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 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 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 Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle

2019 ◽  
Vol 11 (21) ◽  
pp. 2541 ◽  
Author(s):  
Pause ◽  
Raasch ◽  
Marrs ◽  
Csaplovics
Keyword(s):  
Time Series ◽  
Weed Management ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Agricultural Practices ◽  
Series Data ◽  
Herbicide Treatment ◽  
Proof Of Principle ◽  
Sentinel 2

In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring.

Monitoring deciduous tree phenology estimates with Sentinel-2, phenocam and field measurements in Ireland

2021 ◽  
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Tree Canopy ◽  
Deciduous Tree ◽  
Series Data ◽  
Near Surface ◽  
Phenological Data ◽  
Sentinel 2 ◽  
Daily Time

<p>Phenology is an important driver of ecosystem performance. However, studies of phenology in Ireland have been limited by the availability of data at high spatial and temporal resolutions. The new suite of Sentinel-2 sensors, with their enhanced spatial and temporal resolutions might help overcome some of these challenges. Additionally, the presence of red edge bands in the Sentinel-2 sensors provides a unique opportunity to evaluate the performance of different vegetation indices in tracking near surface (phenocam) and ground/laboratory measures of phenology. In this study, we present our initial analyses for the year 2020. Nine common lime trees (Tilia x europaea) on the University College Cork campus (Cork, Ireland) and three undisturbed broadleaf woodland sites from the National Park and Wildlife Services (NPWS) survey were selected. The phenology of these sites was analyzed from satellite derived vegetation indices of NDVI, EVI, GNDVI and NDRE. The available 24 cloud free Sentinel-2 images were pre-processed and interpolated to daily time steps. The start of season (SOS), position of peak (POP) and end of season (EOS) were then extracted from the daily time series using the half amplitude and maximum value method. Similarly, daily data from a phenocam overlooking three of the lime trees were processed to extract the phenological dates. Weekly measurements of leaf chlorophyll or chlorophyll content index (CCI) and maximum photosystem II efficiency (Fv/Fm) by sampling five leaves from each lime tree were made during June to November of 2020. Preliminary results indicate that different vegetation indices vary in their correlation to ground and phenocam observations. The dates of SOS, POP and EOS obtained from Sentinel-2 do not exactly match the ground and phenocam observations, nor are the different indices coincident with each other, with maximum deviations of up to a month and a week for EOS and SOS respectively. The phenological metrics estimated from the EVI time series were in general earlier (i.e. 116, 162 and 270 day of year for SOS, POP and EOS respectively) and those from the NDRE were the last (i.e. 131, 211 and 288 day of year for SOS, POP and EOS respectively). Although local differences were observed in the field, the Sentinel-2 time series data were shown to perform well in tracking the autumn phenology, and in most cases the observed mismatches in phenological data could be ascribed to differences in the scale of observations i.e. pixel vs point comparisons and on spectral basis i.e. sensor vs instrument for measuring CCI. A steeper drop in CCI and Fv/Fm values was also observed in the late autumn period. Such differences in the progression of each time series curve can possibly lead to mismatches in the phenology estimated from vegetation indices and from observations. Other mismatches could also emanate from the fact that field sampling of leaves was done from below the canopy whereas the satellite view of canopy is from the top. Experience from the field revealed differences in the rates of greening and yellowing of the leaves in different regions of the tree canopy.</p>

scholarly journals Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

2019 ◽  
Vol 11 (14) ◽  
pp. 1683 ◽  
Author(s):  
Yangchengsi Zhang ◽  
Long Guo ◽  
Yiyun Chen ◽  
Tiezhu Shi ◽  
Mei Luo ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Carbon ◽  
Time Series Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Series Data ◽  
Landsat 8 ◽  
Auxiliary Variables ◽  
Jianghan Plain ◽  
Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

LamaH: Large-sample Data for Hydrology in Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Mathew Herrnegger ◽  
Frederik Kratzert ◽  
Karsten Schulz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Vegetation Indices ◽  
River Network ◽  
Series Data ◽  
High Temporal Resolution ◽  
Stream Length ◽  
Discharge Data ◽  
Large Sample ◽  
Access To Data

<p>Open large-sample datasets are important for various reasons: i) they enable large-sample analyses, ii) they democratize access to data, iii) they enable large-sample comparative studies and foster reproducibility, and iv) they are a key driver for recent developments of machine-learning based modelling approaches.</p><p>Recently, various large-sample datasets have been released (e.g. different country-specific CAMELS datasets), however, all of them contain only data of individual catchments distributed across entire countries and not connected river networks.</p><p>Here, we present LamaH, a new dataset covering all of Austria and the foreign upstream areas of the Danube, spanning a total of 170.000 km² in 9 different countries with discharge observations for 882 gauges. The dataset also includes 15 different meteorological time series, derived from ERA5-Land, for two different basin delineations: First, corresponding to the entire upstream area of a particular gauge, and second, corresponding only to the area between a particular gauge and its upstream gauges. The time series data for both, meteorological and discharge data, is included in hourly and daily resolution and covers a period of over 35 years (with some exceptions in discharge data for a couple of gauges).</p><p>Sticking closely to the CAMELS datasets, LamaH also contains more than 60 catchment attributes, derived for both types of basin delineations. The attributes include climatic, hydrological and vegetation indices, land cover information, as well as soil, geological and topographical properties. Additionally, the runoff gauges are classified by over 20 different attributes, including information about human impact and indicators for data quality and completeness. Lastly, LamaH also contains attributes for the river network itself, like gauge topology, stream length and the slope between two sequential gauges.</p><p>Given the scope of LamaH, we hope that this dataset will serve as a solid database for further investigations in various tasks of hydrology. The extent of data combined with the interconnected river network and the high temporal resolution of the time series might reveal deeper insights into water transfer and storage with appropriate methods of modelling.</p>

scholarly journals Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

2021 ◽  
Author(s):  
Sadnan Al Manir ◽  
Justin Niestroy ◽  
Maxwell Adam Levinson ◽  
Timothy Clark
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Defeasible Reasoning ◽  
Series Data ◽  
Inference Rules ◽  
Deep Networks ◽  
Evidence Graph ◽  
Over Time

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

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