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Change year maps for large-scale developments. The Woodlands, corresponding with bounding box 6 in Figure 6(a) (a-c) and Cinco Ranch, corresponding with bounding box 7 in Figure 6(a) (d-f). Classification coloration is consistent with legends in Figure 4-6, with darker reds indicating higher proportions of impervious surface.
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In 2017, Hurricane Harvey caused substantial loss of life and property in the swiftly urbanizing region of Houston, TX. Now in its wake, researchers are tasked with investigating how to plan for and mitigate the impact of similar events in the future, despite expectations of increased storm intensity and frequency as well as accelerating urbanizati...
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Convolutional Neural Networks (CNNs) are models that are utilized extensively for the hierarchical extraction of features. Vision transformers (ViTs), through the use of a self-attention mechanism, have recently achieved superior modeling of global contextual information compared to CNNs. However, to realize their image classification strength, ViT...
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... With the accumulation of remote sensing time series data and advancements in addressing missing data, the availability of time series data has significantly improved. Many algorithms or models have been proposed to handle remote sensing time series data for land cover classification (Fang et al. 2020;Hakkenberg et al. 2019;Pelletier, Webb, and Petitjean 2019). Traditional machine learning algorithms (e.g. ...
Preparing regular time series optical remote sensing data is a difficult task due to the influences of frequently cloudy and rainy days. The irregular data and their forms severely limit the data’s ability to be analysed and modelled for vegetation classification. However, how irregular time series data affect vegetation classification in deep learning models is poorly understood. To address these questions, this research preprocessed the 2019-2021 time series of Sentinel-2 in both unequal and equal intervals, and transformed them into an image through recurrence plot for each pixel. The initial one-dimension time series (1DTS) and recurrence plot data were then used as input data for three deep learning methods (i.e., Conv1D model based on one-dimensional convolution, GoogLeNet model based on two-dimensional convolution, and CGNet model which fused Conv1D and GoogLeNet) for vegetation classification, respectively. The class separability of the features generated by each model was evaluated and the importance of spectral and temporal features was further examined through gradient backpropagation. The equal-interval time series data significantly improved the classification accuracy with 0.04, 0.13, and 0.09 for Conv1D, GoogLeNet, and CGNet, respectively. The CGNet achieved the highest classification accuracy, indicating that the information from 1DTS and recurrence plot can be a good complementary for vegetation classification. The importance of spectral bands and time showed that the Sentinel-2 red edge-1 spectral band played a critical role in the identification of eucalyptus, loquat, and honey pomelo, but the importance order of bands varied in different vegetation types in GoogLeNet. The time importance varied across different vegetation types but is similar in these deep learning models. This study quantified the impacts of organizational form (1DTS and recurrence plot) of time series data on different models. This research is valuable for us to choose appropriate data structures and efficient deep learning models for vegetation classification.
... Several other classification methods have been developed and used for LULC production [3,4]. Unfortunately, only a few LULC categories are recognized since a single-temporal RS picture only offers the prompt spectrum of the land surface, and the characteristics that may be employed for categorization are rare [5]. For the categories of various ...
This research examines a multi-attention residual integrated network with an enhanced fireworks algorithm for remote sensing image classification. Remote sensing (RS) picture classification is important for land cover mapping, environmental monitoring, and urban planning. Remote sensing image classification is important in earth observation since the military and commercial sectors have focused on it. Due to RS data's high complexity and limited labelled examples, classifying RS pictures is difficult. Deep Learning (DL) techniques have made great strides in RS image categorization, expanding this field's potential. This research introduces Multi-Attention Residual Integrated Network with Enhanced Fireworks Algorithm (MAR-EFA) to improve hyper spectral image identification. MARIN-EFA improves feature fusion and removes unneeded features to overcome technique constraints. The suggested method weights features using different attention models. These characteristics are then carefully extracted and integrated using a residual network. Final contextual semantic integration on deeply fused features is done with a Bi-LSTM network. Our population-based Enhanced Fireworks Algorithm (EFA) is inspired by fireworks' explosive performance and optimises MARIN parameters. Attention techniques and an improved optimisation algorithm improve performance over current systems. Numerous Eurosat dataset studies were assessed using various performance indicators. The simulation results show that MARIN-EFA outperforms current methods. The suggested technique shows promise for improving RS picture classification and allowing more accurate and reliable data categorization.
