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 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 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.

scholarly journals Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

2020 ◽  
Vol 12 (8) ◽  
pp. 1313 ◽  
Author(s):  
Muhammad Moshiur Rahman ◽  
Andrew Robson
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Quadratic Model ◽  
Series Data ◽  
Landsat 8 ◽  
Normalised Difference Vegetation Index ◽  
Block Level ◽  
Sugarcane Yield ◽  
Growing Region

Early prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons’ harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named ‘bins’. Cloud free (<20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each ‘bin’ was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha-1) achieved for the respective bin over the five growing years, producing strong correlations (R2 = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha-1) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R2 = 0.87 and RMSE = 11.33 (t·ha-1)) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach.

scholarly journals Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3068 ◽  
Author(s):  
Marta Prada ◽  
Carlos Cabo ◽  
Rocío Hernández-Clemente ◽  
Alberto Hornero ◽  
Juan Majada ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Management ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Landsat 8 ◽  
Sweet Chestnut ◽  
Over Time ◽  
Forest Managers ◽  
Sentinel 2

Forest management treatments often translate into changes in forest structure. Understanding and assessing how forests react to these changes is key for forest managers to develop and follow sustainable practices. A strategy to remotely monitor the development of the canopy after thinning using satellite imagery time-series data is presented. The aim was to identify optimal remote sensing Vegetation Indices (VIs) to use as time-sensitive indicators of the early response of vegetation after the thinning of sweet chestnut (Castanea Sativa Mill.) coppice. For this, the changes produced at the canopy level by different thinning treatments and their evolution over time (2014–2019) were extracted from VI values corresponding to two trials involving 33 circular plots (r = 10 m). Plots were subjected to one of the following forest management treatments: Control with no intervention (2800–3300 stems ha−1), Treatment 1, one thinning leaving a living stock density of 900–600 stems ha−1 and Treatment 2, a more intensive thinning, leaving 400 stems ha−1. Time series data from Landsat-8 and Sentinel-2 were collected to calculate values for different VIs. Canopy development was computed by comparing the area under curves (AUCs) of different VI time-series annually throughout the study period. Soil-Line VIs were compared to the Normalized Vegetation Index (NDVI) revealing that the Second Modified Chlorophyll Absorption Ratio Index (MCARI2) more clearly demonstrated canopy evolution tendencies over time than the NDVI. MCARI2 data from both L8 and S2 reflected how the influence of treatment on the canopy cover decreases over the years, providing significant differences in the thinning year and the year after. Metrics derived from the MCARI2 time-series also demonstrated the capacity of the canopy to recovery to pretreatment coverage levels. The AUC method generates a specific V-shaped time-signature, the vertex of which coincides with the thinning event and, as such, provides forest managers with another tool to assist decision making in the development of sustainable forest management strategies.

scholarly journals Melt pond retrieval based on LinearPolar algorithm using Landsat data

2021 ◽  
Author(s):  
Yuqing Qin ◽  
Jie Su ◽  
Mingfeng Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Relative Error ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Landsat 8 ◽  
Landsat Data ◽  
Melt Pond ◽  
Melt Ponds ◽  
Sentinel 2

&lt;p&gt;The formation and distribution of melt ponds also have an important influence on the Arctic climate. It is necessary to obtain more accurate information of melt ponds on Arctic sea ice by remote sensing. Present large-scale melt pond products, especially melt pond fraction (MPF), still need a lot of verification, and it is a good way to use the very high resolution optical satellite remote sensing data to verify the retrieval MPF of low-resolution melt pond results.&lt;/p&gt;&lt;p&gt;Most MPF algorithm such as Markus (Markus, et al., 2003) and PCA (Rosel et al., 2011) relying on fixed melt pond albedo, LinearPolar algorithm (Wang et. al., 2020) considers that the albedo of melt ponds albedo is variable, it has been proved the retrieval results of this algorithm has a high accuracy of the MPF than that of the previous algorithm based on Sentinel-2 data in Wang et al.&amp;#8217;s work. In this paper, we applied this algorithm to Landsat 8 data. Meanwhile, Sentinel-2 data as well as SVM and ISODATA method are used as the comparison and verification data. The results show that the MPF obtained from Landsat 8 using LinearPolar algorithm is the much more closer to Sentinel-2 than Markus and PCA algorithms, and the correlation coefficients of the two MPF is as high as 0.95. The overall relative error of LinearPolar algorithm is 53.5% and 46.4% lower than Markus and PCA algorithms, respectively. And in the cases without obvious melt ponds, the relative error is reduced more than that with obvious melt ponds. This is because LinearPolar algorithm can identify 100% dark melt ponds and relatively small-scale melt ponds, and the latter contributes more to MPF retrieval.&lt;/p&gt;&lt;p&gt;The application of LinearPolar algorithm on Landsat can cover a wider range than Sentinel and enhance the verification efficiency. Moreover, because of the longer time series of Landsat data than Sentinel data, the long-term variation trend of sea ice in fixed areas can be monitored.&lt;/p&gt;

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