LamaH: Large-sample Data for Hydrology in Central Europe

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.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.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 km&#178; 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).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.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.

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NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-w2-2020-41-2020 ◽

2020 ◽

Vol IV-3/W2-2020 ◽

pp. 41-46

Author(s):

D. Ratha ◽

D. Mandal ◽

S. Dey ◽

A. Bhattacharya ◽

A. Frery ◽

...

Keyword(s):

Time Series ◽

Vegetation Index ◽

Time Series Data ◽

Geodesic Distance ◽

Vegetation Indices ◽

Series Data ◽

Biophysical Parameters ◽

Area Index ◽

Plant Area Index ◽

Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

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PREDICTING SUGARCANE YIELDS IN KHUZESTAN USING A LARGE TIME-SERIES OF REMOTE SENSING IMAGERY REGION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-645-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 645-648 ◽

Cited By ~ 1

Author(s):

M. Khosravirad ◽

M. Omid ◽

F. Sarmadian ◽

S. Hosseinpour

Keyword(s):

Time Series ◽

Vegetation Index ◽

Time Series Data ◽

Vegetation Indices ◽

Series Data ◽

Linear Regression Models ◽

Khuzestan Province ◽

Second Growth ◽

Ndvi Time Series ◽

Sugarcane Yield

Abstract. This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ = 1.4) vegetation index relative to first period of growth with R2 = 0.66 and RMSE = 3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R2 = 0.66 and RMSE = 3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R2 = 0.78 and RMSE = 3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.

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Pendugaan Data Runtun Waktu Debit Aliran Sungai Cikeruh dengan Metode Thomas-Fiering

Jurnal Riset Matematika ◽

10.29313/jrm.v1i1.104 ◽

2021 ◽

Vol 1 (1) ◽

pp. 13-20

Author(s):

Meiske Shabrina Pesik ◽

Didi Suhaedi ◽

M. Yusuf Fajar

Keyword(s):

Time Series ◽

River Flow ◽

Time Series Data ◽

Seasonal Pattern ◽

Series Data ◽

Discharge Data ◽

Flow Discharge ◽

Mean Error ◽

The People ◽

Monthly Period

Abstract. The Cikeruh River is a source of water for the people who live in the watershed area. The shift in land management has resulted in problems in the availability of water resources. As a policy to overcome this problem, an estimation of the flow rate of the Cikeruh river was carried out. Cikeruh river flow discharge data is observational data with a monthly period included in time series data or time series data. This data has a seasonal pattern so that the method that can be used to predict the discharge data is the Thomas-Fiering Method. To estimate the discharge data for 2018, the Cikeruh river flow discharge data were used every month from 2011 to 2017 as many as 84 historical data. Then after getting the results of the 2018 debit data estimation, the mean error value calculated using Thomas-Fiering was 0.0291. Abstrak. Sungai Cikeruh merupakan sumber air bagi masyarakat yang bermukim di wilayah daerah aliran sungai. Terjadinya pergeseran tata kelola lahan mengakibatkan permasalahan ketersediaan sumber daya air. Sebagai suatu kebijakan untuk mengatasi permasalahan tersebut maka dilakukan pendugaan debit aliran sungai Cikeruh. Data debit aliran sungai Cikeruh merupakan data pengamatan dengan periode bulanan yang termasuk dalam data time series atau data runtun waktu. Data ini memiliki pola musiman sehingga metode yang dapat digunakan untuk membuat pendugaan data debit adalah Metode Thomas-Fiering. Untuk menduga data debit tahun 2018 digunakan data debit aliran sungai Cikeruh setiap bulannya dari tahun 2011 sampai 2017 sebanyak 84 data historis. Kemudian setelah mendapatkan hasil pendugaan data debit tahun 2018 didapatkan nilai Mean Error perhitungan menggunakan Thomas-Fiering adalah 0.0291.

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Study on The Physical Characteristics and Hydrology of 15 Watershed in East Java

Forum Geografi ◽

10.23917/forgeo.v27i2.2374 ◽

2013 ◽

Vol 27 (2) ◽

pp. 159

Author(s):

Indarto Indarto

Keyword(s):

Time Series ◽

Time Series Data ◽

Daily Rainfall ◽

Study Data ◽

Series Data ◽

Simple Arithmetic ◽

Discharge Data ◽

Average Value ◽

Physical Data ◽

Areal Rainfall

The study demonstrated the application of statistical method to describe physical and hydro-meteorological characteristics by means of time series analysis. Fifteen(15) watersheds in East Java were selected for this study. Data input for the analysis include: physical data, rainfall and discharge. Physical data of the watershed (topography, river network, land use, and soil type) are extracted from existing database and treated using GIS Software. Daily rainfall data were collected from existing pluviometers around the region. Daily discharge data were obtained from measurement station located at the outlet of each watershed. Areal Rainfall for each watershed was determined using average value of existing pluviometers around the watershed and determined using simple arithmetic method. These time series data are then imported to RAP (River Analysis Package). Analysis on the RAP, include: general statistical, flow duration curve (FDC), and baseflow analysis. The result then presented in graphic and tables. Research shows that among the watersheds have different physical and hydrological characteristics.

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

Remote Sensing ◽

10.3390/rs11212541 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2541 ◽

Cited By ~ 2

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.