... Multitemporal images have rich temporal features, which also significantly increases the amount of data. When training deep learning models, multitemporal images are usually stacked along the time dimension and trained on a per-pixel or multidimensional image block basis [12,76,77]. This method ensures data integrity and reduces the difficulty of manually selecting features, but it often leads to data redundancy and increased computation costs. ...
The rapid advancement of remote sensing technology has significantly enhanced the temporal resolution of remote sensing data. Multitemporal remote sensing image classification can extract richer spatiotemporal features. However, this also presents the challenge of mining massive data features. In response to this challenge, deep learning methods have become prevalent in machine learning and have been widely applied in remote sensing due to their ability to handle large datasets. The combination of remote sensing classification and deep learning has become a trend and has developed rapidly in recent years. However, there is a lack of summary and discussion on the research status and trends in multitemporal images. This review retrieved and screened 170 papers and proposed a research framework for this field. It includes retrieval statistics from existing research, preparation of multitemporal datasets, sample acquisition, an overview of typical models, and a discussion of application status. Finally, this paper discusses current problems and puts forward prospects for the future from three directions: adaptability between deep learning models and multitemporal classification, prospects for high-resolution image applications, and large-scale monitoring and model generalization. The aim is to help readers quickly understand the research process and application status of this field.
... However, during high flows, these systems initially bypass alluvial sediment carried by floodwaters. Over the past 200 years across North America, alluvial sediment volumes have increased 10-fold due to land development (Kemp et al., 2020); Houston developed land area grew by 30% from 1997 to 2017 (Hakkenberg et al., 2019). As floodwaters recede, the system shifts to both in-channel and overbank deposition, which increases in magnitude downstream as sediment falls out of suspension (Fig. 3). ...
Southeastern Texas (United States) recorded one of the largest flooding events in U.S. history during Hurricane Harvey (25−31 August 2017), mobilizing millions of cubic meters of sediment in Houston fluvial-estuarine systems. We conducted an integrated quantitative analysis to determine the net minimum volume of sediment transported during the storm using subaerial elevation change, satellite and ground-based images, and sediment dredging reports along major waterways. The 12 fluvial-estuarine streams and two controlled reservoir drainages in the Houston area transported a minimum of ∼2.723 × 107 m3 of sediment. This volume is ∼6−51 times larger than the average annual volume of sediment delivered to Galveston Bay in modern times (in the past 200 yr), and ∼30−118 times larger when compared to Holocene rates. Nearly ∼26% of the measured volume was deposited in Addicks and Barker reservoirs, decreasing holding capacities by ∼1.2% and ∼1.6%, respectively. In the stream drainages, sediment was mobilized from west-northwest of Houston and pulsed toward Galveston Bay, highlighting the extreme short-term variability in sediment delivery. Sediment flux through the Houston region during Harvey is an example of sediment storage followed by a pulsed delivery of high sediment volume rather than continuous delivery of sediment. Comparison of sediment volumes transported through natural and modified drainages through Houston demonstrates that channel modification resulted in significant bypass of sediment downstream. Urban watershed management is more effective when continual updates are implemented based on regional circumstances rather than based on historical fluxes.
... Many classification algorithms, including support vector machine (SVM), maximum likelihood (ML), naive Bayesian (NB), decision tree (DT), K-nearest neighbour (KNN), multi-layer perceptron (MLP) and deep belief network (DBN), have been developed and applied to LULC production (Dou and Chen, 2017a;Maxwell et al., 2018;Du, et al., 2020). However, single-temporal RS image only provides the instantaneous spectrum of the land surface, and the features that can be used for classification is infrequent, resulting in very few recognitions of LULC categories (Gómez, et al., 2016;Hakkenberg et al., 2018). Achieving high classification accuracy using single-temporal RS image is difficult especially for the categories of different crops and other vegetations (Zhong et al., 2019;Tsai et al., 2018). ...
... Classification methods can be roughly divided into two types according to the structure of TSI. The first is to stack multi-temporal images by time sequence and classify them with SVM, random forest (RF), and other classifiers (Hakkenberg et al., 2018;Whelen and Siqueira, 2018;Gong et al., 2019). This method performs well in producing annual LULC, such as finer resolution observation and monitoring of global land cover (Gong et al., 2019). ...
