scholarly journals Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 4400
Author(s):  
Rongkun Zhao ◽  
Yuechen Li ◽  
Jin Chen ◽  
Mingguo Ma ◽  
Lei Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Fusion ◽  
Survey Data ◽  
Paddy Rice ◽  
Spatiotemporal Data ◽  
Series Data ◽  
Landsat 8 ◽  
Rice Area ◽  
Sentinel 2

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

scholarly journals An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications

2019 ◽  
Vol 11 (24) ◽  
pp. 2927
Author(s):  
Hongcan Guan ◽  
Yanjun Su ◽  
Tianyu Hu ◽  
Jin Chen ◽  
Qinghua Guo
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Selection Procedure ◽  
Vegetation Mapping ◽  
Spatiotemporal Data ◽  
Landsat 8 ◽  
Fusion Model ◽  
Average Decrease ◽  
Object Based ◽  
Sentinel 2

Spatiotemporal data fusion is a key technique for generating unified time-series images from various satellite platforms to support the mapping and monitoring of vegetation. However, the high similarity in the reflectance spectrum of different vegetation types brings an enormous challenge in the similar pixel selection procedure of spatiotemporal data fusion, which may lead to considerable uncertainties in the fusion. Here, we propose an object-based spatiotemporal data-fusion framework to replace the original similar pixel selection procedure with an object-restricted method to address this issue. The proposed framework can be applied to any spatiotemporal data-fusion algorithm based on similar pixels. In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. The results show that the proposed object-based framework can improve all three data-fusion algorithms significantly by delineating vegetation boundaries more clearly, and the improvements on FSDAF is the greatest among all three algorithms, which has an average decrease of 2.8% in relative root-mean-square error (rRMSE) in all sensor combinations. Moreover, the improvement on fusing Sentinel 2 and Landsat 8 images is more significant (an average decrease of 2.5% in rRMSE). By using the fused images generated from the proposed object-based framework, we can improve the vegetation mapping result by significantly reducing the “pepper-salt” effect. We believe that the proposed object-based framework has great potential to be used in generating time-series high-resolution remote-sensing data for vegetation mapping applications.

scholarly journals Multi-Dimensional Remote Sensing Analysis Documents Beaver-Induced Permafrost Degradation, Seward Peninsula, Alaska

2021 ◽  
Vol 13 (23) ◽  
pp. 4863
Author(s):  
Benjamin M. Jones ◽  
Ken D. Tape ◽  
Jason A. Clark ◽  
Allen C. Bondurant ◽  
Melissa K. Ward Jones ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Unmanned Aircraft ◽  
The Arctic ◽  
Series Data ◽  
Landsat 8 ◽  
Permafrost Degradation ◽  
Channel Network ◽  
Seward Peninsula ◽  
Sentinel 2

Beavers have established themselves as a key component of low arctic ecosystems over the past several decades. Beavers are widely recognized as ecosystem engineers, but their effects on permafrost-dominated landscapes in the Arctic remain unclear. In this study, we document the occurrence, reconstruct the timing, and highlight the effects of beaver activity on a small creek valley confined by ice-rich permafrost on the Seward Peninsula, Alaska using multi-dimensional remote sensing analysis of satellite (Landsat-8, Sentinel-2, Planet CubeSat, and DigitalGlobe Inc./MAXAR) and unmanned aircraft systems (UAS) imagery. Beaver activity along the study reach of Swan Lake Creek appeared between 2006 and 2011 with the construction of three dams. Between 2011 and 2017, beaver dam numbers increased, with the peak occurring in 2017 (n = 9). Between 2017 and 2019, the number of dams decreased (n = 6), while the average length of the dams increased from 20 to 33 m. Between 4 and 20 August 2019, following a nine-day period of record rainfall (>125 mm), the well-established dam system failed, triggering the formation of a beaver-induced permafrost degradation feature. During the decade of beaver occupation between 2011 and 2021, the creek valley widened from 33 to 180 m (~450% increase) and the length of the stream channel network increased from ~0.6 km to more than 1.9 km (220% increase) as a result of beaver engineering and beaver-induced permafrost degradation. Comparing vegetation (NDVI) and snow (NDSI) derived indices from Sentinel-2 time-series data acquired between 2017 and 2021 for the beaver-induced permafrost degradation feature and a nearby unaffected control site, showed that peak growing season NDVI was lowered by 23% and that it extended the length of the snow-cover period by 19 days following the permafrost disturbance. Our analysis of multi-dimensional remote sensing data highlights several unique aspects of beaver engineering impacts on ice-rich permafrost landscapes. Our detailed reconstruction of the beaver-induced permafrost degradation event may also prove useful for identifying degradation of ice-rich permafrost in optical time-series datasets across regional scales. Future field- and remote sensing-based observations of this site, and others like it, will provide valuable information for the NSF-funded Arctic Beaver Observation Network (A-BON) and the third phase of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Field Campaign.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest 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.

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 Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2236 ◽  
Author(s):  
Viviana Gavilán ◽  
Mario Lillo-Saavedra ◽  
Eduardo Holzapfel ◽  
Diego Rivera ◽  
Angel García-Pedrero
Keyword(s):  
Water Management ◽  
Time Series ◽  
Water Balance ◽  
Distribution System ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Sentinel 2

Efficient water management in agriculture requires a precise estimate of evapotranspiration ( E T ). Although local measurements can be used to estimate surface energy balance components, these values cannot be extrapolated to large areas due to the heterogeneity and complexity of agriculture environment. This extrapolation can be done using satellite images that provide information in visible and thermal infrared region of the electromagnetic spectrum; however, most current satellite sensors do not provide this end, but they do include a set of spectral bands that allow the radiometric behavior of vegetation that is highly correlated with the E T . In this context, our working hypothesis states that it is possible to generate a strategy of integration and harmonization of the Normalized Difference Vegetation Index ( N D V I ) obtained from Landsat-8 ( L 8 ) and Sentinel-2 ( S 2 ) sensors in order to obtain an N D V I time series used to estimate E T through fit equations specific to each crop type during an agricultural season (December 2017–March 2018). Based on the obtained results it was concluded that it is possible to estimate E T using an N D V I time series by integrating data from both sensors L 8 and S 2 , which allowed to carry out an updated seasonal water balance over study site, improving the irrigation water management both at plot and water distribution system scale.

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.

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