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Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis

Forum Geografi ◽

10.23917/forgeo.v29i1.786 ◽

2015 ◽

Vol 29 (1) ◽

Cited By ~ 1

Author(s):

Sri Hartini ◽

Muhammad Pramono Hadi ◽

Sudibyakto Sudibyakto ◽

Aris Poniman

Keyword(s):

Time Series ◽

River Discharge ◽

Time Series Data ◽

Series Data ◽

Var Model ◽

Time Interval ◽

Discharge Data ◽

Vector Auto Regression ◽

The Relationship ◽

Auto Regression

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

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Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data

Remote Sensing ◽

10.3390/rs13224522 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4522

Author(s):

Yupeng Kang ◽

Xinli Hu ◽

Qingyan Meng ◽

Youfeng Zou ◽

Linlin Zhang ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Classification Accuracy ◽

Vegetation Index ◽

Time Series Data ◽

Vegetation Indices ◽

Series Data ◽

Red Edge ◽

Ndvi Time Series ◽

Crop Classification

Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.

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A new approach for suspended sediment load calculation based on generated flow discharge considering climate change

Water Science & Technology Water Supply ◽

10.2166/ws.2021.069 ◽

2021 ◽

Author(s):

Arash Adib ◽

Ozgur Kisi ◽

Shekoofeh Khoramgah ◽

Hamid Reza Gafouri ◽

Ali Liaghat ◽

...

Keyword(s):

Time Series ◽

Monte Carlo ◽

Sediment Load ◽

Time Series Data ◽

Series Data ◽

Suspended Sediment Load ◽

Discharge Time ◽

Discharge Data ◽

New Approach ◽

Flow Discharge

Abstract Use of general circulation models (GCMs) is common for forecasting of hydrometric and meteorological parameters, but the uncertainty of these models is high. This study developed a new approach for calculation of suspended sediment load (SSL) using historical flow discharge data and SSL data of the Idanak hydrometric station on the Marun River (in the southwest of Iran) from 1968 to 2014. This approach derived sediment rating relation by observed data and determined trend of flow discharge time series data by Mann-Kendall nonparametric trend (MK) test and Theil-Sen approach (TSA). Then, the SSL was calculated for a future period based on forecasted flow discharge data by TSA. Also, one hundred annual and monthly flow discharge time series data (for the duration of 40 years) were generated by the Markov chain and the Monte Carlo (MC) methods and it calculated 90% of total prediction uncertainty bounds for flow discharge time series data by Latin Hypercube Sampling (LHS) on Monte Carlo (MC). It is observed that flow discharge and SSL will increase in summer and will reduce in spring. Also, the annual amount of SSL will reduce from 2,811.15 ton/day to 1,341.25 and 962.05 ton/day in the near and far future, respectively.

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

Remote Sensing ◽

10.3390/rs12183068 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3068 ◽

Cited By ~ 1

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.

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ASSESSING IMPACTS OF CLIMATE CHANGE ON TEAK AND SAL LANDSCAPE USING MODIS TIME SERIES DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-305-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 305-313 ◽

Cited By ~ 1

Author(s):

M. K. Patasaraiya ◽

B. Sinha ◽

J. Bisaria ◽

S. Saran ◽

R. K. Jaiswal

Keyword(s):

Climate Change ◽

Time Series ◽

Forest Ecosystems ◽

Time Series Data ◽

Vegetation Indices ◽

Central India ◽

Series Data ◽

Impact Of Climate Change ◽

Increasing Trend ◽

The Impact

Abstract. Climate change poses a severe threat to the forest ecosystems by impacting its productivity, species composition and forest biodiversity at global and regional level. The scientific community all over the world is using remote sensing techniques to monitor and assess the impact of climate change on forest ecosystems. The consistent time series data provided by MODIS is immensely used for developing a different type of Vegetation indices like NDVI (Normalized difference vegetation indices) products at different spatial and temporal resolution. These vegetation indices have significant potential to detect forest growth and health, vegetation seasonality and different phenological events like budding and flowering. The current study aims to understand the impact of climate change on Teak and Sal forest of STR (Satpura tiger reserve) in central India by using Landsat and MODIS time series data. The rationale for taking STR as study site was to attribute the changes exclusively to climate change as there is no anthropogenic disturbance in STR. A change detection analysis was carried out to detect changes between the period 2017 and 1990 using Landsat data of October month. To understand the inter-annual and seasonal variation of Teak and Sal forests, freely available MOD13Q1 product (250m, 16 days’ interval) was used to extract NDVI values for each month and four seasons (DJF, JJAS, ON, MAM) for the period 2000 to 2015. The climatic data (rainfall and temperature) was sourced from IMD (India Meteorological Department) at different resolutions (1, 0.5 and 0.25 degree) for the given period of the study. A correlation analysis was done to establish a causal relationship between climate variable (temperature and rainfall) and vegetation health (NDVI) on a different temporal scale of annual, seasonal and month. The study found an increasing trend in annual mean temperature and no consistent trend in total annual rainfall over the period 2000 to 2015. The maximum percentage change was observed in minimum temperature over the period 2000 to 2015. The average annual NDVI of Teak and Sal forests showed an increasing trend however, no trend was observed in seasonal and monthly NDVI over the same period. The maximum and minimum NDVI was found in the post-monsoon months (ON) and summer months (MAM) respectively. As STR is a Teak and Sal dominated landscape, the findings of the current study can also be applied in developing silvicultural and adaptation strategies for other Teak and Sal dominated landscapes of central India.

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