Recently, time series image (TSI) has been reported to be an effective resource to mapping fine land use/land cover (LULC), and deep learning, in particular, has been gaining growing attention in this field. However, deep learning methods using single classifier need further improvement for accurate TSI classification owing to the 1D temporal properties and insufficient dense time series of the remote sensing images. To overcome such disadvantages, we proposed an innovative approach involving construction of TSI and combination of deep learning and multiple classifiers system (MCS). Firstly, we used a normalised difference index (NDI) to establish an NDIs-based TSI and then designed a framework consisting of a deep learning-based feature extractor and multiple classifiers system (MCS) based classification model to classify the TSI. With the new approach, our experiments were conducted on Landsat images located in two counties, Sutter and Kings in California, United States. The experimental results indicate that our proposed method achieves great progress on accuracy improvement and LULC mapping, outperforming classifications using comparative deep learning and non-deep learning methods.
... Driven by a demand for spatio-temporal accuracy and consistency across the multidecadal imagery time series (where individual images may vary due to seasonal illumination angles and atmospheric conditions), the land cover change dataset was generated using a threepart algorithmic procedure: (1) automatic adaptive signature generalization (Dannenberg et al. 2016) for automated training data selection from NLCD classifications from 2001, 2006, and 2011(Homer et al. 2015, (2) machine learning image classification using random forests to classify atmospherically-corrected image spectra to one of the four aforementioned developed classes (Hakkenberg et al. 2020), and (3) spatio-temporal filtering to reduce erroneous classifications due to clouds, atmospheric contamination, and other sources of data noise and model errors among the 153 billion pixels classified (Hakkenberg et al. 2019). All classifications were validated using independent, multi-temporal fine-resolution imagery from the Ikonos, Quickbird, and Worldview sensors (Hakkenberg et al. 2019). ...
... Driven by a demand for spatio-temporal accuracy and consistency across the multidecadal imagery time series (where individual images may vary due to seasonal illumination angles and atmospheric conditions), the land cover change dataset was generated using a threepart algorithmic procedure: (1) automatic adaptive signature generalization (Dannenberg et al. 2016) for automated training data selection from NLCD classifications from 2001, 2006, and 2011(Homer et al. 2015, (2) machine learning image classification using random forests to classify atmospherically-corrected image spectra to one of the four aforementioned developed classes (Hakkenberg et al. 2020), and (3) spatio-temporal filtering to reduce erroneous classifications due to clouds, atmospheric contamination, and other sources of data noise and model errors among the 153 billion pixels classified (Hakkenberg et al. 2019). All classifications were validated using independent, multi-temporal fine-resolution imagery from the Ikonos, Quickbird, and Worldview sensors (Hakkenberg et al. 2019). ...
Urbanization results in increasing impervious surfaces with the potential to threaten fragile environments and heighten flood risks. In the United States, research on the social processes driving urbanization has tended to focus on the twenty-first century, but less is known about how temporal trends arose from the spatial layout of the urban land upon which this growth was founded. To address this gap, we present a novel interdisciplinary synthesis using neighborhood-level census data in tandem with a satellite-derived annual land cover change time series to assess the role of race, affluence, and socioeconomic status in shaping spatio-temporal urbanization in the Houston metropolitan area from 1997−2016. Results from cross-sectional and temporal regression models indicate that while social dynamics associated with historical versus recent urbanization are related, they are not identical. Thus, while temporal change in Houston's urbanization is driven primarily by socioeconomic status, the social dynamics associated with spatial disparities in urbanization relate primarily to race, regardless of socioeconomic status. These results are noteworthy as urbanization in Houston does not fully comport with existing theoretical perspectives or with empirical findings nationally. Instead, we suggest these findings reflect the city’s politics and culture surrounding land use. Thus, beyond its important social and environmental implications, this study affirms the utility of fusing socio-demographic data with satellite remote sensing of urban growth, and highlights the value of the socioenvironmental succession framework for characterizing urbanization as a recursive process in space and time.
... In addition to flooding resulting from extreme precipitation from hurricanes and cyclones over the study area, intense rainfall in the upstream watersheds of the San Jacinto and Brazos rivers that pass through the study area ( Figure 1) further complicate the flooding problem [21]. Over time, urbanization and other intense anthropogenic activities across the study area have led to changes in the land cover and land surface elevation, which are believed to have aggravated the impacts of flooding [14,22,23]. [22] estimated that over the past two decades , nearly 2040 km 2 (±400 km 2 ) of land in the Greater Houston area has been changed to less permeable developed land cover, out of which 14% were wetland areas. ...
... Over time, urbanization and other intense anthropogenic activities across the study area have led to changes in the land cover and land surface elevation, which are believed to have aggravated the impacts of flooding [14,22,23]. [22] estimated that over the past two decades , nearly 2040 km 2 (±400 km 2 ) of land in the Greater Houston area has been changed to less permeable developed land cover, out of which 14% were wetland areas. ...
The Greater Houston metropolitan area has experienced recurring flooding events in the past two decades related to tropical cyclones and heavy inland rainfall. With the projected recurrence of severe weather events, an approach that outlines the susceptibility of different localities within the study area to potential floods based on analyses of the impacts from earlier events would be beneficial. We applied a novel C-band Sentinel-1 Synthetic Aperture Radar (SAR)-based flood detection method to map floodwater distribution following three recent severe weather events with the goal of identifying areas that are prone to future flood hazards. Attempts were made to calibrate and validate the C-band-based results and analyses to compensate for possible sources of error. These included qualitative and quantitative assessments on L-band aerial SAR data, as well as aerial imagery acquired after one of the events. The findings included the following: (1) most urban centers of Harris county, with few exceptions, are not believed to be prone to flooding hazards in contrast to the densely populated areas on the outskirts of Harris county; (2) nearly 44% of the mapped flood-prone areas lie within a 1 km distance of major drainage networks; (3) areas experiencing high subsidence rates have persistently experienced flooding, possibly exacerbated by morphological changes to the land surface induced by subsidence.
... Driven by a demand for spatio-temporal accuracy and consistency across the multidecadal imagery time series (where individual images may vary due to seasonal illumination angles and atmospheric conditions), the land cover change dataset was generated using a threepart algorithmic procedure: (1) automatic adaptive signature generalization (Dannenberg et al. 2016) for automated training data selection from NLCD classifications from 2001, 2006, and 2011(Homer et al. 2015, (2) machine learning image classification using random forests to classify atmospherically-corrected image spectra to one of the four aforementioned developed classes (Hakkenberg et al. 2020), and (3) spatio-temporal filtering to reduce erroneous classifications due to clouds, atmospheric contamination, and other sources of data noise and model errors among the 153 billion pixels classified (Hakkenberg et al. 2019). All classifications were validated using independent, multi-temporal fine-resolution imagery from the Ikonos, Quickbird, and Worldview sensors (Hakkenberg et al. 2019). ...
... Driven by a demand for spatio-temporal accuracy and consistency across the multidecadal imagery time series (where individual images may vary due to seasonal illumination angles and atmospheric conditions), the land cover change dataset was generated using a threepart algorithmic procedure: (1) automatic adaptive signature generalization (Dannenberg et al. 2016) for automated training data selection from NLCD classifications from 2001, 2006, and 2011(Homer et al. 2015, (2) machine learning image classification using random forests to classify atmospherically-corrected image spectra to one of the four aforementioned developed classes (Hakkenberg et al. 2020), and (3) spatio-temporal filtering to reduce erroneous classifications due to clouds, atmospheric contamination, and other sources of data noise and model errors among the 153 billion pixels classified (Hakkenberg et al. 2019). All classifications were validated using independent, multi-temporal fine-resolution imagery from the Ikonos, Quickbird, and Worldview sensors (Hakkenberg et al. 2019). ...
Urbanization results in increasing impervious surfaces with the potential to threaten fragile environments and heighten flood risks. In the United States, research on the social processes driving urbanization has tended to focus on the twenty-first century, but less is known about how temporal trends arose from the spatial layout of developed land upon which this growth was founded. To address this gap, we present a novel interdisciplinary synthesis using neighborhood-level census data in tandem with a satellite-derived annual land cover change time series to assess the role of race, affluence, and socioeconomic status in shaping spatio-temporal urbanization in the Houston metropolitan area from 1997-2016. Results from cross-sectional and temporal regression models indicate that while social dynamics associated with historical versus recent urbanization are related, they are not identical. Thus, while temporal change in urbanization is driven primarily by socioeconomic status, the social dynamics associated with spatial disparities in urbanization relate primarily to race, regardless of socioeconomic status. The results are noteworthy as urbanization in Houston does not fully comport with existing theoretical perspectives or with empirical findings nationally. Instead, we suggest these findings reflect the city's politics and culture surrounding land use. Thus, beyond its important social and environmental implications, this study affirms the utility of fusing socio-demographic data with satellite remote sensing of urban growth, and highlights the value of the socioenvironmental succession framework for characterizing urbanization as a recursive process in space and time.
... Among the clustering methods, common methods include means clustering and fuzzy C-means (FCM) clustering. Hakkenberg uses the principal component analysis method to extract change features, and use the means method to cluster pixels into two categories to obtain a different map [45]. ...
This paper proposes a multi-temporal image change detection algorithm based on adaptive parameter estimation, which is used to solve the problems of severe interference of coherent speckle noise and the retention of detailed information about changing regions in synthetic aperture radar remote sensing images. The change area in the initial differential image has local consistency and global prominence. By detecting the significant area to locate similar change areas, the coherent speckle noise outside the area can be eliminated. The use of hierarchical FCM clustering to automatically generate training samples can improve the reliability of training samples. In addition, in order to increase the distinction between the changed area and the non-changed area, a sparse automatic encoder is used to extract the changed features and generate a change detection map. Experiments using 4 sets of SAR images show that the algorithm can effectively reduce the effect of speckle noise on detection accuracy, the extraction of changing areas is more complete and meticulous, and the false detection rate is greatly reduced. Since the images in different time phases will be disturbed by weather, clouds, sea water, etc., the target segmentation algorithm can be used to extract the target of interest and highlight the changing area. Principal component analysis and kmeans clustering method are used to reduce the influence of isolated pixels, and change information is extracted to obtain different images. The experiment uses four sets of image data of islands and reefs. The experiment proves that the algorithm can well eliminate external interference, improve the accuracy of change detection, and have a good detection effect on the area of islands and reefs. The adaptive parameter estimation plays a good role in the detection of changing areas, and the visual effect is better, which can improve the accuracy of the detection results.
... Bi-temporal change detection using image pairs has been used effectively to quantify state change (e.g. land cover class) or relative change in surface characteristics between two dates, but is unable to capture higher-order temporal dynamics, including gradual change, periodicity, and change rates [2]. Reflecting the demand for more temporally-frequent land surface data products for disparate applications from land cover change to biophysical land surface models [3], [4], the use of multitemporal image time series has increased rapidly [5]. ...
The characterization of fine temporal-resolution land surface dynamics from broadband optical satellite sensors is constrained by sparse acquisitions of high-quality imagery; interscene variation in radiometric, phenological, atmospheric, and illumination conditions; and subpixel variability in heterogeneous environments. In this letter, we address these concerns by developing and testing the automatic adaptive signature generalization and regression (AASGr) algorithm. Provided a robust reference map corresponding to the date of one image, AASGr automates the prediction of continuous fields maps from imagery time series that is adaptive to the spectral and radiometric characteristics of each target image and thereby requires neither atmospheric correction nor data normalization. We tested AASGr on a 22-year Landsat time series to quantify subannual impervious fractional cover dynamics in Houston, TX--an area characterized by a high degree of spatial heterogeneity in surface cover and high frequency in land cover change. The map time series achieved high accuracy in a three-part validation procedure and reveals spatio-temporal dynamics of urban intensification and extensification at a level of detail previously elusive in discrete classifications or coarse temporal-resolution map products. The automation of continuous fields time series is enabling a new generation of land surface products capable of characterizing precise morphologies along a continuum of spatio-temporal change. While AASGr was applied here to predict subpixel impervious fractional cover from Landsat imagery, the method is generalizable to a range of imagery and applications requiring dense continuous fields time series with uncertainty estimates of geophysical and biochemical characteristics, such as leaf area index, biomass, and albedo.
