data mining techniques for the land cover change detection problem.

Constructing a change magnitude map (CMM) is a key component of binary change detection. Recently, integrating multiple features to obtain a comprehensive CMM has become a popular research topic. However, the current integration approaches mainly utilize simple spectral CMMs that are derived based on a single spectral change index (e.g. image difference, Euclidean distance, and change vector analysis), which is not sufficient for addressing complex land cover changes. In this study, we propose a spectrally enhanced multi-feature fusion (SeMF) method with CMM integration for effective change detection. Seven commonly used spectral change indices are analysed from the aspects of the spectral value and spectral shape; two of these indices are selected to construct the optimal spectral-based CMM, which is more efficient, robust and stable than the single spectral change indices. The rotation-invariant local binary patterns (RiLBP) and Canny methods are further used for CMM generation via the textural and shape features, respectively. These three types of CMMs are adaptively assigned weights by using an information entropy-based fusion strategy and ultimately integrated into a comprehensive CMM. Two groups of experiments with Landsat 8 Operational Land Imager (OLI) and Gaofen (GF)-1 images are designed to verify the effectiveness of the SeMF method. The experimental results indicate that the SeMF method is superior to both spectral feature-based and multi-feature-based change detection methods.

As a result of the development of geo-technologies, in recent years, software has emerged that has provided the possibility of deriving information. The most important of these are geographic information systems (GIS), which are many sources in satellite data from multi-spectral satellites, The source via the Internet is one of the most important modern means of providing large data that varied between maps and satellite images (LANDSAT-SPOT-NASA). The study of land cover changes is one of the most important subjects of interest to the sciences and in particular the geographical research related to the environment and its effects on humans and living organisms. In the current research, the data of interactive open source maps were adopted, which provided the possibility of analysis. Using the Change Matters esri, it is one of the most efficient locations for detection of land cover changes by dealing with visuals for different periods of time. The study also reviewed some of the open source websites dealing with GIS to indicate their importance and role in the analysis and production of maps. The research aims to employ open-source digital technologies via the web to extract information about land cover changes.

Deep learning technology plays an important role in land cover change detection (LCCD) with bitemporal remote sensing images (RSIs). In particular, the classical UNet can provide promising detection accuracies and visual performance for LCCD with RSIs. This study proposes a novel change detection approach named difference feature guidance and multi-scale feature-fusion (DFG-MFF) network based on UNet to further improve the performance of LCCD with RSIs. First, a difference feature module is adopted to guide the change feature learning process. A multi-scale fusion module is then developed to capture the multi-scale features of ground targets with various shapes and sizes. Finally, a composite loss function composed of dice loss function, focal loss function and mean squared error loss function is utilized to train the DFG-MFF network. Experiments are performed on one pairwise homogeneous dataset and two pairs of heterogeneous datasets, and the comparison with eight state-of-the-art methods well demonstrated the feasibility and advantages of the proposed network for LCCD with RSIs.

Due to the lack of support for a high-resolution image in a short time, land cover change detection is always applied on the multitemporal remote-sensed images with different resolutions. The coarse-resolution image contains a large number of mixed pixels, which can seriously limit the utility of the change detection. Soft classification (SC) can be applied to improve this situation through deriving the abundances and generating the fractional change map, but it cannot provide the spatial distribution of the subpixels. Subpixel mapping (SPM) is a potential solution to resolve this problem, and is designed to use the proportions to obtain a sharpened thematic map at a subpixel scale. Based on this thought, the subpixel scale land cover change mapping result can be realized by integrating these two key techniques. However, in practice, there is a serious limitation to the detail and accuracy of the result, because when the scale factor between the different resolution images is large, the subpixel configuration is complex and the data volumes will be amplified. Moreover, with the high proportion of the changed area in the whole image, the change detection process at the subpixel level gets more difficult. The SPM technique is generally performed based only on the abundances of each and the spatial dependence assumption, so it cannot satisfy the demand. In order to overcome this shortcoming, several new reasonable subpixel scale change detection rules are defined in this paper, which dictate the land cover change map must be constructed according to the existing high-resolution image. The output from SC and prior information of the subpixel feature arrangement is applied into a modified cellular automata (CA) model, which can be regarded as a more reasonable tool to monitor the subpixel changes to resolve the big-data problem. Two experiments are performed and the results prove that the proposed method can effectively improve the accuracy of the change detection maps of the spatial distribution in a subpixel scale.

Land cover change in the upstream area of the Jeneberang Watershed, South Sulawesi, has altered river hydrological dynamics and amplified the impacts of increasingly intense extreme rainfall associated with climate change, thereby elevating the risk of flash floods in downstream regions. This study aims to examine the relationship between upstream watershed degradation, trends in extreme rainfall, and the escalation of flash flood risk through an integrated spatial–temporal analysis. A quantitative approach was employed using secondary data, including multitemporal satellite imagery from 2003–2023, 20 years of daily rainfall records, documented flood events, and population data. The analysis involved land cover classification and change detection using GIS, trend testing of extreme rainfall with the Mann–Kendall method, estimation of runoff coefficients and peak discharge, and risk modeling based on the Hazard–Vulnerability–Exposure framework. The results indicate a 17.8% decline in forest cover in the upstream area, associated with a 19% increase in the runoff coefficient and a 24% rise in estimated peak discharge during high-intensity rainfall events. Maximum daily rainfall intensity also shows a statistically significant upward trend (p&lt;0.05). Risk mapping reveals a 29% expansion of high-risk zones, particularly in densely populated downstream areas. These findings demonstrate that the interaction between land use change and intensifying extreme rainfall exacerbates watershed hydrological vulnerability, providing a scientific basis for upstream restoration policies and ecosystem-based climate change adaptation strategies in the Jeneberang Watershed

Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection.