scholarly journals Sequence Image Datasets Construction via Deep Convolution Networks

2021 ◽  
Vol 13 (9) ◽  
pp. 1853
Author(s):  
Xing Jin ◽  
Ping Tang ◽  
Zheng Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Short Term Memory ◽  
Input Sequence ◽  
Optical Data ◽  
Landsat 8 ◽  
Multiple Time ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Image Datasets

Remote-sensing time-series datasets are significant for global change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors such as cloud noise for optical data. Image transformation is the method that is often used to deal with this issue. This paper considers the deep convolution networks to learn the complex mapping between sequence images, called adaptive filter generation network (AdaFG), convolution long short-term memory network (CLSTM), and cycle-consistent generative adversarial network (CyGAN) for construction of sequence image datasets. AdaFG network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. CLSTM network can map between different images using the state information of multiple time-series images. CyGAN network can map an image from a source domain to a target domain without additional information. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the deep convolution networks are effective to produce high-quality time-series image datasets, and the data-driven deep convolution networks can better simulate complex and diverse nonlinear data information.

scholarly journals Sequence Image Interpolation via Separable Convolution Network

2021 ◽  
Vol 13 (2) ◽  
pp. 296
Author(s):  
Xing Jin ◽  
Ping Tang ◽  
Thomas Houet ◽  
Thomas Corpetti ◽  
Emilien Gence Alvarez-Vanhard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Data ◽  
Global Environmental Change ◽  
Input Sequence ◽  
Image Interpolation ◽  
Optical Data ◽  
Series Data ◽  
Landsat 8 ◽  
Deep Model

Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors, such as cloud noise for optical data. Image interpolation is the method that is often used to deal with this issue. This paper considers the deep learning method to learn the complex mapping of an interpolated intermediate image from predecessor and successor images, called separable convolution network for sequence image interpolation. The separable convolution network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the method is effective to produce high-quality time-series interpolated images, and the data-driven deep model can better simulate complex and diverse nonlinear image data information.

Mapping and monitoring of landslide-dammed lakes using Sentinel-2 time series - a case study after the 2016 Kaikōura Earthquake in New Zealand

2020 ◽  
Author(s):  
Lorena Abad ◽  
Daniel Hölbling ◽  
Raphael Spiekermann ◽  
Zahra Dabiri ◽  
Günther Prasicek ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Google Earth ◽  
Aerial Photographs ◽  
Optical Data ◽  
Landsat 8 ◽  
Multiple Time ◽  
Potential Hazard ◽  
Sentinel 2

<p>On November 14, 2016, a 7.8 magnitude earthquake struck the Kaikōura region on the South Island of New Zealand. The event triggered numerous landslides, which dammed rivers in the area and led to the formation of hundreds of dammed lakes. Landslide-dammed lakes constitute a natural risk, given their propensity to breach, which can lead to flooding of downstream settlements and infrastructure. Hence, detecting and monitoring dammed lakes is a key step for risk management strategies. Aerial photographs and helicopter reconnaissance are frequently used for damage assessments following natural hazard events. However, repeated acquisitions of aerial photographs and on-site examinations are time-consuming and expensive. Moreover, such assessments commonly only take place immediately after an event, and long-term monitoring is rarely performed at larger scales.</p><p>Satellite imagery can support mapping and monitoring tasks by providing an overview of the affected area in multiple time steps following the main triggering event without deploying major resources. In this study, we present an automated approach to detect landslide-dammed lakes using Sentinel-2 optical data through the Google Earth Engine (GEE). Our approach consists of a water detection algorithm adapted from Donchyts et al., 2016 [1], where a dynamic threshold is applied to the Normalized Difference Water Index (NDWI). The water bodies are detected on pre- and post-event monthly mosaics, where the cloud coverage of the composed images is below 30 %, resulting in one pre-event (December 2015) and 14 post-event monthly mosaics. Subsequently, a differencing change detection method is performed between pre- and post-event mosaics. This allows for continuous monitoring of the lake status, and for the detection of new lakes forming in the area at different points in time.</p><p>A random sample of lakes delineated from Google Earth high-resolution imagery, acquired right after the Kaikōura earthquake, was used for validation. The pixels categorized as ‘dammed lakes’ were intersected with the validation data set, resulting in a detection rate of 70 % of the delineated lakes. Ten key dams, identified by local authorities as a potential hazard, were further examined and monitored to identify lake area changes in multiple time steps, from December 2016 to March 2019. Taking advantage of the GEE cloud computing capabilities, the proposed automated approach allows fast time series analysis of large areas. It can be applied to other regions where landslide-dammed lakes need to be monitored over long time scales (months – years). Furthermore, the approach could be combined with outburst flood modeling and simulation to support initial rapid risk assessment.</p><p> [1]   Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., & van de Giesen, N. (2016). A 30 m resolution surface water mask including estimation of positional and thematic differences using Landsat 8, SRTM and OpenStreetMap: A case study in the Murray-Darling basin, Australia. Remote Sensing, 8(5).</p><div> <div> </div> </div>

Forest disturbances detection in Vietnam, Cambodia and Laos using Sentinel-1 data

2021 ◽  
Author(s):  
Stéphane Mermoz ◽  
Alexandre Bouvet ◽  
Marie Ballère ◽  
Thierry Koleck ◽  
Thuy Le Toan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
French Guiana ◽  
The United States ◽  
Habitat Destruction ◽  
Detection Methods ◽  
Optical Data ◽  
Forest Loss ◽  
Forest Disturbances ◽  
Synthetic Aperture Radar Data

<p>Over the last 25 years, the world’s forests have undergone substantial changes. Deforestation and forest degradation in particular contribute greatly to biodiversity loss through habitat destruction, soil erosion, terrestrial water cycle disturbances and anthropogenic CO2 emissions. In certain regions and countries, the changes have been more rapid, which is the case in the Greater Mekong sub-region recognized as deforestation hotspot (FAO, 2020). In this region, illegal and unsustainable logging and conversion of forests for agriculture, construction of dams and infrastructure are the direct causes of deforestation. Effective tools are therefore urgently needed to survey illegal logging operations which cause widespread concern in the region.</p><p>Monitoring systems based on optical data, such as the UMD/GLAD Deforestation alerts implemented on the Global Forest Watch platform, are limited by the important cloud cover which causes delays in the detections. However, it has been demonstrated in the last few years that forest losses can be timely monitored using dense time series of (synthetic aperture) radar data acquired by Sentinel-1 satellites, developed in the frame of the European Union’s Earth observation Copernicus programme. Ballère et al. (2021) showed for example that 80% of the forest losses due to gold mining in French Guiana are detected first by Sentinel-1-based forest loss detection methods compared with optical-based methods, sometimes by several months. Methods based on Sentinel-1 have been successfully applied at the local scale (Bouvet et al., 2018, Reiche et al., 2018) and can be adapted and tested at the national scale (Ballère et al., 2020).</p><p>We show here the main results of the SOFT project funded by ESA in the frame of the EO Science for Society open calls. The overall SOFT project goal is to provide validated forest loss maps every month over Vietnam, Cambodia and Laos with a minimum mapping unit of 0.04 ha, using Sentinel-1 data. The results confirm the analysis of the deforestation fronts published recently by the WWF (Pacheco et al., 2021), showing that Eastern Cambodia, and Southern and Northern Laos are currently forest disturbances hotspots.</p><p> </p><p>References:</p><p>Ballère et al., (2021). SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery. <em>Remote Sensing of Environment</em>, <em>252</em>, 112159.</p><p>Bouvet et al., (2018). Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. <em>Remote Sensing</em>, <em>10</em>(8), 1250.</p><p>FAO. Global Forest Resources Assessment; Technical Report; Food and Agriculture Association of the United-States: Rome, Italy, 2020.</p><p>Pacheco et al., 2021. Deforestation fronts: Drivers and responses in a changing world. WWF, Gland, Switzerland</p><div>Reiche et al., (2018). Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. <em>Remote Sensing of Environment</em>, <em>204</em>, 147-161.</div>

scholarly journals Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia

2020 ◽  
Vol 12 (1) ◽  
pp. 197
Author(s):  
Debbie Chamberlain ◽  
Stuart Phinn ◽  
Hugh Possingham
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Great Barrier Reef ◽  
National Park ◽  
Landsat 8 ◽  
Barrier Reef ◽  
Change Analysis ◽  
Estuarine Wetland ◽  
Open Forest

Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends divulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 139 ◽  
Author(s):  
Yingying Yang ◽  
Taixia Wu ◽  
Shudong Wang ◽  
Jing Li ◽  
Farhan Muhanmmad
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Distribution Data ◽  
Landsat 8 ◽  
Oxygen Balance

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

Mining Climate and Remote Sensing Time Series to Improve Monitoring of Sugar Cane Fields

2011 ◽  
pp. 50-72
Author(s):  
Luciana Romani ◽  
Elaine de Sousa ◽  
Marcela Ribeiro ◽  
Ana de Ávila ◽  
Jurandir Zullo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Sugar Cane ◽  
Computational Models ◽  
Fractal Theory ◽  
Remote Sensing Data ◽  
Real Data ◽  
Multiple Time ◽  
Climate Data ◽  
Sustainable Usage

This chapter discusses how to take advantage of computational models to analyze and extract useful information from time series of climate data and remote sensing images. This kind of data has been used for researching on climate changes, as well as to help on improving yield forecasting of agricultural crops and increasing the sustainable usage of the soil. The authors present three techniques based on the Fractal Theory, data streams and time series mining: the FDASE algorithm, to identify correlated attributes; a method that combines intrinsic dimension measurements with statistical analysis, to monitor evolving climate and remote sensing data; and the CLIPSMiner algorithm applied to multiple time series of continuous climate data, to identify relevant and extreme patterns. The experiments with real data show that data mining is a valuable tool to help agricultural entrepreneurs and government on monitoring sugar cane areas, helping to make the production more useful to the country and to the environment.

Uncertainty Estimation for a Global Inland Surface Water Time-Series

2021 ◽  
Author(s):  
Stefan Mayr ◽  
Igor Klein ◽  
Martin Rutzinger ◽  
Claudia Kuenzer
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Water ◽  
Fresh Water ◽  
Water Dynamics ◽  
Landsat 8 ◽  
Spatiotemporal Resolution ◽  
Observational Uncertainty ◽  
Moderate Resolution ◽  
Sentinel 2

<p>Fresh water is vital for life on the planet. Satellite remote sensing time-series are well suited to monitor global surface water dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on inland surface water. However, operating on diurnal- and global spatiotemporal resolution comes with certain drawbacks. As the time-series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, data gaps due to cloud coverage or invalid observations have to be interpolated. Furthermore, the moderate resolution of 250 m merely allows coarse pixel based areal estimations of surface water extent. To unlock the full potential of this dataset, information on associated uncertainty is essential. Therefore, we introduce several auxiliary layers aiming to address interpolation and quantification uncertainty. The probability of interpolated pixels to be covered by water is given by consideration of different temporal and spatial characteristics inherent to the time-series. Resulting temporal probability layers are evaluated by introducing artificial gaps in the original time-series and determining deviations to the known true state. To assess observational uncertainty in case of valid observations, relative datapoint (pixel) locations in feature space are utilized together with previously established temporal information in a linear mixture model. The hereby obtained classification probability also reveals sub-pixel information, which can enhance the product’s quantitative capabilities. Functionality is evaluated in 32 regions of interest across the globe by comparison to reference data derived from Landsat 8 and Sentinel-2 images. Results show an improved accuracy for partially water covered pixels (6.21 %), and that by uncertainty consideration, more comprehensive and reliable time-series information is achieved.</p><p><strong>Keywords:</strong> Fresh water, Landsat 8, MODIS, remote sensing, probability, Sentinel-2, sub-pixel scale, validation, water fraction.</p>

scholarly journals A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering

2019 ◽  
Vol 11 (11) ◽  
pp. 1358 ◽  
Author(s):  
Xingli Qin ◽  
Jie Yang ◽  
Pingxiang Li ◽  
Weidong Sun ◽  
Wei Liu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Synthetic Aperture Radar ◽  
Information Extraction ◽  
Transfer Learning ◽  
Remote Sensing Image ◽  
Synthetic Aperture ◽  
Target Domain ◽  
Time Series Clustering ◽  
Time Series Images

The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning methods often cannot make full use of the time-series information of the images, relying too much on the labeled samples in the target domain. Furthermore, the speckle noise inherent in synthetic aperture radar (SAR) imagery aggravates the difficulty of the manual selection of labeled samples, so these methods have difficulty in meeting the processing requirements of large data volumes and high efficiency. In lieu of these problems and the spatio-temporal relational knowledge of objects in time-series images, this paper introduces the theory of time-series clustering and proposes a new three-phase time-series clustering algorithm. Due to the full use of the inherent characteristics of the PolSAR images, this algorithm can accurately transfer the labels of the source domain samples to those samples that have not changed in the whole time series without relying on the target domain labeled samples, so as to realize transductive sample label transfer for PolSAR time-series images. Experiments were carried out using three different sets of PolSAR time-series images and the proposed method was compared with two of the existing methods. The experimental results showed that the transfer precision of the proposed method reaches a high level with different data and different objects and it performs significantly better than the existing methods. With strong reliability and practicability, the proposed method can provide a new solution for the rapid information extraction of remote sensing image time series.

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